diff --git a/README.md b/README.md
index 5d8a04e..a0761ef 100644
--- a/README.md
+++ b/README.md
@@ -255,4 +255,5 @@ PS:
 - [ ] download / upload experiment logs through timestamp.
 - [ ] add a document to the plot function.
 - [ ] allow sync LOG only or ALL TYPE LOGS. 
-- [ ] support aim and smarter logger.
+- [x] support aim and smarter logger.
+- [ ] add unit_test to ckp loader.
diff --git a/RLA/__init__.py b/RLA/__init__.py
index e69de29..796741c 100644
--- a/RLA/__init__.py
+++ b/RLA/__init__.py
@@ -0,0 +1,3 @@
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_plot.plot_func_v2 import plot_func
\ No newline at end of file
diff --git a/RLA/easy_log/const.py b/RLA/easy_log/const.py
index 82f200d..4eaa5d9 100644
--- a/RLA/easy_log/const.py
+++ b/RLA/easy_log/const.py
@@ -6,5 +6,14 @@
 ARCHIVED_TABLE = 'arc'
 default_log_types = [LOG, CODE, CHECKPOINT, ARCHIVE_TESTER, OTHER_RESULTS]
 
-class LOAD_TESTER_MODE:
-    FORK_TO_NEW = 'fork'
\ No newline at end of file
+
+class LoadTesterMode:
+    FORK_TO_NEW = 'fork'
+
+
+# option: 'stdout', 'log', 'tensorboard', 'csv'
+class LogDataType:
+    TB = 'tensorboard'
+    CSV = 'csv'
+    TXT = 'log'
+    STDOUT = 'stdout'
diff --git a/RLA/easy_log/exp_loader.py b/RLA/easy_log/exp_loader.py
index 043e22f..b9a82b7 100644
--- a/RLA/easy_log/exp_loader.py
+++ b/RLA/easy_log/exp_loader.py
@@ -72,7 +72,8 @@ def import_hyper_parameters(self, hp_to_overwrite: Optional[list] = None, sync_t
         else:
             return argparse.Namespace(**exp_manager.hyper_param)
 
-    def load_from_record_date(self, var_prefix: Optional[str] = None, variable_list: Optional[list]=None, verbose=True):
+    def load_from_record_date(self, var_prefix: Optional[str] = None, variable_list: Optional[list]=None, verbose=True,
+                              ckp_index: Optional[int]=None):
         """
 
         :param var_prefix: the prefix of namescope (for tf) to load. Set to '' to load all of the parameters.
@@ -81,9 +82,8 @@ def load_from_record_date(self, var_prefix: Optional[str] = None, variable_list:
         """
         if self.is_valid_config:
             loaded_tester = Tester.load_tester(self.load_date, self.task_name, self.data_root)
-            if verbose:
-                print("attrs of the loaded tester")
-                pprint(loaded_tester.__dict__)
+            print("attrs of the loaded tester")
+            pprint(loaded_tester.__dict__)
             # load checkpoint
             load_res = {}
             if var_prefix is not None:
diff --git a/RLA/easy_log/log_tools.py b/RLA/easy_log/log_tools.py
index 1f0df8e..87654ef 100644
--- a/RLA/easy_log/log_tools.py
+++ b/RLA/easy_log/log_tools.py
@@ -12,6 +12,10 @@
 import yaml
 import pandas as pd
 import csv
+import dill
+
+import json
+from RLA.easy_log.tester import Tester
 
 class Filter(object):
     ALL = 'all'
@@ -27,7 +31,14 @@ def __init__(self, optional_log_type=None):
         self.log_types = default_log_types.copy()
         if optional_log_type is not None:
             self.log_types.extend(optional_log_type)
-
+    
+    def is_valid_index(self, regex):
+        if re.search(r'\d{4}/\d{2}/\d{2}/\d{2}-\d{2}-\d{2}-\d{6}', regex):
+            target_reg = re.search(r'\d{4}/\d{2}/\d{2}/\d{2}-\d{2}-\d{2}-\d{6}', regex).group(0)
+        else:
+            target_reg = None
+        return target_reg
+    
     def _find_small_timestep_log(self, proj_root, task_table_name, regex, timstep_upper_bound=np.inf, timestep_lower_bound=0):
         small_timestep_regs = []
         root_dir_regex = osp.join(proj_root, LOG, task_table_name, regex)
@@ -35,10 +46,7 @@ def _find_small_timestep_log(self, proj_root, task_table_name, regex, timstep_up
             print("searching dirs", root_dir)
             if os.path.exists(root_dir):
                 for file_list in os.walk(root_dir):
-                    if re.search(r'\d{4}/\d{2}/\d{2}/\d{2}-\d{2}-\d{2}-\d{6}', file_list[0]):
-                        target_reg = re.search(r'\d{4}/\d{2}/\d{2}/\d{2}-\d{2}-\d{2}-\d{6}', file_list[0]).group(0)
-                    else:
-                        target_reg = None
+                    target_reg = self.is_valid_index(file_list[0])
                     if target_reg is not None:
                         if LOG in root_dir_regex:
                             try:
@@ -236,6 +244,7 @@ def archive_log(self, skip_ask=False):
             print("do archive ...")
             self._archive_log(show=False)
 
+
 class ViewLogTool(BasicLogTool):
     def __init__(self, proj_root, task_table_name, regex, *args, **kwargs):
         self.proj_root = proj_root
@@ -248,10 +257,7 @@ def _view_log(self, regex):
         for root_dir in glob.glob(root_dir_regex):
             if os.path.exists(root_dir):
                 for file_list in os.walk(root_dir):
-                    if re.search(r'\d{4}/\d{2}/\d{2}/\d{2}-\d{2}-\d{2}-\d{6}', file_list[0]):
-                        target_reg = re.search(r'\d{4}/\d{2}/\d{2}/\d{2}-\d{2}-\d{2}-\d{6}', file_list[0]).group(0)
-                    else:
-                        target_reg = None
+                    target_reg = self.is_valid_index(file_list[0])
                     if target_reg is not None:
                         backup_file = file_list[0] + '/backup.txt'
                         if file_list[1] == ['tb'] or os.path.exists(backup_file):  # in root of logdir
@@ -268,3 +274,45 @@ def view_log(self, skip_ask=False):
                 s = input("press y to view \n ")
             if s == 'y':
                 self._view_log(regex=res[0] + '*')
+
+
+class PrettyPlotterTool(BasicLogTool):
+    def __init__(self, proj_root, task_table_name, regex, *args, **kwargs):
+        self.proj_root = proj_root
+        self.task_table_name = task_table_name
+        self.regex = regex
+        super(PrettyPlotterTool, self).__init__(*args, **kwargs)
+
+    def json_dump(self, location):
+        target_index = self.is_valid_index(location)
+        if target_index is not None:
+
+            json_location = None
+            try:
+                exp_manager = dill.load(open(location, 'rb'))
+                assert isinstance(exp_manager, Tester)
+                formatted_log_name = exp_manager.log_name_formatter(exp_manager.get_task_table_name(),
+                                                                    exp_manager.record_date)
+                params = exp_manager.hyper_param
+                params['formatted_log_name'] = formatted_log_name
+
+                json_location = exp_manager.log_name_formatter(
+                    osp.join(self.proj_root, LOG, exp_manager.get_task_table_name()), exp_manager.record_date) + '/'
+                json.dump(params, open(osp.join(json_location, 'parameter.json'), 'w'))
+                print("gen:", osp.join(json_location, 'parameter.json'))
+            except FileNotFoundError as e:
+                print("log file cannot found", json_location)
+            except EOFError as e:
+                print("log file broken", json_location)
+
+    def gen_json(self, regex):
+        root_dir_regex = osp.join(self.proj_root, ARCHIVE_TESTER, self.task_table_name, regex)
+        for root_dir in glob.glob(root_dir_regex):
+            if os.path.exists(root_dir):
+                if osp.isdir(root_dir):
+                    for file_list in os.walk(root_dir):
+                        for file in file_list[2]:
+                            location = osp.join(file_list[0], file)
+                            self.json_dump(location)
+                else:
+                    self.json_dump(root_dir)
diff --git a/RLA/easy_log/logger.py b/RLA/easy_log/logger.py
index 7f6ed4d..8bcccb2 100644
--- a/RLA/easy_log/logger.py
+++ b/RLA/easy_log/logger.py
@@ -252,11 +252,21 @@ def __init__(self, dir, framework):
         path = osp.join(osp.abspath(dir), prefix)
         if self.framework == FRAMEWORK.tensorflow:
             import tensorflow as tf
-            self.tb_writer = tf.summary.FileWriter(path)
+            from tensorflow.python import pywrap_tensorflow
+
             self.tbx_writer = None
             from tensorflow.python import pywrap_tensorflow
             from tensorflow.core.util import event_pb2
             from tensorflow.python.util import compat
+            try:
+                self.tb_writer = tf.summary.FileWriter(path)
+                self.tf = tf
+            except AttributeError as e:
+                # tf.compat.v1.disable_eager_execution()
+                # from tensorflow.python.client import _pywrap_events_writer
+                self.tb_writer = tf.summary.create_file_writer(path) # tf.compat.v1.summary.FileWriter(path)
+                # tf.compat.v1.disable_eager_execution()
+                # tf = tf.compat.v1
             self.tf = tf
             self.event_pb2 = event_pb2
             self.pywrap_tensorflow = pywrap_tensorflow
@@ -284,10 +294,16 @@ def writer(self):
     def add_hyper_params_to_tb(self, hyper_param, metric_dict=None):
         if self.framework == FRAMEWORK.tensorflow:
             import tensorflow as tf
-            with tf.Session(graph=tf.Graph()) as sess:
-                hyperparameters = [tf.convert_to_tensor([k, str(v)]) for k, v in hyper_param.items()]
-                summary = sess.run(tf.summary.text('hyperparameters', tf.stack(hyperparameters)))
-                self.tb_writer.add_summary(summary, self.step)
+            try:
+                with tf.Session(graph=tf.Graph()) as sess:
+                    hyperparameters = [tf.convert_to_tensor([k, str(v)]) for k, v in hyper_param.items()]
+                    summary = sess.run(tf.summary.text('hyperparameters', tf.stack(hyperparameters)))
+                    self.tb_writer.add_summary(summary, self.step)
+            except AttributeError as e:
+                tf.compat.v1.enable_eager_execution()
+                with self.tb_writer.as_default():
+                    hyperparameters = [tf.convert_to_tensor([k, str(v)]) for k, v in hyper_param.items()]
+                    tf.summary.text('hyperparameters', tf.stack(hyperparameters), step=self.step)
         elif self.framework == FRAMEWORK.torch:
             import pprint
             if metric_dict is None:
@@ -300,14 +316,20 @@ def add_hyper_params_to_tb(self, hyper_param, metric_dict=None):
 
     def writekvs(self, kvs):
         if self.framework == FRAMEWORK.tensorflow:
-            def summary_val(k, v):
-                kwargs = {'tag': k, 'simple_value': float(v)}
-                return self.tf.Summary.Value(**kwargs)
-            summary = self.tf.Summary(value=[summary_val(k, v) for k, v in kvs.items()])
-            event = self.event_pb2.Event(wall_time=time.time(), summary=summary)
-            event.step = self.step  # is there any reason why you'd want to specify the step?
-            self.writer.add_event(event)
-            self.writer.flush()
+            try:
+                def summary_val(k, v):
+                    kwargs = {'tag': k, 'simple_value': float(v)}
+                    return self.tf.Summary.Value(**kwargs)
+                summary = self.tf.Summary(value=[summary_val(k, v) for k, v in kvs.items()])
+                event = self.event_pb2.Event(wall_time=time.time(), summary=summary)
+                event.step = self.step  # is there any reason why you'd want to specify the step?
+                self.writer.add_event(event)
+                self.writer.flush()
+            except AttributeError as e:
+                self.tf.compat.v1.enable_eager_execution()
+                with self.tb_writer.as_default():
+                    for k, v in kvs.items():
+                        self.tf.summary.scalar(k, v, step=self.step)
         elif self.framework == FRAMEWORK.torch:
             def summary_val(k, v):
                 kwargs = {'tag': k, 'scalar_value': float(v), 'global_step': self.step}
@@ -395,7 +417,7 @@ def ma_record_tabular(key, val, record_len, ignore_nan=False, exclude:Optional[U
     if len(ma_dict[key]) == record_len:
         record_tabular(key, np.mean(ma_dict[key]), exclude)
 
-def logkv(key, val, exclude:Optional[Union[str, Tuple[str, ...]]]=None):
+def logkv(key, val, exclude:Optional[Union[str, Tuple[str, ...]]]=None, freq:Optional[int]=None):
     """
     Log a value of some diagnostic
     Call this once for each diagnostic quantity, each iteration
@@ -404,7 +426,8 @@ def logkv(key, val, exclude:Optional[Union[str, Tuple[str, ...]]]=None):
     :param key: (Any) save to log this key
     :param val: (Any) save to log this value
     """
-    get_current().logkv(key, val, exclude)
+    if freq is None or timestep() % freq == 0:
+        get_current().logkv(key, val, exclude)
 
 
 def log_from_tf_summary(summary):
diff --git a/RLA/easy_log/tester.py b/RLA/easy_log/tester.py
index 46233bb..d98e69d 100644
--- a/RLA/easy_log/tester.py
+++ b/RLA/easy_log/tester.py
@@ -15,7 +15,6 @@
 import os.path as osp
 import pprint
 
-import numpy as np
 import tensorboardX
 
 from RLA.easy_log.time_step import time_step_holder
@@ -162,6 +161,15 @@ def configure(self, task_table_name: str, rla_config: Union[str, dict], data_roo
         for k, v in self.private_config.items():
             logger.info("k: {}, v: {}".format(k, v))
 
+    def get_task_table_name(self):
+        task_table_name = getattr(self, 'task_table_name', None)
+        if task_table_name is None:
+            task_table_name = getattr(self, 'task_name', None)
+            print("[WARN] you are using an old-version RLA. "
+                  "Some attributes' name have been changed (task_name->task_table_name).")
+            if task_table_name is None:
+                raise RuntimeError("invalid ExpManager: task_table_name cannot be found", )
+        return task_table_name
 
     def set_hyper_param(self, **argkw):
         """
@@ -218,13 +226,7 @@ def update_log_files_location(self, root:str):
         """
         self.data_root = root
 
-        task_table_name = getattr(self, 'task_table_name', None)
-        if task_table_name is None:
-            task_table_name = getattr(self, 'task_name', None)
-            print("[WARN] you are using an old-version RLA. "
-                  "Some attributes' name have been changed (task_name->task_table_name).")
-            if task_table_name is None:
-                raise RuntimeError("invalid ExpManager: task_table_name cannot be found", )
+        task_table_name = self.get_task_table_name()
         code_dir, _ = self.__create_file_directory(osp.join(self.data_root, CODE, task_table_name), '', is_file=False)
         log_dir, _ = self.__create_file_directory(osp.join(self.data_root, LOG, task_table_name), '', is_file=False)
         self.pkl_dir, self.pkl_file = self.__create_file_directory(osp.join(self.data_root, ARCHIVE_TESTER, task_table_name), '.pkl')
@@ -431,15 +433,9 @@ def log_file_finder(cls, record_date, task_table_name='train', file_root='../che
                 raise NotImplementedError
             for search_item in search_list:
                 if search_item.startswith(str(record_date.strftime("%H-%M-%S-%f"))):
-                    try:
-                        split_dir = search_item.split('_')
-                        assert len(split_dir) >= 2
-                        info = " ".join(split_dir[2:])
-                    except AssertionError as e:
-                        split_dir = search_item.split(' ')
-                        # self.__ipaddr = split_dir[1]
-                        info = "_".join(split_dir[2:])
-                        print("[WARN] We find an old-version experiment data.")
+                    split_dir = search_item.split(' ')
+                    # self.__ipaddr = split_dir[1]
+                    info = " ".join(split_dir[2:])
                     logger.info("load data: \n ts {}, \n ip {}, \n info {}".format(split_dir[0], split_dir[1], info))
                     file_found = search_item
                     break
@@ -625,6 +621,7 @@ def load_checkpoint(self, ckp_index=None):
             all_ckps = os.listdir(self.checkpoint_dir)
             ites = []
             for ckps in all_ckps:
+                print("ckps", ckps)
                 ites.append(int(ckps.split('checkpoint-')[1].split('.pt')[0]))
             idx = np.argsort(ites)
             all_ckps = np.array(all_ckps)[idx]
@@ -632,7 +629,6 @@ def load_checkpoint(self, ckp_index=None):
             pprint.pprint(all_ckps)
             if ckp_index is None:
                 ckp_index = all_ckps[-1].split('checkpoint-')[1].split('.pt')[0]
-            print("loaded checkpoints:", "checkpoint-{}.pt".format(ckp_index))
             return ckp_index, torch.load(self.checkpoint_dir + "checkpoint-{}.pt".format(ckp_index))
 
     def auto_parse_info(self):
diff --git a/RLA/easy_log/time_used_recorder.py b/RLA/easy_log/time_used_recorder.py
new file mode 100644
index 0000000..21771da
--- /dev/null
+++ b/RLA/easy_log/time_used_recorder.py
@@ -0,0 +1,38 @@
+# Created by xionghuichen at 2022/7/29
+# Email: chenxh@lamda.nju.edu.cn
+from RLA.easy_log import logger
+import time
+
+
+rc_start_time = {}
+
+
+def time_record(name):
+    """
+    record the consumed time of your code snippet. call this function to start a recorder.
+    "name" is identifier to distinguish different recorder and record different snippets at the same time.
+    call time_record_end to end a recorder.
+    :param name: identifier of your code snippet.
+    :type name: str
+    :return:
+    :rtype:
+    """
+    assert name not in rc_start_time
+    rc_start_time[name] = time.time()
+
+
+def time_record_end(name):
+    """
+    record the consumed time of your code snippet. call this function to start a recorder.
+    "name" is identifier to distinguish different recorder and record different snippets at the same time.
+    call time_record_end to end a recorder.
+    :param name: identifier of your code snippet.
+    :type name: str
+    :return:
+    :rtype:
+    """
+    end_time = time.time()
+    start_time = rc_start_time[name]
+    logger.record_tabular("time_used/{}".format(name), end_time - start_time)
+    logger.info("[test] func {0} time used {1:.2f}".format(name, end_time - start_time))
+    del rc_start_time[name]
\ No newline at end of file
diff --git a/RLA/easy_plot/plot_func.py b/RLA/easy_plot/plot_func.py
index 79c6c22..5fb6da1 100644
--- a/RLA/easy_plot/plot_func.py
+++ b/RLA/easy_plot/plot_func.py
@@ -6,6 +6,7 @@
 from RLA.easy_log import logger
 from RLA.easy_plot import plot_util
 from RLA.const import DEFAULT_X_NAME
+from RLA.easy_log.const import LOG, ARCHIVE_TESTER
 
 
 def default_key_to_legend(parse_list, y_name):
@@ -72,7 +73,7 @@ def picture_split(taskpath, single_name=None, param_keys=None, y_names=None,
 
 def csv_to_xy(r, x_name, y_name, scale_dict, x_bound=None, x_start=None, y_bound=None, remove_outlier=False):
 
-    df = r.progress.copy().reset_index() # ['progress']
+    df = r.progress.copy().reset_index()
     if df is None:
         logger.warn("empty df!")
         return [], []
@@ -117,6 +118,7 @@ def plot_res_func(prefix_dir:str, regs, param_keys,
                   remove_outlier=False, xlabel=None,
                   key_to_legend_fn=None,
                   verbose=True, *args, **kwargs):
+    logger.warn("the function is deprecated. please check the plot_func_v2 as the new implementation")
     dirs = []
     if key_to_legend_fn is None:
         key_to_legend_fn = default_key_to_legend
@@ -132,7 +134,6 @@ def plot_res_func(prefix_dir:str, regs, param_keys,
 
         log_found = glob.glob(osp.join(prefix_dir, regex_str))
         dirs.extend(log_found)
-        # print("regex str :{}. log found".format(regex_str))
         reg_group[regex_str] = []
 
         for log in log_found:
@@ -151,9 +152,6 @@ def plot_res_func(prefix_dir:str, regs, param_keys,
     # if misc_scale_index is None:
     #     misc_scale_index = []
     for i in range(len(value_keys)):
-        # if i in misc_scale_index:
-        #     scale_dict[value_keys[i]] = misc_scale[misc_scale_index.index(i)]
-        # else:
         final_scale_dict[value_keys[i]] = lambda x: x
     if scale_dict is not None:
         final_scale_dict.update(scale_dict)
@@ -170,7 +168,7 @@ def plot_res_func(prefix_dir:str, regs, param_keys,
         group_fn = lambda r: picture_split(taskpath=r, param_keys=param_keys, y_names=y_names,
                                            key_to_legend_fn=key_to_legend_fn)
 
-    _, _, lgd, texts, g2lf = plot_util.plot_results(results, xy_fn= lambda r, y_names: csv_to_xy(r, DEFAULT_X_NAME, y_names,
+    _, _, lgd, texts, g2lf, score_results = plot_util.plot_results(results, xy_fn= lambda r, y_names: csv_to_xy(r, DEFAULT_X_NAME, y_names,
                                                                                            final_scale_dict, x_start=x_start, y_bound=y_bound,
                                                                                            remove_outlier=remove_outlier),
                            # xy_fn=lambda r: ts2xy(r['monitor'], 'info/TimestepsSoFar', 'diff/driver_1_2_std'),
@@ -194,7 +192,8 @@ def plot_res_func(prefix_dir:str, regs, param_keys,
             plt.savefig(file_name, bbox_extra_artists=tuple(texts), bbox_inches='tight')
         print("saved location: {}".format(file_name))
     plt.show()
-    return g2lf
+    return g2lf, score_results
+
 
 def scale_index_to_dict(measure, scale_index, scale):
     scale_dict = {}
diff --git a/RLA/easy_plot/plot_func_v2.py b/RLA/easy_plot/plot_func_v2.py
new file mode 100644
index 0000000..1acb282
--- /dev/null
+++ b/RLA/easy_plot/plot_func_v2.py
@@ -0,0 +1,182 @@
+# Created by xionghuichen at 2022/8/10
+# Email: chenxh@lamda.nju.edu.cn
+import glob
+import os.path as osp
+import os
+import dill
+import copy
+import numpy as np
+from typing import Dict, List, Tuple, Type, Union, Optional, Callable
+import matplotlib.pyplot as plt
+
+from RLA import logger
+from RLA.const import DEFAULT_X_NAME
+from RLA.query_tool import experiment_data_query
+from RLA.easy_plot import plot_util
+from RLA.easy_log.const import LOG, ARCHIVE_TESTER, OTHER_RESULTS
+
+
+
+def default_key_to_legend(parse_list, y_name):
+    task_split_key = '.'.join(parse_list)
+    return task_split_key + ' eval:' + y_name
+
+
+def plot_func(data_root:str, task_table_name:str, regs:list, split_keys:list, metrics:list,
+              use_buf=False, verbose=True,
+              xlim: Optional[tuple] = None,
+              xlabel: Optional[str] = DEFAULT_X_NAME, ylabel: Optional[str] = None,
+              scale_dict: Optional[dict] = None, replace_legend_keys: Optional[list] = None,
+              key_to_legend_fn: Optional[Callable] = default_key_to_legend,
+              save_name: Optional[str] = None, *args, **kwargs):
+    """
+    A high-level matplotlib plotter.
+    The function is to load your experiments and plot curves.
+    You can group several experiments into a single figure through this function.
+    It is completed by loading experiments satisfying [data_root, task_table_name, regs] pattern,
+    grouping by "split_keys" or by the "regs" terms (see replace_legend_keys), and plotting the customized "metrics".
+
+    The function support several configure to customize the figure, including xlim, xlabel, ylabel, key_to_legend_fn, etc.
+    The function also supports several configure to post-process your log data, including resample, smooth_step, scale_dict, key_to_legend_fn, etc.
+    The function also supports several configure to beautify the figure, see the parameters of plot_util.plot_results.
+
+    :param data_root:
+    :type data_root:
+    :param task_table_name:
+    :type task_table_name:
+    :param regs:
+    :type regs:
+    :param split_keys:
+    :type split_keys:
+    :param metrics:
+    :type metrics:
+    :param use_buf: use the preloaded csv data instead of loading from scratch
+    :type use_buf: bool
+    :param xlim: set the x limits of the x axes.
+    :type xlim: Optional[tuple]
+    :param xlabel: set the label of the y axes.
+    :type xlabel: Optional[str]
+    :param ylabel: set the label of the y axes.
+    :type ylabel: Optional[str]
+    :param scale_dict: a function dict, to map the value of the metrics through customize functions.
+    e.g.,set metrics = ['return'], scale_dict = {'return': lambda x: np.log(x)}, then we will plot a log-scale return.
+    :type scale_dict: Optional[dict]
+    :param args: set the label of the y axes.
+
+    :return:
+    :rtype:
+    """
+    results = []
+    reg_group = {}
+    for reg in regs:
+        reg_group[reg] = []
+        print("searching", reg)
+        tester_dict = experiment_data_query(data_root, task_table_name, reg, ARCHIVE_TESTER)
+        log_dict = experiment_data_query(data_root, task_table_name, reg, LOG)
+        counter = 0
+        for k, v in log_dict.items():
+            result = plot_util.load_results(v.dirname, names=metrics + [DEFAULT_X_NAME],
+                                   x_bound=[DEFAULT_X_NAME, xlim], use_buf=use_buf)
+            if len(result) == 0:
+                continue
+            assert len(result) == 1
+            result = result[0]
+            if verbose:
+                print("find log", v.dirname)
+            counter += 1
+            result.hyper_param = tester_dict[k].exp_manager.hyper_param
+            results.append(result)
+            reg_group[reg].append(result)
+        print("find log number", counter)
+    final_scale_dict = {}
+    for m in metrics:
+        final_scale_dict[m] = lambda x: x
+    if scale_dict is not None:
+        final_scale_dict.update(scale_dict)
+
+    y_names = metrics # []
+    if ylabel is None:
+        ylabel = metrics
+
+    if replace_legend_keys is not None:
+        assert len(replace_legend_keys) == len(regs) and len(metrics) == 1,  \
+            "In manual legend-key mode, the number of keys should be one-to-one matched with regs"
+        # if len(replace_legend_keys) == len(regs):
+        group_fn = lambda r: split_by_reg(taskpath=r, reg_group=reg_group, y_names=y_names)
+    else:
+        group_fn = lambda r: picture_split(taskpath=r, split_keys=split_keys, y_names=y_names,
+                                           key_to_legend_fn=key_to_legend_fn)
+    _, _, lgd, texts, g2lf, score_results = \
+        plot_util.plot_results(results, xy_fn= lambda r, y_names: csv_to_xy(r, DEFAULT_X_NAME, y_names, final_scale_dict),
+                           group_fn=group_fn, average_group=True, ylabel=ylabel, xlabel=xlabel, replace_legend_keys=replace_legend_keys, *args, **kwargs)
+    print("--- complete process ---")
+    if save_name is not None:
+        import os
+        file_name = osp.join(data_root, OTHER_RESULTS, 'easy_plot', save_name)
+        os.makedirs(os.path.dirname(file_name), exist_ok=True)
+
+        if lgd is not None:
+            plt.savefig(file_name, bbox_extra_artists=tuple([lgd] + texts), bbox_inches='tight')
+        else:
+            plt.savefig(file_name, bbox_extra_artists=tuple(texts), bbox_inches='tight')
+        print("saved location: {}".format(file_name))
+    plt.show()
+    return g2lf, score_results
+
+
+def split_by_reg(taskpath, reg_group, y_names):
+    task_split_key = "None"
+    for i , reg_k in enumerate(reg_group.keys()):
+        if taskpath.dirname in reg_group[reg_k]:
+            assert task_split_key == "None", "one experiment should belong to only one reg_group"
+            task_split_key = str(i)
+    assert len(y_names) == 1
+    return task_split_key, y_names
+
+
+def split_by_task(taskpath, split_keys, y_names, key_to_legend_fn):
+    pair_delimiter = '&'
+    kv_delimiter = '='
+    parse_list = []
+    for split_key in split_keys:
+        if split_key in taskpath.hyper_param:
+            parse_list.append(split_key + '=' + str(taskpath.hyper_param[split_key]))
+        else:
+            parse_list.append(split_key + '=NF')
+    param_keys = []
+    for y_name in y_names:
+        param_keys.append(key_to_legend_fn(parse_list, y_name))
+    return param_keys, y_names
+
+
+def picture_split(taskpath, single_name=None, split_keys=None, y_names=None,
+                  key_to_legend_fn=None):
+    if single_name is not None:
+        return single_name, None
+    else:
+        return split_by_task(taskpath, split_keys, y_names, key_to_legend_fn=key_to_legend_fn)
+
+
+def csv_to_xy(r, x_name, y_name, scale_dict, x_bound=None, x_start=None):
+
+    df = r.progress.copy().reset_index() # ['progress']
+    if df is None:
+        logger.warn("empty df!")
+        return [], []
+    if y_name not in list(df.columns):
+        return None
+    df.drop(df[np.isnan(df[x_name])].index, inplace=True)
+    df.drop(df[np.isnan(df[y_name])].index, inplace=True)
+    # pd = pd.dropna(axis=0, how='any')
+    x = df[x_name]
+    y = df[y_name]
+    if x_bound is None:
+        x_bound = x.max()
+    if x_start is None:
+        x_start = x.min()
+    filter_index = (x <= x_bound) & (x >= x_start)
+    x = x[filter_index]
+    y = y[filter_index]
+
+    y = scale_dict[y_name](y)
+    return x, y
diff --git a/RLA/easy_plot/plot_util.py b/RLA/easy_plot/plot_util.py
index 573ce44..42779aa 100644
--- a/RLA/easy_plot/plot_util.py
+++ b/RLA/easy_plot/plot_util.py
@@ -11,6 +11,7 @@
 sns.set_style('darkgrid', {'legend.frameon':True})
 
 
+
 def smooth(y, radius, mode='two_sided', valid_only=False):
     '''
     Smooth signal y, where radius is determines the size of the window
@@ -151,8 +152,16 @@ def symmetric_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_
     ys[count_ys < low_counts_threshold] = np.nan
     return xs, ys, count_ys
 
-Result = namedtuple('Result', 'monitor progress dirname metadata')
-Result.__new__.__defaults__ = (None,) * len(Result._fields)
+# Result = namedtuple('Result', 'monitor progress dirname metadata hyper_param')
+# Result.__new__.__defaults__ = (None,) * len(Result._fields)
+
+class Result:
+    def __init__(self, monitor=None, progress=None, dirname=None, metadata=None, hyper_param=None):
+        self.monitor = monitor
+        self.progress = progress
+        self.dirname = dirname
+        self.metadata = metadata
+        self.hyper_param = hyper_param
 
 def word_replace(string):
     return string.replace('/', '--').replace("\'", "||")
@@ -217,24 +226,32 @@ def load_results(root_dir_or_dirs, names, x_bound, enable_progress=True, use_buf
                                 reader = pd.read_csv(progcsv, chunksize=100000,  quoting=csv.QUOTE_NONE,
                                                      encoding='utf-8', index_col=False, comment='#')
                                 raw_df = pd.DataFrame()
+
                                 for chunk in reader:
                                     slim_chunk = chunk
-                                    if set(names).issubset(slim_chunk.columns):
-                                        slim_chunk = slim_chunk[names]
-                                        if x_bound[1] is not None:
+                                    # if set(names).issubset(slim_chunk.columns):
+                                    existed_names = []
+                                    for name in names:
+                                        if name not in slim_chunk.columns:
+                                            print("[error keys]: {}".format(name))
+                                        else:
+                                            existed_names.append(name)
+                                    if len(existed_names) == 0:
+                                        raise RuntimeError("all value_keys cannot be found.")
+                                    slim_chunk = slim_chunk[existed_names]
+                                    if x_bound[1] is not None:
+                                        if isinstance(x_bound[1], tuple):
+                                            slim_chunk = slim_chunk[np.logical_and(slim_chunk[x_bound[0]] < x_bound[1][1],
+                                                                                   slim_chunk[x_bound[0]] > x_bound[1][0])]
+                                        else:
                                             slim_chunk = slim_chunk[slim_chunk[x_bound[0]] < x_bound[1]]
-                                        raw_df = pd.concat([raw_df, slim_chunk], ignore_index=True)
-                                    else:
-                                        raise RuntimeError
+                                    raw_df = pd.concat([raw_df, slim_chunk], ignore_index=True)
                                 import csv
                                 raw_df.to_csv(buf_csv, index=False)
                             result['progress'] = raw_df
                         except pandas.errors.EmptyDataError:
                             print('skipping progress file in ', dirname, 'empty data')
                         except Exception as e:
-                            for name in names:
-                                if name not in slim_chunk.columns:
-                                    print("[error keys]: {}".format(name))
                             print("other read error :", e)
 
                     else:
@@ -339,6 +356,7 @@ def plot_results(
                                               See docstrings for decay_steps in symmetric_ema or one_sided_ema functions.
 
     '''
+    score_results = {}
     if vary_len_plot:
         assert resample <= 0, "plot varied length averaged lines only allowed in unresample mode."
     if colors is None:
@@ -395,10 +413,7 @@ def plot_results(
         idx_row = isplit // ncols
         idx_col = isplit % ncols
         ax = axarr[idx_row][idx_col]
-        # 单张图片可能有多个数据源
         for result in sresults:
-            # 一个数据源目前只能画一条线，我把这里变成多个group应该就可以了
-            # 并且多个实验只要group名字相同，就应该使用相同的group
             result_groups, y_names = group_fn(result)
             for group, y_name in zip(result_groups, y_names):
                 g2c[group] += 1
@@ -420,7 +435,6 @@ def plot_results(
                     if resample:
                         x, y, counts = symmetric_ema(x, y, x[0], x[-1], resample, decay_steps=smooth_step)
                     l, = ax.plot(x, y, color=colors[groups.index(group) % len(colors)])
-                    # l 应该是对应线的结果？
                     g2l[group] = l
 
         if average_group:
@@ -508,14 +522,17 @@ def allequal(qs):
                         res = g2lf[original_legend_keys[index] + '-se']
                         res[0].update(props={"color": colors[index % len(colors)]})
                         print("{}-err : ({:.2f} \pm {:.2f})".format(legend_keys[index], res[1][-1], res[2][-1]))
+                        score_results[legend_keys[index]+'-err'] = [res[1][-1], res[2][-1]]
                     if shaded_std:
                         res = g2lf[original_legend_keys[index] + '-ss']
                         res[0].update(props={"color": colors[index % len(colors)]})
                         print("{}-std :({:.2f} \pm {:.2f})".format(legend_keys[index], res[1][-1], res[2][-1]))
+                        score_results[legend_keys[index]+'-std'] = [res[1][-1], res[2][-1]]
                     if shaded_range:
                         res = g2lf[original_legend_keys[index] + '-sr']
                         res[0].update(props={"color": colors[index % len(colors)]})
                         print("{}-range : ({:.2f}, {:.2f})".format(legend_keys[index], res[1][-1], res[2][-1]))
+                        score_results[legend_keys[index]+'-range'] = [res[1][-1], res[2][-1]]
 
             if bound_line is not None:
                 for bl in bound_line:
@@ -569,7 +586,7 @@ def allequal(qs):
         plt.title(title, fontsize=18)
     else:
         plt.gcf().subplots_adjust(bottom=0.12, left=0.12)
-    return f, axarr, lgd, texts, g2lf
+    return f, axarr, lgd, texts, g2lf, score_results
 
 def regression_analysis(df):
     xcols = list(df.columns.copy())
diff --git a/RLA/query_tool.py b/RLA/query_tool.py
new file mode 100644
index 0000000..6f1a89f
--- /dev/null
+++ b/RLA/query_tool.py
@@ -0,0 +1,85 @@
+# Created by xionghuichen at 2022/8/10
+# Email: chenxh@lamda.nju.edu.cn
+
+import glob
+import os.path as osp
+import os
+import dill
+import re
+import copy
+from RLA.easy_log.const import LOG, ARCHIVE_TESTER, LogDataType
+from RLA.easy_log.tester import Tester
+
+
+class BasicQueryResult(object):
+    def __init__(self, dirname):
+        self.dirname = dirname
+
+class ArchiveQueryResult(BasicQueryResult):
+    def __init__(self, exp_manager, dirname):
+        super(ArchiveQueryResult, self).__init__(dirname)
+        assert isinstance(exp_manager, Tester)
+        self.exp_manager = exp_manager
+
+class LogQueryResult(BasicQueryResult):
+    def __init__(self, dirname):
+        super(LogQueryResult, self).__init__(dirname)
+
+def extract_valid_index(regex):
+    if re.search(r'\d{4}/\d{2}/\d{2}/\d{2}-\d{2}-\d{2}-\d{6}', regex):
+        target_reg = re.search(r'\d{4}/\d{2}/\d{2}/\d{2}-\d{2}-\d{2}-\d{6}', regex).group(0)
+    else:
+        target_reg = None
+    return target_reg
+
+def experiment_data_query(data_root, task_table_name, reg, data_type):
+    if data_type == LOG:
+        return _log_data_query(data_root, task_table_name, reg)
+    elif data_type == ARCHIVE_TESTER:
+        return _archive_tester_query(data_root, task_table_name, reg)
+    else:
+        raise NotImplementedError
+
+
+def _archive_tester_query(data_root, task_table_name, reg):
+    experiment_data_dict = {}
+    root_dir_regex = osp.join(data_root, ARCHIVE_TESTER, task_table_name, reg)
+    for root_dir in glob.glob(root_dir_regex):
+        if os.path.exists(root_dir):
+            if osp.isdir(root_dir):
+                for file_list in os.walk(root_dir):
+                    for file in file_list[2]:
+                        location = osp.join(file_list[0], file)
+                        exp_manager = dill.load(open(location, 'rb'))
+                        dirname = location.split('.pkl')[0]
+                        key = extract_valid_index(location)
+                        experiment_data_dict[key] = ArchiveQueryResult(dirname=dirname, exp_manager=exp_manager)
+            else:
+                location = root_dir
+                key = extract_valid_index(location)
+                exp_manager = dill.load(open(location, 'rb'))
+                dirname = location.split('.pkl')[0]
+                experiment_data_dict[key] = ArchiveQueryResult(dirname=dirname, exp_manager=exp_manager)
+    return experiment_data_dict
+
+
+def _log_data_query(data_root, task_table_name, reg):
+    experiment_data_dict = {}
+    root_dir_regex = osp.join(data_root, LOG, task_table_name, reg)
+    for root_dir in glob.glob(root_dir_regex):
+        if os.path.exists(root_dir):
+            if osp.isdir(root_dir):
+                for file_list in os.walk(root_dir):
+                    for file in file_list[2]:
+                        if 'progress.csv' in file:
+                            location = osp.join(file_list[0], file)
+                            key = extract_valid_index(location)
+                            dirname = osp.dirname(location)
+                            experiment_data_dict[key] = LogQueryResult(dirname=dirname)
+                            break
+            else:
+                location = root_dir
+                dirname = osp.dirname(location)
+                experiment_data_dict[key] = LogQueryResult(dirname=dirname)
+    return experiment_data_dict
+
diff --git a/RLA/utils/utils.py b/RLA/utils/utils.py
index b6a0476..9138586 100644
--- a/RLA/utils/utils.py
+++ b/RLA/utils/utils.py
@@ -31,4 +31,7 @@ def load_yaml(path):
         private_config = yaml.load(fs)
     except TypeError:
         private_config = yaml.safe_load(fs)
-    return private_config
\ No newline at end of file
+    return private_config
+
+def optional_set(new_val, old_val):
+    return new_val if new_val is not None else old_val
\ No newline at end of file
diff --git a/example/plot_res.ipynb b/example/plot_res.ipynb
index 6653046..ce2a009 100644
--- a/example/plot_res.ipynb
+++ b/example/plot_res.ipynb
@@ -1,8 +1,63 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "72bf0fba",
+   "metadata": {},
+   "source": [
+    "# V2"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
+   "id": "cb6f1787",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from RLA.easy_plot.plot_func_v2 import plot_func"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "984c6140",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mAssertionError\u001B[0m                            Traceback (most recent call last)",
+      "\u001B[0;32m/var/folders/tv/tvd4k0qs49q3fcm30tq61mx80000gp/T/ipykernel_4915/3456245110.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      4\u001B[0m     \u001B[0;34m'2022/03/01/21-[12]*'\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m ]\n\u001B[0;32m----> 6\u001B[0;31m \u001B[0m_\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mplot_func\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata_root\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mdata_root\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtask_table_name\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mtask\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mregs\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mregs\u001B[0m \u001B[0;34m,\u001B[0m \u001B[0msplit_keys\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'learning_rate'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmetrics\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'perf/mse'\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
+      "\u001B[0;32m~/projects/RLAssistant/RLA/easy_plot/plot_func_v2.py\u001B[0m in \u001B[0;36mplot_func\u001B[0;34m(data_root, task_table_name, regs, split_keys, metrics, use_buf, xlim, ylim, xlabel, ylabel, scale_dict, replace_legend_keys, key_to_legend_fn, save_name, *args, **kwargs)\u001B[0m\n\u001B[1;32m    100\u001B[0m     \u001B[0m_\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0m_\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mlgd\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtexts\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mg2lf\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mscore_results\u001B[0m \u001B[0;34m=\u001B[0m\u001B[0;31m \u001B[0m\u001B[0;31m\\\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    101\u001B[0m         plot_util.plot_results(results, xy_fn= lambda r, y_names: csv_to_xy(r, DEFAULT_X_NAME, y_names, final_scale_dict),\n\u001B[0;32m--> 102\u001B[0;31m                            group_fn=group_fn, average_group=True, ylabel=ylabel, xlabel=xlabel, replace_legend_keys=replace_legend_keys, *args, **kwargs)\n\u001B[0m\u001B[1;32m    103\u001B[0m     \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"--- complete process ---\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    104\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0msave_name\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+      "\u001B[0;32m~/projects/RLAssistant/RLA/easy_plot/plot_util.py\u001B[0m in \u001B[0;36mplot_results\u001B[0;34m(allresults, xy_fn, split_fn, group_fn, average_group, shaded_std, shaded_err, shaded_range, figsize, legend_outside, resample, vary_len_plot, smooth_step, tiling, xlabel, ylabel, title, replace_legend_keys, replace_legend_sort, pretty, bound_line, colors, log, ylim, xlim, show_number, skip_legend, rescale_idx)\u001B[0m\n\u001B[1;32m    363\u001B[0m         \u001B[0msplitkey\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0msplit_fn\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mresult\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    364\u001B[0m         \u001B[0msk2r\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0msplitkey\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mresult\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 365\u001B[0;31m     \u001B[0;32massert\u001B[0m \u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msk2r\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m>\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    366\u001B[0m     \u001B[0;32massert\u001B[0m \u001B[0misinstance\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mresample\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mint\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"0: don't resample. <integer>: that many samples\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    367\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0mtiling\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0;34m'vertical'\u001B[0m \u001B[0;32mor\u001B[0m \u001B[0mtiling\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+      "\u001B[0;31mAssertionError\u001B[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "data_root='simplest_code'\n",
+    "task = 'demo_task'\n",
+    "regs = [\n",
+    "    '2022/03/01/21-[12]*'\n",
+    "]\n",
+    "_ = plot_func(data_root=data_root, task_table_name=task, regs=regs , split_keys=['learning_rate'], metrics=['perf/mse'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "589e5737",
+   "metadata": {},
+   "source": [
+    "# V1 (deprecated)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
    "id": "a54ad55b",
    "metadata": {},
    "outputs": [],
@@ -20,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "be8dbbe0",
    "metadata": {},
    "outputs": [
@@ -216,9 +271,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "meituan",
    "language": "python",
-   "name": "python3"
+   "name": "meituan"
   },
   "language_info": {
    "codemirror_mode": {
@@ -235,4 +290,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/rla_scripts/start_pretty_plotter.py b/rla_scripts/start_pretty_plotter.py
new file mode 100644
index 0000000..8217d2d
--- /dev/null
+++ b/rla_scripts/start_pretty_plotter.py
@@ -0,0 +1,56 @@
+"""
+A script to start a server of the pretty plotter.
+
+"""
+import os
+
+from RLA.easy_log.log_tools import PrettyPlotterTool, Filter
+import argparse
+from RLA.rla_argparser import boolean_flag
+from config import *
+
+from smart_logger.front_page.page import start_page_server
+import smart_logger.common.plot_config as plot_config
+import smart_logger.common.page_config as page_config
+
+
+def argsparser():
+    parser = argparse.ArgumentParser("Delete Log")
+    # reduce setting
+    parser.add_argument('--task_table_name', type=str, default="")
+    parser.add_argument('--regex', type=str)
+    parser.add_argument('--timestep_bound', type=int, default=100)
+    parser.add_argument('--delete_type', type=str, default=Filter.ALL)
+    parser.add_argument('--workspace_path', '-wks', type=str, default='~/.pretty_plotter_cache',
+                        help="Path to the workspace, used to saving cache data")
+    parser.add_argument('--user_name', '-u', type=str, default='user',
+                        help="user name")
+    parser.add_argument('--password', '-pw', type=str, default='123456',
+                        help="password")
+    parser.add_argument('--port', '-p', type=int, default=7005, help="Server port")
+    boolean_flag(parser, 'start_server', default=False)
+    args = parser.parse_args()
+    return args
+
+if __name__=='__main__':
+    args = argsparser()
+    filter = Filter()
+    filter.config(type=args.delete_type, timstep_bound=args.timestep_bound)
+    tool = PrettyPlotterTool(proj_root=DATA_ROOT, task_table_name=args.task_table_name, regex=args.regex)
+    tool.gen_json(args.regex)
+    if args.start_server:
+        plot_config.DATA_PATH = os.path.abspath(DATA_ROOT)
+        page_config.WORKSPAPCE = os.path.abspath(os.path.expanduser(args.workspace_path))
+
+        plot_config.DATA_MERGER = []
+        plot_config.PLOTTING_XY = []
+        plot_config.PLOT_LOG_PATH = f"{plot_config.DATA_PATH}"
+        plot_config.PLOT_FIGURE_SAVING_PATH = f"{os.path.join(os.path.dirname(plot_config.DATA_PATH), 'figure')}"
+
+        page_config.WEB_RAM_PATH = f"{page_config.WORKSPAPCE}/WEB_ROM"
+        page_config.CONFIG_PATH = f"{page_config.WEB_RAM_PATH}/configs"
+        page_config.FIGURE_PATH = f"{page_config.WEB_RAM_PATH}/figures"
+        page_config.PORT = args.port
+        page_config.USER_NAME = args.user_name
+        page_config.PASSWD = args.password
+        start_page_server()
diff --git a/setup.py b/setup.py
index ebafbfe..d1fb8b1 100644
--- a/setup.py
+++ b/setup.py
@@ -22,6 +22,7 @@
             "seaborn",
             "pathspec",
             'tensorboardX', 
-            'pysftp'
+            'pysftp',
+            'typing'
         ]
     )
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+model_checkpoint_path: "checkpoint-900"
+all_model_checkpoint_paths: "checkpoint-900"
diff --git a/test/test_data_root/checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint-900.data-00000-of-00001 b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint-900.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint-900.index b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint-900.index
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diff --git a/test/test_data_root/checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint-900.meta b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint-900.meta
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diff --git a/test/test_data_root/checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint
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--- /dev/null
+++ b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint	
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint-900"
+all_model_checkpoint_paths: "checkpoint-900"
diff --git a/test/test_data_root/checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint-900.data-00000-of-00001 b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint-900.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint-900.index b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint-900.index
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diff --git a/test/test_data_root/checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint
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+++ b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint	
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint-900"
+all_model_checkpoint_paths: "checkpoint-900"
diff --git a/test/test_data_root/checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint-900.data-00000-of-00001 b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint-900.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint-900.meta b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint-900.meta
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diff --git a/test/test_data_root/checkpoint/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint
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+++ b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint	
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint-300"
+all_model_checkpoint_paths: "checkpoint-300"
diff --git a/test/test_data_root/checkpoint/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint-300.data-00000-of-00001 b/test/test_data_root/checkpoint/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint-300.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/checkpoint
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--- /dev/null
+++ b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/checkpoint	
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint-0"
+all_model_checkpoint_paths: "checkpoint-0"
diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/checkpoint-0.data-00000-of-00001 b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/checkpoint-0.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/checkpoint-0.index b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/checkpoint-0.index
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint
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--- /dev/null
+++ b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint	
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint-800"
+all_model_checkpoint_paths: "checkpoint-800"
diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint-800.data-00000-of-00001 b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint-800.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint
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+++ b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint	
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint-80"
+all_model_checkpoint_paths: "checkpoint-80"
diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint-80.data-00000-of-00001 b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint-80.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint-80.index b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint-80.index
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/checkpoint-80.pt b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/checkpoint-80.pt
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint
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+++ b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint	
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint-80"
+all_model_checkpoint_paths: "checkpoint-80"
diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint-80.data-00000-of-00001 b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint-80.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint-80.index b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint-80.index
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/checkpoint-80.pt b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/checkpoint-80.pt
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/checkpoint
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+++ b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/checkpoint	
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint-80"
+all_model_checkpoint_paths: "checkpoint-80"
diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/checkpoint-80.data-00000-of-00001 b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/checkpoint-80.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/checkpoint-80.pt b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/checkpoint-80.pt
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/checkpoint
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+++ b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/checkpoint
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+all_model_checkpoint_paths: "checkpoint-80"
diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/checkpoint-80.data-00000-of-00001 b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/checkpoint-80.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/checkpoint-80.pt b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/checkpoint-80.pt
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/checkpoint
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/checkpoint
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+++ b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/checkpoint
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint-0"
+all_model_checkpoint_paths: "checkpoint-0"
diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/checkpoint-0.data-00000-of-00001 b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/checkpoint-0.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/checkpoint
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+++ b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/checkpoint
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/checkpoint
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/checkpoint-0.data-00000-of-00001 b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/checkpoint-0.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/checkpoint
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+++ b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/checkpoint
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint-0"
+all_model_checkpoint_paths: "checkpoint-0"
diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/checkpoint-0.data-00000-of-00001 b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/checkpoint-0.data-00000-of-00001
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/checkpoint-0.meta b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/checkpoint-0.meta
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diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/checkpoint b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/checkpoint
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+++ b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/checkpoint
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint-0"
+all_model_checkpoint_paths: "checkpoint-0"
diff --git a/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/checkpoint-0.data-00000-of-00001 b/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/checkpoint-0.data-00000-of-00001
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diff --git a/test/test_data_root/code/__init__.py b/test/test_data_root/code/__init__.py
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diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/__init__.py
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diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/project/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/project/__init__.py
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diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/project/main.py b/test/test_data_root/code/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/project/main.py
new file mode 100644
index 0000000..d5be684
--- /dev/null
+++ b/test/test_data_root/code/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/project/main.py	
@@ -0,0 +1,79 @@
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.tools import time_record, time_record_end
+from RLA.easy_log.simple_mat_plot import simple_plot
+from RLA.rla_argparser import arg_parser_postprocess
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import argparse
+
+# phase 1: init your hyper-parameters
+def get_param():
+    parser = argparse.ArgumentParser("Tensorflow Implementation of Variational Sequence")
+    parser.add_argument('--seed', help='RNG seed', type=int, default=88)
+    parser.add_argument('--env_id', help='environment ID', default='Test-v1')
+    parser.add_argument('--learning_rate', help='a hyperparameter', default=1e-4, type=float)
+    parser.add_argument('--input_size', help='a hyperparameter', default=16, type=int)
+    parser = arg_parser_postprocess(parser)
+    args = parser.parse_args()
+    kwargs = vars(args)
+    exp_manager.set_hyper_param(**kwargs)
+    exp_manager.add_record_param(["env_id", "learning_rate"])
+    return kwargs
+
+# phase 2: init the RLA experiment manager.
+kwargs = get_param()
+task_name = 'demo_task'
+rla_data_root = '../'
+exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+exp_manager.log_files_gen()
+exp_manager.print_args()
+
+# start_epoch = 0
+# phase 3: [optional] resume from a historical experiment.
+from RLA.easy_log.exp_loader import exp_loader
+exp_loader.fork_log_files()
+start_epoch, load_res, hist_variables = exp_loader.load_from_record_date(variable_list=['iv'])
+
+# phase 4: write your code.
+import tensorflow as tf
+X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+l = X_ph
+for _ in range(3):
+    l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+loss = tf.reduce_mean(np.square(out - y_ph))
+opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+
+sess = tf.Session().__enter__()
+sess.run(tf.variables_initializer(tf.global_variables()))
+
+exp_manager.new_saver(var_prefix='', max_to_keep=1)
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+for i in range(start_epoch, 1000):
+    exp_manager.time_step_holder.set_time(i)
+    x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+    y = target_func(x_input)
+    loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph:x_input, y_ph: y})[:-1]
+    logger.ma_record_tabular("perf/mse", loss_out, 10)
+    logger.record_tabular("y_out", np.mean(y))
+    logger.dump_tabular()
+    if i % 100 == 0:
+        exp_manager.save_checkpoint()
+    if i % 10 == 0:
+        def plot_func():
+            import matplotlib.pyplot as plt
+            testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=16, axis=-1)
+            testY = target_func(testX)
+            predY = sess.run(out, feed_dict={X_ph: testX})
+            plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+            plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+        mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+    logger.dump_tabular()
\ No newline at end of file
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/project/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/project/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/project/main.py b/test/test_data_root/code/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/project/main.py
new file mode 100644
index 0000000..e8f57e4
--- /dev/null
+++ b/test/test_data_root/code/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/project/main.py	
@@ -0,0 +1,79 @@
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.tools import time_record, time_record_end
+from RLA.easy_log.simple_mat_plot import simple_plot
+from RLA.rla_argparser import arg_parser_postprocess
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import argparse
+
+# phase 1: init your hyper-parameters
+def get_param():
+    parser = argparse.ArgumentParser("Tensorflow Implementation of Variational Sequence")
+    parser.add_argument('--seed', help='RNG seed', type=int, default=88)
+    parser.add_argument('--env_id', help='environment ID', default='Test-v1')
+    parser.add_argument('--learning_rate', help='a hyperparameter', default=1e-2, type=float)
+    parser.add_argument('--input_size', help='a hyperparameter', default=16, type=int)
+    parser = arg_parser_postprocess(parser)
+    args = parser.parse_args()
+    kwargs = vars(args)
+    exp_manager.set_hyper_param(**kwargs)
+    exp_manager.add_record_param(["env_id", "learning_rate"])
+    return kwargs
+
+# phase 2: init the RLA experiment manager.
+kwargs = get_param()
+task_name = 'demo_task'
+rla_data_root = '../'
+exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+exp_manager.log_files_gen()
+exp_manager.print_args()
+
+# start_epoch = 0
+# phase 3: [optional] resume from a historical experiment.
+from RLA.easy_log.exp_loader import exp_loader
+exp_loader.fork_log_files()
+start_epoch, load_res, hist_variables = exp_loader.load_from_record_date(variable_list=['iv'])
+
+# phase 4: write your code.
+import tensorflow as tf
+X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+l = X_ph
+for _ in range(3):
+    l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+loss = tf.reduce_mean(np.square(out - y_ph))
+opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+
+sess = tf.Session().__enter__()
+sess.run(tf.variables_initializer(tf.global_variables()))
+
+exp_manager.new_saver(var_prefix='', max_to_keep=1)
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+for i in range(start_epoch, 1000):
+    exp_manager.time_step_holder.set_time(i)
+    x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+    y = target_func(x_input)
+    loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph:x_input, y_ph: y})[:-1]
+    logger.ma_record_tabular("perf/mse", loss_out, 10)
+    logger.record_tabular("y_out", np.mean(y))
+    logger.dump_tabular()
+    if i % 100 == 0:
+        exp_manager.save_checkpoint()
+    if i % 10 == 0:
+        def plot_func():
+            import matplotlib.pyplot as plt
+            testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=16, axis=-1)
+            testY = target_func(testX)
+            predY = sess.run(out, feed_dict={X_ph: testX})
+            plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+            plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+        mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+    logger.dump_tabular()
\ No newline at end of file
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/project/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/project/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/project/main.py b/test/test_data_root/code/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/project/main.py
new file mode 100644
index 0000000..247969a
--- /dev/null
+++ b/test/test_data_root/code/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/project/main.py	
@@ -0,0 +1,92 @@
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.tools import time_record, time_record_end
+from RLA.easy_log.simple_mat_plot import simple_plot
+from RLA.rla_argparser import arg_parser_postprocess
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import argparse
+
+# phase 1: init your hyper-parameters
+def get_param():
+    parser = argparse.ArgumentParser("Tensorflow Implementation of Variational Sequence")
+    parser.add_argument('--seed', help='RNG seed', type=int, default=888)
+    parser.add_argument('--env_id', help='environment ID', default='Test-v1')
+    parser.add_argument('--learning_rate', help='a hyperparameter', default=1e-2, type=float)
+    parser.add_argument('--input_size', help='a hyperparameter', default=16, type=int)
+    parser = arg_parser_postprocess(parser)
+    args = parser.parse_args()
+    kwargs = vars(args)
+    exp_manager.set_hyper_param(**kwargs)
+    exp_manager.add_record_param(["env_id", "learning_rate", "seed"])
+    return kwargs
+
+# phase 2: init the RLA experiment manager.
+kwargs = get_param()
+
+task_name = 'demo_task'
+rla_data_root = '../'
+exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+exp_manager.log_files_gen()
+exp_manager.print_args()
+
+def set_global_seeds(seed):
+    """
+    set the seed for python random, tensorflow, numpy and gym spaces
+
+    :param seed: (int) the seed
+    """
+    import random
+    tf.set_random_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+set_global_seeds(kwargs["seed"])
+
+# phase 3: [optional] resume from a historical experiment.
+from RLA.easy_log.exp_loader import exp_loader
+exp_loader.fork_log_files()
+start_epoch, load_res, hist_variables = exp_loader.load_from_record_date(variable_list=['iv'])
+
+# phase 4: write your code.
+import tensorflow as tf
+X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+l = X_ph
+for _ in range(3):
+    l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+loss = tf.reduce_mean(np.square(out - y_ph))
+opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+
+sess = tf.Session().__enter__()
+sess.run(tf.variables_initializer(tf.global_variables()))
+
+exp_manager.new_saver(var_prefix='', max_to_keep=1)
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+for i in range(start_epoch, 1000):
+    exp_manager.time_step_holder.set_time(i)
+    x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+    y = target_func(x_input)
+    loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph:x_input, y_ph: y})[:-1]
+    logger.ma_record_tabular("perf/mse", loss_out, 10)
+    logger.record_tabular("y_out", np.mean(y))
+    logger.dump_tabular()
+    if i % 100 == 0:
+        exp_manager.save_checkpoint()
+    if i % 10 == 0:
+        def plot_func():
+            import matplotlib.pyplot as plt
+            testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=16, axis=-1)
+            testY = target_func(testX)
+            predY = sess.run(out, feed_dict={X_ph: testX})
+            plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+            plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+        mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+    logger.dump_tabular()
\ No newline at end of file
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/project/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/project/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/project/main.py b/test/test_data_root/code/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/project/main.py
new file mode 100644
index 0000000..d1450a6
--- /dev/null
+++ b/test/test_data_root/code/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/project/main.py	
@@ -0,0 +1,92 @@
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.tools import time_record, time_record_end
+from RLA.easy_log.simple_mat_plot import simple_plot
+from RLA.rla_argparser import arg_parser_postprocess
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import tensorflow as tf
+import argparse
+
+# phase 1: init your hyper-parameters
+def get_param():
+    parser = argparse.ArgumentParser("Tensorflow Implementation of Variational Sequence")
+    parser.add_argument('--seed', help='RNG seed', type=int, default=8888)
+    parser.add_argument('--env_id', help='environment ID', default='Test-v1')
+    parser.add_argument('--learning_rate', help='a hyperparameter', default=1e-2, type=float)
+    parser.add_argument('--input_size', help='a hyperparameter', default=16, type=int)
+    parser = arg_parser_postprocess(parser)
+    args = parser.parse_args()
+    kwargs = vars(args)
+    exp_manager.set_hyper_param(**kwargs)
+    exp_manager.add_record_param(["env_id", "learning_rate", "seed"])
+    return kwargs
+
+# phase 2: init the RLA experiment manager.
+kwargs = get_param()
+
+task_name = 'demo_task'
+rla_data_root = '../'
+exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+exp_manager.log_files_gen()
+exp_manager.print_args()
+
+def set_global_seeds(seed):
+    """
+    set the seed for python random, tensorflow, numpy and gym spaces
+
+    :param seed: (int) the seed
+    """
+    import random
+    tf.set_random_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+set_global_seeds(kwargs["seed"])
+
+# phase 3: [optional] resume from a historical experiment.
+from RLA.easy_log.exp_loader import exp_loader
+exp_loader.fork_log_files()
+start_epoch, load_res, hist_variables = exp_loader.load_from_record_date(variable_list=['iv'])
+
+# phase 4: write your code.
+X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+l = X_ph
+for _ in range(3):
+    l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+loss = tf.reduce_mean(np.square(out - y_ph))
+opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+
+sess = tf.Session().__enter__()
+sess.run(tf.variables_initializer(tf.global_variables()))
+
+exp_manager.new_saver(var_prefix='', max_to_keep=1)
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+for i in range(start_epoch, 1000):
+    exp_manager.time_step_holder.set_time(i)
+    x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+    y = target_func(x_input)
+    loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph:x_input, y_ph: y})[:-1]
+    logger.ma_record_tabular("perf/mse", loss_out, 10)
+    logger.record_tabular("y_out", np.mean(y))
+    logger.dump_tabular()
+    if i % 100 == 0:
+        exp_manager.save_checkpoint()
+    if i % 10 == 0:
+        def plot_func():
+            import matplotlib.pyplot as plt
+            testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=16, axis=-1)
+            testY = target_func(testX)
+            predY = sess.run(out, feed_dict={X_ph: testX})
+            plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+            plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+        mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+    logger.dump_tabular()
\ No newline at end of file
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/project/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/project/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/project/main.py b/test/test_data_root/code/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/project/main.py
new file mode 100644
index 0000000..a634d64
--- /dev/null
+++ b/test/test_data_root/code/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/project/main.py	
@@ -0,0 +1,92 @@
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.tools import time_record, time_record_end
+from RLA.easy_log.simple_mat_plot import simple_plot
+from RLA.rla_argparser import arg_parser_postprocess
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import tensorflow as tf
+import argparse
+
+# phase 1: init your hyper-parameters
+def get_param():
+    parser = argparse.ArgumentParser("Tensorflow Implementation of Variational Sequence")
+    parser.add_argument('--seed', help='RNG seed', type=int, default=888)
+    parser.add_argument('--env_id', help='environment ID', default='Test-v1')
+    parser.add_argument('--learning_rate', help='a hyperparameter', default=1e-2, type=float)
+    parser.add_argument('--input_size', help='a hyperparameter', default=16, type=int)
+    parser = arg_parser_postprocess(parser)
+    args = parser.parse_args()
+    kwargs = vars(args)
+    exp_manager.set_hyper_param(**kwargs)
+    exp_manager.add_record_param(["env_id", "learning_rate", "seed"])
+    return kwargs
+
+# phase 2: init the RLA experiment manager.
+kwargs = get_param()
+
+task_name = 'demo_task'
+rla_data_root = '../'
+exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+exp_manager.log_files_gen()
+exp_manager.print_args()
+
+def set_global_seeds(seed):
+    """
+    set the seed for python random, tensorflow, numpy and gym spaces
+
+    :param seed: (int) the seed
+    """
+    import random
+    tf.set_random_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+set_global_seeds(kwargs["seed"])
+
+# phase 3: [optional] resume from a historical experiment.
+from RLA.easy_log.exp_loader import exp_loader
+exp_loader.fork_log_files()
+start_epoch, load_res, hist_variables = exp_loader.load_from_record_date(variable_list=['iv'])
+
+# phase 4: write your code.
+X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+l = X_ph
+for _ in range(3):
+    l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+loss = tf.reduce_mean(np.square(out - y_ph))
+opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+
+sess = tf.Session().__enter__()
+sess.run(tf.variables_initializer(tf.global_variables()))
+
+exp_manager.new_saver(var_prefix='', max_to_keep=1)
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+for i in range(start_epoch, 1000):
+    exp_manager.time_step_holder.set_time(i)
+    x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+    y = target_func(x_input)
+    loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph:x_input, y_ph: y})[:-1]
+    logger.ma_record_tabular("perf/mse", loss_out, 10)
+    logger.record_tabular("y_out", np.mean(y))
+    logger.dump_tabular()
+    if i % 100 == 0:
+        exp_manager.save_checkpoint()
+    if i % 10 == 0:
+        def plot_func():
+            import matplotlib.pyplot as plt
+            testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=16, axis=-1)
+            testY = target_func(testX)
+            predY = sess.run(out, feed_dict={X_ph: testX})
+            plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+            plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+        mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+    logger.dump_tabular()
\ No newline at end of file
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/project/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/project/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/project/main.py b/test/test_data_root/code/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/project/main.py
new file mode 100644
index 0000000..0b55177
--- /dev/null
+++ b/test/test_data_root/code/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/project/main.py	
@@ -0,0 +1,92 @@
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.tools import time_record, time_record_end
+from RLA.easy_log.simple_mat_plot import simple_plot
+from RLA.rla_argparser import arg_parser_postprocess
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import tensorflow as tf
+import argparse
+
+# phase 1: init your hyper-parameters
+def get_param():
+    parser = argparse.ArgumentParser("Tensorflow Implementation of Variational Sequence")
+    parser.add_argument('--seed', help='RNG seed', type=int, default=888)
+    parser.add_argument('--env_id', help='environment ID', default='Test-v1')
+    parser.add_argument('--learning_rate', help='a hyperparameter', default=1e-3, type=float)
+    parser.add_argument('--input_size', help='a hyperparameter', default=16, type=int)
+    parser = arg_parser_postprocess(parser)
+    args = parser.parse_args()
+    kwargs = vars(args)
+    exp_manager.set_hyper_param(**kwargs)
+    exp_manager.add_record_param(["env_id", "learning_rate", "seed"])
+    return kwargs
+
+# phase 2: init the RLA experiment manager.
+kwargs = get_param()
+
+task_name = 'demo_task'
+rla_data_root = '../'
+exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+exp_manager.log_files_gen()
+exp_manager.print_args()
+
+def set_global_seeds(seed):
+    """
+    set the seed for python random, tensorflow, numpy and gym spaces
+
+    :param seed: (int) the seed
+    """
+    import random
+    tf.set_random_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+set_global_seeds(kwargs["seed"])
+
+# phase 3: [optional] resume from a historical experiment.
+from RLA.easy_log.exp_loader import exp_loader
+exp_loader.fork_log_files()
+start_epoch, load_res, hist_variables = exp_loader.load_from_record_date(variable_list=['iv'])
+
+# phase 4: write your code.
+X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+l = X_ph
+for _ in range(3):
+    l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+loss = tf.reduce_mean(np.square(out - y_ph))
+opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+
+sess = tf.Session().__enter__()
+sess.run(tf.variables_initializer(tf.global_variables()))
+
+exp_manager.new_saver(var_prefix='', max_to_keep=1)
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+for i in range(start_epoch, 1000):
+    exp_manager.time_step_holder.set_time(i)
+    x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+    y = target_func(x_input)
+    loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph:x_input, y_ph: y})[:-1]
+    logger.ma_record_tabular("perf/mse", loss_out, 10)
+    logger.record_tabular("y_out", np.mean(y))
+    logger.dump_tabular()
+    if i % 100 == 0:
+        exp_manager.save_checkpoint()
+    if i % 10 == 0:
+        def plot_func():
+            import matplotlib.pyplot as plt
+            testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=16, axis=-1)
+            testY = target_func(testX)
+            predY = sess.run(out, feed_dict={X_ph: testX})
+            plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+            plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+        mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+    logger.dump_tabular()
\ No newline at end of file
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/project/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/project/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/project/main.py b/test/test_data_root/code/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/project/main.py
new file mode 100644
index 0000000..335f7e4
--- /dev/null
+++ b/test/test_data_root/code/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/project/main.py	
@@ -0,0 +1,92 @@
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.tools import time_record, time_record_end
+from RLA.easy_log.simple_mat_plot import simple_plot
+from RLA.rla_argparser import arg_parser_postprocess
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import tensorflow as tf
+import argparse
+
+# phase 1: init your hyper-parameters
+def get_param():
+    parser = argparse.ArgumentParser("Tensorflow Implementation of Variational Sequence")
+    parser.add_argument('--seed', help='RNG seed', type=int, default=88)
+    parser.add_argument('--env_id', help='environment ID', default='Test-v1')
+    parser.add_argument('--learning_rate', help='a hyperparameter', default=1e-3, type=float)
+    parser.add_argument('--input_size', help='a hyperparameter', default=16, type=int)
+    parser = arg_parser_postprocess(parser)
+    args = parser.parse_args()
+    kwargs = vars(args)
+    exp_manager.set_hyper_param(**kwargs)
+    exp_manager.add_record_param(["env_id", "learning_rate", "seed"])
+    return kwargs
+
+# phase 2: init the RLA experiment manager.
+kwargs = get_param()
+
+task_name = 'demo_task'
+rla_data_root = '../'
+exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+exp_manager.log_files_gen()
+exp_manager.print_args()
+
+def set_global_seeds(seed):
+    """
+    set the seed for python random, tensorflow, numpy and gym spaces
+
+    :param seed: (int) the seed
+    """
+    import random
+    tf.set_random_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+set_global_seeds(kwargs["seed"])
+
+# phase 3: [optional] resume from a historical experiment.
+from RLA.easy_log.exp_loader import exp_loader
+exp_loader.fork_log_files()
+start_epoch, load_res, hist_variables = exp_loader.load_from_record_date(variable_list=['iv'])
+
+# phase 4: write your code.
+X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+l = X_ph
+for _ in range(3):
+    l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+loss = tf.reduce_mean(np.square(out - y_ph))
+opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+
+sess = tf.Session().__enter__()
+sess.run(tf.variables_initializer(tf.global_variables()))
+
+exp_manager.new_saver(var_prefix='', max_to_keep=1)
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+for i in range(start_epoch, 1000):
+    exp_manager.time_step_holder.set_time(i)
+    x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+    y = target_func(x_input)
+    loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph:x_input, y_ph: y})[:-1]
+    logger.ma_record_tabular("perf/mse", loss_out, 10)
+    logger.record_tabular("y_out", np.mean(y))
+    logger.dump_tabular()
+    if i % 100 == 0:
+        exp_manager.save_checkpoint()
+    if i % 10 == 0:
+        def plot_func():
+            import matplotlib.pyplot as plt
+            testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=16, axis=-1)
+            testY = target_func(testX)
+            predY = sess.run(out, feed_dict={X_ph: testX})
+            plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+            plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+        mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+    logger.dump_tabular()
\ No newline at end of file
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/project/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/project/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/project/main.py b/test/test_data_root/code/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/project/main.py
new file mode 100644
index 0000000..335f7e4
--- /dev/null
+++ b/test/test_data_root/code/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/project/main.py	
@@ -0,0 +1,92 @@
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.tools import time_record, time_record_end
+from RLA.easy_log.simple_mat_plot import simple_plot
+from RLA.rla_argparser import arg_parser_postprocess
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import tensorflow as tf
+import argparse
+
+# phase 1: init your hyper-parameters
+def get_param():
+    parser = argparse.ArgumentParser("Tensorflow Implementation of Variational Sequence")
+    parser.add_argument('--seed', help='RNG seed', type=int, default=88)
+    parser.add_argument('--env_id', help='environment ID', default='Test-v1')
+    parser.add_argument('--learning_rate', help='a hyperparameter', default=1e-3, type=float)
+    parser.add_argument('--input_size', help='a hyperparameter', default=16, type=int)
+    parser = arg_parser_postprocess(parser)
+    args = parser.parse_args()
+    kwargs = vars(args)
+    exp_manager.set_hyper_param(**kwargs)
+    exp_manager.add_record_param(["env_id", "learning_rate", "seed"])
+    return kwargs
+
+# phase 2: init the RLA experiment manager.
+kwargs = get_param()
+
+task_name = 'demo_task'
+rla_data_root = '../'
+exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+exp_manager.log_files_gen()
+exp_manager.print_args()
+
+def set_global_seeds(seed):
+    """
+    set the seed for python random, tensorflow, numpy and gym spaces
+
+    :param seed: (int) the seed
+    """
+    import random
+    tf.set_random_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+set_global_seeds(kwargs["seed"])
+
+# phase 3: [optional] resume from a historical experiment.
+from RLA.easy_log.exp_loader import exp_loader
+exp_loader.fork_log_files()
+start_epoch, load_res, hist_variables = exp_loader.load_from_record_date(variable_list=['iv'])
+
+# phase 4: write your code.
+X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+l = X_ph
+for _ in range(3):
+    l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+loss = tf.reduce_mean(np.square(out - y_ph))
+opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+
+sess = tf.Session().__enter__()
+sess.run(tf.variables_initializer(tf.global_variables()))
+
+exp_manager.new_saver(var_prefix='', max_to_keep=1)
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+for i in range(start_epoch, 1000):
+    exp_manager.time_step_holder.set_time(i)
+    x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+    y = target_func(x_input)
+    loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph:x_input, y_ph: y})[:-1]
+    logger.ma_record_tabular("perf/mse", loss_out, 10)
+    logger.record_tabular("y_out", np.mean(y))
+    logger.dump_tabular()
+    if i % 100 == 0:
+        exp_manager.save_checkpoint()
+    if i % 10 == 0:
+        def plot_func():
+            import matplotlib.pyplot as plt
+            testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=16, axis=-1)
+            testY = target_func(testX)
+            predY = sess.run(out, feed_dict={X_ph: testX})
+            plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+            plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+        mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+    logger.dump_tabular()
\ No newline at end of file
diff --git a/test/test_data_root/code/demo_task/2022/03/01/__init__.py b/test/test_data_root/code/demo_task/2022/03/01/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/03/__init__.py b/test/test_data_root/code/demo_task/2022/03/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/2022/__init__.py b/test/test_data_root/code/demo_task/2022/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/demo_task/__init__.py b/test/test_data_root/code/demo_task/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/proj/__init__.py b/test/test_data_root/code/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/proj/test_manager.py b/test/test_data_root/code/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/proj/test_manager.py
new file mode 100644
index 0000000..68d7ce5
--- /dev/null
+++ b/test/test_data_root/code/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/proj/test_manager.py	
@@ -0,0 +1,21 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+import os
+
+
+class ManagerTest(BaseTest):
+    def _init_proj(self, hp):
+        task_name = 'test_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_proj_init(self):
+        hp = {
+            'hp1': 1,
+            'hp2': 2,
+        }
+        exp_manager.set_hyper_param(**hp)
+        exp_manager.add_record_param(['hp1'])
+        self._init_proj(hp)
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/proj/test_manager.py
new file mode 100644
index 0000000..787034a
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/proj/test_manager.py	
@@ -0,0 +1,21 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+import os
+
+
+class ManagerTest(BaseTest):
+    def _init_proj(self, hp):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_proj_init(self):
+        hp = {
+            'hp1': 1,
+            'hp2': 2,
+        }
+        exp_manager.set_hyper_param(**hp)
+        exp_manager.add_record_param(['hp1'])
+        self._init_proj(hp)
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/proj/test_manager.py
new file mode 100644
index 0000000..c93837a
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/proj/test_manager.py	
@@ -0,0 +1,72 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import os
+
+
+class ManagerTest(BaseTest):
+    def _init_proj(self):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_proj_init(self):
+        hp = {
+            'hp1': 1,
+            'hp2': 2,
+        }
+        exp_manager.set_hyper_param(**hp)
+        exp_manager.add_record_param(['hp1'])
+        self._init_proj()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 2,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+
+        def target_func(x):
+            return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+        for i in range(0, 1000):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 100 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=16, axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..597d175
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,72 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import os
+
+
+class ManagerTest(BaseTest):
+    def _init_proj(self):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_proj_init(self):
+        hp = {
+            'hp1': 1,
+            'hp2': 2,
+        }
+        exp_manager.set_hyper_param(**hp)
+        exp_manager.add_record_param(['hp1'])
+        self._init_proj()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+
+        def target_func(x):
+            return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+        for i in range(0, 1000):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 100 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=16, axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..39b6d03
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,72 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import os
+
+
+class ManagerTest(BaseTest):
+    def _init_proj(self):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path='../rla_config.yaml', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_proj_init(self):
+        hp = {
+            'hp1': 1,
+            'hp2': 2,
+        }
+        exp_manager.set_hyper_param(**hp)
+        exp_manager.add_record_param(['hp1'])
+        self._init_proj()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+
+        def target_func(x):
+            return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..a8e901c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,103 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP
+        from torch import nn
+        from torch.nn import functional as F
+
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(x_input), y)
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", mse_loss.numpy(), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b93481b
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,48 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..bcfa54a
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,102 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP, get_device
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(th.as_tensor(x_input).to(get_device('auto')))
+            mse_loss = F.mse_loss(mlp(x_input), y)
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", mse_loss.numpy(), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b93481b
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,48 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0fe6413
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,102 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP, get_device
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(th.as_tensor(x_input).to(get_device('auto'))), y)
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", mse_loss.numpy(), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b93481b
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,48 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..fe5ca4c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,103 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP, get_device
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(th.as_tensor(x_input).to(get_device('auto'))), y)
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", mse_loss.numpy(), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b93481b
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,48 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..fe5ca4c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,103 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP, get_device
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(th.as_tensor(x_input).to(get_device('auto'))), y)
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", mse_loss.numpy(), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b93481b
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,48 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..123152a
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,102 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP, get_device, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", mse_loss.numpy(), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..d986016
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,102 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP, get_device, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..9177256
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,111 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP, get_device, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..13b83a8
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,112 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            logger.dump_tabular()
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP, get_device, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..917c83c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,113 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP, get_device, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..343eee0
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,114 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP, get_device, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..343eee0
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/proj/test_manager.py	
@@ -0,0 +1,114 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+import os
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _init_proj(self, config_name='rla_config.yaml'):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        exp_manager.configure(task_name, private_config_path=f'../{config_name}', data_root=rla_data_root)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj()
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        self._init_proj(config_name='rla_config_torch.yaml')
+        from torch_net import MLP, get_device, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=3e-4)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_torch(self):
+        pass
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..fece1f1
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,132 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+import yaml
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(f'../../../example/rla_config.yaml')
+
+    def _init_proj(self, config_yaml):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root='../')
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+
+    # def sent_to_remote(self):
+    #     kwargs = {
+    #         'input_size': 16,
+    #         'learning_rate': 0.0001,
+    #     }
+    #     exp_manager.set_hyper_param(**kwargs)
+    #     exp_manager.add_record_param(['input_size'])
+    #     self._init_proj(config_name='rla_config_torch.yaml')
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..fece1f1
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,132 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+import yaml
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(f'../../../example/rla_config.yaml')
+
+    def _init_proj(self, config_yaml):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root='../')
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+
+    # def sent_to_remote(self):
+    #     kwargs = {
+    #         'input_size': 16,
+    #         'learning_rate': 0.0001,
+    #     }
+    #     exp_manager.set_hyper_param(**kwargs)
+    #     exp_manager.add_record_param(['input_size'])
+    #     self._init_proj(config_name='rla_config_torch.yaml')
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/proj/private_config.py
new file mode 100644
index 0000000..6e361c5
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/proj/private_config.py	
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='meituan'
+passward='meituan'
+remote_root='/Users/xionghuichen/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..94a3963
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,150 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+import yaml
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(f'../../../example/rla_config.yaml')
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root='../', **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.passward
+        yaml['REMOTE_SETTING']['remote_data_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/proj/private_config.py
new file mode 100644
index 0000000..6e361c5
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/proj/private_config.py	
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='meituan'
+passward='meituan'
+remote_root='/Users/xionghuichen/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..94a3963
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,150 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+import yaml
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(f'../../../example/rla_config.yaml')
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root='../', **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.passward
+        yaml['REMOTE_SETTING']['remote_data_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/proj/private_config.py
new file mode 100644
index 0000000..6e361c5
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/proj/private_config.py	
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='meituan'
+passward='meituan'
+remote_root='/Users/xionghuichen/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..dbdc8fa
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,150 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+import yaml
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(f'../../../example/rla_config.yaml')
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root='../', **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'ftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.passward
+        yaml['REMOTE_SETTING']['remote_data_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/proj/private_config.py
new file mode 100644
index 0000000..6e361c5
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/proj/private_config.py	
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='meituan'
+passward='meituan'
+remote_root='/Users/xionghuichen/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..dbdc8fa
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,150 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+import yaml
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(f'../../../example/rla_config.yaml')
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root='../', **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'ftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.passward
+        yaml['REMOTE_SETTING']['remote_data_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/proj/private_config.py
new file mode 100644
index 0000000..6e361c5
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/proj/private_config.py	
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='meituan'
+passward='meituan'
+remote_root='/Users/xionghuichen/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..dbdc8fa
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/proj/test_manager.py	
@@ -0,0 +1,150 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+import yaml
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(f'../../../example/rla_config.yaml')
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root='../', **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'ftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.passward
+        yaml['REMOTE_SETTING']['remote_data_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/proj/torch_net.py	
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..6e361c5
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='meituan'
+passward='meituan'
+remote_root='/Users/xionghuichen/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..dbdc8fa
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/proj/test_manager.py
@@ -0,0 +1,150 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+import yaml
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(f'../../../example/rla_config.yaml')
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root='../', **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'ftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.passward
+        yaml['REMOTE_SETTING']['remote_data_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..6e361c5
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='meituan'
+passward='meituan'
+remote_root='/Users/xionghuichen/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..dbdc8fa
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/proj/test_manager.py
@@ -0,0 +1,150 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+import yaml
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(f'../../../example/rla_config.yaml')
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root='../', **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'ftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.passward
+        yaml['REMOTE_SETTING']['remote_data_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..5f01e9d
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='set-zw-yarn-training527294.mt'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..dbdc8fa
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/proj/test_manager.py
@@ -0,0 +1,150 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+import yaml
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(f'../../../example/rla_config.yaml')
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = '../../test_data_root'
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root='../', **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'ftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.passward
+        yaml['REMOTE_SETTING']['remote_data_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..a5f671e
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/proj/test_manager.py
@@ -0,0 +1,153 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'ftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.passward
+        yaml['REMOTE_SETTING']['remote_data_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..a5f671e
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,153 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'ftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.passward
+        yaml['REMOTE_SETTING']['remote_data_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..a5f671e
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,153 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        import numpy as np
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(np.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'ftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.passward
+        yaml['REMOTE_SETTING']['remote_data_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..ab36011
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+passward='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..8c07721
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,152 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..0b3b456
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,158 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..ef81802
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,159 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v1'
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v2'
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..ef81802
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,159 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v1'
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v2'
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..ef81802
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,159 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v1'
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v2'
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..ef81802
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,159 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v1'
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v2'
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..ef81802
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,159 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v1'
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v2'
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..ef81802
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/test_manager.py
@@ -0,0 +1,159 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v1'
+        except AttributeError as e:
+            tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v2'
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/proj/__init__.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/proj/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/proj/private_config.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/proj/private_config.py
new file mode 100644
index 0000000..a560f5c
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/proj/private_config.py
@@ -0,0 +1,5 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+username='hadoop-peisongpa'
+password='wangli78'
+remote_root='/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root'
\ No newline at end of file
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/proj/test_manager.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/proj/test_manager.py
new file mode 100644
index 0000000..a357b5b
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/proj/test_manager.py
@@ -0,0 +1,159 @@
+from test._base import BaseTest
+from RLA.easy_log.tester import exp_manager
+from RLA.easy_log import logger
+from RLA.easy_log.complex_data_recorder import MatplotlibRecorder as mpr
+import numpy as np
+from RLA.utils.utils import load_yaml
+import os
+
+
+def target_func(x):
+    return np.tanh(np.mean(x, axis=-1, keepdims=True))
+
+
+RLA_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+DATABASE_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+CODE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+class ManagerTest(BaseTest):
+
+    def _load_rla_config(self):
+        return load_yaml(os.path.join(RLA_REPO_ROOT, 'example/rla_config.yaml'))
+
+    def _init_proj(self, config_yaml, **kwargs):
+        task_name = 'test_manger_demo_task'
+        rla_data_root = os.path.join(DATABASE_ROOT, 'test_data_root')
+        config_yaml['BACKUP_CONFIG']['backup_code_dir'] = ['proj']
+        exp_manager.configure(task_name, private_config_path=config_yaml, data_root=rla_data_root,
+                              code_root=CODE_ROOT, **kwargs)
+        exp_manager.log_files_gen()
+        exp_manager.print_args()
+
+    def test_log_tf(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        self._init_proj(yaml)
+        import tensorflow as tf
+        try:
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v1'
+        except AttributeError as e:
+            # tf.compat.v1.disable_eager_execution()
+            tf = tf.compat.v1
+            X_ph = tf.placeholder(dtype=tf.float32, shape=[None, kwargs["input_size"]], name='x')
+            version = 'v2'
+        y_ph = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x')
+        l = X_ph
+        # build a neural network
+        for _ in range(3):
+            l = tf.nn.tanh(tf.layers.dense(l, 64, kernel_initializer=tf.keras.initializers.glorot_normal))
+
+        out = tf.layers.dense(l, 1, kernel_initializer=tf.keras.initializers.glorot_normal)
+        loss = tf.reduce_mean(tf.square(out - y_ph))
+        opt = tf.train.AdamOptimizer(learning_rate=kwargs["learning_rate"]).minimize(loss)
+        sess = tf.Session().__enter__()
+        sess.run(tf.variables_initializer(tf.global_variables()))
+
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        # synthetic target function.
+
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            y = target_func(x_input)
+            loss_out, y_pred = sess.run([loss, out, opt], feed_dict={X_ph: x_input, y_ph: y})[:-1]
+            # moving averaged
+            logger.ma_record_tabular("perf/mse", loss_out, 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 20 == 0:
+                exp_manager.save_checkpoint()
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testY = target_func(testX)
+                    predY = sess.run(out, feed_dict={X_ph: testX})
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            logger.dump_tabular()
+
+
+    def test_load_checkpoint_tf(self):
+        pass
+
+    def test_log_torch(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        yaml['DL_FRAMEWORK'] = 'torch'
+        self._init_proj(yaml)
+        from test.test_proj.proj.torch_net import MLP, to_tensor
+        from torch import nn
+        from torch.nn import functional as F
+        import torch as th
+        mlp = MLP(feature_dim=kwargs['input_size'], net_arch=[64, 64, 64], activation_fn=nn.Tanh)
+        exp_manager.new_saver(var_prefix='', max_to_keep=1)
+        optimizer = th.optim.Adam(mlp.parameters(), lr=kwargs['learning_rate'])
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            x_input = np.random.normal(0, 3, [64, kwargs["input_size"]])
+            x_input = x_input.astype(np.float32)
+            y = target_func(x_input)
+            mse_loss = F.mse_loss(mlp(to_tensor(x_input)), to_tensor(y))
+            optimizer.zero_grad()
+            mse_loss.backward()
+            optimizer.step()
+            logger.ma_record_tabular("perf/mse", np.mean(mse_loss.detach().cpu().numpy()), 10)
+            logger.record_tabular("y_out", np.mean(y))
+            if i % 10 == 0:
+                def plot_func():
+                    import matplotlib.pyplot as plt
+                    testX = np.repeat(np.expand_dims(np.arange(-10, 10, 0.1), axis=-1), repeats=kwargs["input_size"], axis=-1)
+                    testX = testX.astype(np.float32)
+                    testY = target_func(testX)
+                    predY = mlp(to_tensor(testX)).detach().cpu().numpy()
+                    plt.plot(testX.mean(axis=-1), predY.mean(axis=-1), label='pred')
+                    plt.plot(testX.mean(axis=-1), testY.mean(axis=-1), label='real')
+                mpr.pretty_plot_wrapper('react_func', plot_func, xlabel='x', ylabel='y', title='react test')
+            if i % 20 == 0:
+                exp_manager.save_checkpoint(model_dict={'mlp': mlp.state_dict(), 'opt': optimizer.state_dict(), 'epoch': i})
+                pass
+            logger.dump_tabular()
+
+    def test_load_checkpoint_torch(self):
+        pass
+
+    def test_sent_to_master(self):
+        kwargs = {
+            'input_size': 16,
+            'learning_rate': 0.0001,
+        }
+        exp_manager.set_hyper_param(**kwargs)
+        exp_manager.add_record_param(['input_size'])
+        yaml = self._load_rla_config()
+        from test.test_proj.proj import private_config
+        yaml['DL_FRAMEWORK'] = 'torch'
+        yaml['SEND_LOG_FILE'] = True
+        yaml['REMOTE_SETTING']['ftp_server'] = '127.0.0.1'
+        yaml['REMOTE_SETTING']['file_transfer_protocol'] = 'sftp'
+        yaml['REMOTE_SETTING']['username'] = private_config.username
+        yaml['REMOTE_SETTING']['password'] = private_config.password
+        yaml['REMOTE_SETTING']['remote_log_root'] = private_config.remote_root
+
+        self._init_proj(yaml, is_master_node=False)
+        for i in range(0, 100):
+            exp_manager.time_step_holder.set_time(i)
+            logger.record_tabular("i", i)
+            logger.dump_tabular()
+            if i % 10 == 0:
+                exp_manager.sync_log_file()
diff --git a/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/proj/torch_net.py b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/proj/torch_net.py
new file mode 100644
index 0000000..b9f0232
--- /dev/null
+++ b/test/test_data_root/code/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/proj/torch_net.py
@@ -0,0 +1,52 @@
+# Created by xionghuichen at 2022/6/22
+# Email: chenxh@lamda.nju.edu.cn
+
+import torch as th
+from torch import nn
+from typing import Dict, List, Tuple, Type, Union
+
+
+
+def get_device(device: Union[th.device, str] = "auto") -> th.device:
+    """
+    Retrieve PyTorch device.
+    It checks that the requested device is available first.
+    For now, it supports only cpu and cuda.
+    By default, it tries to use the gpu.
+
+    :param device: One for 'auto', 'cuda', 'cpu'
+    :return:
+    """
+    # Cuda by default
+    if device == "auto":
+        device = "cuda"
+    # Force conversion to th.device
+    device = th.device(device)
+
+    # Cuda not available
+    if device.type == th.device("cuda").type and not th.cuda.is_available():
+        return th.device("cpu")
+    return device
+
+def to_tensor(x, device="auto"):
+    return th.as_tensor(x).to(get_device('auto'))
+
+class MLP(nn.Module):
+    def __init__(self, feature_dim: int, net_arch: List[int], activation_fn: Type[nn.Module],
+                 device: Union[th.device, str] = "auto"):
+        super(MLP, self).__init__()
+        device = get_device(device)
+        net = []
+        last_layer_dim_shared = feature_dim
+        for layer in net_arch:
+            net.append(nn.Linear(last_layer_dim_shared, layer))  # add linear of size layer
+            net.append(activation_fn())
+            last_layer_dim_shared = layer
+        net.append(nn.Linear(last_layer_dim_shared, 1))
+        self.net = nn.Sequential(*net).to(device)
+
+
+    def forward(self, features: th.Tensor) -> th.Tensor:
+        return self.net(features)
+
+
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/backup.txt b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/backup.txt
new file mode 100644
index 0000000..1d3db32
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/backup.txt	
@@ -0,0 +1,7 @@
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 88
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/log.txt b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/log.txt
new file mode 100644
index 0000000..321a321
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/log.txt	
@@ -0,0 +1,5048 @@
+Logging to ../log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/
+log dir: ../log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/
+pkl_file: ../archive_tester/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001.pkl
+checkpoint_dir: ../checkpoint/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/
+results_dir: ../results/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 88
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.00551  |
+---------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/checkpoint 0
+---------------------------------------------------------
+| time-step | 1        |
+| y_out     | -0.0437  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 2        |
+| y_out     | 0.0211   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 3        |
+| y_out     | 0.0542   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 4        |
+| y_out     | -0.0968  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 5        |
+| y_out     | -0.123   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 6        |
+| y_out     | -0.0233  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 7        |
+| y_out     | 0.0479   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 8        |
+| y_out     | 0.101    |
+---------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.7536595 |
+| time-step | 9         |
+| y_out     | 0.00496   |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.7614303 |
+| time-step | 10        |
+| y_out     | 0.0406    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.73954237 |
+| time-step | 11         |
+| y_out     | 0.0193     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.70386356 |
+| time-step | 12         |
+| y_out     | 0.0374     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.6770209 |
+| time-step | 13        |
+| y_out     | 0.00551   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.65267324 |
+| time-step | 14         |
+| y_out     | 0.0266     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.6346815 |
+| time-step | 15        |
+| y_out     | -0.0881   |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.6258823 |
+| time-step | 16        |
+| y_out     | 0.1       |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.5979749 |
+| time-step | 17        |
+| y_out     | -0.0309   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.55063057 |
+| time-step | 18         |
+| y_out     | -0.0441    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.5483422 |
+| time-step | 19        |
+| y_out     | -0.0449   |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.5084941 |
+| time-step | 20        |
+| y_out     | 0.104     |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.50091726 |
+| time-step | 21         |
+| y_out     | -0.0949    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.49691707 |
+| time-step | 22         |
+| y_out     | -0.125     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.47591686 |
+| time-step | 23         |
+| y_out     | 0.0839     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.46730766 |
+| time-step | 24         |
+| y_out     | -0.00879   |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.4655877 |
+| time-step | 25        |
+| y_out     | 0.0547    |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.4504355 |
+| time-step | 26        |
+| y_out     | -0.0216   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.43825817 |
+| time-step | 27         |
+| y_out     | -0.019     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.42810416 |
+| time-step | 28         |
+| y_out     | 0.0864     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.4091662 |
+| time-step | 29        |
+| y_out     | 0.0262    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.39756814 |
+| time-step | 30         |
+| y_out     | 0.122      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.39547896 |
+| time-step | 31         |
+| y_out     | -0.0351    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.38193163 |
+| time-step | 32         |
+| y_out     | 0.0113     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.3848196 |
+| time-step | 33        |
+| y_out     | -0.053    |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.3804757 |
+| time-step | 34        |
+| y_out     | 0.0572    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.36593375 |
+| time-step | 35         |
+| y_out     | 0.017      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.35520807 |
+| time-step | 36         |
+| y_out     | -0.0157    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.34942326 |
+| time-step | 37         |
+| y_out     | 0.0236     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.34835225 |
+| time-step | 38         |
+| y_out     | -0.0641    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.34093562 |
+| time-step | 39         |
+| y_out     | -0.0648    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.33941054 |
+| time-step | 40         |
+| y_out     | 0.0845     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.3291096 |
+| time-step | 41        |
+| y_out     | -0.0178   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.32265082 |
+| time-step | 42         |
+| y_out     | -0.0342    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.3030842 |
+| time-step | 43        |
+| y_out     | -0.116    |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2845729 |
+| time-step | 44        |
+| y_out     | 0.0727    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.27790684 |
+| time-step | 45         |
+| y_out     | -0.0115    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.27230132 |
+| time-step | 46         |
+| y_out     | 0.046      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26273435 |
+| time-step | 47         |
+| y_out     | -0.0139    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25933737 |
+| time-step | 48         |
+| y_out     | -0.00529   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26412326 |
+| time-step | 49         |
+| y_out     | -0.0303    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.27383024 |
+| time-step | 50         |
+| y_out     | 0.077      |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2696325 |
+| time-step | 51        |
+| y_out     | 0.0933    |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2703403 |
+| time-step | 52        |
+| y_out     | -0.0672   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26812977 |
+| time-step | 53         |
+| y_out     | -0.0344    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26405814 |
+| time-step | 54         |
+| y_out     | -0.012     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26761395 |
+| time-step | 55         |
+| y_out     | 0.0193     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26213422 |
+| time-step | 56         |
+| y_out     | -0.0438    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2602182 |
+| time-step | 57        |
+| y_out     | -0.0301   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26066226 |
+| time-step | 58         |
+| y_out     | -0.0592    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2479376 |
+| time-step | 59        |
+| y_out     | -0.0131   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23060067 |
+| time-step | 60         |
+| y_out     | 0.0163     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22659326 |
+| time-step | 61         |
+| y_out     | -0.0223    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2289532 |
+| time-step | 62        |
+| y_out     | -0.00366  |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21603683 |
+| time-step | 63         |
+| y_out     | 0.146      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22301443 |
+| time-step | 64         |
+| y_out     | 0.0111     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21527323 |
+| time-step | 65         |
+| y_out     | 0.0736     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2119784 |
+| time-step | 66        |
+| y_out     | 0.0203    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21069415 |
+| time-step | 67         |
+| y_out     | 0.0439     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20798007 |
+| time-step | 68         |
+| y_out     | 0.121      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20300722 |
+| time-step | 69         |
+| y_out     | -0.0586    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20228119 |
+| time-step | 70         |
+| y_out     | 0.00133    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20275107 |
+| time-step | 71         |
+| y_out     | -0.0316    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19388804 |
+| time-step | 72         |
+| y_out     | -0.0021    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20016971 |
+| time-step | 73         |
+| y_out     | 0.0199     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18662246 |
+| time-step | 74         |
+| y_out     | -0.109     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18414605 |
+| time-step | 75         |
+| y_out     | 0.0153     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18662009 |
+| time-step | 76         |
+| y_out     | -0.0261    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18146111 |
+| time-step | 77         |
+| y_out     | 0.11       |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17184725 |
+| time-step | 78         |
+| y_out     | 0.0392     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17073518 |
+| time-step | 79         |
+| y_out     | 0.0281     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16503216 |
+| time-step | 80         |
+| y_out     | -0.00368   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16086186 |
+| time-step | 81         |
+| y_out     | -0.0177    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1619516 |
+| time-step | 82        |
+| y_out     | -0.151    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15664205 |
+| time-step | 83         |
+| y_out     | 0.0902     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16488114 |
+| time-step | 84         |
+| y_out     | -0.0866    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15822244 |
+| time-step | 85         |
+| y_out     | 0.0288     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15081719 |
+| time-step | 86         |
+| y_out     | -0.0976    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1494141 |
+| time-step | 87        |
+| y_out     | 0.0185    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14710765 |
+| time-step | 88         |
+| y_out     | 0.0195     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14720626 |
+| time-step | 89         |
+| y_out     | 0.025      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14446992 |
+| time-step | 90         |
+| y_out     | -0.0801    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1501849 |
+| time-step | 91        |
+| y_out     | -0.0318   |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1425922 |
+| time-step | 92        |
+| y_out     | -0.166    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14148389 |
+| time-step | 93         |
+| y_out     | 0.0941     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13228774 |
+| time-step | 94         |
+| y_out     | -0.126     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13850872 |
+| time-step | 95         |
+| y_out     | 0.0134     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13413472 |
+| time-step | 96         |
+| y_out     | -0.0185    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1341801 |
+| time-step | 97        |
+| y_out     | 0.0388    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13406035 |
+| time-step | 98         |
+| y_out     | -0.181     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13296397 |
+| time-step | 99         |
+| y_out     | -0.0134    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13536148 |
+| time-step | 100        |
+| y_out     | 0.0598     |
+-----------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/checkpoint 100
+-----------------------------------------------------------
+| perf/mse  | 0.12632269 |
+| time-step | 101        |
+| y_out     | 0.0589     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12709916 |
+| time-step | 102        |
+| y_out     | 0.0696     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12770632 |
+| time-step | 103        |
+| y_out     | -0.0157    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12637927 |
+| time-step | 104        |
+| y_out     | 0.0332     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11823742 |
+| time-step | 105        |
+| y_out     | 0.0241     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.120560944 |
+| time-step | 106         |
+| y_out     | 0.00443     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.115595795 |
+| time-step | 107         |
+| y_out     | -0.0997     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11589529 |
+| time-step | 108        |
+| y_out     | -0.0741    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.112142526 |
+| time-step | 109         |
+| y_out     | -0.0855     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10772308 |
+| time-step | 110        |
+| y_out     | 0.0742     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10829158 |
+| time-step | 111        |
+| y_out     | 0.0626     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11135219 |
+| time-step | 112        |
+| y_out     | -0.0171    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10839609 |
+| time-step | 113        |
+| y_out     | 0.0416     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.110289335 |
+| time-step | 114         |
+| y_out     | 0.0192      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11743257 |
+| time-step | 115        |
+| y_out     | 6.44e-05   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11489244 |
+| time-step | 116        |
+| y_out     | 0.0768     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.115033686 |
+| time-step | 117         |
+| y_out     | -0.0542     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1143599 |
+| time-step | 118       |
+| y_out     | 0.0674    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11436614 |
+| time-step | 119        |
+| y_out     | -0.123     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11219598 |
+| time-step | 120        |
+| y_out     | 0.0667     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10956015 |
+| time-step | 121        |
+| y_out     | -0.0453    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10427566 |
+| time-step | 122        |
+| y_out     | -0.133     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.106453106 |
+| time-step | 123         |
+| y_out     | 0.0293      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.103135526 |
+| time-step | 124         |
+| y_out     | -0.113      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09769255 |
+| time-step | 125        |
+| y_out     | 0.0371     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.095179215 |
+| time-step | 126         |
+| y_out     | -0.151      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.093893126 |
+| time-step | 127         |
+| y_out     | -0.00293    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09305087 |
+| time-step | 128        |
+| y_out     | -0.0497    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.090363435 |
+| time-step | 129         |
+| y_out     | -0.0671     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09022226 |
+| time-step | 130        |
+| y_out     | -0.0083    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09041275 |
+| time-step | 131        |
+| y_out     | 0.0349     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09001038 |
+| time-step | 132        |
+| y_out     | 0.0924     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.091183744 |
+| time-step | 133         |
+| y_out     | -0.0376     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09292256 |
+| time-step | 134        |
+| y_out     | 0.0182     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09379208 |
+| time-step | 135        |
+| y_out     | -0.0393    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09531312 |
+| time-step | 136        |
+| y_out     | 0.0207     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10008483 |
+| time-step | 137        |
+| y_out     | 0.0256     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.099591166 |
+| time-step | 138         |
+| y_out     | -0.0905     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.101162255 |
+| time-step | 139         |
+| y_out     | 0.0803      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10505815 |
+| time-step | 140        |
+| y_out     | -0.024     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10293009 |
+| time-step | 141        |
+| y_out     | 0.199      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10702111 |
+| time-step | 142        |
+| y_out     | -0.11      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10858023 |
+| time-step | 143        |
+| y_out     | 0.00967    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10915722 |
+| time-step | 144        |
+| y_out     | -0.0721    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10915925 |
+| time-step | 145        |
+| y_out     | 0.0344     |
+-----------------------------------------------------------
+---------------------------------------------------------
+| perf/mse  | 0.108269 |
+| time-step | 146      |
+| y_out     | 0.122    |
+---------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10186194 |
+| time-step | 147        |
+| y_out     | -0.0252    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09985083 |
+| time-step | 148        |
+| y_out     | -0.0865    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.100750186 |
+| time-step | 149         |
+| y_out     | -0.0829     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09781083 |
+| time-step | 150        |
+| y_out     | -0.00408   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.098306686 |
+| time-step | 151         |
+| y_out     | 0.00946     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09458697 |
+| time-step | 152        |
+| y_out     | 0.0787     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09106726 |
+| time-step | 153        |
+| y_out     | -0.0298    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08781679 |
+| time-step | 154        |
+| y_out     | -0.0823    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08761193 |
+| time-step | 155        |
+| y_out     | 0.083      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08527299 |
+| time-step | 156        |
+| y_out     | 0.0795     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.085666336 |
+| time-step | 157         |
+| y_out     | -0.0396     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08647408 |
+| time-step | 158        |
+| y_out     | 0.136      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08341716 |
+| time-step | 159        |
+| y_out     | -0.0295    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08342825 |
+| time-step | 160        |
+| y_out     | 0.0676     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.080938205 |
+| time-step | 161         |
+| y_out     | -0.0227     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.077774964 |
+| time-step | 162         |
+| y_out     | 0.00395     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07537802 |
+| time-step | 163        |
+| y_out     | -0.0223    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.076626234 |
+| time-step | 164         |
+| y_out     | 0.126       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07307533 |
+| time-step | 165        |
+| y_out     | 0.00617    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.072913185 |
+| time-step | 166         |
+| y_out     | 0.0102      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07236985 |
+| time-step | 167        |
+| y_out     | -0.00032   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06986389 |
+| time-step | 168        |
+| y_out     | 0.0378     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06895031 |
+| time-step | 169        |
+| y_out     | -0.0129    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07149744 |
+| time-step | 170        |
+| y_out     | -0.0368    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.071288966 |
+| time-step | 171         |
+| y_out     | 0.0103      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07429742 |
+| time-step | 172        |
+| y_out     | 0.0913     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.071485594 |
+| time-step | 173         |
+| y_out     | -0.175      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.069862366 |
+| time-step | 174         |
+| y_out     | -0.0778     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07156961 |
+| time-step | 175        |
+| y_out     | -0.0153    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07435675 |
+| time-step | 176        |
+| y_out     | 0.0275     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07573581 |
+| time-step | 177        |
+| y_out     | 0.0197     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07466517 |
+| time-step | 178        |
+| y_out     | -0.0543    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07821276 |
+| time-step | 179        |
+| y_out     | 0.0708     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.073047474 |
+| time-step | 180         |
+| y_out     | -0.0602     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07462281 |
+| time-step | 181        |
+| y_out     | 0.0483     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07356569 |
+| time-step | 182        |
+| y_out     | -0.131     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07592135 |
+| time-step | 183        |
+| y_out     | -0.0958    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07851888 |
+| time-step | 184        |
+| y_out     | 0.0167     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.081421696 |
+| time-step | 185         |
+| y_out     | 0.0582      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.079103336 |
+| time-step | 186         |
+| y_out     | 0.0396      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.080199204 |
+| time-step | 187         |
+| y_out     | 0.0395      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.081093356 |
+| time-step | 188         |
+| y_out     | -0.0652     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07874111 |
+| time-step | 189        |
+| y_out     | 0.0111     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0806599 |
+| time-step | 190       |
+| y_out     | 0.0319    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08369358 |
+| time-step | 191        |
+| y_out     | -0.000837  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08218878 |
+| time-step | 192        |
+| y_out     | -0.0748    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08088047 |
+| time-step | 193        |
+| y_out     | 0.0121     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07906091 |
+| time-step | 194        |
+| y_out     | -0.0555    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0741044 |
+| time-step | 195       |
+| y_out     | 0.0385    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.072809376 |
+| time-step | 196         |
+| y_out     | -0.0434     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07125479 |
+| time-step | 197        |
+| y_out     | -0.0625    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0719644 |
+| time-step | 198       |
+| y_out     | -0.00296  |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07198538 |
+| time-step | 199        |
+| y_out     | 0.0256     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.070659146 |
+| time-step | 200         |
+| y_out     | 0.00947     |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/checkpoint 200
+------------------------------------------------------------
+| perf/mse  | 0.066339575 |
+| time-step | 201         |
+| y_out     | 0.0186      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0688318 |
+| time-step | 202       |
+| y_out     | -0.0365   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06774326 |
+| time-step | 203        |
+| y_out     | -0.0144    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06734158 |
+| time-step | 204        |
+| y_out     | 0.0731     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06919502 |
+| time-step | 205        |
+| y_out     | -0.0726    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07262932 |
+| time-step | 206        |
+| y_out     | -0.0551    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07139654 |
+| time-step | 207        |
+| y_out     | -0.0569    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07193249 |
+| time-step | 208        |
+| y_out     | -0.0411    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0712597 |
+| time-step | 209       |
+| y_out     | 0.13      |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07278111 |
+| time-step | 210        |
+| y_out     | -0.036     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.073609285 |
+| time-step | 211         |
+| y_out     | -0.102      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07077061 |
+| time-step | 212        |
+| y_out     | -0.0259    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07455244 |
+| time-step | 213        |
+| y_out     | 0.00398    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07383256 |
+| time-step | 214        |
+| y_out     | 0.0402     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07556737 |
+| time-step | 215        |
+| y_out     | -0.0695    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07411288 |
+| time-step | 216        |
+| y_out     | -0.0859    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07299921 |
+| time-step | 217        |
+| y_out     | -0.0889    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07033751 |
+| time-step | 218        |
+| y_out     | -0.00653   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07161388 |
+| time-step | 219        |
+| y_out     | -0.0263    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.068507046 |
+| time-step | 220         |
+| y_out     | 0.00187     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07103853 |
+| time-step | 221        |
+| y_out     | 0.0216     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07235795 |
+| time-step | 222        |
+| y_out     | 0.0768     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06829778 |
+| time-step | 223        |
+| y_out     | -0.0146    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06932568 |
+| time-step | 224        |
+| y_out     | -0.101     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.067998655 |
+| time-step | 225         |
+| y_out     | 0.0729      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06521767 |
+| time-step | 226        |
+| y_out     | 0.0368     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06395949 |
+| time-step | 227        |
+| y_out     | 0.0319     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.065621085 |
+| time-step | 228         |
+| y_out     | 0.118       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.063318364 |
+| time-step | 229         |
+| y_out     | -0.0347     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06854433 |
+| time-step | 230        |
+| y_out     | -0.0651    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06686604 |
+| time-step | 231        |
+| y_out     | -0.0858    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06609942 |
+| time-step | 232        |
+| y_out     | -0.0199    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.067278266 |
+| time-step | 233         |
+| y_out     | -0.0539     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06675903 |
+| time-step | 234        |
+| y_out     | -0.0116    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06772205 |
+| time-step | 235        |
+| y_out     | -0.0356    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07046996 |
+| time-step | 236        |
+| y_out     | 0.0635     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07163824 |
+| time-step | 237        |
+| y_out     | -0.0102    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.072859146 |
+| time-step | 238         |
+| y_out     | 0.0101      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.075486556 |
+| time-step | 239         |
+| y_out     | 0.054       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07305653 |
+| time-step | 240        |
+| y_out     | 0.0395     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0736026 |
+| time-step | 241       |
+| y_out     | 0.0439    |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0765899 |
+| time-step | 242       |
+| y_out     | 0.0296    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.078019395 |
+| time-step | 243         |
+| y_out     | -0.0235     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07583042 |
+| time-step | 244        |
+| y_out     | 0.068      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07330941 |
+| time-step | 245        |
+| y_out     | 0.0146     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07313369 |
+| time-step | 246        |
+| y_out     | -0.0334    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07583009 |
+| time-step | 247        |
+| y_out     | 0.0507     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07508434 |
+| time-step | 248        |
+| y_out     | 0.129      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07270874 |
+| time-step | 249        |
+| y_out     | 0.0809     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07287582 |
+| time-step | 250        |
+| y_out     | 0.0678     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07310078 |
+| time-step | 251        |
+| y_out     | 0.0395     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07047896 |
+| time-step | 252        |
+| y_out     | 0.0997     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.068328634 |
+| time-step | 253         |
+| y_out     | -0.0101     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0712144 |
+| time-step | 254       |
+| y_out     | 0.0344    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07390735 |
+| time-step | 255        |
+| y_out     | -0.0288    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07300605 |
+| time-step | 256        |
+| y_out     | 0.0205     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07213883 |
+| time-step | 257        |
+| y_out     | 0.0732     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0714982 |
+| time-step | 258       |
+| y_out     | 0.0955    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06990965 |
+| time-step | 259        |
+| y_out     | -0.00153   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06994686 |
+| time-step | 260        |
+| y_out     | -0.0676    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06896425 |
+| time-step | 261        |
+| y_out     | 0.0343     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06738427 |
+| time-step | 262        |
+| y_out     | 0.0586     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06569174 |
+| time-step | 263        |
+| y_out     | -0.0664    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062795445 |
+| time-step | 264         |
+| y_out     | -0.121      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06213799 |
+| time-step | 265        |
+| y_out     | -0.0332    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06258695 |
+| time-step | 266        |
+| y_out     | -0.0204    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059867553 |
+| time-step | 267         |
+| y_out     | -0.141      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062483363 |
+| time-step | 268         |
+| y_out     | -0.0263     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06525875 |
+| time-step | 269        |
+| y_out     | 0.146      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.064568534 |
+| time-step | 270         |
+| y_out     | 0.00328     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.064529285 |
+| time-step | 271         |
+| y_out     | -0.0416     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06593606 |
+| time-step | 272        |
+| y_out     | -0.0559    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.069381185 |
+| time-step | 273         |
+| y_out     | 0.123       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.070490465 |
+| time-step | 274         |
+| y_out     | 0.12        |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06832643 |
+| time-step | 275        |
+| y_out     | 0.0563     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06861231 |
+| time-step | 276        |
+| y_out     | -0.0026    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06918214 |
+| time-step | 277        |
+| y_out     | -0.00258   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06731231 |
+| time-step | 278        |
+| y_out     | 0.0572     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06561977 |
+| time-step | 279        |
+| y_out     | 0.0937     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06458853 |
+| time-step | 280        |
+| y_out     | 0.0662     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.063012086 |
+| time-step | 281         |
+| y_out     | 0.0796      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061319232 |
+| time-step | 282         |
+| y_out     | 0.015       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059950896 |
+| time-step | 283         |
+| y_out     | 0.072       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060632158 |
+| time-step | 284         |
+| y_out     | -0.0569     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.063623376 |
+| time-step | 285         |
+| y_out     | 0.0925      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06250887 |
+| time-step | 286        |
+| y_out     | 0.0206     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0653263 |
+| time-step | 287       |
+| y_out     | -0.0156   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06339609 |
+| time-step | 288        |
+| y_out     | 0.0196     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06357501 |
+| time-step | 289        |
+| y_out     | 0.0795     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06568088 |
+| time-step | 290        |
+| y_out     | 0.0329     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06486209 |
+| time-step | 291        |
+| y_out     | -0.013     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.066144176 |
+| time-step | 292         |
+| y_out     | -0.122      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06583441 |
+| time-step | 293        |
+| y_out     | -0.0591    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06567614 |
+| time-step | 294        |
+| y_out     | 0.0184     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062682025 |
+| time-step | 295         |
+| y_out     | 0.0398      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062473517 |
+| time-step | 296         |
+| y_out     | 0.127       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060914338 |
+| time-step | 297         |
+| y_out     | -0.106      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06190338 |
+| time-step | 298        |
+| y_out     | 0.0476     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06371783 |
+| time-step | 299        |
+| y_out     | -0.0939    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060892522 |
+| time-step | 300         |
+| y_out     | -0.0253     |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/checkpoint 300
+------------------------------------------------------------
+| perf/mse  | 0.061561406 |
+| time-step | 301         |
+| y_out     | -0.0156     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05978496 |
+| time-step | 302        |
+| y_out     | 0.0918     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058757592 |
+| time-step | 303         |
+| y_out     | -0.0838     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05701039 |
+| time-step | 304        |
+| y_out     | -0.00366   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058056284 |
+| time-step | 305         |
+| y_out     | 0.105       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05757015 |
+| time-step | 306        |
+| y_out     | -0.0512    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056466363 |
+| time-step | 307         |
+| y_out     | -0.00967    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057786595 |
+| time-step | 308         |
+| y_out     | -0.109      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056100674 |
+| time-step | 309         |
+| y_out     | -0.0396     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.055892337 |
+| time-step | 310         |
+| y_out     | 0.0777      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056663238 |
+| time-step | 311         |
+| y_out     | -0.0471     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05775717 |
+| time-step | 312        |
+| y_out     | 0.038      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05662114 |
+| time-step | 313        |
+| y_out     | -0.000669  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05946677 |
+| time-step | 314        |
+| y_out     | 0.0189     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05735783 |
+| time-step | 315        |
+| y_out     | -0.0556    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058225967 |
+| time-step | 316         |
+| y_out     | -0.0532     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059217285 |
+| time-step | 317         |
+| y_out     | -0.177      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05691106 |
+| time-step | 318        |
+| y_out     | 0.0787     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057017483 |
+| time-step | 319         |
+| y_out     | -0.0396     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059242982 |
+| time-step | 320         |
+| y_out     | 0.0429      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05944742 |
+| time-step | 321        |
+| y_out     | 0.0364     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061292928 |
+| time-step | 322         |
+| y_out     | -0.0288     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06390854 |
+| time-step | 323        |
+| y_out     | 0.162      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061706442 |
+| time-step | 324         |
+| y_out     | 0.0108      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.063138455 |
+| time-step | 325         |
+| y_out     | -0.0383     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062838145 |
+| time-step | 326         |
+| y_out     | -0.00194    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06128608 |
+| time-step | 327        |
+| y_out     | 0.00518    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060820688 |
+| time-step | 328         |
+| y_out     | 0.0311      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05965125 |
+| time-step | 329        |
+| y_out     | 0.0881     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057916872 |
+| time-step | 330         |
+| y_out     | 0.0465      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058904983 |
+| time-step | 331         |
+| y_out     | 0.0514      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057457566 |
+| time-step | 332         |
+| y_out     | -0.0436     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058152925 |
+| time-step | 333         |
+| y_out     | -0.0629     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05647705 |
+| time-step | 334        |
+| y_out     | -0.0741    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05727185 |
+| time-step | 335        |
+| y_out     | -0.043     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056803744 |
+| time-step | 336         |
+| y_out     | 0.0188      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05602798 |
+| time-step | 337        |
+| y_out     | -0.0476    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058072366 |
+| time-step | 338         |
+| y_out     | 0.065       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060007293 |
+| time-step | 339         |
+| y_out     | -0.015      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060237087 |
+| time-step | 340         |
+| y_out     | 0.0302      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060214512 |
+| time-step | 341         |
+| y_out     | 0.163       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058044933 |
+| time-step | 342         |
+| y_out     | -0.0272     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05564768 |
+| time-step | 343        |
+| y_out     | 0.0726     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056173064 |
+| time-step | 344         |
+| y_out     | -0.0447     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05405266 |
+| time-step | 345        |
+| y_out     | -0.173     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.053636093 |
+| time-step | 346         |
+| y_out     | -0.0615     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05649879 |
+| time-step | 347        |
+| y_out     | 0.0727     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.053624135 |
+| time-step | 348         |
+| y_out     | 0.0927      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.052668493 |
+| time-step | 349         |
+| y_out     | -0.0842     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.054834854 |
+| time-step | 350         |
+| y_out     | 0.103       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.054926433 |
+| time-step | 351         |
+| y_out     | 0.119       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.055627823 |
+| time-step | 352         |
+| y_out     | -0.206      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.055149816 |
+| time-step | 353         |
+| y_out     | 0.109       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.055934835 |
+| time-step | 354         |
+| y_out     | -0.0192     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056229435 |
+| time-step | 355         |
+| y_out     | 0.0897      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05643161 |
+| time-step | 356        |
+| y_out     | 0.0995     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.054255534 |
+| time-step | 357         |
+| y_out     | 0.00171     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05489844 |
+| time-step | 358        |
+| y_out     | 0.0214     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.055008076 |
+| time-step | 359         |
+| y_out     | 0.0315      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.052582312 |
+| time-step | 360         |
+| y_out     | 0.0174      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051539678 |
+| time-step | 361         |
+| y_out     | 0.0424      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05143289 |
+| time-step | 362        |
+| y_out     | -0.0295    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051551152 |
+| time-step | 363         |
+| y_out     | -0.0789     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051219992 |
+| time-step | 364         |
+| y_out     | -0.063      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.053554337 |
+| time-step | 365         |
+| y_out     | 0.121       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05462113 |
+| time-step | 366        |
+| y_out     | 0.0401     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05408623 |
+| time-step | 367        |
+| y_out     | -0.0178    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05427552 |
+| time-step | 368        |
+| y_out     | -0.0197    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.052317858 |
+| time-step | 369         |
+| y_out     | -0.0143     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050932724 |
+| time-step | 370         |
+| y_out     | 0.0134      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050129782 |
+| time-step | 371         |
+| y_out     | -0.0464     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.052606583 |
+| time-step | 372         |
+| y_out     | -0.0506     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.053700425 |
+| time-step | 373         |
+| y_out     | 0.0201      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.053138226 |
+| time-step | 374         |
+| y_out     | -0.000939   |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04954926 |
+| time-step | 375        |
+| y_out     | 0.118      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04844753 |
+| time-step | 376        |
+| y_out     | -0.0964    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047648974 |
+| time-step | 377         |
+| y_out     | -0.0457     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047716357 |
+| time-step | 378         |
+| y_out     | 0.0286      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047496177 |
+| time-step | 379         |
+| y_out     | -0.00471    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049459416 |
+| time-step | 380         |
+| y_out     | -0.0768     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05048915 |
+| time-step | 381        |
+| y_out     | 0.127      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048765484 |
+| time-step | 382         |
+| y_out     | 0.0225      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048090927 |
+| time-step | 383         |
+| y_out     | -0.0885     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049119245 |
+| time-step | 384         |
+| y_out     | 0.0405      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050285257 |
+| time-step | 385         |
+| y_out     | 0.00977     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04998021 |
+| time-step | 386        |
+| y_out     | 0.0622     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051143028 |
+| time-step | 387         |
+| y_out     | -0.00915    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050728135 |
+| time-step | 388         |
+| y_out     | 0.0286      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05251382 |
+| time-step | 389        |
+| y_out     | -0.00823   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051797282 |
+| time-step | 390         |
+| y_out     | -0.0732     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0528268 |
+| time-step | 391       |
+| y_out     | 0.0221    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051988743 |
+| time-step | 392         |
+| y_out     | 0.0759      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05017187 |
+| time-step | 393        |
+| y_out     | -0.00241   |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0493968 |
+| time-step | 394       |
+| y_out     | 0.0433    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048933208 |
+| time-step | 395         |
+| y_out     | 0.00741     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049795378 |
+| time-step | 396         |
+| y_out     | -0.0133     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05128135 |
+| time-step | 397        |
+| y_out     | -0.0568    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049810044 |
+| time-step | 398         |
+| y_out     | 0.172       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050452936 |
+| time-step | 399         |
+| y_out     | -0.0236     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051612623 |
+| time-step | 400         |
+| y_out     | 0.00315     |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/checkpoint 400
+------------------------------------------------------------
+| perf/mse  | 0.049298503 |
+| time-step | 401         |
+| y_out     | -0.0815     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04917501 |
+| time-step | 402        |
+| y_out     | -0.00737   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050548345 |
+| time-step | 403         |
+| y_out     | -0.105      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05184231 |
+| time-step | 404        |
+| y_out     | -0.0992    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051629175 |
+| time-step | 405         |
+| y_out     | 0.0327      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050516494 |
+| time-step | 406         |
+| y_out     | -0.099      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04838071 |
+| time-step | 407        |
+| y_out     | 0.0216     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049961284 |
+| time-step | 408         |
+| y_out     | 0.162       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04932687 |
+| time-step | 409        |
+| y_out     | 0.0375     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04691658 |
+| time-step | 410        |
+| y_out     | 0.0337     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04760117 |
+| time-step | 411        |
+| y_out     | 0.113      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04678657 |
+| time-step | 412        |
+| y_out     | 0.0494     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04491849 |
+| time-step | 413        |
+| y_out     | -0.133     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042869948 |
+| time-step | 414         |
+| y_out     | -0.101      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04428022 |
+| time-step | 415        |
+| y_out     | 0.0261     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04513421 |
+| time-step | 416        |
+| y_out     | 0.0331     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04536701 |
+| time-step | 417        |
+| y_out     | 0.0109     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045004413 |
+| time-step | 418         |
+| y_out     | -0.0477     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04228895 |
+| time-step | 419        |
+| y_out     | -0.0488    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04272204 |
+| time-step | 420        |
+| y_out     | -0.206     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04088345 |
+| time-step | 421        |
+| y_out     | 0.032      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042330705 |
+| time-step | 422         |
+| y_out     | -0.0214     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0448047 |
+| time-step | 423       |
+| y_out     | 0.0421    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04717904 |
+| time-step | 424        |
+| y_out     | 0.00916    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04613499 |
+| time-step | 425        |
+| y_out     | -0.0104    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0451278 |
+| time-step | 426       |
+| y_out     | 0.0292    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045091845 |
+| time-step | 427         |
+| y_out     | -0.0652     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047298737 |
+| time-step | 428         |
+| y_out     | -0.092      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048587345 |
+| time-step | 429         |
+| y_out     | 0.0756      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04786981 |
+| time-step | 430        |
+| y_out     | -0.0936    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046995595 |
+| time-step | 431         |
+| y_out     | 0.0636      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04719137 |
+| time-step | 432        |
+| y_out     | 0.157      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04599424 |
+| time-step | 433        |
+| y_out     | 0.0661     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044415057 |
+| time-step | 434         |
+| y_out     | -0.0576     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044502713 |
+| time-step | 435         |
+| y_out     | 0.0653      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0450804 |
+| time-step | 436       |
+| y_out     | -0.0376   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04378108 |
+| time-step | 437        |
+| y_out     | -0.0145    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042750355 |
+| time-step | 438         |
+| y_out     | 0.00341     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041916072 |
+| time-step | 439         |
+| y_out     | -0.0945     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041096076 |
+| time-step | 440         |
+| y_out     | 0.0314      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04207965 |
+| time-step | 441        |
+| y_out     | -0.0212    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04124843 |
+| time-step | 442        |
+| y_out     | -0.0166    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04065743 |
+| time-step | 443        |
+| y_out     | 0.00908    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04054389 |
+| time-step | 444        |
+| y_out     | -0.0226    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04050336 |
+| time-step | 445        |
+| y_out     | 0.0884     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039309554 |
+| time-step | 446         |
+| y_out     | 0.0395      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041436084 |
+| time-step | 447         |
+| y_out     | 0.0281      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039484333 |
+| time-step | 448         |
+| y_out     | -0.0726     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04045566 |
+| time-step | 449        |
+| y_out     | -0.0639    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041988704 |
+| time-step | 450         |
+| y_out     | 0.0996      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043819435 |
+| time-step | 451         |
+| y_out     | 0.142       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045196947 |
+| time-step | 452         |
+| y_out     | -0.0309     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046382874 |
+| time-step | 453         |
+| y_out     | 0.112       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045947142 |
+| time-step | 454         |
+| y_out     | -5.64e-05   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045975145 |
+| time-step | 455         |
+| y_out     | 0.00517     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04711535 |
+| time-step | 456        |
+| y_out     | 0.0274     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046774935 |
+| time-step | 457         |
+| y_out     | -0.124      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04663525 |
+| time-step | 458        |
+| y_out     | 0.0198     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04687738 |
+| time-step | 459        |
+| y_out     | 0.00411    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046076596 |
+| time-step | 460         |
+| y_out     | 0.0193      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04469601 |
+| time-step | 461        |
+| y_out     | -0.0325    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043347925 |
+| time-step | 462         |
+| y_out     | 0.101       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042185884 |
+| time-step | 463         |
+| y_out     | -0.0211     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041989654 |
+| time-step | 464         |
+| y_out     | -0.0237     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042105682 |
+| time-step | 465         |
+| y_out     | 0.0226      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040245485 |
+| time-step | 466         |
+| y_out     | -0.0359     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039194096 |
+| time-step | 467         |
+| y_out     | -0.0236     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039975733 |
+| time-step | 468         |
+| y_out     | -0.0261     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038464148 |
+| time-step | 469         |
+| y_out     | 0.0744      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038968343 |
+| time-step | 470         |
+| y_out     | 0.0589      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03878829 |
+| time-step | 471        |
+| y_out     | -0.078     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039112873 |
+| time-step | 472         |
+| y_out     | 0.0266      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03935024 |
+| time-step | 473        |
+| y_out     | -0.0559    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0407045 |
+| time-step | 474       |
+| y_out     | 0.0479    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04014684 |
+| time-step | 475        |
+| y_out     | -0.0073    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04010058 |
+| time-step | 476        |
+| y_out     | -0.061     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040465403 |
+| time-step | 477         |
+| y_out     | 0.0758      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040812254 |
+| time-step | 478         |
+| y_out     | -0.0687     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040099453 |
+| time-step | 479         |
+| y_out     | -0.0585     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04009784 |
+| time-step | 480        |
+| y_out     | -0.105     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040263597 |
+| time-step | 481         |
+| y_out     | -0.0528     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040755503 |
+| time-step | 482         |
+| y_out     | 0.0978      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040466808 |
+| time-step | 483         |
+| y_out     | 0.0249      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039717473 |
+| time-step | 484         |
+| y_out     | 0.0131      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04026102 |
+| time-step | 485        |
+| y_out     | 0.167      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041761823 |
+| time-step | 486         |
+| y_out     | -0.0314     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042971868 |
+| time-step | 487         |
+| y_out     | -0.176      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042814843 |
+| time-step | 488         |
+| y_out     | -0.0925     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04446888 |
+| time-step | 489        |
+| y_out     | -0.0604    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045741346 |
+| time-step | 490         |
+| y_out     | -0.155      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045773074 |
+| time-step | 491         |
+| y_out     | -0.1        |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04578921 |
+| time-step | 492        |
+| y_out     | 0.0311     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04678817 |
+| time-step | 493        |
+| y_out     | 0.083      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047098078 |
+| time-step | 494         |
+| y_out     | -0.0127     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046221964 |
+| time-step | 495         |
+| y_out     | 0.0307      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046291053 |
+| time-step | 496         |
+| y_out     | -0.106      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044057168 |
+| time-step | 497         |
+| y_out     | 0.0425      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044664633 |
+| time-step | 498         |
+| y_out     | -0.107      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045550805 |
+| time-step | 499         |
+| y_out     | -0.0175     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043581955 |
+| time-step | 500         |
+| y_out     | 0.016       |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/checkpoint 500
+----------------------------------------------------------
+| perf/mse  | 0.0433195 |
+| time-step | 501       |
+| y_out     | -0.0539   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043495655 |
+| time-step | 502         |
+| y_out     | 0.0775      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043606438 |
+| time-step | 503         |
+| y_out     | -0.0818     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0419158 |
+| time-step | 504       |
+| y_out     | 0.0413    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042762235 |
+| time-step | 505         |
+| y_out     | -0.0734     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04218278 |
+| time-step | 506        |
+| y_out     | 0.0339     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04287967 |
+| time-step | 507        |
+| y_out     | 0.0724     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0417406 |
+| time-step | 508       |
+| y_out     | 0.0412    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040139075 |
+| time-step | 509         |
+| y_out     | -0.0413     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041674577 |
+| time-step | 510         |
+| y_out     | -0.0385     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04270089 |
+| time-step | 511        |
+| y_out     | -0.0657    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041781474 |
+| time-step | 512         |
+| y_out     | 0.0757      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041109428 |
+| time-step | 513         |
+| y_out     | -0.0585     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04133248 |
+| time-step | 514        |
+| y_out     | -0.141     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04113532 |
+| time-step | 515        |
+| y_out     | 0.00362    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04176956 |
+| time-step | 516        |
+| y_out     | 0.0542     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040870108 |
+| time-step | 517         |
+| y_out     | -0.0301     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042523883 |
+| time-step | 518         |
+| y_out     | 0.124       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042741444 |
+| time-step | 519         |
+| y_out     | 0.0612      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0416047 |
+| time-step | 520       |
+| y_out     | -0.0764   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040262688 |
+| time-step | 521         |
+| y_out     | -0.0142     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041239403 |
+| time-step | 522         |
+| y_out     | -0.0748     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041240614 |
+| time-step | 523         |
+| y_out     | -0.00354    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042331543 |
+| time-step | 524         |
+| y_out     | 0.0199      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04086683 |
+| time-step | 525        |
+| y_out     | -0.0124    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039828677 |
+| time-step | 526         |
+| y_out     | 0.0876      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039771628 |
+| time-step | 527         |
+| y_out     | -0.0468     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038845032 |
+| time-step | 528         |
+| y_out     | -0.0454     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040037535 |
+| time-step | 529         |
+| y_out     | 0.0757      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04109566 |
+| time-step | 530        |
+| y_out     | 0.0987     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040208463 |
+| time-step | 531         |
+| y_out     | 0.0253      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039192673 |
+| time-step | 532         |
+| y_out     | -0.0193     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039382868 |
+| time-step | 533         |
+| y_out     | -0.0567     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039696056 |
+| time-step | 534         |
+| y_out     | -0.0118     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040804677 |
+| time-step | 535         |
+| y_out     | -0.0303     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04089757 |
+| time-step | 536        |
+| y_out     | -0.026     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04123577 |
+| time-step | 537        |
+| y_out     | 0.00811    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040962234 |
+| time-step | 538         |
+| y_out     | -0.0729     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0419768 |
+| time-step | 539       |
+| y_out     | 0.0183    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03992503 |
+| time-step | 540        |
+| y_out     | -0.0245    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041555304 |
+| time-step | 541         |
+| y_out     | -0.0704     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04217988 |
+| time-step | 542        |
+| y_out     | -0.0395    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04279113 |
+| time-step | 543        |
+| y_out     | -0.0782    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041717317 |
+| time-step | 544         |
+| y_out     | -0.0516     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041593306 |
+| time-step | 545         |
+| y_out     | 0.0356      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040710445 |
+| time-step | 546         |
+| y_out     | -0.0156     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041528393 |
+| time-step | 547         |
+| y_out     | 0.0811      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04133732 |
+| time-step | 548        |
+| y_out     | -0.139     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03972288 |
+| time-step | 549        |
+| y_out     | 0.0141     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043103877 |
+| time-step | 550         |
+| y_out     | 0.0483      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04295221 |
+| time-step | 551        |
+| y_out     | 0.144      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041297276 |
+| time-step | 552         |
+| y_out     | 0.075       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040485527 |
+| time-step | 553         |
+| y_out     | 0.0263      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040230997 |
+| time-step | 554         |
+| y_out     | -0.04       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039685823 |
+| time-step | 555         |
+| y_out     | 0.0929      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04062644 |
+| time-step | 556        |
+| y_out     | 0.0698     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03977953 |
+| time-step | 557        |
+| y_out     | 0.111      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039497703 |
+| time-step | 558         |
+| y_out     | -0.0431     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039897688 |
+| time-step | 559         |
+| y_out     | -0.0915     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03709321 |
+| time-step | 560        |
+| y_out     | -0.0733    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036519706 |
+| time-step | 561         |
+| y_out     | -0.0789     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038696013 |
+| time-step | 562         |
+| y_out     | 0.117       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03883735 |
+| time-step | 563        |
+| y_out     | 0.0783     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03916894 |
+| time-step | 564        |
+| y_out     | 0.116      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03973155 |
+| time-step | 565        |
+| y_out     | -0.0242    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041335262 |
+| time-step | 566         |
+| y_out     | 0.147       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041378096 |
+| time-step | 567         |
+| y_out     | 0.0208      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0416981 |
+| time-step | 568       |
+| y_out     | 0.131     |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043603305 |
+| time-step | 569         |
+| y_out     | -0.106      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044673316 |
+| time-step | 570         |
+| y_out     | 0.0356      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04417503 |
+| time-step | 571        |
+| y_out     | 0.0524     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042526834 |
+| time-step | 572         |
+| y_out     | 0.00476     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04173949 |
+| time-step | 573        |
+| y_out     | 0.0393     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041181777 |
+| time-step | 574         |
+| y_out     | 0.0579      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04162394 |
+| time-step | 575        |
+| y_out     | -0.0316    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040213548 |
+| time-step | 576         |
+| y_out     | 0.0637      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04056842 |
+| time-step | 577        |
+| y_out     | 0.00894    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041636225 |
+| time-step | 578         |
+| y_out     | -0.0247     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038060185 |
+| time-step | 579         |
+| y_out     | 0.0615      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037322216 |
+| time-step | 580         |
+| y_out     | -0.00985    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037494082 |
+| time-step | 581         |
+| y_out     | 0.00326     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03757308 |
+| time-step | 582        |
+| y_out     | -0.0333    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038125496 |
+| time-step | 583         |
+| y_out     | -0.038      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038185593 |
+| time-step | 584         |
+| y_out     | -0.0036     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037031632 |
+| time-step | 585         |
+| y_out     | 0.0674      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03636884 |
+| time-step | 586        |
+| y_out     | -0.0126    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036718212 |
+| time-step | 587         |
+| y_out     | -0.0889     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035738792 |
+| time-step | 588         |
+| y_out     | 0.0737      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03630426 |
+| time-step | 589        |
+| y_out     | -0.0699    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03629414 |
+| time-step | 590        |
+| y_out     | -0.0244    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03701472 |
+| time-step | 591        |
+| y_out     | -0.0592    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036893435 |
+| time-step | 592         |
+| y_out     | 0.0298      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03701152 |
+| time-step | 593        |
+| y_out     | -0.0296    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037567534 |
+| time-step | 594         |
+| y_out     | 0.0333      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03869937 |
+| time-step | 595        |
+| y_out     | 0.054      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040331863 |
+| time-step | 596         |
+| y_out     | -0.095      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03968043 |
+| time-step | 597        |
+| y_out     | -0.0178    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038565416 |
+| time-step | 598         |
+| y_out     | 0.106       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03983199 |
+| time-step | 599        |
+| y_out     | -0.0518    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040780813 |
+| time-step | 600         |
+| y_out     | 0.163       |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/checkpoint 600
+-----------------------------------------------------------
+| perf/mse  | 0.04040748 |
+| time-step | 601        |
+| y_out     | -0.0434    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039763443 |
+| time-step | 602         |
+| y_out     | 0.11        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040080987 |
+| time-step | 603         |
+| y_out     | -0.0373     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039690845 |
+| time-step | 604         |
+| y_out     | 0.0147      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038111765 |
+| time-step | 605         |
+| y_out     | -0.0195     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036243714 |
+| time-step | 606         |
+| y_out     | 0.0947      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03605535 |
+| time-step | 607        |
+| y_out     | 0.0687     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037156858 |
+| time-step | 608         |
+| y_out     | 0.0169      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03555821 |
+| time-step | 609        |
+| y_out     | -0.00921   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035048187 |
+| time-step | 610         |
+| y_out     | 0.00486     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034824394 |
+| time-step | 611         |
+| y_out     | 0.0217      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035158224 |
+| time-step | 612         |
+| y_out     | 0.0197      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034627605 |
+| time-step | 613         |
+| y_out     | 0.00414     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036983654 |
+| time-step | 614         |
+| y_out     | -0.111      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038132165 |
+| time-step | 615         |
+| y_out     | 0.0494      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03982105 |
+| time-step | 616        |
+| y_out     | -0.0528    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03999034 |
+| time-step | 617        |
+| y_out     | 0.112      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039819263 |
+| time-step | 618         |
+| y_out     | 0.0283      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040619727 |
+| time-step | 619         |
+| y_out     | 0.0497      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040472355 |
+| time-step | 620         |
+| y_out     | 0.0712      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03979265 |
+| time-step | 621        |
+| y_out     | -0.0643    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03958496 |
+| time-step | 622        |
+| y_out     | -0.0376    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041450303 |
+| time-step | 623         |
+| y_out     | 0.0885      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03933242 |
+| time-step | 624        |
+| y_out     | 0.108      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039508883 |
+| time-step | 625         |
+| y_out     | 0.0362      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037417866 |
+| time-step | 626         |
+| y_out     | 0.0181      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03711041 |
+| time-step | 627        |
+| y_out     | -0.00268   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037317857 |
+| time-step | 628         |
+| y_out     | -0.00291    |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0383118 |
+| time-step | 629       |
+| y_out     | -0.0435   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037956122 |
+| time-step | 630         |
+| y_out     | -0.0328     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038879532 |
+| time-step | 631         |
+| y_out     | 0.0857      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03920067 |
+| time-step | 632        |
+| y_out     | 0.0979     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038225017 |
+| time-step | 633         |
+| y_out     | 0.0652      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037791915 |
+| time-step | 634         |
+| y_out     | -0.003      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037692644 |
+| time-step | 635         |
+| y_out     | -0.0781     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03763432 |
+| time-step | 636        |
+| y_out     | 0.0707     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037584264 |
+| time-step | 637         |
+| y_out     | -0.0472     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036727577 |
+| time-step | 638         |
+| y_out     | 0.00734     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03474323 |
+| time-step | 639        |
+| y_out     | -0.111     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034990508 |
+| time-step | 640         |
+| y_out     | -0.0782     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035130512 |
+| time-step | 641         |
+| y_out     | 0.0261      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035033457 |
+| time-step | 642         |
+| y_out     | -0.0644     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034520645 |
+| time-step | 643         |
+| y_out     | 0.0249      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034130104 |
+| time-step | 644         |
+| y_out     | -0.0691     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033446677 |
+| time-step | 645         |
+| y_out     | 0.0629      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034531206 |
+| time-step | 646         |
+| y_out     | -0.0221     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035306662 |
+| time-step | 647         |
+| y_out     | -0.0768     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03565969 |
+| time-step | 648        |
+| y_out     | -0.0286    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035442043 |
+| time-step | 649         |
+| y_out     | 0.0749      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035059936 |
+| time-step | 650         |
+| y_out     | -0.0146     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034617852 |
+| time-step | 651         |
+| y_out     | 0.0715      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033822127 |
+| time-step | 652         |
+| y_out     | -0.0111     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032898072 |
+| time-step | 653         |
+| y_out     | 0.0236      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033201367 |
+| time-step | 654         |
+| y_out     | 0.125       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033928987 |
+| time-step | 655         |
+| y_out     | -0.0798     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03349203 |
+| time-step | 656        |
+| y_out     | -0.00448   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032486964 |
+| time-step | 657         |
+| y_out     | 0.111       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031624325 |
+| time-step | 658         |
+| y_out     | 0.0595      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033516604 |
+| time-step | 659         |
+| y_out     | 0.0642      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03387384 |
+| time-step | 660        |
+| y_out     | -0.0176    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03485232 |
+| time-step | 661        |
+| y_out     | -0.0974    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036046077 |
+| time-step | 662         |
+| y_out     | 0.0135      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03621802 |
+| time-step | 663        |
+| y_out     | -0.0573    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035767984 |
+| time-step | 664         |
+| y_out     | -0.102      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0345046 |
+| time-step | 665       |
+| y_out     | 0.0752    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034522187 |
+| time-step | 666         |
+| y_out     | 0.00163     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036395587 |
+| time-step | 667         |
+| y_out     | 0.04        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037666894 |
+| time-step | 668         |
+| y_out     | 0.0146      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0355506 |
+| time-step | 669       |
+| y_out     | 0.0179    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034635972 |
+| time-step | 670         |
+| y_out     | 0.00883     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03435846 |
+| time-step | 671        |
+| y_out     | -0.0762    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034817446 |
+| time-step | 672         |
+| y_out     | -0.0712     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034293998 |
+| time-step | 673         |
+| y_out     | 0.0252      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03492614 |
+| time-step | 674        |
+| y_out     | 0.0816     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035841107 |
+| time-step | 675         |
+| y_out     | -0.167      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036643483 |
+| time-step | 676         |
+| y_out     | 0.0915      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035110187 |
+| time-step | 677         |
+| y_out     | 0.0336      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034637656 |
+| time-step | 678         |
+| y_out     | 0.00567     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034472078 |
+| time-step | 679         |
+| y_out     | 0.0149      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034322552 |
+| time-step | 680         |
+| y_out     | 0.0319      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033315394 |
+| time-step | 681         |
+| y_out     | 0.0619      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031849127 |
+| time-step | 682         |
+| y_out     | -0.034      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032420613 |
+| time-step | 683         |
+| y_out     | 0.0341      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031600617 |
+| time-step | 684         |
+| y_out     | -0.0433     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031020794 |
+| time-step | 685         |
+| y_out     | -0.0167     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029522315 |
+| time-step | 686         |
+| y_out     | 0.103       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030101735 |
+| time-step | 687         |
+| y_out     | 0.0171      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029611489 |
+| time-step | 688         |
+| y_out     | 0.035       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030553326 |
+| time-step | 689         |
+| y_out     | 0.072       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031130135 |
+| time-step | 690         |
+| y_out     | 0.0693      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031407844 |
+| time-step | 691         |
+| y_out     | -0.0939     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033279028 |
+| time-step | 692         |
+| y_out     | -0.109      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033729516 |
+| time-step | 693         |
+| y_out     | 0.0145      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033430513 |
+| time-step | 694         |
+| y_out     | 0.0954      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034287043 |
+| time-step | 695         |
+| y_out     | 0.102       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034985255 |
+| time-step | 696         |
+| y_out     | 0.113       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03490349 |
+| time-step | 697        |
+| y_out     | -0.0601    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034853287 |
+| time-step | 698         |
+| y_out     | -0.141      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034514718 |
+| time-step | 699         |
+| y_out     | 0.00826     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03458406 |
+| time-step | 700        |
+| y_out     | -0.00751   |
+-----------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/checkpoint 700
+------------------------------------------------------------
+| perf/mse  | 0.033880405 |
+| time-step | 701         |
+| y_out     | -0.0406     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03241182 |
+| time-step | 702        |
+| y_out     | 0.0285     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030682286 |
+| time-step | 703         |
+| y_out     | -0.0627     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031068558 |
+| time-step | 704         |
+| y_out     | 0.0351      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030623829 |
+| time-step | 705         |
+| y_out     | 0.0698      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030694833 |
+| time-step | 706         |
+| y_out     | 0.0778      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031009803 |
+| time-step | 707         |
+| y_out     | 0.0685      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03075462 |
+| time-step | 708        |
+| y_out     | -0.00235   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031132001 |
+| time-step | 709         |
+| y_out     | -0.04       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032163657 |
+| time-step | 710         |
+| y_out     | -0.00144    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031868786 |
+| time-step | 711         |
+| y_out     | -0.0717     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031574074 |
+| time-step | 712         |
+| y_out     | -0.0264     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03337201 |
+| time-step | 713        |
+| y_out     | 0.0215     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033705104 |
+| time-step | 714         |
+| y_out     | -0.0207     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033566993 |
+| time-step | 715         |
+| y_out     | -0.0749     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033646517 |
+| time-step | 716         |
+| y_out     | -0.0401     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033064827 |
+| time-step | 717         |
+| y_out     | -0.0377     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032034695 |
+| time-step | 718         |
+| y_out     | 0.0351      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031348057 |
+| time-step | 719         |
+| y_out     | 0.0401      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030188825 |
+| time-step | 720         |
+| y_out     | -0.0856     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030938143 |
+| time-step | 721         |
+| y_out     | -0.0408     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031626992 |
+| time-step | 722         |
+| y_out     | -0.0702     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03152649 |
+| time-step | 723        |
+| y_out     | 0.0833     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03144444 |
+| time-step | 724        |
+| y_out     | -0.0535    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03161285 |
+| time-step | 725        |
+| y_out     | -0.0357    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03192906 |
+| time-step | 726        |
+| y_out     | 0.0422     |
+-----------------------------------------------------------
+---------------------------------------------------------
+| perf/mse  | 0.031762 |
+| time-step | 727      |
+| y_out     | -0.074   |
+---------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03284171 |
+| time-step | 728        |
+| y_out     | -0.0334    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03496663 |
+| time-step | 729        |
+| y_out     | 0.0321     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03502272 |
+| time-step | 730        |
+| y_out     | -0.0666    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034854148 |
+| time-step | 731         |
+| y_out     | 0.0921      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034256183 |
+| time-step | 732         |
+| y_out     | -0.0104     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03399481 |
+| time-step | 733        |
+| y_out     | 0.0709     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034413703 |
+| time-step | 734         |
+| y_out     | -0.0454     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033731613 |
+| time-step | 735         |
+| y_out     | -0.0567     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033232715 |
+| time-step | 736         |
+| y_out     | 0.0943      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033324506 |
+| time-step | 737         |
+| y_out     | -0.0534     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03399422 |
+| time-step | 738        |
+| y_out     | -0.0444    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031683613 |
+| time-step | 739         |
+| y_out     | -0.00581    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03147561 |
+| time-step | 740        |
+| y_out     | -0.00476   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033192992 |
+| time-step | 741         |
+| y_out     | 0.0681      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033889122 |
+| time-step | 742         |
+| y_out     | -0.128      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03453921 |
+| time-step | 743        |
+| y_out     | 0.0614     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034295876 |
+| time-step | 744         |
+| y_out     | -0.114      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03441958 |
+| time-step | 745        |
+| y_out     | -0.0234    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034893565 |
+| time-step | 746         |
+| y_out     | -0.00544    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035000503 |
+| time-step | 747         |
+| y_out     | -0.0958     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034186408 |
+| time-step | 748         |
+| y_out     | -0.0123     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03489658 |
+| time-step | 749        |
+| y_out     | -0.194     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034544032 |
+| time-step | 750         |
+| y_out     | 0.0714      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033502202 |
+| time-step | 751         |
+| y_out     | 0.065       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033249952 |
+| time-step | 752         |
+| y_out     | 0.0638      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032732934 |
+| time-step | 753         |
+| y_out     | 0.00302     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032715775 |
+| time-step | 754         |
+| y_out     | -0.03       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034088694 |
+| time-step | 755         |
+| y_out     | 0.154       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034077838 |
+| time-step | 756         |
+| y_out     | -0.00674    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03454442 |
+| time-step | 757        |
+| y_out     | 0.04       |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035190124 |
+| time-step | 758         |
+| y_out     | 0.0838      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03517934 |
+| time-step | 759        |
+| y_out     | -0.0254    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03540191 |
+| time-step | 760        |
+| y_out     | 0.104      |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0355314 |
+| time-step | 761       |
+| y_out     | -0.0936   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035840582 |
+| time-step | 762         |
+| y_out     | 0.0396      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035787575 |
+| time-step | 763         |
+| y_out     | 0.0322      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035666965 |
+| time-step | 764         |
+| y_out     | 0.00436     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03417643 |
+| time-step | 765        |
+| y_out     | -0.0576    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033591874 |
+| time-step | 766         |
+| y_out     | -0.0522     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032676984 |
+| time-step | 767         |
+| y_out     | -0.027      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033677064 |
+| time-step | 768         |
+| y_out     | 0.0796      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03439086 |
+| time-step | 769        |
+| y_out     | 0.0594     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034344353 |
+| time-step | 770         |
+| y_out     | 0.0414      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03289576 |
+| time-step | 771        |
+| y_out     | -0.0117    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032238055 |
+| time-step | 772         |
+| y_out     | -0.148      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032016914 |
+| time-step | 773         |
+| y_out     | 0.0391      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032675076 |
+| time-step | 774         |
+| y_out     | 0.0218      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032914568 |
+| time-step | 775         |
+| y_out     | -0.0503     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03223046 |
+| time-step | 776        |
+| y_out     | 0.0149     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031958494 |
+| time-step | 777         |
+| y_out     | 0.0107      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03102837 |
+| time-step | 778        |
+| y_out     | -0.0888    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030204069 |
+| time-step | 779         |
+| y_out     | 0.0867      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031328745 |
+| time-step | 780         |
+| y_out     | 0.0751      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031813454 |
+| time-step | 781         |
+| y_out     | 0.0828      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031569373 |
+| time-step | 782         |
+| y_out     | 0.0166      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030353406 |
+| time-step | 783         |
+| y_out     | 0.0582      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029569939 |
+| time-step | 784         |
+| y_out     | -0.0847     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028947726 |
+| time-step | 785         |
+| y_out     | -0.00751    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029482957 |
+| time-step | 786         |
+| y_out     | 0.0227      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029256474 |
+| time-step | 787         |
+| y_out     | -0.0829     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028287593 |
+| time-step | 788         |
+| y_out     | 0.0452      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027608458 |
+| time-step | 789         |
+| y_out     | 0.0493      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026024941 |
+| time-step | 790         |
+| y_out     | 0.0735      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026200688 |
+| time-step | 791         |
+| y_out     | -0.0822     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027343327 |
+| time-step | 792         |
+| y_out     | 0.0103      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028491667 |
+| time-step | 793         |
+| y_out     | 0.0159      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028277855 |
+| time-step | 794         |
+| y_out     | 0.0354      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028639372 |
+| time-step | 795         |
+| y_out     | 0.071       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027921278 |
+| time-step | 796         |
+| y_out     | -0.049      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02821945 |
+| time-step | 797        |
+| y_out     | -0.0486    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02897954 |
+| time-step | 798        |
+| y_out     | 0.0341     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029815987 |
+| time-step | 799         |
+| y_out     | -0.0627     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030650765 |
+| time-step | 800         |
+| y_out     | 0.00245     |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/checkpoint 800
+------------------------------------------------------------
+| perf/mse  | 0.029749114 |
+| time-step | 801         |
+| y_out     | 0.0606      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030450022 |
+| time-step | 802         |
+| y_out     | -0.0143     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029640784 |
+| time-step | 803         |
+| y_out     | 0.0054      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029577825 |
+| time-step | 804         |
+| y_out     | 0.14        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029527217 |
+| time-step | 805         |
+| y_out     | 0.0279      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029465567 |
+| time-step | 806         |
+| y_out     | 0.0478      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02942439 |
+| time-step | 807        |
+| y_out     | 0.0227     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029973114 |
+| time-step | 808         |
+| y_out     | -0.0968     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02865826 |
+| time-step | 809        |
+| y_out     | 0.103      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02827696 |
+| time-step | 810        |
+| y_out     | 0.00753    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029177418 |
+| time-step | 811         |
+| y_out     | 0.0428      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027264377 |
+| time-step | 812         |
+| y_out     | -0.0166     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02793624 |
+| time-step | 813        |
+| y_out     | -0.107     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028114026 |
+| time-step | 814         |
+| y_out     | 0.0876      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028837245 |
+| time-step | 815         |
+| y_out     | -0.0863     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029435512 |
+| time-step | 816         |
+| y_out     | -0.0488     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02895026 |
+| time-step | 817        |
+| y_out     | 0.0702     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027444452 |
+| time-step | 818         |
+| y_out     | 0.113       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02928554 |
+| time-step | 819        |
+| y_out     | -0.0172    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02921555 |
+| time-step | 820        |
+| y_out     | 0.172      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029565636 |
+| time-step | 821         |
+| y_out     | -0.00874    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030052274 |
+| time-step | 822         |
+| y_out     | -0.0021     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030542308 |
+| time-step | 823         |
+| y_out     | -0.0329     |
+------------------------------------------------------------
+---------------------------------------------------------
+| perf/mse  | 0.030573 |
+| time-step | 824      |
+| y_out     | 0.0276   |
+---------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029855018 |
+| time-step | 825         |
+| y_out     | 0.00139     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029474149 |
+| time-step | 826         |
+| y_out     | -0.0141     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030186277 |
+| time-step | 827         |
+| y_out     | 0.0737      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03139303 |
+| time-step | 828        |
+| y_out     | 0.0197     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03030757 |
+| time-step | 829        |
+| y_out     | -0.0406    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031952985 |
+| time-step | 830         |
+| y_out     | -0.105      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0318633 |
+| time-step | 831       |
+| y_out     | -0.04     |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031874202 |
+| time-step | 832         |
+| y_out     | 0.00901     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032290168 |
+| time-step | 833         |
+| y_out     | -0.093      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031817093 |
+| time-step | 834         |
+| y_out     | 0.0472      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031733997 |
+| time-step | 835         |
+| y_out     | 0.0468      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03252268 |
+| time-step | 836        |
+| y_out     | 0.0532     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032422867 |
+| time-step | 837         |
+| y_out     | 0.0573      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032111738 |
+| time-step | 838         |
+| y_out     | -0.0552     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03298972 |
+| time-step | 839        |
+| y_out     | 0.0844     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03135136 |
+| time-step | 840        |
+| y_out     | -0.0525    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030794963 |
+| time-step | 841         |
+| y_out     | 0.0768      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030718062 |
+| time-step | 842         |
+| y_out     | -0.0651     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029161686 |
+| time-step | 843         |
+| y_out     | -0.0209     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030745342 |
+| time-step | 844         |
+| y_out     | 0.0223      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031048447 |
+| time-step | 845         |
+| y_out     | -0.0104     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030652534 |
+| time-step | 846         |
+| y_out     | 0.076       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030912261 |
+| time-step | 847         |
+| y_out     | -0.0457     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03114942 |
+| time-step | 848        |
+| y_out     | -0.0474    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030947322 |
+| time-step | 849         |
+| y_out     | 0.0276      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031459086 |
+| time-step | 850         |
+| y_out     | 0.0533      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03158531 |
+| time-step | 851        |
+| y_out     | -0.126     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03080064 |
+| time-step | 852        |
+| y_out     | -0.0452    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031007767 |
+| time-step | 853         |
+| y_out     | 0.0355      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030812617 |
+| time-step | 854         |
+| y_out     | 0.0452      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030408556 |
+| time-step | 855         |
+| y_out     | 0.043       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031355392 |
+| time-step | 856         |
+| y_out     | -0.0708     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030533958 |
+| time-step | 857         |
+| y_out     | -0.0976     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029457297 |
+| time-step | 858         |
+| y_out     | 0.193       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028317515 |
+| time-step | 859         |
+| y_out     | 0.0419      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0276384 |
+| time-step | 860       |
+| y_out     | -0.012    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02652489 |
+| time-step | 861        |
+| y_out     | 0.0513     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027203146 |
+| time-step | 862         |
+| y_out     | -0.052      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02691786 |
+| time-step | 863        |
+| y_out     | 0.0679     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026311576 |
+| time-step | 864         |
+| y_out     | 0.143       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026107114 |
+| time-step | 865         |
+| y_out     | 0.0747      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024599604 |
+| time-step | 866         |
+| y_out     | 0.055       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02489976 |
+| time-step | 867        |
+| y_out     | -0.105     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025397714 |
+| time-step | 868         |
+| y_out     | -0.117      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02566601 |
+| time-step | 869        |
+| y_out     | -0.0137    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026150549 |
+| time-step | 870         |
+| y_out     | 0.0308      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026716184 |
+| time-step | 871         |
+| y_out     | -0.0314     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026029903 |
+| time-step | 872         |
+| y_out     | 0.0212      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026441425 |
+| time-step | 873         |
+| y_out     | -0.0552     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025501873 |
+| time-step | 874         |
+| y_out     | -0.0837     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02523778 |
+| time-step | 875        |
+| y_out     | -0.164     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025822561 |
+| time-step | 876         |
+| y_out     | -0.0131     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026206236 |
+| time-step | 877         |
+| y_out     | -0.123      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02624933 |
+| time-step | 878        |
+| y_out     | -0.165     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026718482 |
+| time-step | 879         |
+| y_out     | 0.124       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025950437 |
+| time-step | 880         |
+| y_out     | 0.00619     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026844483 |
+| time-step | 881         |
+| y_out     | -0.0278     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026983282 |
+| time-step | 882         |
+| y_out     | -0.0564     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02738451 |
+| time-step | 883        |
+| y_out     | 0.00291    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028599177 |
+| time-step | 884         |
+| y_out     | 0.002       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029451102 |
+| time-step | 885         |
+| y_out     | -0.0123     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028934142 |
+| time-step | 886         |
+| y_out     | 0.0422      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028447041 |
+| time-step | 887         |
+| y_out     | -0.161      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028218012 |
+| time-step | 888         |
+| y_out     | 0.0181      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02758134 |
+| time-step | 889        |
+| y_out     | -0.0215    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027639583 |
+| time-step | 890         |
+| y_out     | -0.0336     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027555833 |
+| time-step | 891         |
+| y_out     | 0.0725      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028048292 |
+| time-step | 892         |
+| y_out     | -0.0274     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027543291 |
+| time-step | 893         |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027465697 |
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+| y_out     | -0.126      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028316656 |
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+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02879639 |
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+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028723842 |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028936356 |
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+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02920806 |
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+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028817976 |
+| time-step | 900         |
+| y_out     | -0.0324     |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/checkpoint 900
+------------------------------------------------------------
+| perf/mse  | 0.028033573 |
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+-----------------------------------------------------------
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+| perf/mse  | 0.02495483 |
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/progress-[||perf--mse||, ||timestep||].csv b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/progress-[||perf--mse||, ||timestep||].csv
new file mode 100644
index 0000000..2708d8e
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/progress-[||perf--mse||, ||timestep||].csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/progress.csv b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/progress.csv
new file mode 100644
index 0000000..40ade58
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/progress.csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/tb/events/events.out.tfevents.1646140386.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/tb/events/events.out.tfevents.1646140386.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..644ee98
Binary files /dev/null and b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/tb/events/events.out.tfevents.1646140386.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/warn.txt b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/warn.txt
new file mode 100644
index 0000000..f7e0929
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-13-03-230208 11.0.91.89 &env_id=Test-v1&learning_rate=0.0001/warn.txt	
@@ -0,0 +1,8 @@
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/backup.txt b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/backup.txt
new file mode 100644
index 0000000..0dc856b
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/backup.txt	
@@ -0,0 +1,7 @@
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.01
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 88
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/log.txt b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/log.txt
new file mode 100644
index 0000000..3c5378d
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/log.txt	
@@ -0,0 +1,5048 @@
+Logging to ../log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/
+log dir: ../log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/
+pkl_file: ../archive_tester/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01.pkl
+checkpoint_dir: ../checkpoint/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/
+results_dir: ../results/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.01
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 88
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | -0.0733  |
+---------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/checkpoint 0
+---------------------------------------------------------
+| time-step | 1        |
+| y_out     | 0.0674   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 2        |
+| y_out     | -0.0221  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 3        |
+| y_out     | 0.0157   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 4        |
+| y_out     | -0.0122  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 5        |
+| y_out     | -0.122   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 6        |
+| y_out     | -0.0238  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 7        |
+| y_out     | 0.0193   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 8        |
+| y_out     | 0.074    |
+---------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26031023 |
+| time-step | 9          |
+| y_out     | 0.00655    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21351269 |
+| time-step | 10         |
+| y_out     | 0.0145     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18336979 |
+| time-step | 11         |
+| y_out     | -0.0601    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15505561 |
+| time-step | 12         |
+| y_out     | 0.000709   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13367245 |
+| time-step | 13         |
+| y_out     | -0.103     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12379172 |
+| time-step | 14         |
+| y_out     | -0.0563    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1196655 |
+| time-step | 15        |
+| y_out     | 0.0535    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10415129 |
+| time-step | 16         |
+| y_out     | 0.000784   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09134625 |
+| time-step | 17         |
+| y_out     | -0.0506    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07917899 |
+| time-step | 18         |
+| y_out     | 0.0583     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07524745 |
+| time-step | 19         |
+| y_out     | -0.0654    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06702645 |
+| time-step | 20         |
+| y_out     | -0.0397    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061725102 |
+| time-step | 21          |
+| y_out     | -0.0573     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057398804 |
+| time-step | 22          |
+| y_out     | -0.00388    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.055527873 |
+| time-step | 23          |
+| y_out     | 0.00144     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05091731 |
+| time-step | 24         |
+| y_out     | -0.0146    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04761921 |
+| time-step | 25         |
+| y_out     | 0.0195     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043828513 |
+| time-step | 26          |
+| y_out     | 0.0372      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041592363 |
+| time-step | 27          |
+| y_out     | -0.0433     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039872997 |
+| time-step | 28          |
+| y_out     | -0.0343     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037364412 |
+| time-step | 29          |
+| y_out     | -0.0109     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035988085 |
+| time-step | 30          |
+| y_out     | -0.0835     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036347017 |
+| time-step | 31          |
+| y_out     | -0.0531     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035212535 |
+| time-step | 32          |
+| y_out     | -0.0974     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032537967 |
+| time-step | 33          |
+| y_out     | 0.0847      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03250489 |
+| time-step | 34         |
+| y_out     | 0.0319     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031569332 |
+| time-step | 35          |
+| y_out     | 0.0534      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029569631 |
+| time-step | 36          |
+| y_out     | 0.0642      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026859771 |
+| time-step | 37          |
+| y_out     | 0.0876      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024990764 |
+| time-step | 38          |
+| y_out     | 0.107       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02430394 |
+| time-step | 39         |
+| y_out     | -0.0244    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023186047 |
+| time-step | 40          |
+| y_out     | 0.00518     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020670911 |
+| time-step | 41          |
+| y_out     | -0.0359     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0198818 |
+| time-step | 42        |
+| y_out     | 0.0908    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019569468 |
+| time-step | 43          |
+| y_out     | -0.0447     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018605178 |
+| time-step | 44          |
+| y_out     | -0.00846    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01806185 |
+| time-step | 45         |
+| y_out     | 0.0322     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01870245 |
+| time-step | 46         |
+| y_out     | 0.0359     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018544752 |
+| time-step | 47          |
+| y_out     | 0.118       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017930094 |
+| time-step | 48          |
+| y_out     | -0.0887     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016933622 |
+| time-step | 49          |
+| y_out     | 0.0639      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016479034 |
+| time-step | 50          |
+| y_out     | -0.0348     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016017461 |
+| time-step | 51          |
+| y_out     | -0.0767     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015107287 |
+| time-step | 52          |
+| y_out     | -0.0663     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014478793 |
+| time-step | 53          |
+| y_out     | -0.0666     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013966963 |
+| time-step | 54          |
+| y_out     | 0.00879     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013132383 |
+| time-step | 55          |
+| y_out     | -0.0423     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012082603 |
+| time-step | 56          |
+| y_out     | 0.088       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011799296 |
+| time-step | 57          |
+| y_out     | 0.00427     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01203368 |
+| time-step | 58         |
+| y_out     | -0.0279    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011613684 |
+| time-step | 59          |
+| y_out     | 0.076       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011724345 |
+| time-step | 60          |
+| y_out     | -0.0132     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011878715 |
+| time-step | 61          |
+| y_out     | -0.00401    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012243206 |
+| time-step | 62          |
+| y_out     | 0.113       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012304836 |
+| time-step | 63          |
+| y_out     | 0.0154      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012427358 |
+| time-step | 64          |
+| y_out     | -0.0405     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012178169 |
+| time-step | 65          |
+| y_out     | -0.0247     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012319158 |
+| time-step | 66          |
+| y_out     | -0.0597     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012303159 |
+| time-step | 67          |
+| y_out     | -0.0777     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012027314 |
+| time-step | 68          |
+| y_out     | -0.0329     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011956895 |
+| time-step | 69          |
+| y_out     | 0.0964      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011164153 |
+| time-step | 70          |
+| y_out     | -0.0164     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011343891 |
+| time-step | 71          |
+| y_out     | 0.102       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011425108 |
+| time-step | 72          |
+| y_out     | 0.0973      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01146083 |
+| time-step | 73         |
+| y_out     | 0.0222     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011823762 |
+| time-step | 74          |
+| y_out     | 0.0866      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011945274 |
+| time-step | 75          |
+| y_out     | -0.153      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011839587 |
+| time-step | 76          |
+| y_out     | -0.117      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011633513 |
+| time-step | 77          |
+| y_out     | -0.0638     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011656511 |
+| time-step | 78          |
+| y_out     | -0.0167     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01172599 |
+| time-step | 79         |
+| y_out     | -0.0982    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011577224 |
+| time-step | 80          |
+| y_out     | -0.00113    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011471375 |
+| time-step | 81          |
+| y_out     | 0.0153      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011363814 |
+| time-step | 82          |
+| y_out     | 0.0724      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011001265 |
+| time-step | 83          |
+| y_out     | -0.0164     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010873512 |
+| time-step | 84          |
+| y_out     | -0.0532     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01137321 |
+| time-step | 85         |
+| y_out     | 0.046      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01110844 |
+| time-step | 86         |
+| y_out     | -0.0428    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011072291 |
+| time-step | 87          |
+| y_out     | -0.00239    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010852053 |
+| time-step | 88          |
+| y_out     | -0.0804     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011062831 |
+| time-step | 89          |
+| y_out     | -0.0596     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011090169 |
+| time-step | 90          |
+| y_out     | -0.0482     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011159094 |
+| time-step | 91          |
+| y_out     | -0.0419     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01138119 |
+| time-step | 92         |
+| y_out     | 0.0889     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01147932 |
+| time-step | 93         |
+| y_out     | -0.0562    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011230049 |
+| time-step | 94          |
+| y_out     | -0.00708    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01104031 |
+| time-step | 95         |
+| y_out     | -0.0441    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010987503 |
+| time-step | 96          |
+| y_out     | -0.0395     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011121065 |
+| time-step | 97          |
+| y_out     | -0.0647     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010915167 |
+| time-step | 98          |
+| y_out     | -0.0314     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010247631 |
+| time-step | 99          |
+| y_out     | 0.0419      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010305108 |
+| time-step | 100         |
+| y_out     | 0.131       |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/checkpoint 100
+------------------------------------------------------------
+| perf/mse  | 0.010660535 |
+| time-step | 101         |
+| y_out     | -0.0347     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010309954 |
+| time-step | 102         |
+| y_out     | -0.00651    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010640209 |
+| time-step | 103         |
+| y_out     | -0.145      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010558878 |
+| time-step | 104         |
+| y_out     | 0.102       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010505226 |
+| time-step | 105         |
+| y_out     | -0.0213     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010820939 |
+| time-step | 106         |
+| y_out     | 0.0962      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010339354 |
+| time-step | 107         |
+| y_out     | 0.0175      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010475488 |
+| time-step | 108         |
+| y_out     | 0.0066      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010944723 |
+| time-step | 109         |
+| y_out     | 0.108       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010873228 |
+| time-step | 110         |
+| y_out     | -0.0569     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010329481 |
+| time-step | 111         |
+| y_out     | 0.0658      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0100920275 |
+| time-step | 112          |
+| y_out     | 0.116        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009621786 |
+| time-step | 113         |
+| y_out     | -0.0769     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009209978 |
+| time-step | 114         |
+| y_out     | 0.0436      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008970896 |
+| time-step | 115         |
+| y_out     | 0.0435      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00884672 |
+| time-step | 116        |
+| y_out     | 0.134      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009148502 |
+| time-step | 117         |
+| y_out     | 0.13        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008988777 |
+| time-step | 118         |
+| y_out     | -0.0132     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008639535 |
+| time-step | 119         |
+| y_out     | 0.054       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008648804 |
+| time-step | 120         |
+| y_out     | -0.0634     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008502534 |
+| time-step | 121         |
+| y_out     | -0.0703     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008378643 |
+| time-step | 122         |
+| y_out     | 0.0372      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008391513 |
+| time-step | 123         |
+| y_out     | -0.00715    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008633414 |
+| time-step | 124         |
+| y_out     | 0.123       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008756106 |
+| time-step | 125         |
+| y_out     | 0.0371      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008520562 |
+| time-step | 126         |
+| y_out     | -0.0951     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008229163 |
+| time-step | 127         |
+| y_out     | -0.0981     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00833922 |
+| time-step | 128        |
+| y_out     | -0.0449    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008834759 |
+| time-step | 129         |
+| y_out     | 0.0858      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008692222 |
+| time-step | 130         |
+| y_out     | -0.000844   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008742148 |
+| time-step | 131         |
+| y_out     | 0.0313      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00919948 |
+| time-step | 132        |
+| y_out     | 0.0152     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0093329735 |
+| time-step | 133          |
+| y_out     | 0.00194      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0096267015 |
+| time-step | 134          |
+| y_out     | -0.0275      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00945548 |
+| time-step | 135        |
+| y_out     | -0.0337    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009729335 |
+| time-step | 136         |
+| y_out     | 0.016       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0101147685 |
+| time-step | 137          |
+| y_out     | 0.131        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009680775 |
+| time-step | 138         |
+| y_out     | -0.0648     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0092971735 |
+| time-step | 139          |
+| y_out     | -0.0343      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009679349 |
+| time-step | 140         |
+| y_out     | 0.136       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009776584 |
+| time-step | 141         |
+| y_out     | -0.0136     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009558963 |
+| time-step | 142         |
+| y_out     | 0.00176     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0096537825 |
+| time-step | 143          |
+| y_out     | 0.157        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009238768 |
+| time-step | 144         |
+| y_out     | -0.0999     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009039121 |
+| time-step | 145         |
+| y_out     | 0.0282      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009134624 |
+| time-step | 146         |
+| y_out     | -0.0482     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008712808 |
+| time-step | 147         |
+| y_out     | -0.0459     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008852864 |
+| time-step | 148         |
+| y_out     | -0.00626    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008944349 |
+| time-step | 149         |
+| y_out     | 0.00774     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008603865 |
+| time-step | 150         |
+| y_out     | -0.0209     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008121284 |
+| time-step | 151         |
+| y_out     | 0.0948      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007980691 |
+| time-step | 152         |
+| y_out     | -0.0431     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076287724 |
+| time-step | 153          |
+| y_out     | -0.0117      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076569216 |
+| time-step | 154          |
+| y_out     | -0.0862      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00757899 |
+| time-step | 155        |
+| y_out     | -0.0688    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070884502 |
+| time-step | 156          |
+| y_out     | 0.00218      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007514848 |
+| time-step | 157         |
+| y_out     | 0.0939      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074733854 |
+| time-step | 158          |
+| y_out     | 0.0565       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007117582 |
+| time-step | 159         |
+| y_out     | -0.0453     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007110757 |
+| time-step | 160         |
+| y_out     | 0.0345      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071210256 |
+| time-step | 161          |
+| y_out     | -0.0508      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069531323 |
+| time-step | 162          |
+| y_out     | 0.0665       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0072190105 |
+| time-step | 163          |
+| y_out     | -0.0518      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006955318 |
+| time-step | 164         |
+| y_out     | -0.00207    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070700618 |
+| time-step | 165          |
+| y_out     | -0.00238     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007488442 |
+| time-step | 166         |
+| y_out     | 0.0604      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007107269 |
+| time-step | 167         |
+| y_out     | -0.114      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007080859 |
+| time-step | 168         |
+| y_out     | -0.0185     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007167834 |
+| time-step | 169         |
+| y_out     | 0.0771      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069548436 |
+| time-step | 170          |
+| y_out     | -0.0688      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070898533 |
+| time-step | 171          |
+| y_out     | 0.00152      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007524592 |
+| time-step | 172         |
+| y_out     | 0.0702      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071449913 |
+| time-step | 173          |
+| y_out     | 0.0714       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071817026 |
+| time-step | 174          |
+| y_out     | 0.0292       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007101299 |
+| time-step | 175         |
+| y_out     | 0.00933     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0068930946 |
+| time-step | 176          |
+| y_out     | 0.0788       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0066107744 |
+| time-step | 177          |
+| y_out     | -0.00554     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064534033 |
+| time-step | 178          |
+| y_out     | -0.0581      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006479381 |
+| time-step | 179         |
+| y_out     | -0.0563     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063861934 |
+| time-step | 180          |
+| y_out     | 0.133        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064704185 |
+| time-step | 181          |
+| y_out     | -0.175       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006569355 |
+| time-step | 182         |
+| y_out     | -0.132      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067012487 |
+| time-step | 183          |
+| y_out     | -0.0501      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0068758912 |
+| time-step | 184          |
+| y_out     | -0.00386     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069683283 |
+| time-step | 185          |
+| y_out     | 0.00795      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006742413 |
+| time-step | 186         |
+| y_out     | -0.069      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070079966 |
+| time-step | 187          |
+| y_out     | -0.0244      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074630426 |
+| time-step | 188          |
+| y_out     | -0.0627      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071394816 |
+| time-step | 189          |
+| y_out     | 0.137        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0075824684 |
+| time-step | 190          |
+| y_out     | 0.0159       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007266394 |
+| time-step | 191         |
+| y_out     | -0.0235     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006797208 |
+| time-step | 192         |
+| y_out     | 0.0197      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00663698 |
+| time-step | 193        |
+| y_out     | 0.119      |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064894995 |
+| time-step | 194          |
+| y_out     | -0.119       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065049604 |
+| time-step | 195          |
+| y_out     | 0.079        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065847985 |
+| time-step | 196          |
+| y_out     | 0.0854       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006266739 |
+| time-step | 197         |
+| y_out     | -0.022      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005874417 |
+| time-step | 198         |
+| y_out     | -0.0101     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060962318 |
+| time-step | 199          |
+| y_out     | 0.148        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056852703 |
+| time-step | 200          |
+| y_out     | 0.00375      |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/checkpoint 200
+-------------------------------------------------------------
+| perf/mse  | 0.0056905826 |
+| time-step | 201          |
+| y_out     | -0.0461      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056968005 |
+| time-step | 202          |
+| y_out     | -0.0284      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059170853 |
+| time-step | 203          |
+| y_out     | -0.0369      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005800717 |
+| time-step | 204         |
+| y_out     | 0.0524      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054552252 |
+| time-step | 205          |
+| y_out     | 0.00161      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005533154 |
+| time-step | 206         |
+| y_out     | 0.194       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005827756 |
+| time-step | 207         |
+| y_out     | 0.0368      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059373435 |
+| time-step | 208          |
+| y_out     | 0.00418      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006007456 |
+| time-step | 209         |
+| y_out     | -0.0446     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005976817 |
+| time-step | 210         |
+| y_out     | 0.0574      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061158566 |
+| time-step | 211          |
+| y_out     | 0.0554       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005955133 |
+| time-step | 212         |
+| y_out     | -0.07       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005852655 |
+| time-step | 213         |
+| y_out     | 0.0174      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058372607 |
+| time-step | 214          |
+| y_out     | 0.0388       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061067664 |
+| time-step | 215          |
+| y_out     | -0.0266      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058830264 |
+| time-step | 216          |
+| y_out     | -0.0452      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059009306 |
+| time-step | 217          |
+| y_out     | 0.154        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057516405 |
+| time-step | 218          |
+| y_out     | -0.0367      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005689205 |
+| time-step | 219         |
+| y_out     | -0.0868     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005685425 |
+| time-step | 220         |
+| y_out     | -0.124      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055836244 |
+| time-step | 221          |
+| y_out     | 0.0853       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005851838 |
+| time-step | 222         |
+| y_out     | 0.000309    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005647618 |
+| time-step | 223         |
+| y_out     | 0.141       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056630773 |
+| time-step | 224          |
+| y_out     | -0.114       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005676373 |
+| time-step | 225         |
+| y_out     | -0.0723     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00574146 |
+| time-step | 226        |
+| y_out     | -0.0297    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056439033 |
+| time-step | 227          |
+| y_out     | -0.036       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056503955 |
+| time-step | 228          |
+| y_out     | -0.0478      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005390475 |
+| time-step | 229         |
+| y_out     | 0.029       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054510543 |
+| time-step | 230          |
+| y_out     | 0.0827       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005657955 |
+| time-step | 231         |
+| y_out     | 0.0621      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054920064 |
+| time-step | 232          |
+| y_out     | -0.0329      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057251537 |
+| time-step | 233          |
+| y_out     | -0.0822      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057805083 |
+| time-step | 234          |
+| y_out     | 0.071        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005538959 |
+| time-step | 235         |
+| y_out     | -0.0181     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005525657 |
+| time-step | 236         |
+| y_out     | 0.132       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056345984 |
+| time-step | 237          |
+| y_out     | 0.014        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057900175 |
+| time-step | 238          |
+| y_out     | 0.0269       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058385995 |
+| time-step | 239          |
+| y_out     | 0.0633       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005932725 |
+| time-step | 240         |
+| y_out     | 0.0878      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059852153 |
+| time-step | 241          |
+| y_out     | -0.0502      |
+-------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0059008 |
+| time-step | 242       |
+| y_out     | 0.0314    |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058916872 |
+| time-step | 243          |
+| y_out     | 0.0125       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006113454 |
+| time-step | 244         |
+| y_out     | 0.0267      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006300458 |
+| time-step | 245         |
+| y_out     | 0.038       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006252008 |
+| time-step | 246         |
+| y_out     | 0.041       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006121675 |
+| time-step | 247         |
+| y_out     | 0.0694      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006164758 |
+| time-step | 248         |
+| y_out     | -0.0384     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063201278 |
+| time-step | 249          |
+| y_out     | 0.026        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061799046 |
+| time-step | 250          |
+| y_out     | 0.0308       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056938594 |
+| time-step | 251          |
+| y_out     | 0.0242       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005799958 |
+| time-step | 252         |
+| y_out     | 0.0937      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057879714 |
+| time-step | 253          |
+| y_out     | 0.0304       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054863626 |
+| time-step | 254          |
+| y_out     | 0.0163       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005453122 |
+| time-step | 255         |
+| y_out     | 0.0353      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055143707 |
+| time-step | 256          |
+| y_out     | -0.00536     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053968215 |
+| time-step | 257          |
+| y_out     | -0.141       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053051896 |
+| time-step | 258          |
+| y_out     | 0.071        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053464267 |
+| time-step | 259          |
+| y_out     | 0.0116       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053339684 |
+| time-step | 260          |
+| y_out     | 0.0113       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005537163 |
+| time-step | 261         |
+| y_out     | 0.005       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056337644 |
+| time-step | 262          |
+| y_out     | 0.0265       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055603823 |
+| time-step | 263          |
+| y_out     | -0.0274      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055800793 |
+| time-step | 264          |
+| y_out     | -0.0619      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005743446 |
+| time-step | 265         |
+| y_out     | -0.0378     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057869824 |
+| time-step | 266          |
+| y_out     | -0.0436      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058582886 |
+| time-step | 267          |
+| y_out     | 0.196        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057696034 |
+| time-step | 268          |
+| y_out     | 0.0295       |
+-------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0058006 |
+| time-step | 269       |
+| y_out     | 0.00851   |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058288774 |
+| time-step | 270          |
+| y_out     | 0.0299       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005862211 |
+| time-step | 271         |
+| y_out     | 0.0698      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055047823 |
+| time-step | 272          |
+| y_out     | -0.0172      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054702526 |
+| time-step | 273          |
+| y_out     | 0.0411       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055995346 |
+| time-step | 274          |
+| y_out     | 0.121        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051725837 |
+| time-step | 275          |
+| y_out     | 0.0267       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005104615 |
+| time-step | 276         |
+| y_out     | -0.00406    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004917266 |
+| time-step | 277         |
+| y_out     | 0.075       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051001003 |
+| time-step | 278          |
+| y_out     | 0.0736       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049710376 |
+| time-step | 279          |
+| y_out     | -0.0587      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051388624 |
+| time-step | 280          |
+| y_out     | 0.0122       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050842958 |
+| time-step | 281          |
+| y_out     | 0.0693       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005420475 |
+| time-step | 282         |
+| y_out     | 0.0741      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053245174 |
+| time-step | 283          |
+| y_out     | -0.00115     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053305677 |
+| time-step | 284          |
+| y_out     | -0.0295      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005521316 |
+| time-step | 285         |
+| y_out     | -0.0894     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053106486 |
+| time-step | 286          |
+| y_out     | 0.00486      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00569501 |
+| time-step | 287        |
+| y_out     | 0.013      |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054959194 |
+| time-step | 288          |
+| y_out     | -0.0979      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005306167 |
+| time-step | 289         |
+| y_out     | -0.0268     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005315619 |
+| time-step | 290         |
+| y_out     | 0.0255      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005450518 |
+| time-step | 291         |
+| y_out     | -0.0844     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051769167 |
+| time-step | 292          |
+| y_out     | 0.208        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005234494 |
+| time-step | 293         |
+| y_out     | -0.0382     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005097677 |
+| time-step | 294         |
+| y_out     | -0.039      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005130583 |
+| time-step | 295         |
+| y_out     | 0.0344      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005126798 |
+| time-step | 296         |
+| y_out     | -0.00368    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048689307 |
+| time-step | 297          |
+| y_out     | 0.0587       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00480453 |
+| time-step | 298        |
+| y_out     | -0.0115    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004892632 |
+| time-step | 299         |
+| y_out     | -0.168      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045462097 |
+| time-step | 300          |
+| y_out     | 0.0511       |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/checkpoint 300
+------------------------------------------------------------
+| perf/mse  | 0.004359688 |
+| time-step | 301         |
+| y_out     | 0.0158      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004396187 |
+| time-step | 302         |
+| y_out     | 0.00161     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045867893 |
+| time-step | 303          |
+| y_out     | -0.00954     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044956906 |
+| time-step | 304          |
+| y_out     | 0.0204       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004379426 |
+| time-step | 305         |
+| y_out     | -0.0783     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004473804 |
+| time-step | 306         |
+| y_out     | -0.0598     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043944838 |
+| time-step | 307          |
+| y_out     | -0.136       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045137657 |
+| time-step | 308          |
+| y_out     | -0.0457      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045762104 |
+| time-step | 309          |
+| y_out     | 0.0418       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047158636 |
+| time-step | 310          |
+| y_out     | -0.0554      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00456644 |
+| time-step | 311        |
+| y_out     | -0.085     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004636108 |
+| time-step | 312         |
+| y_out     | 0.0383      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043530185 |
+| time-step | 313          |
+| y_out     | 0.0231       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045791133 |
+| time-step | 314          |
+| y_out     | 0.098        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047738934 |
+| time-step | 315          |
+| y_out     | 0.162        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047483435 |
+| time-step | 316          |
+| y_out     | -0.0338      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004779514 |
+| time-step | 317         |
+| y_out     | 0.0724      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004636596 |
+| time-step | 318         |
+| y_out     | 0.0345      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046113627 |
+| time-step | 319          |
+| y_out     | 0.0211       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004428326 |
+| time-step | 320         |
+| y_out     | -0.0302     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046538087 |
+| time-step | 321          |
+| y_out     | 0.0793       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046236417 |
+| time-step | 322          |
+| y_out     | 0.0291       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004737603 |
+| time-step | 323         |
+| y_out     | 0.0345      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044686804 |
+| time-step | 324          |
+| y_out     | -0.0901      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040447116 |
+| time-step | 325          |
+| y_out     | 0.0969       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004052042 |
+| time-step | 326         |
+| y_out     | -0.0629     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041114376 |
+| time-step | 327          |
+| y_out     | 0.0992       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040254313 |
+| time-step | 328          |
+| y_out     | 0.00626      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039073573 |
+| time-step | 329          |
+| y_out     | 0.0539       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003980251 |
+| time-step | 330         |
+| y_out     | 0.0982      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037475578 |
+| time-step | 331          |
+| y_out     | -0.0355      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003618455 |
+| time-step | 332         |
+| y_out     | -0.0836     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035154175 |
+| time-step | 333          |
+| y_out     | 0.0291       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035970851 |
+| time-step | 334          |
+| y_out     | -0.0406      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036502003 |
+| time-step | 335          |
+| y_out     | -0.0314      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035669014 |
+| time-step | 336          |
+| y_out     | -0.0647      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003383059 |
+| time-step | 337         |
+| y_out     | -0.0222     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034307358 |
+| time-step | 338          |
+| y_out     | -0.0938      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033943546 |
+| time-step | 339          |
+| y_out     | -0.064       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034319449 |
+| time-step | 340          |
+| y_out     | -0.0805      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036122575 |
+| time-step | 341          |
+| y_out     | 0.0189       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037124418 |
+| time-step | 342          |
+| y_out     | -0.0246      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036883645 |
+| time-step | 343          |
+| y_out     | -0.0299      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037975118 |
+| time-step | 344          |
+| y_out     | -0.00647     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038647472 |
+| time-step | 345          |
+| y_out     | -0.135       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039787465 |
+| time-step | 346          |
+| y_out     | -0.108       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004058059 |
+| time-step | 347         |
+| y_out     | 0.097       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040088957 |
+| time-step | 348          |
+| y_out     | 0.0157       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004138083 |
+| time-step | 349         |
+| y_out     | -0.00724    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040200637 |
+| time-step | 350          |
+| y_out     | -0.0345      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038482684 |
+| time-step | 351          |
+| y_out     | 0.000321     |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00375184 |
+| time-step | 352        |
+| y_out     | -0.0134    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003710435 |
+| time-step | 353         |
+| y_out     | 0.139       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035248343 |
+| time-step | 354          |
+| y_out     | 0.124        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036025282 |
+| time-step | 355          |
+| y_out     | 0.0407       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035022337 |
+| time-step | 356          |
+| y_out     | 0.0857       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034867008 |
+| time-step | 357          |
+| y_out     | 0.0444       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034245285 |
+| time-step | 358          |
+| y_out     | 0.0107       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032818771 |
+| time-step | 359          |
+| y_out     | -0.016       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003363476 |
+| time-step | 360         |
+| y_out     | 0.0692      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032919352 |
+| time-step | 361          |
+| y_out     | 0.0153       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032907515 |
+| time-step | 362          |
+| y_out     | -0.121       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033411705 |
+| time-step | 363          |
+| y_out     | -0.0205      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032866194 |
+| time-step | 364          |
+| y_out     | 0.0639       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032076626 |
+| time-step | 365          |
+| y_out     | -0.0391      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032435365 |
+| time-step | 366          |
+| y_out     | 0.097        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031282164 |
+| time-step | 367          |
+| y_out     | -0.0145      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032171588 |
+| time-step | 368          |
+| y_out     | 0.0348       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031775683 |
+| time-step | 369          |
+| y_out     | 0.0723       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030347174 |
+| time-step | 370          |
+| y_out     | 0.0121       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030330648 |
+| time-step | 371          |
+| y_out     | 0.0426       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030724104 |
+| time-step | 372          |
+| y_out     | -0.0397      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030502945 |
+| time-step | 373          |
+| y_out     | 0.0228       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002985191 |
+| time-step | 374         |
+| y_out     | 0.0608      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028567456 |
+| time-step | 375          |
+| y_out     | -0.0859      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027451396 |
+| time-step | 376          |
+| y_out     | -0.101       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026854528 |
+| time-step | 377          |
+| y_out     | 0.114        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027308264 |
+| time-step | 378          |
+| y_out     | -0.0231      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002766747 |
+| time-step | 379         |
+| y_out     | -0.00318    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027897893 |
+| time-step | 380          |
+| y_out     | -0.165       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028267715 |
+| time-step | 381          |
+| y_out     | 0.0794       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027630434 |
+| time-step | 382          |
+| y_out     | 0.126        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027344238 |
+| time-step | 383          |
+| y_out     | -0.114       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002815619 |
+| time-step | 384         |
+| y_out     | -0.00961    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029575939 |
+| time-step | 385          |
+| y_out     | 0.063        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003051009 |
+| time-step | 386         |
+| y_out     | 0.0707      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031477264 |
+| time-step | 387          |
+| y_out     | 0.0476       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030540498 |
+| time-step | 388          |
+| y_out     | 0.0324       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031411506 |
+| time-step | 389          |
+| y_out     | 0.221        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031523048 |
+| time-step | 390          |
+| y_out     | -0.0302      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003295749 |
+| time-step | 391         |
+| y_out     | -0.0629     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032405376 |
+| time-step | 392          |
+| y_out     | -0.00234     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031862468 |
+| time-step | 393          |
+| y_out     | -0.0205      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031066793 |
+| time-step | 394          |
+| y_out     | -0.042       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029888048 |
+| time-step | 395          |
+| y_out     | -0.0261      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028266455 |
+| time-step | 396          |
+| y_out     | 0.00682      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027447361 |
+| time-step | 397          |
+| y_out     | -0.0124      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027547204 |
+| time-step | 398          |
+| y_out     | 0.0341       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026042599 |
+| time-step | 399          |
+| y_out     | -0.136       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027166272 |
+| time-step | 400          |
+| y_out     | 0.015        |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/checkpoint 400
+------------------------------------------------------------
+| perf/mse  | 0.002676873 |
+| time-step | 401         |
+| y_out     | -0.0788     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002675979 |
+| time-step | 402         |
+| y_out     | 0.0993      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026569306 |
+| time-step | 403          |
+| y_out     | -0.0555      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027379273 |
+| time-step | 404          |
+| y_out     | 0.0413       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027675773 |
+| time-step | 405          |
+| y_out     | 0.0476       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028112398 |
+| time-step | 406          |
+| y_out     | 0.068        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028223158 |
+| time-step | 407          |
+| y_out     | -0.0432      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028645063 |
+| time-step | 408          |
+| y_out     | -0.0889      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030290063 |
+| time-step | 409          |
+| y_out     | 0.0359       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002875284 |
+| time-step | 410         |
+| y_out     | -0.00441    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00272735 |
+| time-step | 411        |
+| y_out     | -0.0698    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002780829 |
+| time-step | 412         |
+| y_out     | 0.0261      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029790043 |
+| time-step | 413          |
+| y_out     | 0.0533       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002979803 |
+| time-step | 414         |
+| y_out     | 0.0784      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030229974 |
+| time-step | 415          |
+| y_out     | 0.0637       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031487495 |
+| time-step | 416          |
+| y_out     | -0.119       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032071434 |
+| time-step | 417          |
+| y_out     | 0.14         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032605317 |
+| time-step | 418          |
+| y_out     | 0.0827       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031939088 |
+| time-step | 419          |
+| y_out     | -0.0431      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032815754 |
+| time-step | 420          |
+| y_out     | -0.0434      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003370655 |
+| time-step | 421         |
+| y_out     | 0.0151      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034016422 |
+| time-step | 422          |
+| y_out     | -0.0598      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031806983 |
+| time-step | 423          |
+| y_out     | -0.0882      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031204994 |
+| time-step | 424          |
+| y_out     | -0.0459      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003251347 |
+| time-step | 425         |
+| y_out     | 0.0396      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033099924 |
+| time-step | 426          |
+| y_out     | 0.0266       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033190246 |
+| time-step | 427          |
+| y_out     | 0.0366       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032771747 |
+| time-step | 428          |
+| y_out     | -0.0232      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032331583 |
+| time-step | 429          |
+| y_out     | 0.145        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032746133 |
+| time-step | 430          |
+| y_out     | -0.00724     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033960561 |
+| time-step | 431          |
+| y_out     | 0.102        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034853958 |
+| time-step | 432          |
+| y_out     | -0.00559     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036518336 |
+| time-step | 433          |
+| y_out     | 0.0185       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038200952 |
+| time-step | 434          |
+| y_out     | 0.0693       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003672272 |
+| time-step | 435         |
+| y_out     | -0.018      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037124723 |
+| time-step | 436          |
+| y_out     | -0.00863     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038609966 |
+| time-step | 437          |
+| y_out     | -0.0609      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038170596 |
+| time-step | 438          |
+| y_out     | -0.0829      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036541256 |
+| time-step | 439          |
+| y_out     | 0.0195       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003855871 |
+| time-step | 440         |
+| y_out     | 0.114       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00367743 |
+| time-step | 441        |
+| y_out     | 0.0208     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003791449 |
+| time-step | 442         |
+| y_out     | -0.0272     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038086295 |
+| time-step | 443          |
+| y_out     | -0.059       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036459353 |
+| time-step | 444          |
+| y_out     | 0.123        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038562906 |
+| time-step | 445          |
+| y_out     | -0.0903      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036505803 |
+| time-step | 446          |
+| y_out     | -0.0238      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033768122 |
+| time-step | 447          |
+| y_out     | -0.0213      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033907418 |
+| time-step | 448          |
+| y_out     | -0.00123     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003505027 |
+| time-step | 449         |
+| y_out     | -0.0661     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003177321 |
+| time-step | 450         |
+| y_out     | -0.138      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031363026 |
+| time-step | 451          |
+| y_out     | 0.134        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027600173 |
+| time-step | 452          |
+| y_out     | -0.0716      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002515526 |
+| time-step | 453         |
+| y_out     | 0.0989      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025456373 |
+| time-step | 454          |
+| y_out     | -0.0396      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00228338 |
+| time-step | 455        |
+| y_out     | -0.0403    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023438004 |
+| time-step | 456          |
+| y_out     | 0.0164       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025289222 |
+| time-step | 457          |
+| y_out     | -0.00588     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002416496 |
+| time-step | 458         |
+| y_out     | -0.0819     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025297415 |
+| time-step | 459          |
+| y_out     | 0.0414       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025940458 |
+| time-step | 460          |
+| y_out     | -0.0214      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026486102 |
+| time-step | 461          |
+| y_out     | -0.0646      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002723851 |
+| time-step | 462         |
+| y_out     | 0.025       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028438596 |
+| time-step | 463          |
+| y_out     | -0.0975      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002761069 |
+| time-step | 464         |
+| y_out     | -0.0641     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028378738 |
+| time-step | 465          |
+| y_out     | -0.0553      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027785497 |
+| time-step | 466          |
+| y_out     | 0.0139       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002699546 |
+| time-step | 467         |
+| y_out     | -0.0662     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027790226 |
+| time-step | 468          |
+| y_out     | -0.144       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002822285 |
+| time-step | 469         |
+| y_out     | -0.0368     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002787628 |
+| time-step | 470         |
+| y_out     | -0.0386     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028056267 |
+| time-step | 471          |
+| y_out     | -0.122       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030464206 |
+| time-step | 472          |
+| y_out     | -0.00104     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030079607 |
+| time-step | 473          |
+| y_out     | -0.109       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031134698 |
+| time-step | 474          |
+| y_out     | 0.0334       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030813986 |
+| time-step | 475          |
+| y_out     | -0.0036      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003052196 |
+| time-step | 476         |
+| y_out     | 0.0469      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030555069 |
+| time-step | 477          |
+| y_out     | -0.0625      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029650487 |
+| time-step | 478          |
+| y_out     | -0.0213      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002779623 |
+| time-step | 479         |
+| y_out     | -0.179      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027619165 |
+| time-step | 480          |
+| y_out     | 0.097        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027293614 |
+| time-step | 481          |
+| y_out     | 0.0632       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025570656 |
+| time-step | 482          |
+| y_out     | -0.111       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002629723 |
+| time-step | 483         |
+| y_out     | 0.0248      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024707608 |
+| time-step | 484          |
+| y_out     | -0.0446      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023626718 |
+| time-step | 485          |
+| y_out     | -0.0561      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023490011 |
+| time-step | 486          |
+| y_out     | -0.051       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022600389 |
+| time-step | 487          |
+| y_out     | -0.0461      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002234936 |
+| time-step | 488         |
+| y_out     | -0.089      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023461932 |
+| time-step | 489          |
+| y_out     | 0.1          |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002299776 |
+| time-step | 490         |
+| y_out     | -0.16       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023411014 |
+| time-step | 491          |
+| y_out     | 0.182        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023036448 |
+| time-step | 492          |
+| y_out     | 0.00504      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021829254 |
+| time-step | 493          |
+| y_out     | 0.0272       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021960933 |
+| time-step | 494          |
+| y_out     | -0.151       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023803338 |
+| time-step | 495          |
+| y_out     | 0.0225       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002331805 |
+| time-step | 496         |
+| y_out     | -0.0696     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00233934 |
+| time-step | 497        |
+| y_out     | 0.161      |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024229854 |
+| time-step | 498          |
+| y_out     | 0.00285      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022554558 |
+| time-step | 499          |
+| y_out     | 0.0966       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022238677 |
+| time-step | 500          |
+| y_out     | -0.00512     |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/checkpoint 500
+-------------------------------------------------------------
+| perf/mse  | 0.0021433183 |
+| time-step | 501          |
+| y_out     | -0.0189      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002113535 |
+| time-step | 502         |
+| y_out     | -0.0897     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002100234 |
+| time-step | 503         |
+| y_out     | 0.0105      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021240811 |
+| time-step | 504          |
+| y_out     | -0.0581      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019771294 |
+| time-step | 505          |
+| y_out     | -0.0993      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020351703 |
+| time-step | 506          |
+| y_out     | -0.0601      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020447446 |
+| time-step | 507          |
+| y_out     | -0.00244     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019718576 |
+| time-step | 508          |
+| y_out     | -0.0819      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020063096 |
+| time-step | 509          |
+| y_out     | -0.0449      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020783716 |
+| time-step | 510          |
+| y_out     | -0.0284      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020546622 |
+| time-step | 511          |
+| y_out     | 0.00401      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020450833 |
+| time-step | 512          |
+| y_out     | -0.0165      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021281638 |
+| time-step | 513          |
+| y_out     | -0.0339      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002143634 |
+| time-step | 514         |
+| y_out     | 0.0293      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022015078 |
+| time-step | 515          |
+| y_out     | 0.0224       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021475754 |
+| time-step | 516          |
+| y_out     | 0.0598       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022269427 |
+| time-step | 517          |
+| y_out     | -0.00662     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023432763 |
+| time-step | 518          |
+| y_out     | 0.0485       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002401222 |
+| time-step | 519         |
+| y_out     | 0.0741      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024662379 |
+| time-step | 520          |
+| y_out     | 0.0489       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024707017 |
+| time-step | 521          |
+| y_out     | -0.0195      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025454203 |
+| time-step | 522          |
+| y_out     | -0.0201      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024890187 |
+| time-step | 523          |
+| y_out     | 0.0287       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002505992 |
+| time-step | 524         |
+| y_out     | 0.0109      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026073395 |
+| time-step | 525          |
+| y_out     | 0.00733      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025565855 |
+| time-step | 526          |
+| y_out     | -0.0424      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024391564 |
+| time-step | 527          |
+| y_out     | 0.0453       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024392153 |
+| time-step | 528          |
+| y_out     | -0.089       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024006793 |
+| time-step | 529          |
+| y_out     | -0.043       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002292173 |
+| time-step | 530         |
+| y_out     | -0.0022     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024059769 |
+| time-step | 531          |
+| y_out     | -0.0893      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024055324 |
+| time-step | 532          |
+| y_out     | -0.0218      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024946951 |
+| time-step | 533          |
+| y_out     | -0.0259      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024668395 |
+| time-step | 534          |
+| y_out     | 0.0455       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023326012 |
+| time-step | 535          |
+| y_out     | -0.0783      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002443885 |
+| time-step | 536         |
+| y_out     | -0.0663     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025234304 |
+| time-step | 537          |
+| y_out     | -0.0149      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024664085 |
+| time-step | 538          |
+| y_out     | -0.0484      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002584805 |
+| time-step | 539         |
+| y_out     | -0.0376     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025890027 |
+| time-step | 540          |
+| y_out     | 0.118        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025133213 |
+| time-step | 541          |
+| y_out     | -0.0494      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026952599 |
+| time-step | 542          |
+| y_out     | -0.0879      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027123834 |
+| time-step | 543          |
+| y_out     | 0.0681       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002689264 |
+| time-step | 544         |
+| y_out     | 0.063       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027741035 |
+| time-step | 545          |
+| y_out     | -0.0289      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027414158 |
+| time-step | 546          |
+| y_out     | 0.0981       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027187539 |
+| time-step | 547          |
+| y_out     | 0.0381       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002805574 |
+| time-step | 548         |
+| y_out     | 0.0597      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00282604 |
+| time-step | 549        |
+| y_out     | 0.0145     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028716237 |
+| time-step | 550          |
+| y_out     | -0.00545     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028156699 |
+| time-step | 551          |
+| y_out     | 0.00647      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026442178 |
+| time-step | 552          |
+| y_out     | 0.0445       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025296258 |
+| time-step | 553          |
+| y_out     | 0.0179       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026639043 |
+| time-step | 554          |
+| y_out     | 0.18         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026615947 |
+| time-step | 555          |
+| y_out     | -0.0226      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026516842 |
+| time-step | 556          |
+| y_out     | -0.00498     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026206903 |
+| time-step | 557          |
+| y_out     | 0.00986      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024968733 |
+| time-step | 558          |
+| y_out     | -0.00153     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002526144 |
+| time-step | 559         |
+| y_out     | 0.111       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024777227 |
+| time-step | 560          |
+| y_out     | -0.0197      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024897256 |
+| time-step | 561          |
+| y_out     | -0.0101      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023758558 |
+| time-step | 562          |
+| y_out     | -0.129       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024036537 |
+| time-step | 563          |
+| y_out     | -0.0395      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022878686 |
+| time-step | 564          |
+| y_out     | -0.0903      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020820925 |
+| time-step | 565          |
+| y_out     | 0.0562       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020221476 |
+| time-step | 566          |
+| y_out     | -0.0534      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001976047 |
+| time-step | 567         |
+| y_out     | -0.0749     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019488174 |
+| time-step | 568          |
+| y_out     | -0.0119      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018191446 |
+| time-step | 569          |
+| y_out     | -0.0231      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017933354 |
+| time-step | 570          |
+| y_out     | -0.0903      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017584019 |
+| time-step | 571          |
+| y_out     | -0.0175      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017262285 |
+| time-step | 572          |
+| y_out     | 0.0341       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016989928 |
+| time-step | 573          |
+| y_out     | 0.00451      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016640241 |
+| time-step | 574          |
+| y_out     | -0.0501      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017585425 |
+| time-step | 575          |
+| y_out     | -0.156       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019102652 |
+| time-step | 576          |
+| y_out     | -0.0729      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018732961 |
+| time-step | 577          |
+| y_out     | 0.00512      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018699842 |
+| time-step | 578          |
+| y_out     | -0.0346      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017665562 |
+| time-step | 579          |
+| y_out     | -0.056       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017897019 |
+| time-step | 580          |
+| y_out     | 0.0517       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017840561 |
+| time-step | 581          |
+| y_out     | 0.104        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017592997 |
+| time-step | 582          |
+| y_out     | 0.0892       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017076783 |
+| time-step | 583          |
+| y_out     | 0.0428       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016387952 |
+| time-step | 584          |
+| y_out     | 0.117        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015810037 |
+| time-step | 585          |
+| y_out     | -0.029       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014622647 |
+| time-step | 586          |
+| y_out     | -0.0366      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014957235 |
+| time-step | 587          |
+| y_out     | -0.0571      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014543158 |
+| time-step | 588          |
+| y_out     | -0.0813      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014812534 |
+| time-step | 589          |
+| y_out     | 0.00692      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001367893 |
+| time-step | 590         |
+| y_out     | 0.0753      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013970856 |
+| time-step | 591          |
+| y_out     | -0.119       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015061393 |
+| time-step | 592          |
+| y_out     | 0.0535       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015357451 |
+| time-step | 593          |
+| y_out     | -0.0124      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016547425 |
+| time-step | 594          |
+| y_out     | -0.0396      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016331424 |
+| time-step | 595          |
+| y_out     | 0.0337       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016304584 |
+| time-step | 596          |
+| y_out     | -0.0556      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016652457 |
+| time-step | 597          |
+| y_out     | 0.137        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017786825 |
+| time-step | 598          |
+| y_out     | -0.146       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017203672 |
+| time-step | 599          |
+| y_out     | 0.0228       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001786032 |
+| time-step | 600         |
+| y_out     | 0.0544      |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/checkpoint 600
+-------------------------------------------------------------
+| perf/mse  | 0.0017361853 |
+| time-step | 601          |
+| y_out     | -0.158       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016937058 |
+| time-step | 602          |
+| y_out     | -0.0661      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016890537 |
+| time-step | 603          |
+| y_out     | 0.0392       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016218744 |
+| time-step | 604          |
+| y_out     | 0.073        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001601972 |
+| time-step | 605         |
+| y_out     | -0.0042     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015790925 |
+| time-step | 606          |
+| y_out     | 0.0866       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001564561 |
+| time-step | 607         |
+| y_out     | 0.0371      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015015571 |
+| time-step | 608          |
+| y_out     | 0.00672      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001535351 |
+| time-step | 609         |
+| y_out     | -0.0276     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015155817 |
+| time-step | 610          |
+| y_out     | 0.0612       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015155214 |
+| time-step | 611          |
+| y_out     | 0.0585       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015362937 |
+| time-step | 612          |
+| y_out     | -0.0808      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015615993 |
+| time-step | 613          |
+| y_out     | 0.0438       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015777744 |
+| time-step | 614          |
+| y_out     | -0.000711    |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016566053 |
+| time-step | 615          |
+| y_out     | -0.0558      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016467813 |
+| time-step | 616          |
+| y_out     | 0.139        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016113274 |
+| time-step | 617          |
+| y_out     | 0.0899       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015947443 |
+| time-step | 618          |
+| y_out     | 0.0197       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015654413 |
+| time-step | 619          |
+| y_out     | 0.000611     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015996281 |
+| time-step | 620          |
+| y_out     | 0.0676       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001612214 |
+| time-step | 621         |
+| y_out     | 0.15        |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015322065 |
+| time-step | 622          |
+| y_out     | 0.0423       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015821314 |
+| time-step | 623          |
+| y_out     | -0.0125      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015103245 |
+| time-step | 624          |
+| y_out     | 0.143        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014455387 |
+| time-step | 625          |
+| y_out     | 0.0634       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015303436 |
+| time-step | 626          |
+| y_out     | -0.0292      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015454928 |
+| time-step | 627          |
+| y_out     | -0.0324      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015097434 |
+| time-step | 628          |
+| y_out     | -0.045       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016027406 |
+| time-step | 629          |
+| y_out     | -0.0699      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016061461 |
+| time-step | 630          |
+| y_out     | 0.156        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016446331 |
+| time-step | 631          |
+| y_out     | 0.0275       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016633548 |
+| time-step | 632          |
+| y_out     | 0.0133       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015085164 |
+| time-step | 633          |
+| y_out     | -0.0618      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016138197 |
+| time-step | 634          |
+| y_out     | 0.0466       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016143487 |
+| time-step | 635          |
+| y_out     | 0.102        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016096967 |
+| time-step | 636          |
+| y_out     | -0.094       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015991589 |
+| time-step | 637          |
+| y_out     | 0.0541       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015933309 |
+| time-step | 638          |
+| y_out     | 0.146        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015239471 |
+| time-step | 639          |
+| y_out     | 0.0643       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015452319 |
+| time-step | 640          |
+| y_out     | 0.0564       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015790348 |
+| time-step | 641          |
+| y_out     | -0.0195      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015637142 |
+| time-step | 642          |
+| y_out     | -0.0122      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001596322 |
+| time-step | 643         |
+| y_out     | -0.0364     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016201679 |
+| time-step | 644          |
+| y_out     | 0.0591       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016536374 |
+| time-step | 645          |
+| y_out     | -0.127       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015769384 |
+| time-step | 646          |
+| y_out     | -0.0047      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001606236 |
+| time-step | 647         |
+| y_out     | -0.0209     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016799301 |
+| time-step | 648          |
+| y_out     | -0.00193     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016352783 |
+| time-step | 649          |
+| y_out     | 0.013        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015815415 |
+| time-step | 650          |
+| y_out     | 0.00157      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015611749 |
+| time-step | 651          |
+| y_out     | -0.0634      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015601789 |
+| time-step | 652          |
+| y_out     | -0.028       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015590788 |
+| time-step | 653          |
+| y_out     | 0.0249       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014543917 |
+| time-step | 654          |
+| y_out     | 0.111        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015145291 |
+| time-step | 655          |
+| y_out     | -0.0858      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001510774 |
+| time-step | 656         |
+| y_out     | -0.0187     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015026423 |
+| time-step | 657          |
+| y_out     | -0.0209      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014829512 |
+| time-step | 658          |
+| y_out     | -0.0825      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015394397 |
+| time-step | 659          |
+| y_out     | 0.0509       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016067823 |
+| time-step | 660          |
+| y_out     | -0.0165      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015210335 |
+| time-step | 661          |
+| y_out     | -0.0278      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015509999 |
+| time-step | 662          |
+| y_out     | -0.0483      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015882564 |
+| time-step | 663          |
+| y_out     | -0.0339      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001610259 |
+| time-step | 664         |
+| y_out     | -0.176      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014971425 |
+| time-step | 665          |
+| y_out     | -0.055       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014673668 |
+| time-step | 666          |
+| y_out     | 0.0444       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001393267 |
+| time-step | 667         |
+| y_out     | -0.00825    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013340584 |
+| time-step | 668          |
+| y_out     | -0.00333     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012605786 |
+| time-step | 669          |
+| y_out     | 0.0217       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011792941 |
+| time-step | 670          |
+| y_out     | 0.0266       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012000287 |
+| time-step | 671          |
+| y_out     | -0.0999      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011347297 |
+| time-step | 672          |
+| y_out     | 0.0699       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010909894 |
+| time-step | 673          |
+| y_out     | 0.00284      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010138055 |
+| time-step | 674          |
+| y_out     | 0.0708       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010394409 |
+| time-step | 675          |
+| y_out     | 0.0369       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010418305 |
+| time-step | 676          |
+| y_out     | 0.0754       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010731874 |
+| time-step | 677          |
+| y_out     | 0.000942     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011011984 |
+| time-step | 678          |
+| y_out     | 0.06         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010700924 |
+| time-step | 679          |
+| y_out     | -0.0203      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011639296 |
+| time-step | 680          |
+| y_out     | -0.0141      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011387598 |
+| time-step | 681          |
+| y_out     | 0.0212       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001194834 |
+| time-step | 682         |
+| y_out     | 0.0667      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001205616 |
+| time-step | 683         |
+| y_out     | 0.0462      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013037976 |
+| time-step | 684          |
+| y_out     | 0.00651      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012929349 |
+| time-step | 685          |
+| y_out     | -0.0564      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013014317 |
+| time-step | 686          |
+| y_out     | -0.00108     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013446751 |
+| time-step | 687          |
+| y_out     | 0.0223       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013919927 |
+| time-step | 688          |
+| y_out     | -0.0276      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014260157 |
+| time-step | 689          |
+| y_out     | 0.116        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013734181 |
+| time-step | 690          |
+| y_out     | 0.0455       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014188963 |
+| time-step | 691          |
+| y_out     | 0.104        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013775916 |
+| time-step | 692          |
+| y_out     | 0.0455       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013906041 |
+| time-step | 693          |
+| y_out     | 0.0966       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013371798 |
+| time-step | 694          |
+| y_out     | -0.0381      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013418801 |
+| time-step | 695          |
+| y_out     | 0.111        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014211164 |
+| time-step | 696          |
+| y_out     | -0.0118      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013470534 |
+| time-step | 697          |
+| y_out     | -0.102       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001315282 |
+| time-step | 698         |
+| y_out     | -0.077      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014131407 |
+| time-step | 699          |
+| y_out     | -0.093       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014734557 |
+| time-step | 700          |
+| y_out     | -0.0509      |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/checkpoint 700
+-------------------------------------------------------------
+| perf/mse  | 0.0014704582 |
+| time-step | 701          |
+| y_out     | -0.00454     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015474182 |
+| time-step | 702          |
+| y_out     | 0.0674       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015651288 |
+| time-step | 703          |
+| y_out     | -0.0166      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017819578 |
+| time-step | 704          |
+| y_out     | -0.0351      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018381958 |
+| time-step | 705          |
+| y_out     | 0.0381       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017886732 |
+| time-step | 706          |
+| y_out     | -0.14        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018567903 |
+| time-step | 707          |
+| y_out     | -0.0659      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018964342 |
+| time-step | 708          |
+| y_out     | 0.00112      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001857879 |
+| time-step | 709         |
+| y_out     | -0.0614     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017472741 |
+| time-step | 710          |
+| y_out     | -0.033       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017861892 |
+| time-step | 711          |
+| y_out     | 0.0929       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017209474 |
+| time-step | 712          |
+| y_out     | 0.0251       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017688789 |
+| time-step | 713          |
+| y_out     | -0.0124      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015929053 |
+| time-step | 714          |
+| y_out     | 0.165        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001603996 |
+| time-step | 715         |
+| y_out     | -0.0575     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016065843 |
+| time-step | 716          |
+| y_out     | -0.0294      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015911171 |
+| time-step | 717          |
+| y_out     | -0.0256      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015900119 |
+| time-step | 718          |
+| y_out     | -0.0305      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016427931 |
+| time-step | 719          |
+| y_out     | -0.0563      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017046925 |
+| time-step | 720          |
+| y_out     | -0.00659     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016059477 |
+| time-step | 721          |
+| y_out     | -0.025       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015981235 |
+| time-step | 722          |
+| y_out     | -0.0168      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015169577 |
+| time-step | 723          |
+| y_out     | 0.0864       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014766265 |
+| time-step | 724          |
+| y_out     | -0.0489      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014610193 |
+| time-step | 725          |
+| y_out     | 0.00706      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014538435 |
+| time-step | 726          |
+| y_out     | -0.00828     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014114145 |
+| time-step | 727          |
+| y_out     | 0.000232     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013742128 |
+| time-step | 728          |
+| y_out     | 0.113        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012903323 |
+| time-step | 729          |
+| y_out     | 0.113        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012526093 |
+| time-step | 730          |
+| y_out     | -0.0744      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013000371 |
+| time-step | 731          |
+| y_out     | -0.0465      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012952009 |
+| time-step | 732          |
+| y_out     | -0.0348      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001311125 |
+| time-step | 733         |
+| y_out     | 0.0863      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001357936 |
+| time-step | 734         |
+| y_out     | -0.0123     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012625211 |
+| time-step | 735          |
+| y_out     | 0.018        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012506814 |
+| time-step | 736          |
+| y_out     | 0.149        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012420781 |
+| time-step | 737          |
+| y_out     | -0.0448      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012601435 |
+| time-step | 738          |
+| y_out     | -0.12        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014245887 |
+| time-step | 739          |
+| y_out     | 0.0258       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014529482 |
+| time-step | 740          |
+| y_out     | -0.0333      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00143737 |
+| time-step | 741        |
+| y_out     | -0.0421    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015056904 |
+| time-step | 742          |
+| y_out     | 0.00851      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014822015 |
+| time-step | 743          |
+| y_out     | -0.144       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015202237 |
+| time-step | 744          |
+| y_out     | -0.0552      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016448621 |
+| time-step | 745          |
+| y_out     | 0.0979       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016346775 |
+| time-step | 746          |
+| y_out     | -0.123       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017506303 |
+| time-step | 747          |
+| y_out     | -0.128       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017088789 |
+| time-step | 748          |
+| y_out     | -0.0626      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016231742 |
+| time-step | 749          |
+| y_out     | 0.0195       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016619973 |
+| time-step | 750          |
+| y_out     | -0.00275     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016811952 |
+| time-step | 751          |
+| y_out     | -0.105       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016501654 |
+| time-step | 752          |
+| y_out     | -0.0402      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016279487 |
+| time-step | 753          |
+| y_out     | -0.144       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015456935 |
+| time-step | 754          |
+| y_out     | 0.04         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014450427 |
+| time-step | 755          |
+| y_out     | -0.0277      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014441294 |
+| time-step | 756          |
+| y_out     | -0.085       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013843784 |
+| time-step | 757          |
+| y_out     | 0.00969      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013719222 |
+| time-step | 758          |
+| y_out     | 0.0614       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012864972 |
+| time-step | 759          |
+| y_out     | -0.0584      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012014253 |
+| time-step | 760          |
+| y_out     | 0.0485       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011534083 |
+| time-step | 761          |
+| y_out     | -0.0739      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011047863 |
+| time-step | 762          |
+| y_out     | 0.0311       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011721404 |
+| time-step | 763          |
+| y_out     | 0.0071       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001141215 |
+| time-step | 764         |
+| y_out     | -0.105      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011649055 |
+| time-step | 765          |
+| y_out     | -0.0391      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011541259 |
+| time-step | 766          |
+| y_out     | -0.011       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00104775 |
+| time-step | 767        |
+| y_out     | 0.114      |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011247257 |
+| time-step | 768          |
+| y_out     | -0.0895      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011293389 |
+| time-step | 769          |
+| y_out     | -0.0606      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011369591 |
+| time-step | 770          |
+| y_out     | 0.0634       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012278683 |
+| time-step | 771          |
+| y_out     | 0.046        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012642798 |
+| time-step | 772          |
+| y_out     | 0.00485      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012869451 |
+| time-step | 773          |
+| y_out     | -0.075       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013297705 |
+| time-step | 774          |
+| y_out     | 0.0379       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014186993 |
+| time-step | 775          |
+| y_out     | 0.0641       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014338037 |
+| time-step | 776          |
+| y_out     | -0.0524      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015006268 |
+| time-step | 777          |
+| y_out     | 0.0438       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014273908 |
+| time-step | 778          |
+| y_out     | -0.0373      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014415398 |
+| time-step | 779          |
+| y_out     | 0.0927       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014662341 |
+| time-step | 780          |
+| y_out     | -0.0142      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014124663 |
+| time-step | 781          |
+| y_out     | -0.0195      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013342763 |
+| time-step | 782          |
+| y_out     | -0.116       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012971035 |
+| time-step | 783          |
+| y_out     | -0.0061      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013647781 |
+| time-step | 784          |
+| y_out     | -0.0569      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012315961 |
+| time-step | 785          |
+| y_out     | 0.0728       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013444985 |
+| time-step | 786          |
+| y_out     | -0.024       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013571052 |
+| time-step | 787          |
+| y_out     | 0.155        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015096015 |
+| time-step | 788          |
+| y_out     | -0.122       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015455248 |
+| time-step | 789          |
+| y_out     | 0.0256       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00158327 |
+| time-step | 790        |
+| y_out     | 0.0974     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016062756 |
+| time-step | 791          |
+| y_out     | 0.00874      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001755878 |
+| time-step | 792         |
+| y_out     | 0.12        |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018160173 |
+| time-step | 793          |
+| y_out     | 0.0364       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001725144 |
+| time-step | 794         |
+| y_out     | -0.000483   |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018430974 |
+| time-step | 795          |
+| y_out     | -0.0413      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001736924 |
+| time-step | 796         |
+| y_out     | -0.0119     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017135529 |
+| time-step | 797          |
+| y_out     | 0.0067       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015935239 |
+| time-step | 798          |
+| y_out     | 0.0978       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015165628 |
+| time-step | 799          |
+| y_out     | 0.152        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001427565 |
+| time-step | 800         |
+| y_out     | -0.0389     |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/checkpoint 800
+-------------------------------------------------------------
+| perf/mse  | 0.0014683085 |
+| time-step | 801          |
+| y_out     | 0.0625       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013958691 |
+| time-step | 802          |
+| y_out     | -0.0324      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013081672 |
+| time-step | 803          |
+| y_out     | -0.0387      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013625949 |
+| time-step | 804          |
+| y_out     | 0.0589       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001324336 |
+| time-step | 805         |
+| y_out     | 0.0364      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013276704 |
+| time-step | 806          |
+| y_out     | -0.0553      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001321068 |
+| time-step | 807         |
+| y_out     | -0.0622     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001273401 |
+| time-step | 808         |
+| y_out     | 0.00404     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013072513 |
+| time-step | 809          |
+| y_out     | -0.136       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001314732 |
+| time-step | 810         |
+| y_out     | -0.0756     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012413089 |
+| time-step | 811          |
+| y_out     | -0.00212     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011837102 |
+| time-step | 812          |
+| y_out     | -0.013       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011893586 |
+| time-step | 813          |
+| y_out     | -0.0211      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011229325 |
+| time-step | 814          |
+| y_out     | -0.0414      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010628313 |
+| time-step | 815          |
+| y_out     | -0.0804      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010642329 |
+| time-step | 816          |
+| y_out     | 0.15         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010910208 |
+| time-step | 817          |
+| y_out     | 0.0473       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011212986 |
+| time-step | 818          |
+| y_out     | -0.0432      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011733446 |
+| time-step | 819          |
+| y_out     | -0.00866     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012428403 |
+| time-step | 820          |
+| y_out     | 0.0318       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001248314 |
+| time-step | 821         |
+| y_out     | 0.000581    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012810953 |
+| time-step | 822          |
+| y_out     | 0.0461       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013508647 |
+| time-step | 823          |
+| y_out     | -0.00859     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013772693 |
+| time-step | 824          |
+| y_out     | 0.0947       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013980384 |
+| time-step | 825          |
+| y_out     | 0.074        |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00138821 |
+| time-step | 826        |
+| y_out     | 0.0537     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013410014 |
+| time-step | 827          |
+| y_out     | 0.0943       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013588734 |
+| time-step | 828          |
+| y_out     | 0.0687       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001329851 |
+| time-step | 829         |
+| y_out     | -0.0169     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012443231 |
+| time-step | 830          |
+| y_out     | -0.0312      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012349149 |
+| time-step | 831          |
+| y_out     | -0.145       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012319392 |
+| time-step | 832          |
+| y_out     | -0.12        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011614606 |
+| time-step | 833          |
+| y_out     | 0.0293       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011276733 |
+| time-step | 834          |
+| y_out     | -0.0722      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010807078 |
+| time-step | 835          |
+| y_out     | -0.0739      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010470602 |
+| time-step | 836          |
+| y_out     | -0.0207      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010900875 |
+| time-step | 837          |
+| y_out     | 0.124        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010210907 |
+| time-step | 838          |
+| y_out     | -0.00345     |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00096073514 |
+| time-step | 839           |
+| y_out     | 0.0811        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009749979 |
+| time-step | 840          |
+| y_out     | 0.0687       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000950004 |
+| time-step | 841         |
+| y_out     | 0.0908      |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00090250437 |
+| time-step | 842           |
+| y_out     | -0.0235       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008825554 |
+| time-step | 843          |
+| y_out     | -0.0102      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00088453403 |
+| time-step | 844           |
+| y_out     | 0.0455        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00088095694 |
+| time-step | 845           |
+| y_out     | 0.0073        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00094369927 |
+| time-step | 846           |
+| y_out     | -0.0202       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00089537894 |
+| time-step | 847           |
+| y_out     | -0.012        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009072519 |
+| time-step | 848          |
+| y_out     | -0.0434      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00090577395 |
+| time-step | 849           |
+| y_out     | 0.00482       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00087266986 |
+| time-step | 850           |
+| y_out     | 0.0293        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008591614 |
+| time-step | 851          |
+| y_out     | 0.0289       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008527847 |
+| time-step | 852          |
+| y_out     | 0.0848       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008234946 |
+| time-step | 853          |
+| y_out     | 0.123        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000811027 |
+| time-step | 854         |
+| y_out     | -0.0435     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007940723 |
+| time-step | 855          |
+| y_out     | 0.0199       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007123447 |
+| time-step | 856          |
+| y_out     | -0.0438      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007456819 |
+| time-step | 857          |
+| y_out     | 0.00646      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007648327 |
+| time-step | 858          |
+| y_out     | -0.0322      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00075889734 |
+| time-step | 859           |
+| y_out     | 0.0521        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00075010036 |
+| time-step | 860           |
+| y_out     | 0.00192       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00075250247 |
+| time-step | 861           |
+| y_out     | -0.0174       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00079283176 |
+| time-step | 862           |
+| y_out     | -0.0545       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00079905073 |
+| time-step | 863           |
+| y_out     | 0.0395        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00083459896 |
+| time-step | 864           |
+| y_out     | 0.0235        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008675875 |
+| time-step | 865          |
+| y_out     | 0.114        |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00089134945 |
+| time-step | 866           |
+| y_out     | 0.0404        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008580085 |
+| time-step | 867          |
+| y_out     | -0.00098     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008368505 |
+| time-step | 868          |
+| y_out     | -0.0246      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008652442 |
+| time-step | 869          |
+| y_out     | 0.0326       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000889382 |
+| time-step | 870         |
+| y_out     | -0.103      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009333716 |
+| time-step | 871          |
+| y_out     | 0.0174       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00090401433 |
+| time-step | 872           |
+| y_out     | 0.161         |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009614569 |
+| time-step | 873          |
+| y_out     | 0.0103       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00094830774 |
+| time-step | 874           |
+| y_out     | -0.0654       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009295023 |
+| time-step | 875          |
+| y_out     | 0.035        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009667568 |
+| time-step | 876          |
+| y_out     | -0.0175      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010561368 |
+| time-step | 877          |
+| y_out     | -0.0648      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011167463 |
+| time-step | 878          |
+| y_out     | 0.0742       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010872628 |
+| time-step | 879          |
+| y_out     | -0.0489      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010925243 |
+| time-step | 880          |
+| y_out     | -0.0742      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010628749 |
+| time-step | 881          |
+| y_out     | -0.0404      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011242713 |
+| time-step | 882          |
+| y_out     | -0.00225     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010577763 |
+| time-step | 883          |
+| y_out     | 0.0801       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010546888 |
+| time-step | 884          |
+| y_out     | 0.0478       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010812151 |
+| time-step | 885          |
+| y_out     | 0.0659       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010511944 |
+| time-step | 886          |
+| y_out     | -0.0589      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009707517 |
+| time-step | 887          |
+| y_out     | -0.00206     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009104345 |
+| time-step | 888          |
+| y_out     | -0.00883     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009550046 |
+| time-step | 889          |
+| y_out     | 0.105        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009679996 |
+| time-step | 890          |
+| y_out     | -0.00534     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009916811 |
+| time-step | 891          |
+| y_out     | 0.0967       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009463288 |
+| time-step | 892          |
+| y_out     | 0.101        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009712934 |
+| time-step | 893          |
+| y_out     | 0.0861       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009714325 |
+| time-step | 894          |
+| y_out     | -0.0928      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00095690443 |
+| time-step | 895           |
+| y_out     | 0.023         |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00092052575 |
+| time-step | 896           |
+| y_out     | 0.00601       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00091311504 |
+| time-step | 897           |
+| y_out     | -0.0262       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00096675847 |
+| time-step | 898           |
+| y_out     | 0.069         |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009459886 |
+| time-step | 899          |
+| y_out     | -0.0118      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009249424 |
+| time-step | 900          |
+| y_out     | -0.0401      |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/checkpoint 900
+-------------------------------------------------------------
+| perf/mse  | 0.0008659684 |
+| time-step | 901          |
+| y_out     | 0.0251       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00087528676 |
+| time-step | 902           |
+| y_out     | 0.017         |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008926176 |
+| time-step | 903          |
+| y_out     | -0.0452      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008806799 |
+| time-step | 904          |
+| y_out     | -0.0547      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009170914 |
+| time-step | 905          |
+| y_out     | 0.0967       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000926555 |
+| time-step | 906         |
+| y_out     | 0.0579      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009437663 |
+| time-step | 907          |
+| y_out     | -0.0616      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00088814646 |
+| time-step | 908           |
+| y_out     | 0.00115       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008562465 |
+| time-step | 909          |
+| y_out     | -0.084       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008396214 |
+| time-step | 910          |
+| y_out     | -0.137       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008597195 |
+| time-step | 911          |
+| y_out     | 0.012        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008533724 |
+| time-step | 912          |
+| y_out     | 0.115        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008084908 |
+| time-step | 913          |
+| y_out     | -0.089       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007821477 |
+| time-step | 914          |
+| y_out     | 0.0517       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007664033 |
+| time-step | 915          |
+| y_out     | 0.0025       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00075741246 |
+| time-step | 916           |
+| y_out     | -0.0125       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007603846 |
+| time-step | 917          |
+| y_out     | -0.00113     |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00076967257 |
+| time-step | 918           |
+| y_out     | 0.032         |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008119568 |
+| time-step | 919          |
+| y_out     | -0.0186      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00080182095 |
+| time-step | 920           |
+| y_out     | -0.0766       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00081231864 |
+| time-step | 921           |
+| y_out     | 0.0521        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008213698 |
+| time-step | 922          |
+| y_out     | 0.0289       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00084424485 |
+| time-step | 923           |
+| y_out     | -0.0586       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008768731 |
+| time-step | 924          |
+| y_out     | 0.0607       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00085797894 |
+| time-step | 925           |
+| y_out     | -0.00773      |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008982549 |
+| time-step | 926          |
+| y_out     | -0.0431      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008574022 |
+| time-step | 927          |
+| y_out     | 0.015        |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00083125883 |
+| time-step | 928           |
+| y_out     | 0.0657        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007904358 |
+| time-step | 929          |
+| y_out     | -0.129       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000884543 |
+| time-step | 930         |
+| y_out     | -0.105      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008903014 |
+| time-step | 931          |
+| y_out     | 0.0638       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00090680167 |
+| time-step | 932           |
+| y_out     | 0.042         |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008860087 |
+| time-step | 933          |
+| y_out     | -0.0562      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00088993553 |
+| time-step | 934           |
+| y_out     | -0.0414       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009092687 |
+| time-step | 935          |
+| y_out     | 0.022        |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00093270215 |
+| time-step | 936           |
+| y_out     | 0.0173        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010453409 |
+| time-step | 937          |
+| y_out     | 0.0519       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010436832 |
+| time-step | 938          |
+| y_out     | 0.0327       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010768556 |
+| time-step | 939          |
+| y_out     | -0.0136      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011166579 |
+| time-step | 940          |
+| y_out     | -0.00695     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011120068 |
+| time-step | 941          |
+| y_out     | -0.0337      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010790712 |
+| time-step | 942          |
+| y_out     | 0.109        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010900488 |
+| time-step | 943          |
+| y_out     | -0.00302     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010846038 |
+| time-step | 944          |
+| y_out     | -0.0841      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010881622 |
+| time-step | 945          |
+| y_out     | 0.106        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010369209 |
+| time-step | 946          |
+| y_out     | 0.0858       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000958197 |
+| time-step | 947         |
+| y_out     | -0.0191     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009943258 |
+| time-step | 948          |
+| y_out     | -0.0866      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00095832284 |
+| time-step | 949           |
+| y_out     | 0.0215        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00089429447 |
+| time-step | 950           |
+| y_out     | -0.073        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008669936 |
+| time-step | 951          |
+| y_out     | -0.0502      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00088033994 |
+| time-step | 952           |
+| y_out     | -0.167        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00090450404 |
+| time-step | 953           |
+| y_out     | -0.0431       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009026959 |
+| time-step | 954          |
+| y_out     | 0.0365       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009001582 |
+| time-step | 955          |
+| y_out     | 0.119        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009041087 |
+| time-step | 956          |
+| y_out     | -0.0181      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009324748 |
+| time-step | 957          |
+| y_out     | -0.0288      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009592011 |
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/progress-[||perf--mse||, ||timestep||].csv b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/progress-[||perf--mse||, ||timestep||].csv
new file mode 100644
index 0000000..7be1ad7
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/progress-[||perf--mse||, ||timestep||].csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/progress.csv b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/progress.csv
new file mode 100644
index 0000000..cb571c6
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/progress.csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/tb/events/events.out.tfevents.1646140442.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/tb/events/events.out.tfevents.1646140442.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..211d5ed
Binary files /dev/null and b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/tb/events/events.out.tfevents.1646140442.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/warn.txt b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/warn.txt
new file mode 100644
index 0000000..f7e0929
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-13-58-909376 11.0.91.89 &env_id=Test-v1&learning_rate=0.01/warn.txt	
@@ -0,0 +1,8 @@
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/backup.txt b/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/backup.txt
new file mode 100644
index 0000000..877f0bb
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/backup.txt	
@@ -0,0 +1,7 @@
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.01
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 888
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/log.txt b/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/log.txt
new file mode 100644
index 0000000..89309e1
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/log.txt	
@@ -0,0 +1,12 @@
+Logging to ../log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/
+log dir: ../log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/
+pkl_file: ../archive_tester/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888.pkl
+checkpoint_dir: ../checkpoint/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/
+results_dir: ../results/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.01
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 888
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/progress.csv b/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/tb/events/events.out.tfevents.1646140801.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/tb/events/events.out.tfevents.1646140801.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..f16e2bc
Binary files /dev/null and b/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/tb/events/events.out.tfevents.1646140801.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/warn.txt b/test/test_data_root/log/demo_task/2022/03/01/21-19-57-875205 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/backup.txt b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/backup.txt
new file mode 100644
index 0000000..3fdaba0
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/backup.txt	
@@ -0,0 +1,7 @@
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.01
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 8888
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/log.txt b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/log.txt
new file mode 100644
index 0000000..7266014
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/log.txt	
@@ -0,0 +1,5048 @@
+Logging to ../log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/
+log dir: ../log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/
+pkl_file: ../archive_tester/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888.pkl
+checkpoint_dir: ../checkpoint/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/
+results_dir: ../results/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.01
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 8888
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | -0.013   |
+---------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/checkpoint 0
+---------------------------------------------------------
+| time-step | 1        |
+| y_out     | -0.0398  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 2        |
+| y_out     | -0.0212  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 3        |
+| y_out     | -0.0308  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 4        |
+| y_out     | 0.0359   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 5        |
+| y_out     | -0.0504  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 6        |
+| y_out     | 0.0961   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 7        |
+| y_out     | -0.0264  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 8        |
+| y_out     | 0.0186   |
+---------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22814277 |
+| time-step | 9          |
+| y_out     | -0.0109    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19190815 |
+| time-step | 10         |
+| y_out     | -0.115     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14656934 |
+| time-step | 11         |
+| y_out     | -0.0477    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13418613 |
+| time-step | 12         |
+| y_out     | -0.00868   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12194592 |
+| time-step | 13         |
+| y_out     | -0.0594    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11168168 |
+| time-step | 14         |
+| y_out     | 0.0662     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09495102 |
+| time-step | 15         |
+| y_out     | 0.0553     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08053599 |
+| time-step | 16         |
+| y_out     | -0.0367    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07548128 |
+| time-step | 17         |
+| y_out     | 0.00463    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06800024 |
+| time-step | 18         |
+| y_out     | 0.0338     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06443814 |
+| time-step | 19         |
+| y_out     | 0.127      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061469335 |
+| time-step | 20          |
+| y_out     | -0.0968     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05173979 |
+| time-step | 21         |
+| y_out     | -0.0468    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049125113 |
+| time-step | 22          |
+| y_out     | 0.0437      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047618106 |
+| time-step | 23          |
+| y_out     | -0.112      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04363936 |
+| time-step | 24         |
+| y_out     | -0.114     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040248767 |
+| time-step | 25          |
+| y_out     | 0.078       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038495947 |
+| time-step | 26          |
+| y_out     | 0.0966      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036612876 |
+| time-step | 27          |
+| y_out     | 0.111       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034937687 |
+| time-step | 28          |
+| y_out     | 0.0692      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03362351 |
+| time-step | 29         |
+| y_out     | -0.0105    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032952435 |
+| time-step | 30          |
+| y_out     | 0.087       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030083561 |
+| time-step | 31          |
+| y_out     | -0.117      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028178344 |
+| time-step | 32          |
+| y_out     | 0.0726      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027806658 |
+| time-step | 33          |
+| y_out     | 0.117       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026402492 |
+| time-step | 34          |
+| y_out     | 0.0209      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024513748 |
+| time-step | 35          |
+| y_out     | 0.072       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02449134 |
+| time-step | 36         |
+| y_out     | -0.0442    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02332837 |
+| time-step | 37         |
+| y_out     | 0.054      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020952143 |
+| time-step | 38          |
+| y_out     | 0.0743      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020119164 |
+| time-step | 39          |
+| y_out     | -0.103      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019523617 |
+| time-step | 40          |
+| y_out     | 0.128       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01971373 |
+| time-step | 41         |
+| y_out     | -0.0632    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01886544 |
+| time-step | 42         |
+| y_out     | -0.0268    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01780585 |
+| time-step | 43         |
+| y_out     | 0.05       |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017950684 |
+| time-step | 44          |
+| y_out     | -0.0445     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017043848 |
+| time-step | 45          |
+| y_out     | 0.0608      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01589923 |
+| time-step | 46         |
+| y_out     | 0.0516     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015796915 |
+| time-step | 47          |
+| y_out     | 0.0283      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016570983 |
+| time-step | 48          |
+| y_out     | -0.0478     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01654853 |
+| time-step | 49         |
+| y_out     | -0.0302    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0161605 |
+| time-step | 50        |
+| y_out     | -0.0187   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015983542 |
+| time-step | 51          |
+| y_out     | 0.0531      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015992003 |
+| time-step | 52          |
+| y_out     | -0.0277     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015842691 |
+| time-step | 53          |
+| y_out     | -0.0606     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0153492885 |
+| time-step | 54           |
+| y_out     | -0.0669      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015530897 |
+| time-step | 55          |
+| y_out     | 0.0551      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015361421 |
+| time-step | 56          |
+| y_out     | 0.0145      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014956504 |
+| time-step | 57          |
+| y_out     | -0.0797     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014301039 |
+| time-step | 58          |
+| y_out     | 0.0946      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013976658 |
+| time-step | 59          |
+| y_out     | -0.0388     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014097333 |
+| time-step | 60          |
+| y_out     | -0.0525     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013206692 |
+| time-step | 61          |
+| y_out     | -0.0524     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013091194 |
+| time-step | 62          |
+| y_out     | 0.0681      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012828231 |
+| time-step | 63          |
+| y_out     | 0.00685     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012624028 |
+| time-step | 64          |
+| y_out     | -0.093      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01218119 |
+| time-step | 65         |
+| y_out     | 0.0732     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011941055 |
+| time-step | 66          |
+| y_out     | -0.0298     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011805937 |
+| time-step | 67          |
+| y_out     | -0.00887    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011551429 |
+| time-step | 68          |
+| y_out     | 0.119       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011596314 |
+| time-step | 69          |
+| y_out     | -0.0434     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011252415 |
+| time-step | 70          |
+| y_out     | 0.0613      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011377673 |
+| time-step | 71          |
+| y_out     | -0.0387     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0111884335 |
+| time-step | 72           |
+| y_out     | 0.162        |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01113789 |
+| time-step | 73         |
+| y_out     | -0.0902    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0109994095 |
+| time-step | 74           |
+| y_out     | 0.0356       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010855131 |
+| time-step | 75          |
+| y_out     | -0.0406     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011410418 |
+| time-step | 76          |
+| y_out     | -0.0279     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011704127 |
+| time-step | 77          |
+| y_out     | -0.0779     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011506419 |
+| time-step | 78          |
+| y_out     | 0.0586      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011268602 |
+| time-step | 79          |
+| y_out     | 0.00219     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011008004 |
+| time-step | 80          |
+| y_out     | -0.0336     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011449149 |
+| time-step | 81          |
+| y_out     | -0.0578     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011286937 |
+| time-step | 82          |
+| y_out     | -0.0307     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0108551225 |
+| time-step | 83           |
+| y_out     | 0.0143       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0107244495 |
+| time-step | 84           |
+| y_out     | 0.0582       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010829263 |
+| time-step | 85          |
+| y_out     | 0.0572      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010135734 |
+| time-step | 86          |
+| y_out     | 0.0634      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009615773 |
+| time-step | 87          |
+| y_out     | 0.0266      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009502051 |
+| time-step | 88          |
+| y_out     | -0.0618     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0093239285 |
+| time-step | 89           |
+| y_out     | 0.0806       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009704497 |
+| time-step | 90          |
+| y_out     | -0.022      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008905305 |
+| time-step | 91          |
+| y_out     | -0.00353    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0088629145 |
+| time-step | 92           |
+| y_out     | -0.125       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008705814 |
+| time-step | 93          |
+| y_out     | 0.122       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008940022 |
+| time-step | 94          |
+| y_out     | 0.023       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008700555 |
+| time-step | 95          |
+| y_out     | 0.0543      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008806358 |
+| time-step | 96          |
+| y_out     | -0.0577     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008766229 |
+| time-step | 97          |
+| y_out     | -0.113      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008845379 |
+| time-step | 98          |
+| y_out     | 0.031       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008962333 |
+| time-step | 99          |
+| y_out     | 0.17        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008823075 |
+| time-step | 100         |
+| y_out     | 0.0818      |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/checkpoint 100
+------------------------------------------------------------
+| perf/mse  | 0.009079252 |
+| time-step | 101         |
+| y_out     | 0.0106      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009047341 |
+| time-step | 102         |
+| y_out     | -0.0447     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0092800325 |
+| time-step | 103          |
+| y_out     | 0.0114       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009331655 |
+| time-step | 104         |
+| y_out     | 0.00084     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0093044955 |
+| time-step | 105          |
+| y_out     | -0.00939     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009508592 |
+| time-step | 106         |
+| y_out     | -0.0777     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009845148 |
+| time-step | 107         |
+| y_out     | -0.0397     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00994922 |
+| time-step | 108        |
+| y_out     | -0.0695    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009717304 |
+| time-step | 109         |
+| y_out     | -0.0986     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0097834915 |
+| time-step | 110          |
+| y_out     | -0.0192      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00985957 |
+| time-step | 111        |
+| y_out     | -0.124     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009869351 |
+| time-step | 112         |
+| y_out     | 0.125       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010112503 |
+| time-step | 113         |
+| y_out     | -0.166      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009826284 |
+| time-step | 114         |
+| y_out     | 0.0724      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009966163 |
+| time-step | 115         |
+| y_out     | -0.000702   |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00990772 |
+| time-step | 116        |
+| y_out     | 0.112      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00951389 |
+| time-step | 117        |
+| y_out     | 0.0835     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00927384 |
+| time-step | 118        |
+| y_out     | 0.156      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009128759 |
+| time-step | 119         |
+| y_out     | -0.00471    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00913714 |
+| time-step | 120        |
+| y_out     | 0.033      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008777548 |
+| time-step | 121         |
+| y_out     | -0.0129     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008357844 |
+| time-step | 122         |
+| y_out     | 0.0151      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007956855 |
+| time-step | 123         |
+| y_out     | 0.0291      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007997531 |
+| time-step | 124         |
+| y_out     | 0.0562      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008159223 |
+| time-step | 125         |
+| y_out     | -0.0207     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008003077 |
+| time-step | 126         |
+| y_out     | 0.121       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008236217 |
+| time-step | 127         |
+| y_out     | 0.113       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0082245255 |
+| time-step | 128          |
+| y_out     | -0.0245      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008678203 |
+| time-step | 129         |
+| y_out     | 0.0274      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0085857455 |
+| time-step | 130          |
+| y_out     | -0.0112      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008827664 |
+| time-step | 131         |
+| y_out     | -0.0251     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009627724 |
+| time-step | 132         |
+| y_out     | -0.0354     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009989527 |
+| time-step | 133         |
+| y_out     | 0.149       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010198536 |
+| time-step | 134         |
+| y_out     | 0.146       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010393284 |
+| time-step | 135         |
+| y_out     | -0.0758     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010434011 |
+| time-step | 136         |
+| y_out     | -0.123      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010183383 |
+| time-step | 137         |
+| y_out     | 0.0284      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010108882 |
+| time-step | 138         |
+| y_out     | -0.027      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009743065 |
+| time-step | 139         |
+| y_out     | 0.0661      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009792206 |
+| time-step | 140         |
+| y_out     | 0.134       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009693286 |
+| time-step | 141         |
+| y_out     | 0.00407     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009483784 |
+| time-step | 142         |
+| y_out     | 0.0954      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0090315435 |
+| time-step | 143          |
+| y_out     | -0.0424      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008447139 |
+| time-step | 144         |
+| y_out     | -0.108      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007830776 |
+| time-step | 145         |
+| y_out     | 0.0191      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00749177 |
+| time-step | 146        |
+| y_out     | 0.0101     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007686507 |
+| time-step | 147         |
+| y_out     | 0.101       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0077596516 |
+| time-step | 148          |
+| y_out     | 0.123        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007711702 |
+| time-step | 149         |
+| y_out     | 0.0285      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074007795 |
+| time-step | 150          |
+| y_out     | -0.0279      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073721996 |
+| time-step | 151          |
+| y_out     | -0.129       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074723167 |
+| time-step | 152          |
+| y_out     | -0.0749      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007752657 |
+| time-step | 153         |
+| y_out     | -0.153      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008310327 |
+| time-step | 154         |
+| y_out     | 0.00628     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008291332 |
+| time-step | 155         |
+| y_out     | -0.0507     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008496254 |
+| time-step | 156         |
+| y_out     | 0.0055      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008483608 |
+| time-step | 157         |
+| y_out     | 0.0684      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008220109 |
+| time-step | 158         |
+| y_out     | 0.0358      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008307133 |
+| time-step | 159         |
+| y_out     | -0.0448     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008243485 |
+| time-step | 160         |
+| y_out     | 0.0729      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008122629 |
+| time-step | 161         |
+| y_out     | 0.0673      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007903912 |
+| time-step | 162         |
+| y_out     | 0.12        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007865642 |
+| time-step | 163         |
+| y_out     | -0.0699     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007622161 |
+| time-step | 164         |
+| y_out     | 0.0505      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0079662 |
+| time-step | 165       |
+| y_out     | -0.0156   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007813568 |
+| time-step | 166         |
+| y_out     | -0.0861     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076940046 |
+| time-step | 167          |
+| y_out     | -0.0359      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0077493945 |
+| time-step | 168          |
+| y_out     | -0.0981      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076100677 |
+| time-step | 169          |
+| y_out     | 0.0106       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0077119707 |
+| time-step | 170          |
+| y_out     | 0.0145       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008007618 |
+| time-step | 171         |
+| y_out     | 0.0929      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007861136 |
+| time-step | 172         |
+| y_out     | -0.0823     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007533052 |
+| time-step | 173         |
+| y_out     | 0.0662      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074703456 |
+| time-step | 174          |
+| y_out     | -0.0799      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007024908 |
+| time-step | 175         |
+| y_out     | -0.037      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006973028 |
+| time-step | 176         |
+| y_out     | -0.0607     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006808958 |
+| time-step | 177         |
+| y_out     | -0.171      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067936345 |
+| time-step | 178          |
+| y_out     | 0.0384       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065353974 |
+| time-step | 179          |
+| y_out     | 0.11         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064742193 |
+| time-step | 180          |
+| y_out     | 0.0197       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006289745 |
+| time-step | 181         |
+| y_out     | 0.0243      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062902225 |
+| time-step | 182          |
+| y_out     | -0.0537      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063520125 |
+| time-step | 183          |
+| y_out     | 0.122        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062725493 |
+| time-step | 184          |
+| y_out     | -0.0445      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065097734 |
+| time-step | 185          |
+| y_out     | -0.104       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006561597 |
+| time-step | 186         |
+| y_out     | 0.00215     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00655551 |
+| time-step | 187        |
+| y_out     | 0.0294     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006472918 |
+| time-step | 188         |
+| y_out     | 0.0415      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065273703 |
+| time-step | 189          |
+| y_out     | -0.0114      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063348166 |
+| time-step | 190          |
+| y_out     | 0.0879       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006337526 |
+| time-step | 191         |
+| y_out     | 0.0381      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062272483 |
+| time-step | 192          |
+| y_out     | -0.0767      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062594516 |
+| time-step | 193          |
+| y_out     | 0.104        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006278423 |
+| time-step | 194         |
+| y_out     | 0.123       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061354144 |
+| time-step | 195          |
+| y_out     | -0.108       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060305703 |
+| time-step | 196          |
+| y_out     | 0.0247       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061104754 |
+| time-step | 197          |
+| y_out     | -0.126       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062858225 |
+| time-step | 198          |
+| y_out     | -0.00541     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006298403 |
+| time-step | 199         |
+| y_out     | -0.0273     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006660664 |
+| time-step | 200         |
+| y_out     | -0.0259     |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/checkpoint 200
+------------------------------------------------------------
+| perf/mse  | 0.006327367 |
+| time-step | 201         |
+| y_out     | -0.111      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006286925 |
+| time-step | 202         |
+| y_out     | 0.0238      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063311225 |
+| time-step | 203          |
+| y_out     | 0.0167       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006096818 |
+| time-step | 204         |
+| y_out     | 0.0873      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059129233 |
+| time-step | 205          |
+| y_out     | 0.0648       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059521077 |
+| time-step | 206          |
+| y_out     | 0.0685       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057609743 |
+| time-step | 207          |
+| y_out     | -0.0468      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056522526 |
+| time-step | 208          |
+| y_out     | 0.0401       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057538003 |
+| time-step | 209          |
+| y_out     | -0.00547     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053303726 |
+| time-step | 210          |
+| y_out     | -0.0278      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00543254 |
+| time-step | 211        |
+| y_out     | 0.0257     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054755462 |
+| time-step | 212          |
+| y_out     | 0.103        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005163024 |
+| time-step | 213         |
+| y_out     | 0.0615      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00527807 |
+| time-step | 214        |
+| y_out     | -0.0455    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054206722 |
+| time-step | 215          |
+| y_out     | 0.0102       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005390556 |
+| time-step | 216         |
+| y_out     | -0.0681     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005303591 |
+| time-step | 217         |
+| y_out     | 0.0221      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005322987 |
+| time-step | 218         |
+| y_out     | -0.0352     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051577697 |
+| time-step | 219          |
+| y_out     | -0.118       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005256223 |
+| time-step | 220         |
+| y_out     | -0.0799     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054133954 |
+| time-step | 221          |
+| y_out     | 0.0311       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005216778 |
+| time-step | 222         |
+| y_out     | 0.172       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005268435 |
+| time-step | 223         |
+| y_out     | 0.0651      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005479181 |
+| time-step | 224         |
+| y_out     | 0.0375      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005524743 |
+| time-step | 225         |
+| y_out     | -0.0803     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055872994 |
+| time-step | 226          |
+| y_out     | -0.0981      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005836328 |
+| time-step | 227         |
+| y_out     | -0.00473    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057471474 |
+| time-step | 228          |
+| y_out     | -0.0165      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058778888 |
+| time-step | 229          |
+| y_out     | -0.0929      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005986926 |
+| time-step | 230         |
+| y_out     | 0.00867     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00576377 |
+| time-step | 231        |
+| y_out     | -0.0504    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005870902 |
+| time-step | 232         |
+| y_out     | -0.0191     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058360295 |
+| time-step | 233          |
+| y_out     | 0.0209       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054785404 |
+| time-step | 234          |
+| y_out     | -0.00798     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005676602 |
+| time-step | 235         |
+| y_out     | -0.0782     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005449542 |
+| time-step | 236         |
+| y_out     | 0.0131      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052621923 |
+| time-step | 237          |
+| y_out     | -0.0344      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053039417 |
+| time-step | 238          |
+| y_out     | -0.0572      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005104669 |
+| time-step | 239         |
+| y_out     | 0.00293     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004935091 |
+| time-step | 240         |
+| y_out     | 0.0952      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049037556 |
+| time-step | 241          |
+| y_out     | 0.00735      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00503283 |
+| time-step | 242        |
+| y_out     | 0.119      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005100853 |
+| time-step | 243         |
+| y_out     | 0.0225      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054141255 |
+| time-step | 244          |
+| y_out     | -0.028       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049223904 |
+| time-step | 245          |
+| y_out     | 0.0381       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050099087 |
+| time-step | 246          |
+| y_out     | -0.101       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051783705 |
+| time-step | 247          |
+| y_out     | -0.126       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051969956 |
+| time-step | 248          |
+| y_out     | -0.143       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052441894 |
+| time-step | 249          |
+| y_out     | 0.0634       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053930283 |
+| time-step | 250          |
+| y_out     | 0.131        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053435406 |
+| time-step | 251          |
+| y_out     | 0.0329       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00520441 |
+| time-step | 252        |
+| y_out     | 0.0482     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054052984 |
+| time-step | 253          |
+| y_out     | -0.0533      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005094585 |
+| time-step | 254         |
+| y_out     | 0.00244     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005109689 |
+| time-step | 255         |
+| y_out     | -0.0304     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051714643 |
+| time-step | 256          |
+| y_out     | -0.0606      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004958625 |
+| time-step | 257         |
+| y_out     | 0.0318      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005060304 |
+| time-step | 258         |
+| y_out     | -0.0527     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005055004 |
+| time-step | 259         |
+| y_out     | -0.0706     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005030291 |
+| time-step | 260         |
+| y_out     | -0.0588     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005053456 |
+| time-step | 261         |
+| y_out     | -0.0441     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005302157 |
+| time-step | 262         |
+| y_out     | 0.000941    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049746884 |
+| time-step | 263          |
+| y_out     | 0.000903     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005001827 |
+| time-step | 264         |
+| y_out     | 0.0753      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005208264 |
+| time-step | 265         |
+| y_out     | 0.0602      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052080993 |
+| time-step | 266          |
+| y_out     | 0.0254       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051157377 |
+| time-step | 267          |
+| y_out     | -0.0679      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050438377 |
+| time-step | 268          |
+| y_out     | -0.0426      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005080645 |
+| time-step | 269         |
+| y_out     | 0.012       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048659346 |
+| time-step | 270          |
+| y_out     | 0.0665       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004884166 |
+| time-step | 271         |
+| y_out     | 0.0247      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004600591 |
+| time-step | 272         |
+| y_out     | -0.0263     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004561812 |
+| time-step | 273         |
+| y_out     | 0.0897      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046241884 |
+| time-step | 274          |
+| y_out     | -0.0502      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004417187 |
+| time-step | 275         |
+| y_out     | 0.0404      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042505665 |
+| time-step | 276          |
+| y_out     | -0.0412      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044181906 |
+| time-step | 277          |
+| y_out     | -0.0823      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042009973 |
+| time-step | 278          |
+| y_out     | 0.00324      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00408451 |
+| time-step | 279        |
+| y_out     | -0.022     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041899416 |
+| time-step | 280          |
+| y_out     | -0.0778      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004101356 |
+| time-step | 281         |
+| y_out     | 0.0279      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040584723 |
+| time-step | 282          |
+| y_out     | 0.0292       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003995669 |
+| time-step | 283         |
+| y_out     | -0.0276     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038804102 |
+| time-step | 284          |
+| y_out     | 0.000751     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039048274 |
+| time-step | 285          |
+| y_out     | -0.00911     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038150835 |
+| time-step | 286          |
+| y_out     | 0.0491       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036521622 |
+| time-step | 287          |
+| y_out     | 0.0873       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036560171 |
+| time-step | 288          |
+| y_out     | -0.12        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036707025 |
+| time-step | 289          |
+| y_out     | 0.0652       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003663823 |
+| time-step | 290         |
+| y_out     | 0.0299      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036018915 |
+| time-step | 291          |
+| y_out     | -0.078       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035577565 |
+| time-step | 292          |
+| y_out     | -0.0165      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036325217 |
+| time-step | 293          |
+| y_out     | -0.0148      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035084677 |
+| time-step | 294          |
+| y_out     | 0.0082       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034077198 |
+| time-step | 295          |
+| y_out     | 0.0507       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035068407 |
+| time-step | 296          |
+| y_out     | 0.0536       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003616973 |
+| time-step | 297         |
+| y_out     | 0.0477      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036918302 |
+| time-step | 298          |
+| y_out     | 0.0629       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036206767 |
+| time-step | 299          |
+| y_out     | -0.0455      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035456014 |
+| time-step | 300          |
+| y_out     | 0.151        |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/checkpoint 300
+-------------------------------------------------------------
+| perf/mse  | 0.0036069364 |
+| time-step | 301          |
+| y_out     | 0.00414      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037248996 |
+| time-step | 302          |
+| y_out     | -0.0771      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036344938 |
+| time-step | 303          |
+| y_out     | 0.019        |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00368546 |
+| time-step | 304        |
+| y_out     | 0.0152     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038071903 |
+| time-step | 305          |
+| y_out     | 0.00272      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00399639 |
+| time-step | 306        |
+| y_out     | 0.0207     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039564827 |
+| time-step | 307          |
+| y_out     | 0.0666       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004216424 |
+| time-step | 308         |
+| y_out     | -0.00561    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004179932 |
+| time-step | 309         |
+| y_out     | -0.0278     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042393752 |
+| time-step | 310          |
+| y_out     | 0.192        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004197497 |
+| time-step | 311         |
+| y_out     | -0.0527     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040477975 |
+| time-step | 312          |
+| y_out     | 0.0885       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041513015 |
+| time-step | 313          |
+| y_out     | 0.0581       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041851373 |
+| time-step | 314          |
+| y_out     | 0.0193       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043020644 |
+| time-step | 315          |
+| y_out     | -0.0468      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004193698 |
+| time-step | 316         |
+| y_out     | -0.0791     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041403947 |
+| time-step | 317          |
+| y_out     | 0.111        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039382842 |
+| time-step | 318          |
+| y_out     | -0.0504      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039878357 |
+| time-step | 319          |
+| y_out     | 0.0259       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003963006 |
+| time-step | 320         |
+| y_out     | 0.0499      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040808967 |
+| time-step | 321          |
+| y_out     | -0.00976     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004089148 |
+| time-step | 322         |
+| y_out     | -0.00544    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004174361 |
+| time-step | 323         |
+| y_out     | -0.055      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044125123 |
+| time-step | 324          |
+| y_out     | 0.0192       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043088314 |
+| time-step | 325          |
+| y_out     | 0.00854      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00413171 |
+| time-step | 326        |
+| y_out     | 0.0986     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004287612 |
+| time-step | 327         |
+| y_out     | -0.0847     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043292423 |
+| time-step | 328          |
+| y_out     | -0.00772     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043574073 |
+| time-step | 329          |
+| y_out     | -0.0597      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044156606 |
+| time-step | 330          |
+| y_out     | 0.0193       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004237474 |
+| time-step | 331         |
+| y_out     | 0.0227      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004242272 |
+| time-step | 332         |
+| y_out     | -0.176      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004266515 |
+| time-step | 333         |
+| y_out     | -0.0241     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00423325 |
+| time-step | 334        |
+| y_out     | 0.0392     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041499655 |
+| time-step | 335          |
+| y_out     | 0.000388     |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00454837 |
+| time-step | 336        |
+| y_out     | 0.102      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004348189 |
+| time-step | 337         |
+| y_out     | 0.037       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041687335 |
+| time-step | 338          |
+| y_out     | -0.0224      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044572987 |
+| time-step | 339          |
+| y_out     | 0.0307       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042990986 |
+| time-step | 340          |
+| y_out     | 0.00452      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043040626 |
+| time-step | 341          |
+| y_out     | 0.0181       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004310579 |
+| time-step | 342         |
+| y_out     | 0.143       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004043701 |
+| time-step | 343         |
+| y_out     | -0.103      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038689692 |
+| time-step | 344          |
+| y_out     | -0.000114    |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039927894 |
+| time-step | 345          |
+| y_out     | 0.0177       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036412464 |
+| time-step | 346          |
+| y_out     | 0.0353       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036830255 |
+| time-step | 347          |
+| y_out     | 0.0519       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003789559 |
+| time-step | 348         |
+| y_out     | 0.0664      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034503099 |
+| time-step | 349          |
+| y_out     | -0.0405      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034847078 |
+| time-step | 350          |
+| y_out     | -0.0675      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036763232 |
+| time-step | 351          |
+| y_out     | 0.00809      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036198385 |
+| time-step | 352          |
+| y_out     | 0.0504       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037022172 |
+| time-step | 353          |
+| y_out     | -0.0687      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003784782 |
+| time-step | 354         |
+| y_out     | -0.0298     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036062598 |
+| time-step | 355          |
+| y_out     | -0.0408      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003554086 |
+| time-step | 356         |
+| y_out     | -0.0393     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034277563 |
+| time-step | 357          |
+| y_out     | 0.0919       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032615073 |
+| time-step | 358          |
+| y_out     | 0.0398       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031486116 |
+| time-step | 359          |
+| y_out     | 0.0312       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031012534 |
+| time-step | 360          |
+| y_out     | 0.0303       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029184758 |
+| time-step | 361          |
+| y_out     | 0.164        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002877044 |
+| time-step | 362         |
+| y_out     | 0.0296      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028360656 |
+| time-step | 363          |
+| y_out     | -0.0483      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002589463 |
+| time-step | 364         |
+| y_out     | -0.0896     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025715188 |
+| time-step | 365          |
+| y_out     | 0.0617       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026089766 |
+| time-step | 366          |
+| y_out     | 0.0665       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00266103 |
+| time-step | 367        |
+| y_out     | 0.0504     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027067424 |
+| time-step | 368          |
+| y_out     | 0.073        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027684818 |
+| time-step | 369          |
+| y_out     | -0.00346     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026964836 |
+| time-step | 370          |
+| y_out     | -0.0353      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027253355 |
+| time-step | 371          |
+| y_out     | -0.0335      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026865224 |
+| time-step | 372          |
+| y_out     | -0.0263      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027575567 |
+| time-step | 373          |
+| y_out     | 0.0703       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027883498 |
+| time-step | 374          |
+| y_out     | -0.0049      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028122657 |
+| time-step | 375          |
+| y_out     | 0.0687       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002724332 |
+| time-step | 376         |
+| y_out     | 0.0377      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026641716 |
+| time-step | 377          |
+| y_out     | 0.0227       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027131606 |
+| time-step | 378          |
+| y_out     | 0.0408       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027769406 |
+| time-step | 379          |
+| y_out     | 0.137        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028316758 |
+| time-step | 380          |
+| y_out     | 0.0591       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028969352 |
+| time-step | 381          |
+| y_out     | 0.108        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029437765 |
+| time-step | 382          |
+| y_out     | 0.0515       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002945118 |
+| time-step | 383         |
+| y_out     | -0.00843    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003000104 |
+| time-step | 384         |
+| y_out     | -0.118      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029598312 |
+| time-step | 385          |
+| y_out     | 0.02         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029995074 |
+| time-step | 386          |
+| y_out     | 0.0337       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003022773 |
+| time-step | 387         |
+| y_out     | 0.122       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00293301 |
+| time-step | 388        |
+| y_out     | -0.0468    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00288595 |
+| time-step | 389        |
+| y_out     | 0.0459     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028845086 |
+| time-step | 390          |
+| y_out     | 0.00252      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028931538 |
+| time-step | 391          |
+| y_out     | 0.00519      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029253946 |
+| time-step | 392          |
+| y_out     | -0.0347      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027798438 |
+| time-step | 393          |
+| y_out     | 0.178        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027377517 |
+| time-step | 394          |
+| y_out     | -0.0891      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027091778 |
+| time-step | 395          |
+| y_out     | -0.0417      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027136323 |
+| time-step | 396          |
+| y_out     | 0.0665       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026537026 |
+| time-step | 397          |
+| y_out     | -0.0648      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026035416 |
+| time-step | 398          |
+| y_out     | 0.133        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027148766 |
+| time-step | 399          |
+| y_out     | -0.073       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026764562 |
+| time-step | 400          |
+| y_out     | -0.0106      |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/checkpoint 400
+-------------------------------------------------------------
+| perf/mse  | 0.0025633783 |
+| time-step | 401          |
+| y_out     | 0.0346       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024607638 |
+| time-step | 402          |
+| y_out     | 0.0949       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026140804 |
+| time-step | 403          |
+| y_out     | -0.0286      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028095958 |
+| time-step | 404          |
+| y_out     | -0.00367     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028621678 |
+| time-step | 405          |
+| y_out     | -0.109       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028227596 |
+| time-step | 406          |
+| y_out     | -0.0583      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002918527 |
+| time-step | 407         |
+| y_out     | 0.0483      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002974172 |
+| time-step | 408         |
+| y_out     | 0.0396      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028609713 |
+| time-step | 409          |
+| y_out     | 0.0392       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029443295 |
+| time-step | 410          |
+| y_out     | 0.0361       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029121835 |
+| time-step | 411          |
+| y_out     | 0.0062       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029660575 |
+| time-step | 412          |
+| y_out     | -0.114       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002851428 |
+| time-step | 413         |
+| y_out     | 0.016       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026635982 |
+| time-step | 414          |
+| y_out     | -0.0861      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025814697 |
+| time-step | 415          |
+| y_out     | -0.00389     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025655008 |
+| time-step | 416          |
+| y_out     | -0.0131      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025301003 |
+| time-step | 417          |
+| y_out     | -0.0759      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025324966 |
+| time-step | 418          |
+| y_out     | 0.0873       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024488522 |
+| time-step | 419          |
+| y_out     | -0.0149      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002294812 |
+| time-step | 420         |
+| y_out     | 0.11        |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021909764 |
+| time-step | 421          |
+| y_out     | 0.0141       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002174045 |
+| time-step | 422         |
+| y_out     | 0.135       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021627548 |
+| time-step | 423          |
+| y_out     | -0.0391      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022104885 |
+| time-step | 424          |
+| y_out     | 0.0666       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002318445 |
+| time-step | 425         |
+| y_out     | 0.00703     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024139923 |
+| time-step | 426          |
+| y_out     | 0.0534       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023800575 |
+| time-step | 427          |
+| y_out     | -0.00408     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023304624 |
+| time-step | 428          |
+| y_out     | 0.0466       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002378085 |
+| time-step | 429         |
+| y_out     | -0.0148     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025470653 |
+| time-step | 430          |
+| y_out     | 0.0611       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026202898 |
+| time-step | 431          |
+| y_out     | -0.0319      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002626631 |
+| time-step | 432         |
+| y_out     | -0.0489     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002606416 |
+| time-step | 433         |
+| y_out     | 0.0673      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002634737 |
+| time-step | 434         |
+| y_out     | -0.00585    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025430552 |
+| time-step | 435          |
+| y_out     | 0.0786       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024083047 |
+| time-step | 436          |
+| y_out     | -0.0211      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024108659 |
+| time-step | 437          |
+| y_out     | -0.0536      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024198657 |
+| time-step | 438          |
+| y_out     | 0.0392       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023280159 |
+| time-step | 439          |
+| y_out     | 0.032        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002206035 |
+| time-step | 440         |
+| y_out     | -0.0592     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022109388 |
+| time-step | 441          |
+| y_out     | 0.0456       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021608341 |
+| time-step | 442          |
+| y_out     | -0.0152      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021653138 |
+| time-step | 443          |
+| y_out     | 0.0246       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020893263 |
+| time-step | 444          |
+| y_out     | -0.0421      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020773895 |
+| time-step | 445          |
+| y_out     | -0.0401      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002117512 |
+| time-step | 446         |
+| y_out     | 0.0371      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020872266 |
+| time-step | 447          |
+| y_out     | -0.0795      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019826216 |
+| time-step | 448          |
+| y_out     | 0.111        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020338867 |
+| time-step | 449          |
+| y_out     | -0.0171      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001994757 |
+| time-step | 450         |
+| y_out     | -0.0591     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019529707 |
+| time-step | 451          |
+| y_out     | 0.00818      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019151836 |
+| time-step | 452          |
+| y_out     | -0.135       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018748939 |
+| time-step | 453          |
+| y_out     | -0.0179      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017884662 |
+| time-step | 454          |
+| y_out     | -0.119       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017458296 |
+| time-step | 455          |
+| y_out     | -0.0409      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001717279 |
+| time-step | 456         |
+| y_out     | 0.00683     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016646234 |
+| time-step | 457          |
+| y_out     | 0.0405       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016306092 |
+| time-step | 458          |
+| y_out     | 0.0991       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001587358 |
+| time-step | 459         |
+| y_out     | 0.0868      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015988561 |
+| time-step | 460          |
+| y_out     | -0.157       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015891518 |
+| time-step | 461          |
+| y_out     | -0.0318      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016028725 |
+| time-step | 462          |
+| y_out     | -0.107       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016279299 |
+| time-step | 463          |
+| y_out     | 0.0323       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016414586 |
+| time-step | 464          |
+| y_out     | -0.0725      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016114619 |
+| time-step | 465          |
+| y_out     | 0.0981       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016523786 |
+| time-step | 466          |
+| y_out     | 0.0839       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017532157 |
+| time-step | 467          |
+| y_out     | 0.043        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018698343 |
+| time-step | 468          |
+| y_out     | -0.00574     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019232066 |
+| time-step | 469          |
+| y_out     | 0.0203       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019062788 |
+| time-step | 470          |
+| y_out     | -0.0484      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018841226 |
+| time-step | 471          |
+| y_out     | -0.0602      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018888488 |
+| time-step | 472          |
+| y_out     | -0.012       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018755818 |
+| time-step | 473          |
+| y_out     | -0.0222      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018860779 |
+| time-step | 474          |
+| y_out     | 0.0117       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018893316 |
+| time-step | 475          |
+| y_out     | -0.0112      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018203834 |
+| time-step | 476          |
+| y_out     | -0.0658      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018144085 |
+| time-step | 477          |
+| y_out     | 0.0438       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001791051 |
+| time-step | 478         |
+| y_out     | 0.0419      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001783035 |
+| time-step | 479         |
+| y_out     | 0.03        |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018367197 |
+| time-step | 480          |
+| y_out     | -0.128       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019194249 |
+| time-step | 481          |
+| y_out     | 0.0201       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001959048 |
+| time-step | 482         |
+| y_out     | 0.102       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020989382 |
+| time-step | 483          |
+| y_out     | 0.0736       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021152776 |
+| time-step | 484          |
+| y_out     | 0.00401      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021999185 |
+| time-step | 485          |
+| y_out     | -0.00499     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023299714 |
+| time-step | 486          |
+| y_out     | 0.0252       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022794872 |
+| time-step | 487          |
+| y_out     | -0.141       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022714082 |
+| time-step | 488          |
+| y_out     | -0.0496      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022822493 |
+| time-step | 489          |
+| y_out     | -0.0518      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022240055 |
+| time-step | 490          |
+| y_out     | -0.0068      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002299924 |
+| time-step | 491         |
+| y_out     | 0.105       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002306609 |
+| time-step | 492         |
+| y_out     | 0.07        |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021361548 |
+| time-step | 493          |
+| y_out     | -0.0202      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00217412 |
+| time-step | 494        |
+| y_out     | 0.0222     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021827281 |
+| time-step | 495          |
+| y_out     | 0.015        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021861326 |
+| time-step | 496          |
+| y_out     | 0.0306       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002204362 |
+| time-step | 497         |
+| y_out     | 0.00631     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023612643 |
+| time-step | 498          |
+| y_out     | 0.146        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024137034 |
+| time-step | 499          |
+| y_out     | -0.0306      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023943342 |
+| time-step | 500          |
+| y_out     | 0.0263       |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/checkpoint 500
+-------------------------------------------------------------
+| perf/mse  | 0.0023614059 |
+| time-step | 501          |
+| y_out     | -0.0356      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023051347 |
+| time-step | 502          |
+| y_out     | 0.0396       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023730814 |
+| time-step | 503          |
+| y_out     | -0.0024      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002348591 |
+| time-step | 504         |
+| y_out     | -0.122      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022454252 |
+| time-step | 505          |
+| y_out     | 0.0665       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002097074 |
+| time-step | 506         |
+| y_out     | -0.0185     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021289706 |
+| time-step | 507          |
+| y_out     | 0.0328       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019829744 |
+| time-step | 508          |
+| y_out     | 0.204        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018287562 |
+| time-step | 509          |
+| y_out     | 0.0396       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00184292 |
+| time-step | 510        |
+| y_out     | -0.0483    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017205365 |
+| time-step | 511          |
+| y_out     | -0.0332      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017632047 |
+| time-step | 512          |
+| y_out     | 0.0748       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016770726 |
+| time-step | 513          |
+| y_out     | 0.0971       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016107045 |
+| time-step | 514          |
+| y_out     | -0.0105      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001635182 |
+| time-step | 515         |
+| y_out     | -0.101      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016805979 |
+| time-step | 516          |
+| y_out     | 0.171        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017523505 |
+| time-step | 517          |
+| y_out     | -0.000842    |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017406091 |
+| time-step | 518          |
+| y_out     | -0.0403      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017679014 |
+| time-step | 519          |
+| y_out     | 0.0346       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001789323 |
+| time-step | 520         |
+| y_out     | 0.0515      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018660773 |
+| time-step | 521          |
+| y_out     | 0.0465       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018465763 |
+| time-step | 522          |
+| y_out     | -0.064       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019030398 |
+| time-step | 523          |
+| y_out     | 0.045        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020594574 |
+| time-step | 524          |
+| y_out     | 0.0623       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002119792 |
+| time-step | 525         |
+| y_out     | -0.0969     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020660518 |
+| time-step | 526          |
+| y_out     | -0.0286      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019526805 |
+| time-step | 527          |
+| y_out     | -0.0488      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019263306 |
+| time-step | 528          |
+| y_out     | -0.00397     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019714755 |
+| time-step | 529          |
+| y_out     | 0.082        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020034318 |
+| time-step | 530          |
+| y_out     | -0.0373      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019015165 |
+| time-step | 531          |
+| y_out     | -0.0323      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001945358 |
+| time-step | 532         |
+| y_out     | 0.0312      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018668473 |
+| time-step | 533          |
+| y_out     | 0.0357       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017047767 |
+| time-step | 534          |
+| y_out     | -0.0205      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016586457 |
+| time-step | 535          |
+| y_out     | -0.0547      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016894216 |
+| time-step | 536          |
+| y_out     | -0.0715      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017010166 |
+| time-step | 537          |
+| y_out     | -0.0494      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017229551 |
+| time-step | 538          |
+| y_out     | 0.0526       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001670079 |
+| time-step | 539         |
+| y_out     | 0.0324      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015760452 |
+| time-step | 540          |
+| y_out     | -0.0905      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016725159 |
+| time-step | 541          |
+| y_out     | 0.0194       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016767133 |
+| time-step | 542          |
+| y_out     | 0.0269       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016951036 |
+| time-step | 543          |
+| y_out     | -0.0771      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017222011 |
+| time-step | 544          |
+| y_out     | 0.107        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017621996 |
+| time-step | 545          |
+| y_out     | -0.0625      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017805615 |
+| time-step | 546          |
+| y_out     | -0.0572      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017982196 |
+| time-step | 547          |
+| y_out     | -0.123       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017882833 |
+| time-step | 548          |
+| y_out     | 0.0563       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018448718 |
+| time-step | 549          |
+| y_out     | -0.0932      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019666234 |
+| time-step | 550          |
+| y_out     | -0.0518      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019076975 |
+| time-step | 551          |
+| y_out     | 0.0212       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018715691 |
+| time-step | 552          |
+| y_out     | 0.0205       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019554817 |
+| time-step | 553          |
+| y_out     | 0.0395       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019985035 |
+| time-step | 554          |
+| y_out     | -0.054       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020592315 |
+| time-step | 555          |
+| y_out     | -0.0031      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020653878 |
+| time-step | 556          |
+| y_out     | 0.145        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020838263 |
+| time-step | 557          |
+| y_out     | 0.142        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002095191 |
+| time-step | 558         |
+| y_out     | -0.062      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020432367 |
+| time-step | 559          |
+| y_out     | -0.0425      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020052257 |
+| time-step | 560          |
+| y_out     | -0.13        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020018995 |
+| time-step | 561          |
+| y_out     | -0.112       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001931811 |
+| time-step | 562         |
+| y_out     | -0.137      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019229626 |
+| time-step | 563          |
+| y_out     | 0.119        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019406999 |
+| time-step | 564          |
+| y_out     | -0.0642      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019049175 |
+| time-step | 565          |
+| y_out     | 0.00662      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020095282 |
+| time-step | 566          |
+| y_out     | 0.00461      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019363305 |
+| time-step | 567          |
+| y_out     | -0.11        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018935282 |
+| time-step | 568          |
+| y_out     | -0.0256      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019831448 |
+| time-step | 569          |
+| y_out     | 0.0289       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019363079 |
+| time-step | 570          |
+| y_out     | -0.0631      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019397025 |
+| time-step | 571          |
+| y_out     | 0.0908       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019768483 |
+| time-step | 572          |
+| y_out     | 0.0114       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018837163 |
+| time-step | 573          |
+| y_out     | 0.0546       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018218185 |
+| time-step | 574          |
+| y_out     | 0.0595       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017805112 |
+| time-step | 575          |
+| y_out     | 0.0163       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016203601 |
+| time-step | 576          |
+| y_out     | 0.102        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016064912 |
+| time-step | 577          |
+| y_out     | -0.0839      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017189073 |
+| time-step | 578          |
+| y_out     | 0.0495       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016244536 |
+| time-step | 579          |
+| y_out     | 0.0425       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016255511 |
+| time-step | 580          |
+| y_out     | 0.026        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015507056 |
+| time-step | 581          |
+| y_out     | -0.0805      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015580666 |
+| time-step | 582          |
+| y_out     | -0.0565      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016561073 |
+| time-step | 583          |
+| y_out     | -0.0273      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016211516 |
+| time-step | 584          |
+| y_out     | -0.0519      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015700705 |
+| time-step | 585          |
+| y_out     | -0.101       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015420543 |
+| time-step | 586          |
+| y_out     | 0.0181       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015701067 |
+| time-step | 587          |
+| y_out     | -0.0695      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015230797 |
+| time-step | 588          |
+| y_out     | 0.012        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015124955 |
+| time-step | 589          |
+| y_out     | -0.071       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016209187 |
+| time-step | 590          |
+| y_out     | 0.0149       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017051346 |
+| time-step | 591          |
+| y_out     | -0.181       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018082935 |
+| time-step | 592          |
+| y_out     | -0.0928      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017641919 |
+| time-step | 593          |
+| y_out     | -0.129       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017935472 |
+| time-step | 594          |
+| y_out     | 0.00735      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001810446 |
+| time-step | 595         |
+| y_out     | -0.00512    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018634761 |
+| time-step | 596          |
+| y_out     | 0.101        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018715688 |
+| time-step | 597          |
+| y_out     | 0.0813       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018106379 |
+| time-step | 598          |
+| y_out     | 0.00686      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001833563 |
+| time-step | 599         |
+| y_out     | 0.0333      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017076008 |
+| time-step | 600          |
+| y_out     | 0.0463       |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/checkpoint 600
+-------------------------------------------------------------
+| perf/mse  | 0.0016647311 |
+| time-step | 601          |
+| y_out     | -0.0148      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015571703 |
+| time-step | 602          |
+| y_out     | 0.0114       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015799468 |
+| time-step | 603          |
+| y_out     | 0.188        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015889879 |
+| time-step | 604          |
+| y_out     | -0.0543      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015591546 |
+| time-step | 605          |
+| y_out     | -0.146       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014958208 |
+| time-step | 606          |
+| y_out     | 0.057        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014240805 |
+| time-step | 607          |
+| y_out     | 0.00771      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014367993 |
+| time-step | 608          |
+| y_out     | 0.0338       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014126824 |
+| time-step | 609          |
+| y_out     | 0.124        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014027186 |
+| time-step | 610          |
+| y_out     | 0.00425      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013791907 |
+| time-step | 611          |
+| y_out     | -0.00435     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013263708 |
+| time-step | 612          |
+| y_out     | -0.0379      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012708445 |
+| time-step | 613          |
+| y_out     | -0.0117      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012139344 |
+| time-step | 614          |
+| y_out     | -0.145       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012319413 |
+| time-step | 615          |
+| y_out     | 0.0966       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012522399 |
+| time-step | 616          |
+| y_out     | -0.115       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012734758 |
+| time-step | 617          |
+| y_out     | 0.0192       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012910946 |
+| time-step | 618          |
+| y_out     | -0.0553      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012755672 |
+| time-step | 619          |
+| y_out     | -0.000138    |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012513229 |
+| time-step | 620          |
+| y_out     | -0.0172      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013075816 |
+| time-step | 621          |
+| y_out     | 0.105        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013105611 |
+| time-step | 622          |
+| y_out     | 0.0755       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012796965 |
+| time-step | 623          |
+| y_out     | -0.00479     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012754378 |
+| time-step | 624          |
+| y_out     | -0.0251      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013478979 |
+| time-step | 625          |
+| y_out     | 0.0528       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013711598 |
+| time-step | 626          |
+| y_out     | 0.117        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013171906 |
+| time-step | 627          |
+| y_out     | -0.00492     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013788433 |
+| time-step | 628          |
+| y_out     | 0.0192       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001413046 |
+| time-step | 629         |
+| y_out     | 0.119       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014567596 |
+| time-step | 630          |
+| y_out     | 0.0382       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015997548 |
+| time-step | 631          |
+| y_out     | 0.0201       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015933358 |
+| time-step | 632          |
+| y_out     | 0.00457      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016292523 |
+| time-step | 633          |
+| y_out     | -0.0629      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017311517 |
+| time-step | 634          |
+| y_out     | 0.0339       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016764387 |
+| time-step | 635          |
+| y_out     | -0.0535      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001692792 |
+| time-step | 636         |
+| y_out     | 0.0659      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017229051 |
+| time-step | 637          |
+| y_out     | -0.0543      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016412109 |
+| time-step | 638          |
+| y_out     | 0.108        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017033726 |
+| time-step | 639          |
+| y_out     | 0.106        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001740497 |
+| time-step | 640         |
+| y_out     | 0.0786      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00173686 |
+| time-step | 641        |
+| y_out     | 0.0417     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018697486 |
+| time-step | 642          |
+| y_out     | 0.0818       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002011414 |
+| time-step | 643         |
+| y_out     | 0.0151      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00203693 |
+| time-step | 644        |
+| y_out     | 0.0654     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021282837 |
+| time-step | 645          |
+| y_out     | -0.0887      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021506737 |
+| time-step | 646          |
+| y_out     | 0.0241       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021839333 |
+| time-step | 647          |
+| y_out     | -0.0595      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022453894 |
+| time-step | 648          |
+| y_out     | -0.00917     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002301612 |
+| time-step | 649         |
+| y_out     | -0.128      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002359852 |
+| time-step | 650         |
+| y_out     | 0.00873     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022147992 |
+| time-step | 651          |
+| y_out     | 0.0442       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022218665 |
+| time-step | 652          |
+| y_out     | 0.166        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021261545 |
+| time-step | 653          |
+| y_out     | 0.089        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002227218 |
+| time-step | 654         |
+| y_out     | 0.0595      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021722629 |
+| time-step | 655          |
+| y_out     | 0.058        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021354246 |
+| time-step | 656          |
+| y_out     | -0.0557      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022716355 |
+| time-step | 657          |
+| y_out     | 0.00279      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022492642 |
+| time-step | 658          |
+| y_out     | 0.0415       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002243658 |
+| time-step | 659         |
+| y_out     | 0.0549      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021330744 |
+| time-step | 660          |
+| y_out     | 0.0672       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021061434 |
+| time-step | 661          |
+| y_out     | -0.0542      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002143382 |
+| time-step | 662         |
+| y_out     | 0.00832     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002160568 |
+| time-step | 663         |
+| y_out     | -0.0826     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020056188 |
+| time-step | 664          |
+| y_out     | 0.0141       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019286026 |
+| time-step | 665          |
+| y_out     | -0.00226     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019068992 |
+| time-step | 666          |
+| y_out     | 0.0217       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017698819 |
+| time-step | 667          |
+| y_out     | -0.048       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018374551 |
+| time-step | 668          |
+| y_out     | -0.0398      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001716411 |
+| time-step | 669         |
+| y_out     | -0.16       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017501016 |
+| time-step | 670          |
+| y_out     | 0.0797       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001791881 |
+| time-step | 671         |
+| y_out     | 0.0431      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016325234 |
+| time-step | 672          |
+| y_out     | -0.0591      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015515495 |
+| time-step | 673          |
+| y_out     | 0.0235       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014953509 |
+| time-step | 674          |
+| y_out     | -0.0982      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015398795 |
+| time-step | 675          |
+| y_out     | 0.00852      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015718257 |
+| time-step | 676          |
+| y_out     | 0.00262      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015397812 |
+| time-step | 677          |
+| y_out     | -0.184       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013955629 |
+| time-step | 678          |
+| y_out     | -0.00378     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014330016 |
+| time-step | 679          |
+| y_out     | -0.0143      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014216142 |
+| time-step | 680          |
+| y_out     | 0.0555       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013772298 |
+| time-step | 681          |
+| y_out     | -0.022       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013943024 |
+| time-step | 682          |
+| y_out     | -0.0938      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013373584 |
+| time-step | 683          |
+| y_out     | -0.113       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013251683 |
+| time-step | 684          |
+| y_out     | -0.115       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012640087 |
+| time-step | 685          |
+| y_out     | 0.0606       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012504628 |
+| time-step | 686          |
+| y_out     | 0.0115       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00128381 |
+| time-step | 687        |
+| y_out     | -0.00328   |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013063055 |
+| time-step | 688          |
+| y_out     | -0.0623      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001284523 |
+| time-step | 689         |
+| y_out     | -0.0677     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012096773 |
+| time-step | 690          |
+| y_out     | -0.0219      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012361064 |
+| time-step | 691          |
+| y_out     | -0.0323      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012859232 |
+| time-step | 692          |
+| y_out     | -0.0182      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001388235 |
+| time-step | 693         |
+| y_out     | -0.0276     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014269461 |
+| time-step | 694          |
+| y_out     | 0.0863       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015398476 |
+| time-step | 695          |
+| y_out     | 0.0747       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015594211 |
+| time-step | 696          |
+| y_out     | -0.0563      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015636419 |
+| time-step | 697          |
+| y_out     | 0.0763       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016136247 |
+| time-step | 698          |
+| y_out     | -0.0364      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016037378 |
+| time-step | 699          |
+| y_out     | 0.0728       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016883069 |
+| time-step | 700          |
+| y_out     | 0.00783      |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/checkpoint 700
+------------------------------------------------------------
+| perf/mse  | 0.001653303 |
+| time-step | 701         |
+| y_out     | 0.0408      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015353167 |
+| time-step | 702          |
+| y_out     | -0.00231     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014869093 |
+| time-step | 703          |
+| y_out     | -0.0581      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015535683 |
+| time-step | 704          |
+| y_out     | 0.0357       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014892067 |
+| time-step | 705          |
+| y_out     | 0.076        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001443968 |
+| time-step | 706         |
+| y_out     | -0.0381     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014547679 |
+| time-step | 707          |
+| y_out     | -0.0573      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013978692 |
+| time-step | 708          |
+| y_out     | 0.00577      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014173442 |
+| time-step | 709          |
+| y_out     | -0.052       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013679179 |
+| time-step | 710          |
+| y_out     | -0.0547      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014311712 |
+| time-step | 711          |
+| y_out     | -0.0186      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015632857 |
+| time-step | 712          |
+| y_out     | -0.0165      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016357766 |
+| time-step | 713          |
+| y_out     | 0.0343       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016154259 |
+| time-step | 714          |
+| y_out     | -0.0315      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016406908 |
+| time-step | 715          |
+| y_out     | -0.0977      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016538191 |
+| time-step | 716          |
+| y_out     | -0.0247      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016900437 |
+| time-step | 717          |
+| y_out     | -0.0674      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017150389 |
+| time-step | 718          |
+| y_out     | 0.116        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017206362 |
+| time-step | 719          |
+| y_out     | 0.0558       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017399801 |
+| time-step | 720          |
+| y_out     | 0.105        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016872808 |
+| time-step | 721          |
+| y_out     | 0.0255       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016493325 |
+| time-step | 722          |
+| y_out     | -0.00897     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015894575 |
+| time-step | 723          |
+| y_out     | 0.0214       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015220798 |
+| time-step | 724          |
+| y_out     | 0.0423       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014660312 |
+| time-step | 725          |
+| y_out     | -0.149       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014465445 |
+| time-step | 726          |
+| y_out     | 0.0439       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013714675 |
+| time-step | 727          |
+| y_out     | -0.0451      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013324551 |
+| time-step | 728          |
+| y_out     | 0.104        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001296964 |
+| time-step | 729         |
+| y_out     | 0.00922     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012843091 |
+| time-step | 730          |
+| y_out     | -0.0295      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012795057 |
+| time-step | 731          |
+| y_out     | -0.115       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001335425 |
+| time-step | 732         |
+| y_out     | 0.0276      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012781796 |
+| time-step | 733          |
+| y_out     | -0.13        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012871639 |
+| time-step | 734          |
+| y_out     | 0.0411       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012652508 |
+| time-step | 735          |
+| y_out     | -0.0252      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013218264 |
+| time-step | 736          |
+| y_out     | -0.00834     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013400693 |
+| time-step | 737          |
+| y_out     | -0.0464      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001346376 |
+| time-step | 738         |
+| y_out     | -0.127      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013420184 |
+| time-step | 739          |
+| y_out     | 0.0104       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013943227 |
+| time-step | 740          |
+| y_out     | -0.00848     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013821267 |
+| time-step | 741          |
+| y_out     | -0.0756      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013179875 |
+| time-step | 742          |
+| y_out     | 0.133        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001340097 |
+| time-step | 743         |
+| y_out     | 0.033       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013320694 |
+| time-step | 744          |
+| y_out     | -0.00807     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014017966 |
+| time-step | 745          |
+| y_out     | 0.00146      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013962178 |
+| time-step | 746          |
+| y_out     | -0.0672      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001362267 |
+| time-step | 747         |
+| y_out     | -0.0263     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013533711 |
+| time-step | 748          |
+| y_out     | 0.0287       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001478119 |
+| time-step | 749         |
+| y_out     | 0.0189      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001470939 |
+| time-step | 750         |
+| y_out     | 0.0116      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015264101 |
+| time-step | 751          |
+| y_out     | 0.0431       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015162361 |
+| time-step | 752          |
+| y_out     | 0.0233       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016780451 |
+| time-step | 753          |
+| y_out     | 0.116        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017870836 |
+| time-step | 754          |
+| y_out     | -0.0535      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018707716 |
+| time-step | 755          |
+| y_out     | -0.0674      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019141177 |
+| time-step | 756          |
+| y_out     | 0.00128      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020022332 |
+| time-step | 757          |
+| y_out     | -0.0887      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002312275 |
+| time-step | 758         |
+| y_out     | -0.0378     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021617326 |
+| time-step | 759          |
+| y_out     | 0.0268       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023383517 |
+| time-step | 760          |
+| y_out     | 0.0531       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024080423 |
+| time-step | 761          |
+| y_out     | 0.025        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028254958 |
+| time-step | 762          |
+| y_out     | -0.0871      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028636672 |
+| time-step | 763          |
+| y_out     | 0.113        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028971792 |
+| time-step | 764          |
+| y_out     | -0.0569      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030611062 |
+| time-step | 765          |
+| y_out     | -0.0291      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003123356 |
+| time-step | 766         |
+| y_out     | 0.202       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033468038 |
+| time-step | 767          |
+| y_out     | -0.0833      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034751922 |
+| time-step | 768          |
+| y_out     | 0.0789       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035624946 |
+| time-step | 769          |
+| y_out     | 0.0913       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035701834 |
+| time-step | 770          |
+| y_out     | 0.0972       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036231496 |
+| time-step | 771          |
+| y_out     | 0.0238       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038013689 |
+| time-step | 772          |
+| y_out     | -0.0156      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038031165 |
+| time-step | 773          |
+| y_out     | -0.12        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037162348 |
+| time-step | 774          |
+| y_out     | 0.172        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038241562 |
+| time-step | 775          |
+| y_out     | 0.0191       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039064605 |
+| time-step | 776          |
+| y_out     | -0.0651      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040820898 |
+| time-step | 777          |
+| y_out     | -0.018       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003923612 |
+| time-step | 778         |
+| y_out     | -0.0428     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040517757 |
+| time-step | 779          |
+| y_out     | -0.0855      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040940316 |
+| time-step | 780          |
+| y_out     | 0.118        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041222507 |
+| time-step | 781          |
+| y_out     | 0.0618       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037221876 |
+| time-step | 782          |
+| y_out     | 0.0677       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038733769 |
+| time-step | 783          |
+| y_out     | 0.0403       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003906427 |
+| time-step | 784         |
+| y_out     | 0.0377      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037811347 |
+| time-step | 785          |
+| y_out     | 0.00851      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038334012 |
+| time-step | 786          |
+| y_out     | -0.0366      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036224523 |
+| time-step | 787          |
+| y_out     | 0.0132       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035261575 |
+| time-step | 788          |
+| y_out     | -0.0215      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003479308 |
+| time-step | 789         |
+| y_out     | -0.0225     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033965383 |
+| time-step | 790          |
+| y_out     | 0.0245       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034302995 |
+| time-step | 791          |
+| y_out     | -0.00672     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034377933 |
+| time-step | 792          |
+| y_out     | 0.103        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033153563 |
+| time-step | 793          |
+| y_out     | 0.0284       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033206039 |
+| time-step | 794          |
+| y_out     | 0.0713       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031762372 |
+| time-step | 795          |
+| y_out     | -0.0839      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030405049 |
+| time-step | 796          |
+| y_out     | -0.0533      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029862316 |
+| time-step | 797          |
+| y_out     | -0.0151      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031312604 |
+| time-step | 798          |
+| y_out     | -0.0526      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030505485 |
+| time-step | 799          |
+| y_out     | 0.0587       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031082488 |
+| time-step | 800          |
+| y_out     | -0.00107     |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/checkpoint 800
+------------------------------------------------------------
+| perf/mse  | 0.003044238 |
+| time-step | 801         |
+| y_out     | 0.0254      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029136336 |
+| time-step | 802          |
+| y_out     | 0.0374       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030064166 |
+| time-step | 803          |
+| y_out     | 0.0433       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029224097 |
+| time-step | 804          |
+| y_out     | -0.00395     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030854908 |
+| time-step | 805          |
+| y_out     | -0.0758      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003105039 |
+| time-step | 806         |
+| y_out     | 0.0239      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030560405 |
+| time-step | 807          |
+| y_out     | 0.00547      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030277981 |
+| time-step | 808          |
+| y_out     | -0.0651      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030091149 |
+| time-step | 809          |
+| y_out     | -0.084       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031404537 |
+| time-step | 810          |
+| y_out     | 0.0279       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029760401 |
+| time-step | 811          |
+| y_out     | -0.0107      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00310524 |
+| time-step | 812        |
+| y_out     | 0.0737     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003099827 |
+| time-step | 813         |
+| y_out     | -0.00561    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031548433 |
+| time-step | 814          |
+| y_out     | -0.0921      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002981928 |
+| time-step | 815         |
+| y_out     | 0.0969      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028553135 |
+| time-step | 816          |
+| y_out     | -0.0909      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027719394 |
+| time-step | 817          |
+| y_out     | 0.0153       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025610626 |
+| time-step | 818          |
+| y_out     | 0.0365       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025459956 |
+| time-step | 819          |
+| y_out     | 0.0488       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022457887 |
+| time-step | 820          |
+| y_out     | 0.0183       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022471668 |
+| time-step | 821          |
+| y_out     | 0.0372       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020746323 |
+| time-step | 822          |
+| y_out     | -0.0429      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017842175 |
+| time-step | 823          |
+| y_out     | 0.0169       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001794372 |
+| time-step | 824         |
+| y_out     | -0.0703     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017394575 |
+| time-step | 825          |
+| y_out     | 0.116        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016785773 |
+| time-step | 826          |
+| y_out     | 0.0178       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015636727 |
+| time-step | 827          |
+| y_out     | 0.0597       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014647844 |
+| time-step | 828          |
+| y_out     | -0.00429     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014292076 |
+| time-step | 829          |
+| y_out     | -0.143       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013410391 |
+| time-step | 830          |
+| y_out     | 0.0686       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001295628 |
+| time-step | 831         |
+| y_out     | 0.00827     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012272757 |
+| time-step | 832          |
+| y_out     | -0.0993      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012152568 |
+| time-step | 833          |
+| y_out     | -0.133       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011068927 |
+| time-step | 834          |
+| y_out     | 0.00747      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009882771 |
+| time-step | 835          |
+| y_out     | 0.00998      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009640165 |
+| time-step | 836          |
+| y_out     | -0.0174      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00096680067 |
+| time-step | 837           |
+| y_out     | 0.0209        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009593062 |
+| time-step | 838          |
+| y_out     | 0.00321      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009659175 |
+| time-step | 839          |
+| y_out     | -0.00284     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009616882 |
+| time-step | 840          |
+| y_out     | -0.015       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009863728 |
+| time-step | 841          |
+| y_out     | 0.0355       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00095545565 |
+| time-step | 842           |
+| y_out     | -0.0418       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00091711024 |
+| time-step | 843           |
+| y_out     | -0.119        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009044634 |
+| time-step | 844          |
+| y_out     | 0.0655       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00096740795 |
+| time-step | 845           |
+| y_out     | 0.038         |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010315398 |
+| time-step | 846          |
+| y_out     | -0.0234      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010463413 |
+| time-step | 847          |
+| y_out     | 0.0028       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010367715 |
+| time-step | 848          |
+| y_out     | -0.0646      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009907639 |
+| time-step | 849          |
+| y_out     | -0.00727     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010160106 |
+| time-step | 850          |
+| y_out     | -0.0774      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009677463 |
+| time-step | 851          |
+| y_out     | 0.0286       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000988133 |
+| time-step | 852         |
+| y_out     | -0.0243     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009715197 |
+| time-step | 853          |
+| y_out     | -0.0497      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009843019 |
+| time-step | 854          |
+| y_out     | -0.0267      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00095529656 |
+| time-step | 855           |
+| y_out     | 0.0315        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008970134 |
+| time-step | 856          |
+| y_out     | 0.135        |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00095216464 |
+| time-step | 857           |
+| y_out     | -0.00609      |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00095925445 |
+| time-step | 858           |
+| y_out     | -0.0273       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010169024 |
+| time-step | 859          |
+| y_out     | 0.0359       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009613419 |
+| time-step | 860          |
+| y_out     | -0.033       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010311457 |
+| time-step | 861          |
+| y_out     | 0.0603       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010693133 |
+| time-step | 862          |
+| y_out     | -0.125       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011163289 |
+| time-step | 863          |
+| y_out     | -0.0882      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011282724 |
+| time-step | 864          |
+| y_out     | 0.16         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011724043 |
+| time-step | 865          |
+| y_out     | -0.0117      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012423054 |
+| time-step | 866          |
+| y_out     | 0.0848       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011758931 |
+| time-step | 867          |
+| y_out     | 0.0226       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011413585 |
+| time-step | 868          |
+| y_out     | -0.0101      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011351632 |
+| time-step | 869          |
+| y_out     | 0.012        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011961028 |
+| time-step | 870          |
+| y_out     | -0.0163      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011909097 |
+| time-step | 871          |
+| y_out     | -0.0371      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011376857 |
+| time-step | 872          |
+| y_out     | -0.0785      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011203687 |
+| time-step | 873          |
+| y_out     | 0.0557       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001135746 |
+| time-step | 874         |
+| y_out     | -0.107      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010842151 |
+| time-step | 875          |
+| y_out     | 0.00717      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011084243 |
+| time-step | 876          |
+| y_out     | -0.0247      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011157264 |
+| time-step | 877          |
+| y_out     | -0.0158      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001135641 |
+| time-step | 878         |
+| y_out     | 0.0326      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011050447 |
+| time-step | 879          |
+| y_out     | -0.0235      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010342798 |
+| time-step | 880          |
+| y_out     | 0.0525       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010770974 |
+| time-step | 881          |
+| y_out     | 0.00176      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010736532 |
+| time-step | 882          |
+| y_out     | -0.0586      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001052056 |
+| time-step | 883         |
+| y_out     | 0.0264      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009979216 |
+| time-step | 884          |
+| y_out     | -0.0816      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009599463 |
+| time-step | 885          |
+| y_out     | -0.0555      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00092344393 |
+| time-step | 886           |
+| y_out     | -0.024        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009016281 |
+| time-step | 887          |
+| y_out     | -0.107       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009106365 |
+| time-step | 888          |
+| y_out     | -0.0391      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00091701216 |
+| time-step | 889           |
+| y_out     | -0.0675       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00095373485 |
+| time-step | 890           |
+| y_out     | -0.0587       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00091533596 |
+| time-step | 891           |
+| y_out     | 0.0527        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00094293914 |
+| time-step | 892           |
+| y_out     | -0.095        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009997895 |
+| time-step | 893          |
+| y_out     | -0.00158     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009882915 |
+| time-step | 894          |
+| y_out     | 0.203        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010694165 |
+| time-step | 895          |
+| y_out     | -0.0175      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010512797 |
+| time-step | 896          |
+| y_out     | 0.0559       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010777601 |
+| time-step | 897          |
+| y_out     | -0.0434      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010582496 |
+| time-step | 898          |
+| y_out     | -0.0254      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010238136 |
+| time-step | 899          |
+| y_out     | -0.0249      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010296003 |
+| time-step | 900          |
+| y_out     | -0.0109      |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/checkpoint 900
+-------------------------------------------------------------
+| perf/mse  | 0.0010155601 |
+| time-step | 901          |
+| y_out     | -0.00159     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009807027 |
+| time-step | 902          |
+| y_out     | -0.000358    |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00095560926 |
+| time-step | 903           |
+| y_out     | 0.000188      |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010090296 |
+| time-step | 904          |
+| y_out     | -0.0659      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009772879 |
+| time-step | 905          |
+| y_out     | -0.0181      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009443198 |
+| time-step | 906          |
+| y_out     | 0.228        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009037809 |
+| time-step | 907          |
+| y_out     | -0.00899     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009213003 |
+| time-step | 908          |
+| y_out     | 0.0414       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00093527185 |
+| time-step | 909           |
+| y_out     | -0.0246       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00094244396 |
+| time-step | 910           |
+| y_out     | -0.0647       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009039774 |
+| time-step | 911          |
+| y_out     | 0.128        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009187603 |
+| time-step | 912          |
+| y_out     | -0.0422      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00087986497 |
+| time-step | 913           |
+| y_out     | -0.04         |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008640201 |
+| time-step | 914          |
+| y_out     | 0.0788       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00087483844 |
+| time-step | 915           |
+| y_out     | -0.0404       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00088016654 |
+| time-step | 916           |
+| y_out     | -0.0854       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00090567954 |
+| time-step | 917           |
+| y_out     | -0.000705     |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00090120564 |
+| time-step | 918           |
+| y_out     | 0.115         |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009400115 |
+| time-step | 919          |
+| y_out     | -0.0213      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00093044824 |
+| time-step | 920           |
+| y_out     | -0.0672       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009363359 |
+| time-step | 921          |
+| y_out     | 0.0521       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00094092125 |
+| time-step | 922           |
+| y_out     | -0.123        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009548993 |
+| time-step | 923          |
+| y_out     | -0.0498      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009190457 |
+| time-step | 924          |
+| y_out     | 0.0965       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00087832485 |
+| time-step | 925           |
+| y_out     | -0.0336       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00087282306 |
+| time-step | 926           |
+| y_out     | 0.0406        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00082784134 |
+| time-step | 927           |
+| y_out     | 0.174         |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00079670147 |
+| time-step | 928           |
+| y_out     | -0.000526     |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007303604 |
+| time-step | 929          |
+| y_out     | 0.0578       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00068764924 |
+| time-step | 930           |
+| y_out     | 0.0323        |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000666016 |
+| time-step | 931         |
+| y_out     | -0.0283     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0006311025 |
+| time-step | 932          |
+| y_out     | 0.0276       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00059834 |
+| time-step | 933        |
+| y_out     | 0.106      |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0005966687 |
+| time-step | 934          |
+| y_out     | 0.0326       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0005984531 |
+| time-step | 935          |
+| y_out     | 0.06         |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00057811936 |
+| time-step | 936           |
+| y_out     | -0.0264       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0006007211 |
+| time-step | 937          |
+| y_out     | 0.0973       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00060122716 |
+| time-step | 938           |
+| y_out     | -0.0492       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0006171138 |
+| time-step | 939          |
+| y_out     | 0.0897       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0006120488 |
+| time-step | 940          |
+| y_out     | -0.0187      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00062549027 |
+| time-step | 941           |
+| y_out     | 0.0262        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0006342864 |
+| time-step | 942          |
+| y_out     | 0.0471       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0006736606 |
+| time-step | 943          |
+| y_out     | 0.123        |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00069389667 |
+| time-step | 944           |
+| y_out     | -0.0429       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00071260385 |
+| time-step | 945           |
+| y_out     | 0.00846       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0006981415 |
+| time-step | 946          |
+| y_out     | 0.0161       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000719516 |
+| time-step | 947         |
+| y_out     | 0.00114     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007945042 |
+| time-step | 948          |
+| y_out     | -0.0481      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007949167 |
+| time-step | 949          |
+| y_out     | -0.1         |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00083176774 |
+| time-step | 950           |
+| y_out     | -0.00929      |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008178168 |
+| time-step | 951          |
+| y_out     | 0.0253       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00086033036 |
+| time-step | 952           |
+| y_out     | -0.0442       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00089215627 |
+| time-step | 953           |
+| y_out     | -0.0085       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00090236793 |
+| time-step | 954           |
+| y_out     | 0.00935       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008566807 |
+| time-step | 955          |
+| y_out     | -0.0112      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000869186 |
+| time-step | 956         |
+| y_out     | -0.0411     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000840875 |
+| time-step | 957         |
+| y_out     | -0.0326     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007951775 |
+| time-step | 958          |
+| y_out     | -0.0775      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00078860356 |
+| time-step | 959           |
+| y_out     | -0.011        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00076531066 |
+| time-step | 960           |
+| y_out     | -0.0529       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007517678 |
+| time-step | 961          |
+| y_out     | 0.00401      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0007130546 |
+| time-step | 962          |
+| y_out     | 0.055        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000646938 |
+| time-step | 963         |
+| y_out     | 0.0451      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0006376976 |
+| time-step | 964          |
+| y_out     | 0.00519      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0006450809 |
+| time-step | 965          |
+| y_out     | -0.00816     |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00061867124 |
+| time-step | 966           |
+| y_out     | -0.116        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0006052899 |
+| time-step | 967          |
+| y_out     | -0.0168      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0005576364 |
+| time-step | 968          |
+| y_out     | 0.0532       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0005497619 |
+| time-step | 969          |
+| y_out     | 0.0687       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0005215871 |
+| time-step | 970          |
+| y_out     | 0.0412       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0005198188 |
+| time-step | 971          |
+| y_out     | 0.0852       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00048232466 |
+| time-step | 972           |
+| y_out     | 0.017         |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00047794264 |
+| time-step | 973           |
+| y_out     | 0.0476        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00045661285 |
+| time-step | 974           |
+| y_out     | -0.0411       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00045010308 |
+| time-step | 975           |
+| y_out     | -0.0436       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0004375408 |
+| time-step | 976          |
+| y_out     | -0.049       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0004536722 |
+| time-step | 977          |
+| y_out     | -0.0563      |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00045433608 |
+| time-step | 978           |
+| y_out     | 0.037         |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000455904 |
+| time-step | 979         |
+| y_out     | -0.082      |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00046207328 |
+| time-step | 980           |
+| y_out     | 0.0598        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00046719945 |
+| time-step | 981           |
+| y_out     | 0.0674        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00047198884 |
+| time-step | 982           |
+| y_out     | -0.0538       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0005355911 |
+| time-step | 983          |
+| y_out     | 0.0397       |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00055250636 |
+| time-step | 984           |
+| y_out     | -0.0275       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0006090977 |
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+-------------------------------------------------------------
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+--------------------------------------------------------------
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+-------------------------------------------------------------
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+-------------------------------------------------------------
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+| perf/mse  | 0.0008596025 |
+| time-step | 999          |
+| y_out     | 0.0834       |
+-------------------------------------------------------------
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/progress-[||perf--mse||, ||timestep||].csv b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/progress-[||perf--mse||, ||timestep||].csv
new file mode 100644
index 0000000..913e231
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/progress-[||perf--mse||, ||timestep||].csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/progress.csv b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/progress.csv
new file mode 100644
index 0000000..ad8f519
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/progress.csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/tb/events/events.out.tfevents.1646140823.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/tb/events/events.out.tfevents.1646140823.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..9dcac93
Binary files /dev/null and b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/tb/events/events.out.tfevents.1646140823.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/warn.txt b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/warn.txt
new file mode 100644
index 0000000..f7e0929
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-20-23-748368 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=8888/warn.txt	
@@ -0,0 +1,8 @@
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/backup.txt b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/backup.txt
new file mode 100644
index 0000000..877f0bb
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/backup.txt	
@@ -0,0 +1,7 @@
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.01
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 888
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/log.txt b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/log.txt
new file mode 100644
index 0000000..8017b63
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/log.txt	
@@ -0,0 +1,5048 @@
+Logging to ../log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/
+log dir: ../log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/
+pkl_file: ../archive_tester/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888.pkl
+checkpoint_dir: ../checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/
+results_dir: ../results/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.01
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 888
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
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+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
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+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
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+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.0275   |
+---------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint 0
+---------------------------------------------------------
+| time-step | 1        |
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+---------------------------------------------------------
+---------------------------------------------------------
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+---------------------------------------------------------
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+| time-step | 4        |
+| y_out     | 0.0202   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 5        |
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+| time-step | 6        |
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+| time-step | 7        |
+| y_out     | 0.106    |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 8        |
+| y_out     | -0.0371  |
+---------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25404385 |
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+| y_out     | 0.13       |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21624255 |
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+| perf/mse  | 0.18596694 |
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+| perf/mse  | 0.13950877 |
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+| perf/mse  | 0.13145243 |
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+-----------------------------------------------------------
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+| y_out     | 0.0404     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012292897 |
+| time-step | 85          |
+| y_out     | 0.026       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012500109 |
+| time-step | 86          |
+| y_out     | 0.016       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012856695 |
+| time-step | 87          |
+| y_out     | -0.00893    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012276762 |
+| time-step | 88          |
+| y_out     | 0.000483    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011978991 |
+| time-step | 89          |
+| y_out     | 0.0492      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0118127335 |
+| time-step | 90           |
+| y_out     | -0.062       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011505524 |
+| time-step | 91          |
+| y_out     | 0.0348      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011945838 |
+| time-step | 92          |
+| y_out     | 0.00665     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011627947 |
+| time-step | 93          |
+| y_out     | 0.0926      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011836036 |
+| time-step | 94          |
+| y_out     | -0.0199     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011424037 |
+| time-step | 95          |
+| y_out     | -0.0102     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011475807 |
+| time-step | 96          |
+| y_out     | 0.00391     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011257061 |
+| time-step | 97          |
+| y_out     | 0.0504      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011499898 |
+| time-step | 98          |
+| y_out     | 0.0516      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01183567 |
+| time-step | 99         |
+| y_out     | 0.0785     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011582458 |
+| time-step | 100         |
+| y_out     | 0.0513      |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint 100
+------------------------------------------------------------
+| perf/mse  | 0.012255019 |
+| time-step | 101         |
+| y_out     | 0.0353      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011903219 |
+| time-step | 102         |
+| y_out     | 0.0149      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011506565 |
+| time-step | 103         |
+| y_out     | -0.0444     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011738735 |
+| time-step | 104         |
+| y_out     | 0.0404      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01216323 |
+| time-step | 105        |
+| y_out     | 0.0658     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011550268 |
+| time-step | 106         |
+| y_out     | 0.0308      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011674861 |
+| time-step | 107         |
+| y_out     | 0.0182      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011697022 |
+| time-step | 108         |
+| y_out     | 0.00225     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011616561 |
+| time-step | 109         |
+| y_out     | 0.00584     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011588188 |
+| time-step | 110         |
+| y_out     | 0.0848      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011390748 |
+| time-step | 111         |
+| y_out     | -0.0577     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011482997 |
+| time-step | 112         |
+| y_out     | 0.0462      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011777064 |
+| time-step | 113         |
+| y_out     | 0.0317      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01118183 |
+| time-step | 114        |
+| y_out     | -0.173     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011029539 |
+| time-step | 115         |
+| y_out     | 0.115       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011370791 |
+| time-step | 116         |
+| y_out     | 0.0842      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011132278 |
+| time-step | 117         |
+| y_out     | 0.0645      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010714049 |
+| time-step | 118         |
+| y_out     | -0.0417     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010616899 |
+| time-step | 119         |
+| y_out     | -0.00702    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0107062645 |
+| time-step | 120          |
+| y_out     | 0.0694       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010425675 |
+| time-step | 121         |
+| y_out     | -0.098      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010234612 |
+| time-step | 122         |
+| y_out     | 0.0795      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010139575 |
+| time-step | 123         |
+| y_out     | 0.0151      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009921612 |
+| time-step | 124         |
+| y_out     | 0.0848      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009880524 |
+| time-step | 125         |
+| y_out     | -0.0948     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009619409 |
+| time-step | 126         |
+| y_out     | -0.00282    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009759772 |
+| time-step | 127         |
+| y_out     | 0.0558      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01052406 |
+| time-step | 128        |
+| y_out     | 0.11       |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010426777 |
+| time-step | 129         |
+| y_out     | 0.0238      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01032447 |
+| time-step | 130        |
+| y_out     | 0.062      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010187253 |
+| time-step | 131         |
+| y_out     | -0.0471     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010301651 |
+| time-step | 132         |
+| y_out     | 0.0148      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010122659 |
+| time-step | 133         |
+| y_out     | 0.00518     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01052367 |
+| time-step | 134        |
+| y_out     | 0.0627     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0105337715 |
+| time-step | 135          |
+| y_out     | 0.0728       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011208582 |
+| time-step | 136         |
+| y_out     | -0.0689     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011015279 |
+| time-step | 137         |
+| y_out     | -0.0652     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010486243 |
+| time-step | 138         |
+| y_out     | -0.0274     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010428895 |
+| time-step | 139         |
+| y_out     | 0.0366      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010207997 |
+| time-step | 140         |
+| y_out     | 0.0603      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009946516 |
+| time-step | 141         |
+| y_out     | -0.0266     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009669334 |
+| time-step | 142         |
+| y_out     | -0.00258    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009626919 |
+| time-step | 143         |
+| y_out     | 0.0414      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009529015 |
+| time-step | 144         |
+| y_out     | -0.0229     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009351367 |
+| time-step | 145         |
+| y_out     | -0.0516     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00849803 |
+| time-step | 146        |
+| y_out     | -0.0258    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008623385 |
+| time-step | 147         |
+| y_out     | -0.0717     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008221876 |
+| time-step | 148         |
+| y_out     | 0.087       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007873524 |
+| time-step | 149         |
+| y_out     | 0.0255      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007916184 |
+| time-step | 150         |
+| y_out     | 0.0258      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007804518 |
+| time-step | 151         |
+| y_out     | 0.0218      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008017892 |
+| time-step | 152         |
+| y_out     | -0.0339     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007977711 |
+| time-step | 153         |
+| y_out     | -0.00406    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007700703 |
+| time-step | 154         |
+| y_out     | 0.0881      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007565164 |
+| time-step | 155         |
+| y_out     | -0.0309     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007904165 |
+| time-step | 156         |
+| y_out     | -0.0543     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007764823 |
+| time-step | 157         |
+| y_out     | 0.00624     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007927408 |
+| time-step | 158         |
+| y_out     | -0.0634     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007884043 |
+| time-step | 159         |
+| y_out     | -0.0602     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007820913 |
+| time-step | 160         |
+| y_out     | -0.0593     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007894961 |
+| time-step | 161         |
+| y_out     | 0.0535      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007741635 |
+| time-step | 162         |
+| y_out     | 0.0587      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076313056 |
+| time-step | 163          |
+| y_out     | -0.0478      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007826738 |
+| time-step | 164         |
+| y_out     | 0.012       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0078485925 |
+| time-step | 165          |
+| y_out     | 0.0361       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007923691 |
+| time-step | 166         |
+| y_out     | 0.00821     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076683476 |
+| time-step | 167          |
+| y_out     | -0.0256      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007849395 |
+| time-step | 168         |
+| y_out     | -0.0226     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008243648 |
+| time-step | 169         |
+| y_out     | -0.0258     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008323584 |
+| time-step | 170         |
+| y_out     | 0.0962      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0084346365 |
+| time-step | 171          |
+| y_out     | -0.0559      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008533811 |
+| time-step | 172         |
+| y_out     | 0.00247     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008782488 |
+| time-step | 173         |
+| y_out     | -0.00381    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008873643 |
+| time-step | 174         |
+| y_out     | 0.0387      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008888138 |
+| time-step | 175         |
+| y_out     | 0.134       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008590636 |
+| time-step | 176         |
+| y_out     | 0.0483      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009116266 |
+| time-step | 177         |
+| y_out     | -0.0597     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0089461375 |
+| time-step | 178          |
+| y_out     | -0.0661      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008508241 |
+| time-step | 179         |
+| y_out     | 0.0103      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008263703 |
+| time-step | 180         |
+| y_out     | 0.122       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008159729 |
+| time-step | 181         |
+| y_out     | 0.0553      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0081051495 |
+| time-step | 182          |
+| y_out     | -0.00639     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0077546365 |
+| time-step | 183          |
+| y_out     | -0.0359      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007629416 |
+| time-step | 184         |
+| y_out     | 0.00915     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0075756563 |
+| time-step | 185          |
+| y_out     | 0.00427      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073037795 |
+| time-step | 186          |
+| y_out     | -0.0327      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007007529 |
+| time-step | 187         |
+| y_out     | -0.00937    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070983833 |
+| time-step | 188          |
+| y_out     | 0.0265       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070726215 |
+| time-step | 189          |
+| y_out     | -0.00807     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007085839 |
+| time-step | 190         |
+| y_out     | 0.0366      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070583774 |
+| time-step | 191          |
+| y_out     | 0.0313       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0068005277 |
+| time-step | 192          |
+| y_out     | 0.00746      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00681666 |
+| time-step | 193        |
+| y_out     | -0.0369    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0066086957 |
+| time-step | 194          |
+| y_out     | -0.15        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067581646 |
+| time-step | 195          |
+| y_out     | -0.0317      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006944844 |
+| time-step | 196         |
+| y_out     | -0.0694     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065717967 |
+| time-step | 197          |
+| y_out     | 0.0104       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062894537 |
+| time-step | 198          |
+| y_out     | -0.0295      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063204193 |
+| time-step | 199          |
+| y_out     | -0.0298      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063961274 |
+| time-step | 200          |
+| y_out     | 0.0483       |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint 200
+-------------------------------------------------------------
+| perf/mse  | 0.0062630577 |
+| time-step | 201          |
+| y_out     | -0.0602      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006385171 |
+| time-step | 202         |
+| y_out     | -0.0408     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063078664 |
+| time-step | 203          |
+| y_out     | -0.0162      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063694417 |
+| time-step | 204          |
+| y_out     | 0.0407       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060950117 |
+| time-step | 205          |
+| y_out     | -0.0601      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062709255 |
+| time-step | 206          |
+| y_out     | 0.0248       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065209977 |
+| time-step | 207          |
+| y_out     | 0.0886       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006340006 |
+| time-step | 208         |
+| y_out     | -0.00118    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006474778 |
+| time-step | 209         |
+| y_out     | -0.0274     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063971914 |
+| time-step | 210          |
+| y_out     | -0.0222      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006349617 |
+| time-step | 211         |
+| y_out     | -0.0639     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060998867 |
+| time-step | 212          |
+| y_out     | 0.0993       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061355657 |
+| time-step | 213          |
+| y_out     | -0.0498      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060756244 |
+| time-step | 214          |
+| y_out     | 0.0155       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062592314 |
+| time-step | 215          |
+| y_out     | 0.0158       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005870505 |
+| time-step | 216         |
+| y_out     | 0.139       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005837211 |
+| time-step | 217         |
+| y_out     | -0.0708     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057791797 |
+| time-step | 218          |
+| y_out     | 0.031        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005591608 |
+| time-step | 219         |
+| y_out     | -0.0693     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054245694 |
+| time-step | 220          |
+| y_out     | -0.0219      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056076837 |
+| time-step | 221          |
+| y_out     | -0.0718      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005767855 |
+| time-step | 222         |
+| y_out     | 0.0256      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057838922 |
+| time-step | 223          |
+| y_out     | -0.122       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059193918 |
+| time-step | 224          |
+| y_out     | -0.00652     |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00552589 |
+| time-step | 225        |
+| y_out     | -0.0408    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005704405 |
+| time-step | 226         |
+| y_out     | -0.0613     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054674693 |
+| time-step | 227          |
+| y_out     | 0.0815       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005680097 |
+| time-step | 228         |
+| y_out     | -0.0496     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005847095 |
+| time-step | 229         |
+| y_out     | 0.0999      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061057126 |
+| time-step | 230          |
+| y_out     | -0.0963      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006135303 |
+| time-step | 231         |
+| y_out     | -0.0934     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006066792 |
+| time-step | 232         |
+| y_out     | -0.0973     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005963771 |
+| time-step | 233         |
+| y_out     | -0.0437     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005739975 |
+| time-step | 234         |
+| y_out     | 0.0246      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005813966 |
+| time-step | 235         |
+| y_out     | 0.0326      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00580691 |
+| time-step | 236        |
+| y_out     | -0.00875   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005958374 |
+| time-step | 237         |
+| y_out     | -0.00615    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058803153 |
+| time-step | 238          |
+| y_out     | -0.0254      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058792084 |
+| time-step | 239          |
+| y_out     | -0.0362      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005652588 |
+| time-step | 240         |
+| y_out     | 0.145       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005467559 |
+| time-step | 241         |
+| y_out     | 0.0131      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005469047 |
+| time-step | 242         |
+| y_out     | 0.0501      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054667136 |
+| time-step | 243          |
+| y_out     | 0.0618       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005457503 |
+| time-step | 244         |
+| y_out     | 0.0114      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054744883 |
+| time-step | 245          |
+| y_out     | 0.16         |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005458428 |
+| time-step | 246         |
+| y_out     | -0.0675     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005487296 |
+| time-step | 247         |
+| y_out     | -0.103      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005292962 |
+| time-step | 248         |
+| y_out     | -0.0274     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050754272 |
+| time-step | 249          |
+| y_out     | 0.0853       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005090325 |
+| time-step | 250         |
+| y_out     | -0.012      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052569495 |
+| time-step | 251          |
+| y_out     | -0.0414      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051961266 |
+| time-step | 252          |
+| y_out     | -0.0363      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052097486 |
+| time-step | 253          |
+| y_out     | -0.0717      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005309787 |
+| time-step | 254         |
+| y_out     | -0.0247     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053571933 |
+| time-step | 255          |
+| y_out     | -0.0646      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005246134 |
+| time-step | 256         |
+| y_out     | 0.0245      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005201134 |
+| time-step | 257         |
+| y_out     | 0.105       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054008705 |
+| time-step | 258          |
+| y_out     | -0.0326      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005316445 |
+| time-step | 259         |
+| y_out     | -0.0932     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055139065 |
+| time-step | 260          |
+| y_out     | -0.14        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054254136 |
+| time-step | 261          |
+| y_out     | -0.0657      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054273074 |
+| time-step | 262          |
+| y_out     | -0.0242      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055618426 |
+| time-step | 263          |
+| y_out     | -0.0395      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054359483 |
+| time-step | 264          |
+| y_out     | 0.0155       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055935187 |
+| time-step | 265          |
+| y_out     | 0.106        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005602519 |
+| time-step | 266         |
+| y_out     | 0.0259      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005675219 |
+| time-step | 267         |
+| y_out     | 0.0204      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005508154 |
+| time-step | 268         |
+| y_out     | 0.0821      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0056658 |
+| time-step | 269       |
+| y_out     | 0.0403    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005427112 |
+| time-step | 270         |
+| y_out     | -0.00278    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005184242 |
+| time-step | 271         |
+| y_out     | 0.00855     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054330626 |
+| time-step | 272          |
+| y_out     | 0.00219      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052553625 |
+| time-step | 273          |
+| y_out     | -0.165       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005137074 |
+| time-step | 274         |
+| y_out     | 0.0377      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049403897 |
+| time-step | 275          |
+| y_out     | -0.0623      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004833267 |
+| time-step | 276         |
+| y_out     | 0.0689      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00465387 |
+| time-step | 277        |
+| y_out     | 0.0581     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045900894 |
+| time-step | 278          |
+| y_out     | 0.00191      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004555408 |
+| time-step | 279         |
+| y_out     | 0.000525    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004857788 |
+| time-step | 280         |
+| y_out     | 0.0134      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049341284 |
+| time-step | 281          |
+| y_out     | -0.0934      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047065816 |
+| time-step | 282          |
+| y_out     | 0.0681       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047200955 |
+| time-step | 283          |
+| y_out     | 0.0741       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047404333 |
+| time-step | 284          |
+| y_out     | 0.11         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047004186 |
+| time-step | 285          |
+| y_out     | 0.0532       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048402185 |
+| time-step | 286          |
+| y_out     | 0.125        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004919482 |
+| time-step | 287         |
+| y_out     | 0.0223      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050935135 |
+| time-step | 288          |
+| y_out     | -0.0939      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005063246 |
+| time-step | 289         |
+| y_out     | 0.183       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047773127 |
+| time-step | 290          |
+| y_out     | 0.0312       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004832393 |
+| time-step | 291         |
+| y_out     | 0.00446     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047424813 |
+| time-step | 292          |
+| y_out     | 0.0557       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047391644 |
+| time-step | 293          |
+| y_out     | -0.0188      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004881492 |
+| time-step | 294         |
+| y_out     | 0.0304      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00494863 |
+| time-step | 295        |
+| y_out     | -0.0491    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050296835 |
+| time-step | 296          |
+| y_out     | -0.146       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050590634 |
+| time-step | 297          |
+| y_out     | -0.159       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004910364 |
+| time-step | 298         |
+| y_out     | 0.0645      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048138397 |
+| time-step | 299          |
+| y_out     | -0.0186      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048563695 |
+| time-step | 300          |
+| y_out     | -0.0148      |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint 300
+------------------------------------------------------------
+| perf/mse  | 0.004725837 |
+| time-step | 301         |
+| y_out     | -0.0323     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005021532 |
+| time-step | 302         |
+| y_out     | -0.152      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005168277 |
+| time-step | 303         |
+| y_out     | 0.0425      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005011716 |
+| time-step | 304         |
+| y_out     | 0.0629      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004822015 |
+| time-step | 305         |
+| y_out     | -0.0101     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004661045 |
+| time-step | 306         |
+| y_out     | 0.036       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004567806 |
+| time-step | 307         |
+| y_out     | 0.125       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004523878 |
+| time-step | 308         |
+| y_out     | 0.059       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047308295 |
+| time-step | 309          |
+| y_out     | -0.0111      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004642966 |
+| time-step | 310         |
+| y_out     | -0.0722     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046033063 |
+| time-step | 311          |
+| y_out     | 0.0516       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004354543 |
+| time-step | 312         |
+| y_out     | -0.146      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00422345 |
+| time-step | 313        |
+| y_out     | -0.0754    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042403187 |
+| time-step | 314          |
+| y_out     | 0.0868       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046130084 |
+| time-step | 315          |
+| y_out     | -0.0808      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045602033 |
+| time-step | 316          |
+| y_out     | -0.00468     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044538863 |
+| time-step | 317          |
+| y_out     | 0.0275       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044705262 |
+| time-step | 318          |
+| y_out     | -0.0286      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042575835 |
+| time-step | 319          |
+| y_out     | -0.0981      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004264278 |
+| time-step | 320         |
+| y_out     | 0.0511      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041419743 |
+| time-step | 321          |
+| y_out     | 0.124        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004052129 |
+| time-step | 322         |
+| y_out     | 0.0236      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039242436 |
+| time-step | 323          |
+| y_out     | -0.00865     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037850111 |
+| time-step | 324          |
+| y_out     | -0.0329      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034822044 |
+| time-step | 325          |
+| y_out     | -0.035       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035128954 |
+| time-step | 326          |
+| y_out     | 0.0798       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034760344 |
+| time-step | 327          |
+| y_out     | 0.0074       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003427165 |
+| time-step | 328         |
+| y_out     | -0.0311     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033458625 |
+| time-step | 329          |
+| y_out     | 0.0866       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034201262 |
+| time-step | 330          |
+| y_out     | -0.0399      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038642138 |
+| time-step | 331          |
+| y_out     | -0.02        |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00387008 |
+| time-step | 332        |
+| y_out     | 0.0823     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003879546 |
+| time-step | 333         |
+| y_out     | 0.0606      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003977562 |
+| time-step | 334         |
+| y_out     | 0.027       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041400227 |
+| time-step | 335          |
+| y_out     | -0.0573      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041529005 |
+| time-step | 336          |
+| y_out     | 0.0102       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042182216 |
+| time-step | 337          |
+| y_out     | -0.0295      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041941693 |
+| time-step | 338          |
+| y_out     | 0.0704       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042140465 |
+| time-step | 339          |
+| y_out     | -0.0779      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040386682 |
+| time-step | 340          |
+| y_out     | 0.104        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037491308 |
+| time-step | 341          |
+| y_out     | 0.138        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037041823 |
+| time-step | 342          |
+| y_out     | 0.121        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037164632 |
+| time-step | 343          |
+| y_out     | 0.07         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036125612 |
+| time-step | 344          |
+| y_out     | -0.0978      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033701803 |
+| time-step | 345          |
+| y_out     | -0.174       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003206561 |
+| time-step | 346         |
+| y_out     | 0.0255      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003049308 |
+| time-step | 347         |
+| y_out     | 0.0401      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029211715 |
+| time-step | 348          |
+| y_out     | -0.0165      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029971832 |
+| time-step | 349          |
+| y_out     | -0.123       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031138198 |
+| time-step | 350          |
+| y_out     | -0.0646      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003042331 |
+| time-step | 351         |
+| y_out     | -0.121      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030804093 |
+| time-step | 352          |
+| y_out     | -0.0818      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029486802 |
+| time-step | 353          |
+| y_out     | -0.0533      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002996923 |
+| time-step | 354         |
+| y_out     | 0.178       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029818472 |
+| time-step | 355          |
+| y_out     | -0.0709      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030159433 |
+| time-step | 356          |
+| y_out     | -0.13        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030697635 |
+| time-step | 357          |
+| y_out     | 0.00774      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031509504 |
+| time-step | 358          |
+| y_out     | -0.0525      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030464414 |
+| time-step | 359          |
+| y_out     | 0.011        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028993026 |
+| time-step | 360          |
+| y_out     | 0.0693       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028431062 |
+| time-step | 361          |
+| y_out     | 0.0426       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002842519 |
+| time-step | 362         |
+| y_out     | 0.0647      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003114042 |
+| time-step | 363         |
+| y_out     | -0.00697    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030059158 |
+| time-step | 364          |
+| y_out     | 0.02         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031718377 |
+| time-step | 365          |
+| y_out     | -0.0133      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032990153 |
+| time-step | 366          |
+| y_out     | -0.014       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033004645 |
+| time-step | 367          |
+| y_out     | -0.0538      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034388423 |
+| time-step | 368          |
+| y_out     | 0.00901      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003431335 |
+| time-step | 369         |
+| y_out     | -0.0664     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035038304 |
+| time-step | 370          |
+| y_out     | 0.0625       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003555777 |
+| time-step | 371         |
+| y_out     | -0.0186     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035579181 |
+| time-step | 372          |
+| y_out     | 0.0806       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00344878 |
+| time-step | 373        |
+| y_out     | 0.0213     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003668848 |
+| time-step | 374         |
+| y_out     | -0.0567     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035509455 |
+| time-step | 375          |
+| y_out     | 0.155        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003515996 |
+| time-step | 376         |
+| y_out     | 0.0613      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003621233 |
+| time-step | 377         |
+| y_out     | -0.0217     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035541516 |
+| time-step | 378          |
+| y_out     | -0.000877    |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035739723 |
+| time-step | 379          |
+| y_out     | -0.0459      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035632234 |
+| time-step | 380          |
+| y_out     | -0.0558      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037005625 |
+| time-step | 381          |
+| y_out     | 0.03         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036168557 |
+| time-step | 382          |
+| y_out     | -0.0461      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035267782 |
+| time-step | 383          |
+| y_out     | -0.01        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003529625 |
+| time-step | 384         |
+| y_out     | 0.0198      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034791995 |
+| time-step | 385          |
+| y_out     | -0.0104      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033669532 |
+| time-step | 386          |
+| y_out     | 0.133        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033413756 |
+| time-step | 387          |
+| y_out     | -0.0287      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032457628 |
+| time-step | 388          |
+| y_out     | -0.00364     |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00326313 |
+| time-step | 389        |
+| y_out     | -0.0797    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032733716 |
+| time-step | 390          |
+| y_out     | -0.125       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031835102 |
+| time-step | 391          |
+| y_out     | 0.0131       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032087662 |
+| time-step | 392          |
+| y_out     | -0.0222      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031948138 |
+| time-step | 393          |
+| y_out     | 0.16         |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003007216 |
+| time-step | 394         |
+| y_out     | 0.0122      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029921124 |
+| time-step | 395          |
+| y_out     | 0.0419       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003020805 |
+| time-step | 396         |
+| y_out     | 0.0635      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028801016 |
+| time-step | 397          |
+| y_out     | 0.0965       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029464185 |
+| time-step | 398          |
+| y_out     | 0.0273       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028379273 |
+| time-step | 399          |
+| y_out     | -0.0995      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027249907 |
+| time-step | 400          |
+| y_out     | 0.12         |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint 400
+------------------------------------------------------------
+| perf/mse  | 0.002695386 |
+| time-step | 401         |
+| y_out     | 0.0628      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026794672 |
+| time-step | 402          |
+| y_out     | -0.0479      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002735395 |
+| time-step | 403         |
+| y_out     | 0.0632      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027150828 |
+| time-step | 404          |
+| y_out     | 0.0112       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028524597 |
+| time-step | 405          |
+| y_out     | -0.0853      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027905018 |
+| time-step | 406          |
+| y_out     | -0.034       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002882365 |
+| time-step | 407         |
+| y_out     | 0.0623      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027978588 |
+| time-step | 408          |
+| y_out     | 0.0921       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028928067 |
+| time-step | 409          |
+| y_out     | -0.109       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029323287 |
+| time-step | 410          |
+| y_out     | -0.145       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003114619 |
+| time-step | 411         |
+| y_out     | 0.0572      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032254017 |
+| time-step | 412          |
+| y_out     | 0.0905       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031854133 |
+| time-step | 413          |
+| y_out     | 0.0543       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033857622 |
+| time-step | 414          |
+| y_out     | -0.0221      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003287709 |
+| time-step | 415         |
+| y_out     | -0.107      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032431737 |
+| time-step | 416          |
+| y_out     | 0.0428       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033213347 |
+| time-step | 417          |
+| y_out     | -0.0223      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034633086 |
+| time-step | 418          |
+| y_out     | 0.00784      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003504065 |
+| time-step | 419         |
+| y_out     | 0.0682      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036496674 |
+| time-step | 420          |
+| y_out     | 0.0874       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003693853 |
+| time-step | 421         |
+| y_out     | -0.075      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003573691 |
+| time-step | 422         |
+| y_out     | -0.0513     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037425898 |
+| time-step | 423          |
+| y_out     | 0.0339       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038778633 |
+| time-step | 424          |
+| y_out     | 0.0939       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038061433 |
+| time-step | 425          |
+| y_out     | 0.0731       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004416661 |
+| time-step | 426         |
+| y_out     | -0.0837     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004411501 |
+| time-step | 427         |
+| y_out     | -0.00185    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043706344 |
+| time-step | 428          |
+| y_out     | 0.0527       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047676335 |
+| time-step | 429          |
+| y_out     | -0.0106      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004815673 |
+| time-step | 430         |
+| y_out     | -0.00814    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045618555 |
+| time-step | 431          |
+| y_out     | 0.0389       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048903003 |
+| time-step | 432          |
+| y_out     | 0.0491       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048577874 |
+| time-step | 433          |
+| y_out     | 0.00271      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004568345 |
+| time-step | 434         |
+| y_out     | 0.0816      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047095087 |
+| time-step | 435          |
+| y_out     | 0.0406       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043004802 |
+| time-step | 436          |
+| y_out     | 0.0133       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004158512 |
+| time-step | 437         |
+| y_out     | 0.0795      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004246843 |
+| time-step | 438         |
+| y_out     | -0.0241     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003936456 |
+| time-step | 439         |
+| y_out     | 0.037       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038993838 |
+| time-step | 440          |
+| y_out     | -0.0175      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038526668 |
+| time-step | 441          |
+| y_out     | 0.21         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035223279 |
+| time-step | 442          |
+| y_out     | 0.0588       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034675982 |
+| time-step | 443          |
+| y_out     | -0.105       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003449841 |
+| time-step | 444         |
+| y_out     | 0.0783      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033970077 |
+| time-step | 445          |
+| y_out     | -0.0795      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032083844 |
+| time-step | 446          |
+| y_out     | 0.0126       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032191083 |
+| time-step | 447          |
+| y_out     | 0.0568       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030373132 |
+| time-step | 448          |
+| y_out     | 0.124        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002832634 |
+| time-step | 449         |
+| y_out     | -0.0171     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027092178 |
+| time-step | 450          |
+| y_out     | 0.0421       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027993654 |
+| time-step | 451          |
+| y_out     | -0.0299      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028605787 |
+| time-step | 452          |
+| y_out     | -0.113       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026752297 |
+| time-step | 453          |
+| y_out     | 0.0422       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027000871 |
+| time-step | 454          |
+| y_out     | -0.0146      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025785663 |
+| time-step | 455          |
+| y_out     | -0.0123      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024993517 |
+| time-step | 456          |
+| y_out     | -0.0135      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002478785 |
+| time-step | 457         |
+| y_out     | -0.0189     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024777818 |
+| time-step | 458          |
+| y_out     | 0.125        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002458927 |
+| time-step | 459         |
+| y_out     | -0.0421     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023675757 |
+| time-step | 460          |
+| y_out     | 0.0541       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022650189 |
+| time-step | 461          |
+| y_out     | -0.224       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021926733 |
+| time-step | 462          |
+| y_out     | 0.0484       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022442187 |
+| time-step | 463          |
+| y_out     | 0.0485       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020998898 |
+| time-step | 464          |
+| y_out     | 0.0995       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021024528 |
+| time-step | 465          |
+| y_out     | 0.145        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020906813 |
+| time-step | 466          |
+| y_out     | 0.0149       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020193045 |
+| time-step | 467          |
+| y_out     | 0.0683       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019543953 |
+| time-step | 468          |
+| y_out     | 0.0471       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018932378 |
+| time-step | 469          |
+| y_out     | -0.0139      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019138686 |
+| time-step | 470          |
+| y_out     | -0.0565      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019028299 |
+| time-step | 471          |
+| y_out     | -0.0338      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018164773 |
+| time-step | 472          |
+| y_out     | -0.0366      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017435333 |
+| time-step | 473          |
+| y_out     | 0.0474       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018003061 |
+| time-step | 474          |
+| y_out     | 0.0802       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017218882 |
+| time-step | 475          |
+| y_out     | -0.0423      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001684279 |
+| time-step | 476         |
+| y_out     | 0.0103      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016763661 |
+| time-step | 477          |
+| y_out     | -0.0584      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017205063 |
+| time-step | 478          |
+| y_out     | 0.0925       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017603608 |
+| time-step | 479          |
+| y_out     | 0.0448       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017245576 |
+| time-step | 480          |
+| y_out     | -0.0666      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016624087 |
+| time-step | 481          |
+| y_out     | -0.0149      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016428211 |
+| time-step | 482          |
+| y_out     | -0.0408      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017297517 |
+| time-step | 483          |
+| y_out     | -0.147       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016518139 |
+| time-step | 484          |
+| y_out     | -0.0888      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016908478 |
+| time-step | 485          |
+| y_out     | 0.014        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017396715 |
+| time-step | 486          |
+| y_out     | 0.0581       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017373674 |
+| time-step | 487          |
+| y_out     | 0.088        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016673455 |
+| time-step | 488          |
+| y_out     | 0.0384       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00158371 |
+| time-step | 489        |
+| y_out     | -0.0303    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015815301 |
+| time-step | 490          |
+| y_out     | -0.091       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016661342 |
+| time-step | 491          |
+| y_out     | 0.122        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016753192 |
+| time-step | 492          |
+| y_out     | -0.0255      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016785947 |
+| time-step | 493          |
+| y_out     | -0.0312      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001772495 |
+| time-step | 494         |
+| y_out     | -0.225      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017736319 |
+| time-step | 495          |
+| y_out     | -0.0133      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018837117 |
+| time-step | 496          |
+| y_out     | 0.116        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018875517 |
+| time-step | 497          |
+| y_out     | -0.108       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001921845 |
+| time-step | 498         |
+| y_out     | 0.00924     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002022256 |
+| time-step | 499         |
+| y_out     | -0.0665     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020047543 |
+| time-step | 500          |
+| y_out     | -0.0365      |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint 500
+-------------------------------------------------------------
+| perf/mse  | 0.0020039312 |
+| time-step | 501          |
+| y_out     | -0.0838      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020370446 |
+| time-step | 502          |
+| y_out     | 0.0414       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019321634 |
+| time-step | 503          |
+| y_out     | 0.0964       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019290233 |
+| time-step | 504          |
+| y_out     | 0.0724       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019330776 |
+| time-step | 505          |
+| y_out     | 0.0232       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017912758 |
+| time-step | 506          |
+| y_out     | -0.0263      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017951546 |
+| time-step | 507          |
+| y_out     | 0.0266       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017879081 |
+| time-step | 508          |
+| y_out     | 0.0096       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017211379 |
+| time-step | 509          |
+| y_out     | 0.0371       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017111085 |
+| time-step | 510          |
+| y_out     | 0.0782       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015988341 |
+| time-step | 511          |
+| y_out     | 0.052        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015991902 |
+| time-step | 512          |
+| y_out     | 0.0293       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001596882 |
+| time-step | 513         |
+| y_out     | 0.0149      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014749819 |
+| time-step | 514          |
+| y_out     | 0.118        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014614912 |
+| time-step | 515          |
+| y_out     | -0.12        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014667718 |
+| time-step | 516          |
+| y_out     | 0.0134       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014564025 |
+| time-step | 517          |
+| y_out     | 0.158        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015266333 |
+| time-step | 518          |
+| y_out     | -0.0158      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015924074 |
+| time-step | 519          |
+| y_out     | 0.0606       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016178818 |
+| time-step | 520          |
+| y_out     | -0.193       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016894946 |
+| time-step | 521          |
+| y_out     | 0.153        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016992416 |
+| time-step | 522          |
+| y_out     | -0.0412      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001764079 |
+| time-step | 523         |
+| y_out     | 0.0229      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001823039 |
+| time-step | 524         |
+| y_out     | 0.0509      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018382244 |
+| time-step | 525          |
+| y_out     | 0.132        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018616552 |
+| time-step | 526          |
+| y_out     | 0.0788       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018887825 |
+| time-step | 527          |
+| y_out     | 0.0177       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001908743 |
+| time-step | 528         |
+| y_out     | -0.156      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019206364 |
+| time-step | 529          |
+| y_out     | -0.0269      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019301809 |
+| time-step | 530          |
+| y_out     | -0.0361      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019566207 |
+| time-step | 531          |
+| y_out     | -0.112       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001962641 |
+| time-step | 532         |
+| y_out     | 0.0279      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019991086 |
+| time-step | 533          |
+| y_out     | 0.0202       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019544857 |
+| time-step | 534          |
+| y_out     | -0.000581    |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002035989 |
+| time-step | 535         |
+| y_out     | -0.0285     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021131162 |
+| time-step | 536          |
+| y_out     | 0.157        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021001971 |
+| time-step | 537          |
+| y_out     | 0.0517       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020319205 |
+| time-step | 538          |
+| y_out     | 0.0519       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019359395 |
+| time-step | 539          |
+| y_out     | 0.0454       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019430589 |
+| time-step | 540          |
+| y_out     | 0.107        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019210335 |
+| time-step | 541          |
+| y_out     | -0.0549      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001963705 |
+| time-step | 542         |
+| y_out     | -0.0338     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018713437 |
+| time-step | 543          |
+| y_out     | -0.0635      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002005464 |
+| time-step | 544         |
+| y_out     | 0.0968      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018908065 |
+| time-step | 545          |
+| y_out     | -0.0914      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019246961 |
+| time-step | 546          |
+| y_out     | 0.0598       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019107908 |
+| time-step | 547          |
+| y_out     | 0.0862       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018855829 |
+| time-step | 548          |
+| y_out     | 0.0244       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001898213 |
+| time-step | 549         |
+| y_out     | -0.0861     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001863506 |
+| time-step | 550         |
+| y_out     | -0.0447     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018874655 |
+| time-step | 551          |
+| y_out     | 0.0184       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018460691 |
+| time-step | 552          |
+| y_out     | -0.0198      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019216121 |
+| time-step | 553          |
+| y_out     | 0.000665     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018543961 |
+| time-step | 554          |
+| y_out     | 0.0464       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018949676 |
+| time-step | 555          |
+| y_out     | 0.0697       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018328143 |
+| time-step | 556          |
+| y_out     | -0.0769      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019074312 |
+| time-step | 557          |
+| y_out     | 0.0408       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019691791 |
+| time-step | 558          |
+| y_out     | -0.068       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020057554 |
+| time-step | 559          |
+| y_out     | 0.0929       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020674686 |
+| time-step | 560          |
+| y_out     | -0.114       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020839528 |
+| time-step | 561          |
+| y_out     | -0.0213      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021000248 |
+| time-step | 562          |
+| y_out     | -0.188       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020321535 |
+| time-step | 563          |
+| y_out     | -0.0131      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020988125 |
+| time-step | 564          |
+| y_out     | -0.147       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021145795 |
+| time-step | 565          |
+| y_out     | 0.177        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002098894 |
+| time-step | 566         |
+| y_out     | 0.0247      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020550906 |
+| time-step | 567          |
+| y_out     | -0.00537     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002026525 |
+| time-step | 568         |
+| y_out     | 0.00804     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002051998 |
+| time-step | 569         |
+| y_out     | -0.0161     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019867157 |
+| time-step | 570          |
+| y_out     | 0.0464       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019181123 |
+| time-step | 571          |
+| y_out     | -0.084       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018791903 |
+| time-step | 572          |
+| y_out     | -0.137       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001876292 |
+| time-step | 573         |
+| y_out     | 0.00258     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018547786 |
+| time-step | 574          |
+| y_out     | 0.0532       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018406834 |
+| time-step | 575          |
+| y_out     | 0.0369       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001769655 |
+| time-step | 576         |
+| y_out     | -0.0605     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018028232 |
+| time-step | 577          |
+| y_out     | -0.0378      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017970685 |
+| time-step | 578          |
+| y_out     | -0.000645    |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001787435 |
+| time-step | 579         |
+| y_out     | 0.0332      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017943239 |
+| time-step | 580          |
+| y_out     | -0.0839      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017846429 |
+| time-step | 581          |
+| y_out     | 0.0925       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017564133 |
+| time-step | 582          |
+| y_out     | 0.0828       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017647475 |
+| time-step | 583          |
+| y_out     | 0.000887     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016614299 |
+| time-step | 584          |
+| y_out     | -0.0216      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016063887 |
+| time-step | 585          |
+| y_out     | -0.0399      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016128639 |
+| time-step | 586          |
+| y_out     | -0.00623     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014955291 |
+| time-step | 587          |
+| y_out     | -0.0145      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014464997 |
+| time-step | 588          |
+| y_out     | 0.0105       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001398869 |
+| time-step | 589         |
+| y_out     | 0.0775      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013778738 |
+| time-step | 590          |
+| y_out     | -0.121       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013745308 |
+| time-step | 591          |
+| y_out     | 0.0235       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013672747 |
+| time-step | 592          |
+| y_out     | -0.0151      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013323801 |
+| time-step | 593          |
+| y_out     | -0.0338      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013053135 |
+| time-step | 594          |
+| y_out     | -0.12        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012961403 |
+| time-step | 595          |
+| y_out     | 0.0696       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013024146 |
+| time-step | 596          |
+| y_out     | -0.0881      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013250818 |
+| time-step | 597          |
+| y_out     | 0.0555       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013150477 |
+| time-step | 598          |
+| y_out     | -0.0227      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012520016 |
+| time-step | 599          |
+| y_out     | -0.0145      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012569686 |
+| time-step | 600          |
+| y_out     | 0.114        |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint 600
+-------------------------------------------------------------
+| perf/mse  | 0.0012697636 |
+| time-step | 601          |
+| y_out     | 0.131        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013004772 |
+| time-step | 602          |
+| y_out     | 0.0133       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013117633 |
+| time-step | 603          |
+| y_out     | -0.0552      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013281712 |
+| time-step | 604          |
+| y_out     | -0.0665      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014254451 |
+| time-step | 605          |
+| y_out     | 0.00538      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014005085 |
+| time-step | 606          |
+| y_out     | 0.00538      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013961944 |
+| time-step | 607          |
+| y_out     | 0.0756       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014525477 |
+| time-step | 608          |
+| y_out     | -0.0335      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001519191 |
+| time-step | 609         |
+| y_out     | 0.0427      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015447239 |
+| time-step | 610          |
+| y_out     | 0.00601      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014677106 |
+| time-step | 611          |
+| y_out     | 0.00625      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013843477 |
+| time-step | 612          |
+| y_out     | 0.219        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013700541 |
+| time-step | 613          |
+| y_out     | -0.11        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013428393 |
+| time-step | 614          |
+| y_out     | -0.00335     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012454236 |
+| time-step | 615          |
+| y_out     | 0.0543       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012533384 |
+| time-step | 616          |
+| y_out     | -0.107       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012849139 |
+| time-step | 617          |
+| y_out     | -0.0295      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012527828 |
+| time-step | 618          |
+| y_out     | -0.0533      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012444494 |
+| time-step | 619          |
+| y_out     | -0.0649      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012695559 |
+| time-step | 620          |
+| y_out     | -0.0353      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013112181 |
+| time-step | 621          |
+| y_out     | 0.0668       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013514087 |
+| time-step | 622          |
+| y_out     | -0.158       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013635417 |
+| time-step | 623          |
+| y_out     | -0.00632     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014409134 |
+| time-step | 624          |
+| y_out     | -0.1         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014583239 |
+| time-step | 625          |
+| y_out     | 0.0313       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015712464 |
+| time-step | 626          |
+| y_out     | 0.0669       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015128684 |
+| time-step | 627          |
+| y_out     | 0.0941       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015506728 |
+| time-step | 628          |
+| y_out     | 0.0387       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016227548 |
+| time-step | 629          |
+| y_out     | -0.033       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001630237 |
+| time-step | 630         |
+| y_out     | -0.024      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016448429 |
+| time-step | 631          |
+| y_out     | 0.00958      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015883439 |
+| time-step | 632          |
+| y_out     | -0.0457      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016770326 |
+| time-step | 633          |
+| y_out     | -0.0198      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018076223 |
+| time-step | 634          |
+| y_out     | -0.0647      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018845929 |
+| time-step | 635          |
+| y_out     | -0.0401      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018737372 |
+| time-step | 636          |
+| y_out     | 0.0409       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019149439 |
+| time-step | 637          |
+| y_out     | -0.0455      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001947668 |
+| time-step | 638         |
+| y_out     | -0.0284     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0019284387 |
+| time-step | 639          |
+| y_out     | -0.0589      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018166339 |
+| time-step | 640          |
+| y_out     | 0.0632       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017884426 |
+| time-step | 641          |
+| y_out     | 0.103        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0018237059 |
+| time-step | 642          |
+| y_out     | -0.0126      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017315864 |
+| time-step | 643          |
+| y_out     | 0.0214       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015924254 |
+| time-step | 644          |
+| y_out     | 0.105        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014983196 |
+| time-step | 645          |
+| y_out     | 0.0114       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014402111 |
+| time-step | 646          |
+| y_out     | 0.0177       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014410474 |
+| time-step | 647          |
+| y_out     | 0.0828       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00134639 |
+| time-step | 648        |
+| y_out     | -0.0204    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013404057 |
+| time-step | 649          |
+| y_out     | -0.0327      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014057737 |
+| time-step | 650          |
+| y_out     | 0.0651       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014291888 |
+| time-step | 651          |
+| y_out     | -0.08        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014712964 |
+| time-step | 652          |
+| y_out     | -0.0995      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014658961 |
+| time-step | 653          |
+| y_out     | -0.115       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014968063 |
+| time-step | 654          |
+| y_out     | -0.0168      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015456323 |
+| time-step | 655          |
+| y_out     | 0.0626       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015468011 |
+| time-step | 656          |
+| y_out     | 0.0727       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015656151 |
+| time-step | 657          |
+| y_out     | 0.108        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015823657 |
+| time-step | 658          |
+| y_out     | 0.0207       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015337823 |
+| time-step | 659          |
+| y_out     | -0.0716      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016243138 |
+| time-step | 660          |
+| y_out     | -0.0481      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001617544 |
+| time-step | 661         |
+| y_out     | -0.0941     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016212013 |
+| time-step | 662          |
+| y_out     | 0.0265       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016805325 |
+| time-step | 663          |
+| y_out     | -0.0915      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015839137 |
+| time-step | 664          |
+| y_out     | -0.0857      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015407805 |
+| time-step | 665          |
+| y_out     | 0.0973       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015286522 |
+| time-step | 666          |
+| y_out     | -0.068       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015011401 |
+| time-step | 667          |
+| y_out     | -0.00557     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015630085 |
+| time-step | 668          |
+| y_out     | 0.0137       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015728811 |
+| time-step | 669          |
+| y_out     | 0.047        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015028403 |
+| time-step | 670          |
+| y_out     | -0.0897      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001527854 |
+| time-step | 671         |
+| y_out     | 0.14        |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014969496 |
+| time-step | 672          |
+| y_out     | 0.0075       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001485966 |
+| time-step | 673         |
+| y_out     | 0.0496      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015473317 |
+| time-step | 674          |
+| y_out     | -0.112       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015247725 |
+| time-step | 675          |
+| y_out     | 0.0465       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016368071 |
+| time-step | 676          |
+| y_out     | -0.0682      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016484316 |
+| time-step | 677          |
+| y_out     | -0.0873      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016139641 |
+| time-step | 678          |
+| y_out     | 0.0715       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016643817 |
+| time-step | 679          |
+| y_out     | 0.0785       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016313422 |
+| time-step | 680          |
+| y_out     | -0.0264      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016340213 |
+| time-step | 681          |
+| y_out     | -0.0207      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016779233 |
+| time-step | 682          |
+| y_out     | 0.0018       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016093779 |
+| time-step | 683          |
+| y_out     | -0.0877      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015863193 |
+| time-step | 684          |
+| y_out     | -0.0309      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016307397 |
+| time-step | 685          |
+| y_out     | -0.0555      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015177442 |
+| time-step | 686          |
+| y_out     | -0.209       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015409404 |
+| time-step | 687          |
+| y_out     | 0.0629       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016464142 |
+| time-step | 688          |
+| y_out     | 0.00828      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015846018 |
+| time-step | 689          |
+| y_out     | 0.078        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015498542 |
+| time-step | 690          |
+| y_out     | 0.022        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016153401 |
+| time-step | 691          |
+| y_out     | 0.0494       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016121231 |
+| time-step | 692          |
+| y_out     | -0.0459      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016613256 |
+| time-step | 693          |
+| y_out     | 0.00521      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016914855 |
+| time-step | 694          |
+| y_out     | 0.0997       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016825378 |
+| time-step | 695          |
+| y_out     | -0.013       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001616143 |
+| time-step | 696         |
+| y_out     | 0.0364      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015601993 |
+| time-step | 697          |
+| y_out     | 0.0678       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014514831 |
+| time-step | 698          |
+| y_out     | -0.0304      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001539682 |
+| time-step | 699         |
+| y_out     | 0.0157      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016030623 |
+| time-step | 700          |
+| y_out     | 0.0458       |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint 700
+-------------------------------------------------------------
+| perf/mse  | 0.0014816022 |
+| time-step | 701          |
+| y_out     | -0.0224      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014149598 |
+| time-step | 702          |
+| y_out     | -0.00393     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014740886 |
+| time-step | 703          |
+| y_out     | -0.0477      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014727968 |
+| time-step | 704          |
+| y_out     | 0.047        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014626773 |
+| time-step | 705          |
+| y_out     | -0.0842      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014940612 |
+| time-step | 706          |
+| y_out     | -0.0647      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015993789 |
+| time-step | 707          |
+| y_out     | -0.0351      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015763196 |
+| time-step | 708          |
+| y_out     | -0.0131      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015403478 |
+| time-step | 709          |
+| y_out     | 0.0091       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014987963 |
+| time-step | 710          |
+| y_out     | 0.00544      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015496066 |
+| time-step | 711          |
+| y_out     | 0.00898      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016412217 |
+| time-step | 712          |
+| y_out     | -0.0955      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016250139 |
+| time-step | 713          |
+| y_out     | -0.0102      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015875548 |
+| time-step | 714          |
+| y_out     | -0.143       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016020648 |
+| time-step | 715          |
+| y_out     | -0.145       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016212922 |
+| time-step | 716          |
+| y_out     | 0.0269       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016419597 |
+| time-step | 717          |
+| y_out     | -0.0319      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016763052 |
+| time-step | 718          |
+| y_out     | -0.0583      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016095603 |
+| time-step | 719          |
+| y_out     | -0.0364      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016297493 |
+| time-step | 720          |
+| y_out     | -0.0857      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016001847 |
+| time-step | 721          |
+| y_out     | 0.11         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015412235 |
+| time-step | 722          |
+| y_out     | -0.00827     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001488206 |
+| time-step | 723         |
+| y_out     | -0.102      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014943511 |
+| time-step | 724          |
+| y_out     | -0.0631      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015017873 |
+| time-step | 725          |
+| y_out     | 0.185        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015227173 |
+| time-step | 726          |
+| y_out     | 0.073        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013941253 |
+| time-step | 727          |
+| y_out     | -0.0833      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013641053 |
+| time-step | 728          |
+| y_out     | 0.0851       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013759707 |
+| time-step | 729          |
+| y_out     | -0.00561     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013534677 |
+| time-step | 730          |
+| y_out     | 0.168        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013629955 |
+| time-step | 731          |
+| y_out     | 0.21         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013518177 |
+| time-step | 732          |
+| y_out     | 0.0283       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012926044 |
+| time-step | 733          |
+| y_out     | 0.00764      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012653987 |
+| time-step | 734          |
+| y_out     | 0.0776       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012558422 |
+| time-step | 735          |
+| y_out     | 0.116        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012732265 |
+| time-step | 736          |
+| y_out     | -0.147       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012746044 |
+| time-step | 737          |
+| y_out     | 0.0567       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001308241 |
+| time-step | 738         |
+| y_out     | 0.027       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013368338 |
+| time-step | 739          |
+| y_out     | -0.0568      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013069513 |
+| time-step | 740          |
+| y_out     | 0.0291       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013355837 |
+| time-step | 741          |
+| y_out     | 0.0242       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013662466 |
+| time-step | 742          |
+| y_out     | -0.0933      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013604152 |
+| time-step | 743          |
+| y_out     | -0.0573      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013837991 |
+| time-step | 744          |
+| y_out     | -0.117       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013333956 |
+| time-step | 745          |
+| y_out     | -0.0616      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013569245 |
+| time-step | 746          |
+| y_out     | -0.0717      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014491847 |
+| time-step | 747          |
+| y_out     | -0.0929      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014151782 |
+| time-step | 748          |
+| y_out     | 0.0171       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013548705 |
+| time-step | 749          |
+| y_out     | 0.0459       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014152452 |
+| time-step | 750          |
+| y_out     | 0.00297      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013835679 |
+| time-step | 751          |
+| y_out     | 0.0711       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014039897 |
+| time-step | 752          |
+| y_out     | 0.0382       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014107164 |
+| time-step | 753          |
+| y_out     | 0.115        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013971764 |
+| time-step | 754          |
+| y_out     | -0.107       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014266205 |
+| time-step | 755          |
+| y_out     | -0.0338      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013308247 |
+| time-step | 756          |
+| y_out     | -0.0292      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012277218 |
+| time-step | 757          |
+| y_out     | 0.0919       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012604803 |
+| time-step | 758          |
+| y_out     | 0.0295       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012660809 |
+| time-step | 759          |
+| y_out     | 0.0571       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012378447 |
+| time-step | 760          |
+| y_out     | 0.0135       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012316607 |
+| time-step | 761          |
+| y_out     | -0.0165      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011885753 |
+| time-step | 762          |
+| y_out     | 0.0233       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012399842 |
+| time-step | 763          |
+| y_out     | 0.0698       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012556204 |
+| time-step | 764          |
+| y_out     | -0.0439      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012823503 |
+| time-step | 765          |
+| y_out     | 0.0399       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013519218 |
+| time-step | 766          |
+| y_out     | 0.0114       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013685614 |
+| time-step | 767          |
+| y_out     | 0.0481       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014297662 |
+| time-step | 768          |
+| y_out     | 0.145        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015234398 |
+| time-step | 769          |
+| y_out     | 0.0108       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001632954 |
+| time-step | 770         |
+| y_out     | 0.0683      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016493459 |
+| time-step | 771          |
+| y_out     | 0.0318       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016499363 |
+| time-step | 772          |
+| y_out     | -0.0183      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016605736 |
+| time-step | 773          |
+| y_out     | -0.0147      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016189611 |
+| time-step | 774          |
+| y_out     | 0.0358       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016293947 |
+| time-step | 775          |
+| y_out     | -0.11        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016620125 |
+| time-step | 776          |
+| y_out     | -0.0125      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001639788 |
+| time-step | 777         |
+| y_out     | -0.00572    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015269461 |
+| time-step | 778          |
+| y_out     | 0.0277       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015961269 |
+| time-step | 779          |
+| y_out     | 0.0441       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001488887 |
+| time-step | 780         |
+| y_out     | -0.0428     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001520644 |
+| time-step | 781         |
+| y_out     | 0.0205      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015393305 |
+| time-step | 782          |
+| y_out     | 0.00329      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001533292 |
+| time-step | 783         |
+| y_out     | -0.033      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015550258 |
+| time-step | 784          |
+| y_out     | 0.0859       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001564206 |
+| time-step | 785         |
+| y_out     | 0.0473      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015619909 |
+| time-step | 786          |
+| y_out     | 0.0597       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016349365 |
+| time-step | 787          |
+| y_out     | 0.00654      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016121648 |
+| time-step | 788          |
+| y_out     | 0.00609      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015286787 |
+| time-step | 789          |
+| y_out     | -0.0741      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015791589 |
+| time-step | 790          |
+| y_out     | -0.0774      |
+-------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0015862 |
+| time-step | 791       |
+| y_out     | -0.0318   |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016430316 |
+| time-step | 792          |
+| y_out     | 0.0496       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016612026 |
+| time-step | 793          |
+| y_out     | -0.155       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001705773 |
+| time-step | 794         |
+| y_out     | -0.0302     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017095857 |
+| time-step | 795          |
+| y_out     | -0.0635      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016576644 |
+| time-step | 796          |
+| y_out     | -0.085       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015880729 |
+| time-step | 797          |
+| y_out     | 0.132        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001561817 |
+| time-step | 798         |
+| y_out     | -0.00564    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014963356 |
+| time-step | 799          |
+| y_out     | 0.0217       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013792056 |
+| time-step | 800          |
+| y_out     | 0.047        |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint 800
+-------------------------------------------------------------
+| perf/mse  | 0.0013139077 |
+| time-step | 801          |
+| y_out     | -0.0566      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012064158 |
+| time-step | 802          |
+| y_out     | -0.0814      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011227394 |
+| time-step | 803          |
+| y_out     | 0.0232       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010652235 |
+| time-step | 804          |
+| y_out     | -0.0839      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010063705 |
+| time-step | 805          |
+| y_out     | -0.0185      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009313339 |
+| time-step | 806          |
+| y_out     | 0.0202       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009764189 |
+| time-step | 807          |
+| y_out     | 0.0175       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010051323 |
+| time-step | 808          |
+| y_out     | -0.0374      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009578525 |
+| time-step | 809          |
+| y_out     | -0.0142      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009976508 |
+| time-step | 810          |
+| y_out     | 0.0548       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009936997 |
+| time-step | 811          |
+| y_out     | -0.0721      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009953096 |
+| time-step | 812          |
+| y_out     | 0.0312       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009886217 |
+| time-step | 813          |
+| y_out     | -0.0756      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009773261 |
+| time-step | 814          |
+| y_out     | -0.0122      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009670124 |
+| time-step | 815          |
+| y_out     | 0.031        |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00096423505 |
+| time-step | 816           |
+| y_out     | -0.0113       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00092683884 |
+| time-step | 817           |
+| y_out     | -0.0432       |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009833213 |
+| time-step | 818          |
+| y_out     | 0.0318       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009936083 |
+| time-step | 819          |
+| y_out     | -0.00974     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009949177 |
+| time-step | 820          |
+| y_out     | -0.0265      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010193815 |
+| time-step | 821          |
+| y_out     | -0.0332      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010876535 |
+| time-step | 822          |
+| y_out     | -0.00756     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011380808 |
+| time-step | 823          |
+| y_out     | 0.0223       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012768683 |
+| time-step | 824          |
+| y_out     | -0.0816      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012991218 |
+| time-step | 825          |
+| y_out     | -0.057       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001354258 |
+| time-step | 826         |
+| y_out     | -0.0178     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015106251 |
+| time-step | 827          |
+| y_out     | -0.022       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014538752 |
+| time-step | 828          |
+| y_out     | 0.00573      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016875062 |
+| time-step | 829          |
+| y_out     | 0.04         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016941025 |
+| time-step | 830          |
+| y_out     | -0.0178      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017291667 |
+| time-step | 831          |
+| y_out     | 0.00744      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017540681 |
+| time-step | 832          |
+| y_out     | 0.0129       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001710535 |
+| time-step | 833         |
+| y_out     | 0.0229      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017356438 |
+| time-step | 834          |
+| y_out     | -0.0378      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017026818 |
+| time-step | 835          |
+| y_out     | 0.0315       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017175585 |
+| time-step | 836          |
+| y_out     | 0.0264       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017254393 |
+| time-step | 837          |
+| y_out     | -0.0518      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017319337 |
+| time-step | 838          |
+| y_out     | 0.022        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016571712 |
+| time-step | 839          |
+| y_out     | 0.0864       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016579969 |
+| time-step | 840          |
+| y_out     | -0.0442      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016339527 |
+| time-step | 841          |
+| y_out     | 0.0989       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001519721 |
+| time-step | 842         |
+| y_out     | -0.0865     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015197153 |
+| time-step | 843          |
+| y_out     | -0.0578      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001353838 |
+| time-step | 844         |
+| y_out     | 0.108       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013635682 |
+| time-step | 845          |
+| y_out     | -0.0741      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013530097 |
+| time-step | 846          |
+| y_out     | 0.033        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012082167 |
+| time-step | 847          |
+| y_out     | 0.148        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011938962 |
+| time-step | 848          |
+| y_out     | -0.149       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010359393 |
+| time-step | 849          |
+| y_out     | 0.0301       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010175499 |
+| time-step | 850          |
+| y_out     | -0.00607     |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00094430224 |
+| time-step | 851           |
+| y_out     | 0.0504        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00095826306 |
+| time-step | 852           |
+| y_out     | 0.00774       |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.000947598 |
+| time-step | 853         |
+| y_out     | 0.0141      |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00093136076 |
+| time-step | 854           |
+| y_out     | -0.0472       |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00094816927 |
+| time-step | 855           |
+| y_out     | 0.0527        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00090709666 |
+| time-step | 856           |
+| y_out     | 0.0105        |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.00089000363 |
+| time-step | 857           |
+| y_out     | 0.0963        |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008932337 |
+| time-step | 858          |
+| y_out     | -0.0218      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008874565 |
+| time-step | 859          |
+| y_out     | 0.048        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0008836418 |
+| time-step | 860          |
+| y_out     | 0.0405       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0009614425 |
+| time-step | 861          |
+| y_out     | 0.0649       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010424951 |
+| time-step | 862          |
+| y_out     | -0.024       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010786236 |
+| time-step | 863          |
+| y_out     | 0.00693      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010896754 |
+| time-step | 864          |
+| y_out     | 0.0091       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011004236 |
+| time-step | 865          |
+| y_out     | 0.0925       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0010793271 |
+| time-step | 866          |
+| y_out     | -0.0775      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001067426 |
+| time-step | 867         |
+| y_out     | -0.124      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011082778 |
+| time-step | 868          |
+| y_out     | 0.0417       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011463218 |
+| time-step | 869          |
+| y_out     | -0.0864      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011629704 |
+| time-step | 870          |
+| y_out     | 0.0723       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011140731 |
+| time-step | 871          |
+| y_out     | -0.0334      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011147743 |
+| time-step | 872          |
+| y_out     | 0.0261       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011119181 |
+| time-step | 873          |
+| y_out     | -0.0238      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012028043 |
+| time-step | 874          |
+| y_out     | -0.0195      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012490407 |
+| time-step | 875          |
+| y_out     | 0.0218       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001245074 |
+| time-step | 876         |
+| y_out     | -0.047      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014086334 |
+| time-step | 877          |
+| y_out     | 0.0729       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013753776 |
+| time-step | 878          |
+| y_out     | 0.0964       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014156981 |
+| time-step | 879          |
+| y_out     | -0.0751      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014426743 |
+| time-step | 880          |
+| y_out     | 0.0207       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014448159 |
+| time-step | 881          |
+| y_out     | 0.0422       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001494928 |
+| time-step | 882         |
+| y_out     | -0.0618     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015137955 |
+| time-step | 883          |
+| y_out     | 0.00511      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015403086 |
+| time-step | 884          |
+| y_out     | 0.0583       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014997727 |
+| time-step | 885          |
+| y_out     | 0.0729       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015864294 |
+| time-step | 886          |
+| y_out     | -0.0336      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001590246 |
+| time-step | 887         |
+| y_out     | 0.025       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001572282 |
+| time-step | 888         |
+| y_out     | 0.143       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016519555 |
+| time-step | 889          |
+| y_out     | -0.0502      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016490292 |
+| time-step | 890          |
+| y_out     | -0.0126      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017483182 |
+| time-step | 891          |
+| y_out     | -0.0203      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017382024 |
+| time-step | 892          |
+| y_out     | 0.0486       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016975643 |
+| time-step | 893          |
+| y_out     | 0.0156       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016574562 |
+| time-step | 894          |
+| y_out     | 0.0045       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017741101 |
+| time-step | 895          |
+| y_out     | -0.0852      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017445082 |
+| time-step | 896          |
+| y_out     | -0.0477      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001645507 |
+| time-step | 897         |
+| y_out     | 0.0476      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016793279 |
+| time-step | 898          |
+| y_out     | -0.00897     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015680805 |
+| time-step | 899          |
+| y_out     | -0.109       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0017065794 |
+| time-step | 900          |
+| y_out     | 0.0224       |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/checkpoint 900
+-------------------------------------------------------------
+| perf/mse  | 0.0015966247 |
+| time-step | 901          |
+| y_out     | -0.116       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015480891 |
+| time-step | 902          |
+| y_out     | -0.039       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0016080616 |
+| time-step | 903          |
+| y_out     | -0.0239      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015988548 |
+| time-step | 904          |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015027409 |
+| time-step | 905          |
+| y_out     | 0.0572       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0015189637 |
+| time-step | 906          |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0014535878 |
+| time-step | 907          |
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+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001484911 |
+| time-step | 908         |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.001481435 |
+| time-step | 909         |
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+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013689965 |
+| time-step | 910          |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013781392 |
+| time-step | 911          |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013740219 |
+| time-step | 912          |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013306374 |
+| time-step | 913          |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0013018546 |
+| time-step | 914          |
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+-------------------------------------------------------------
+| perf/mse  | 0.0012775271 |
+| time-step | 915          |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011926976 |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012097347 |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012356499 |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012392241 |
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+-------------------------------------------------------------
+| perf/mse  | 0.0012708695 |
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+-------------------------------------------------------------
+| perf/mse  | 0.0012496191 |
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+-------------------------------------------------------------
+| perf/mse  | 0.0012434265 |
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+-------------------------------------------------------------
+| perf/mse  | 0.0012441714 |
+| time-step | 923          |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0012208009 |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0011978479 |
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+-------------------------------------------------------------
+| perf/mse  | 0.0011823905 |
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+-------------------------------------------------------------
+| perf/mse  | 0.0012313379 |
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+------------------------------------------------------------
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+| perf/mse  | 0.0010376245 |
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+--------------------------------------------------------------
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+| perf/mse  | 0.0007698019 |
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+| perf/mse  | 0.0008218087 |
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+| perf/mse  | 0.00084103865 |
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+| perf/mse  | 0.00087403355 |
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+| perf/mse  | 0.0008673421 |
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+| perf/mse  | 0.0007927171 |
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+-------------------------------------------------------------
+| perf/mse  | 0.0011292533 |
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/progress-[||perf--mse||, ||timestep||].csv b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/progress-[||perf--mse||, ||timestep||].csv
new file mode 100644
index 0000000..76904d5
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/progress-[||perf--mse||, ||timestep||].csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/progress.csv b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/progress.csv
new file mode 100644
index 0000000..58247dd
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/progress.csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/tb/events/events.out.tfevents.1646140978.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/tb/events/events.out.tfevents.1646140978.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..8df0396
Binary files /dev/null and b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/tb/events/events.out.tfevents.1646140978.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/warn.txt b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/warn.txt
new file mode 100644
index 0000000..f7e0929
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-22-58-846147 11.0.91.89 &env_id=Test-v1&learning_rate=0.01&seed=888/warn.txt	
@@ -0,0 +1,8 @@
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/backup.txt b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/backup.txt
new file mode 100644
index 0000000..e0ed068
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/backup.txt	
@@ -0,0 +1,7 @@
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.001
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 888
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/log.txt b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/log.txt
new file mode 100644
index 0000000..1b858fc
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/log.txt	
@@ -0,0 +1,5048 @@
+Logging to ../log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/
+log dir: ../log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/
+pkl_file: ../archive_tester/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888.pkl
+checkpoint_dir: ../checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/
+results_dir: ../results/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.001
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 888
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.0275   |
+---------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint 0
+---------------------------------------------------------
+| time-step | 1        |
+| y_out     | 0.0338   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 2        |
+| y_out     | 0.1      |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 3        |
+| y_out     | -0.052   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 4        |
+| y_out     | 0.0202   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 5        |
+| y_out     | 0.0738   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 6        |
+| y_out     | -0.125   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 7        |
+| y_out     | 0.106    |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 8        |
+| y_out     | -0.0371  |
+---------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2213418 |
+| time-step | 9         |
+| y_out     | 0.13      |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18151541 |
+| time-step | 10         |
+| y_out     | 0.0482     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15199599 |
+| time-step | 11         |
+| y_out     | 0.0499     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13638918 |
+| time-step | 12         |
+| y_out     | 0.0342     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11555026 |
+| time-step | 13         |
+| y_out     | 0.0338     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1025746 |
+| time-step | 14        |
+| y_out     | 0.0288    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08903063 |
+| time-step | 15         |
+| y_out     | -0.00663   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.082288586 |
+| time-step | 16          |
+| y_out     | 0.0607      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.075040475 |
+| time-step | 17          |
+| y_out     | 0.107       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07045758 |
+| time-step | 18         |
+| y_out     | -0.0585    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06912045 |
+| time-step | 19         |
+| y_out     | 0.042      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06851025 |
+| time-step | 20         |
+| y_out     | -0.0466    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.067601785 |
+| time-step | 21          |
+| y_out     | 0.0607      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062033504 |
+| time-step | 22          |
+| y_out     | -0.103      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06083738 |
+| time-step | 23         |
+| y_out     | -0.0759    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056046844 |
+| time-step | 24          |
+| y_out     | 0.0133      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.052001763 |
+| time-step | 25          |
+| y_out     | 0.0665      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050352037 |
+| time-step | 26          |
+| y_out     | 0.0716      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048408873 |
+| time-step | 27          |
+| y_out     | 0.0257      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049339093 |
+| time-step | 28          |
+| y_out     | -0.164      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049158037 |
+| time-step | 29          |
+| y_out     | 0.061       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047397114 |
+| time-step | 30          |
+| y_out     | -0.0206     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04551185 |
+| time-step | 31         |
+| y_out     | -0.202     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047057323 |
+| time-step | 32          |
+| y_out     | -0.044      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046180792 |
+| time-step | 33          |
+| y_out     | 0.114       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04624776 |
+| time-step | 34         |
+| y_out     | 0.0339     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047249004 |
+| time-step | 35          |
+| y_out     | 0.043       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049095653 |
+| time-step | 36          |
+| y_out     | -0.102      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048703287 |
+| time-step | 37          |
+| y_out     | 2.42e-05    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04864519 |
+| time-step | 38         |
+| y_out     | 0.0621     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047138564 |
+| time-step | 39          |
+| y_out     | -0.000787   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045966193 |
+| time-step | 40          |
+| y_out     | -0.0536     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04617403 |
+| time-step | 41         |
+| y_out     | 0.076      |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0450994 |
+| time-step | 42        |
+| y_out     | -0.103    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044573702 |
+| time-step | 43          |
+| y_out     | 0.0325      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043455534 |
+| time-step | 44          |
+| y_out     | -0.00575    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041777693 |
+| time-step | 45          |
+| y_out     | -0.0295     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039458685 |
+| time-step | 46          |
+| y_out     | -0.0945     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040559005 |
+| time-step | 47          |
+| y_out     | -0.082      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0386033 |
+| time-step | 48        |
+| y_out     | 0.0332    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03760356 |
+| time-step | 49         |
+| y_out     | -0.0744    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035813883 |
+| time-step | 50          |
+| y_out     | 0.0279      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035661034 |
+| time-step | 51          |
+| y_out     | -0.0879     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035118055 |
+| time-step | 52          |
+| y_out     | -0.00367    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03511173 |
+| time-step | 53         |
+| y_out     | 0.0312     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035650674 |
+| time-step | 54          |
+| y_out     | 0.089       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035762656 |
+| time-step | 55          |
+| y_out     | -0.00147    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03560549 |
+| time-step | 56         |
+| y_out     | -0.0236    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0349341 |
+| time-step | 57        |
+| y_out     | 0.0138    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03472092 |
+| time-step | 58         |
+| y_out     | 0.0997     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035575476 |
+| time-step | 59          |
+| y_out     | 0.0299      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035381716 |
+| time-step | 60          |
+| y_out     | 0.0243      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034856774 |
+| time-step | 61          |
+| y_out     | 0.116       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034570497 |
+| time-step | 62          |
+| y_out     | 0.0291      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034000043 |
+| time-step | 63          |
+| y_out     | 0.143       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03381919 |
+| time-step | 64         |
+| y_out     | 0.0235     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03339701 |
+| time-step | 65         |
+| y_out     | 0.0922     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0337741 |
+| time-step | 66        |
+| y_out     | 0.0502    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033254325 |
+| time-step | 67          |
+| y_out     | 0.0538      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032633655 |
+| time-step | 68          |
+| y_out     | 0.0655      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03219404 |
+| time-step | 69         |
+| y_out     | -0.0817    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032805838 |
+| time-step | 70          |
+| y_out     | -0.014      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032841913 |
+| time-step | 71          |
+| y_out     | -0.127      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031482257 |
+| time-step | 72          |
+| y_out     | 0.0578      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031539984 |
+| time-step | 73          |
+| y_out     | -0.146      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031192064 |
+| time-step | 74          |
+| y_out     | -0.035      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03175701 |
+| time-step | 75         |
+| y_out     | 0.0706     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031267945 |
+| time-step | 76          |
+| y_out     | 0.147       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029908344 |
+| time-step | 77          |
+| y_out     | -0.0494     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0303464 |
+| time-step | 78        |
+| y_out     | 0.0748    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029448232 |
+| time-step | 79          |
+| y_out     | -0.0401     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028521877 |
+| time-step | 80          |
+| y_out     | 0.0922      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02831181 |
+| time-step | 81         |
+| y_out     | -0.0251    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029110854 |
+| time-step | 82          |
+| y_out     | -0.00572    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028900817 |
+| time-step | 83          |
+| y_out     | 0.107       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028351873 |
+| time-step | 84          |
+| y_out     | 0.0404      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02713016 |
+| time-step | 85         |
+| y_out     | 0.026      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027275499 |
+| time-step | 86          |
+| y_out     | 0.016       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02898648 |
+| time-step | 87         |
+| y_out     | -0.00893   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02910329 |
+| time-step | 88         |
+| y_out     | 0.000483   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028891105 |
+| time-step | 89          |
+| y_out     | 0.0492      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029155355 |
+| time-step | 90          |
+| y_out     | -0.062      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028093165 |
+| time-step | 91          |
+| y_out     | 0.0348      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027509117 |
+| time-step | 92          |
+| y_out     | 0.00665     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02631087 |
+| time-step | 93         |
+| y_out     | 0.0926     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024781937 |
+| time-step | 94          |
+| y_out     | -0.0199     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024651572 |
+| time-step | 95          |
+| y_out     | -0.0102     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0238366 |
+| time-step | 96        |
+| y_out     | 0.00391   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021582127 |
+| time-step | 97          |
+| y_out     | 0.0504      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020574383 |
+| time-step | 98          |
+| y_out     | 0.0516      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02091686 |
+| time-step | 99         |
+| y_out     | 0.0785     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020916494 |
+| time-step | 100         |
+| y_out     | 0.0513      |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint 100
+------------------------------------------------------------
+| perf/mse  | 0.021655126 |
+| time-step | 101         |
+| y_out     | 0.0353      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021175686 |
+| time-step | 102         |
+| y_out     | 0.0149      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020749059 |
+| time-step | 103         |
+| y_out     | -0.0444     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02127223 |
+| time-step | 104        |
+| y_out     | 0.0404     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020565215 |
+| time-step | 105         |
+| y_out     | 0.0658      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020838127 |
+| time-step | 106         |
+| y_out     | 0.0308      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02159388 |
+| time-step | 107        |
+| y_out     | 0.0182     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021593995 |
+| time-step | 108         |
+| y_out     | 0.00225     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02105179 |
+| time-step | 109        |
+| y_out     | 0.00584    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02001243 |
+| time-step | 110        |
+| y_out     | 0.0848     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020365763 |
+| time-step | 111         |
+| y_out     | -0.0577     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020517994 |
+| time-step | 112         |
+| y_out     | 0.0462      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020068152 |
+| time-step | 113         |
+| y_out     | 0.0317      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020240221 |
+| time-step | 114         |
+| y_out     | -0.173      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02081648 |
+| time-step | 115        |
+| y_out     | 0.115      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020471169 |
+| time-step | 116         |
+| y_out     | 0.0842      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019733286 |
+| time-step | 117         |
+| y_out     | 0.0645      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019559998 |
+| time-step | 118         |
+| y_out     | -0.0417     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01958919 |
+| time-step | 119        |
+| y_out     | -0.00702   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019794539 |
+| time-step | 120         |
+| y_out     | 0.0694      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018008139 |
+| time-step | 121         |
+| y_out     | -0.098      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017552197 |
+| time-step | 122         |
+| y_out     | 0.0795      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018022334 |
+| time-step | 123         |
+| y_out     | 0.0151      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017377157 |
+| time-step | 124         |
+| y_out     | 0.0848      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01712075 |
+| time-step | 125        |
+| y_out     | -0.0948    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017562747 |
+| time-step | 126         |
+| y_out     | -0.00282    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017126106 |
+| time-step | 127         |
+| y_out     | 0.0558      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016692223 |
+| time-step | 128         |
+| y_out     | 0.11        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016295671 |
+| time-step | 129         |
+| y_out     | 0.0238      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016403737 |
+| time-step | 130         |
+| y_out     | 0.062       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01732798 |
+| time-step | 131        |
+| y_out     | -0.0471    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017429734 |
+| time-step | 132         |
+| y_out     | 0.0148      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01712677 |
+| time-step | 133        |
+| y_out     | 0.00518    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017159015 |
+| time-step | 134         |
+| y_out     | 0.0627      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016970944 |
+| time-step | 135         |
+| y_out     | 0.0728      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016358491 |
+| time-step | 136         |
+| y_out     | -0.0689     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016560799 |
+| time-step | 137         |
+| y_out     | -0.0652     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016877415 |
+| time-step | 138         |
+| y_out     | -0.0274     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017120881 |
+| time-step | 139         |
+| y_out     | 0.0366      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017035149 |
+| time-step | 140         |
+| y_out     | 0.0603      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016782813 |
+| time-step | 141         |
+| y_out     | -0.0266     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01638801 |
+| time-step | 142        |
+| y_out     | -0.00258   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015880505 |
+| time-step | 143         |
+| y_out     | 0.0414      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015267524 |
+| time-step | 144         |
+| y_out     | -0.0229     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0156107005 |
+| time-step | 145          |
+| y_out     | -0.0516      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015585728 |
+| time-step | 146         |
+| y_out     | -0.0258     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015725685 |
+| time-step | 147         |
+| y_out     | -0.0717     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01553901 |
+| time-step | 148        |
+| y_out     | 0.087      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015121599 |
+| time-step | 149         |
+| y_out     | 0.0255      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015083885 |
+| time-step | 150         |
+| y_out     | 0.0258      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015403369 |
+| time-step | 151         |
+| y_out     | 0.0218      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015506953 |
+| time-step | 152         |
+| y_out     | -0.0339     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015268986 |
+| time-step | 153         |
+| y_out     | -0.00406    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015932553 |
+| time-step | 154         |
+| y_out     | 0.0881      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01559755 |
+| time-step | 155        |
+| y_out     | -0.0309    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015623331 |
+| time-step | 156         |
+| y_out     | -0.0543     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015851144 |
+| time-step | 157         |
+| y_out     | 0.00624     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015993834 |
+| time-step | 158         |
+| y_out     | -0.0634     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01616678 |
+| time-step | 159        |
+| y_out     | -0.0602    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015920011 |
+| time-step | 160         |
+| y_out     | -0.0593     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014885281 |
+| time-step | 161         |
+| y_out     | 0.0535      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014697814 |
+| time-step | 162         |
+| y_out     | 0.0587      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015226157 |
+| time-step | 163         |
+| y_out     | -0.0478     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015763143 |
+| time-step | 164         |
+| y_out     | 0.012       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015888304 |
+| time-step | 165         |
+| y_out     | 0.0361      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015365997 |
+| time-step | 166         |
+| y_out     | 0.00821     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014655883 |
+| time-step | 167         |
+| y_out     | -0.0256     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014129279 |
+| time-step | 168         |
+| y_out     | -0.0226     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013963118 |
+| time-step | 169         |
+| y_out     | -0.0258     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013838867 |
+| time-step | 170         |
+| y_out     | 0.0962      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014186429 |
+| time-step | 171         |
+| y_out     | -0.0559     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0143125625 |
+| time-step | 172          |
+| y_out     | 0.00247      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014567261 |
+| time-step | 173         |
+| y_out     | -0.00381    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013981223 |
+| time-step | 174         |
+| y_out     | 0.0387      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014193992 |
+| time-step | 175         |
+| y_out     | 0.134       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014226446 |
+| time-step | 176         |
+| y_out     | 0.0483      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014416695 |
+| time-step | 177         |
+| y_out     | -0.0597     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01473509 |
+| time-step | 178        |
+| y_out     | -0.0661    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014725288 |
+| time-step | 179         |
+| y_out     | 0.0103      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014622453 |
+| time-step | 180         |
+| y_out     | 0.122       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014817156 |
+| time-step | 181         |
+| y_out     | 0.0553      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015053752 |
+| time-step | 182         |
+| y_out     | -0.00639    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014922845 |
+| time-step | 183         |
+| y_out     | -0.0359     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014320677 |
+| time-step | 184         |
+| y_out     | 0.00915     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0138921235 |
+| time-step | 185          |
+| y_out     | 0.00427      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013571496 |
+| time-step | 186         |
+| y_out     | -0.0327     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013630627 |
+| time-step | 187         |
+| y_out     | -0.00937    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013863456 |
+| time-step | 188         |
+| y_out     | 0.0265      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013980565 |
+| time-step | 189         |
+| y_out     | -0.00807    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014103638 |
+| time-step | 190         |
+| y_out     | 0.0366      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013648671 |
+| time-step | 191         |
+| y_out     | 0.0313      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012918606 |
+| time-step | 192         |
+| y_out     | 0.00746     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012076277 |
+| time-step | 193         |
+| y_out     | -0.0369     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012533133 |
+| time-step | 194         |
+| y_out     | -0.15       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012529286 |
+| time-step | 195         |
+| y_out     | -0.0317     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012729259 |
+| time-step | 196         |
+| y_out     | -0.0694     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0125234425 |
+| time-step | 197          |
+| y_out     | 0.0104       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01248196 |
+| time-step | 198        |
+| y_out     | -0.0295    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012001619 |
+| time-step | 199         |
+| y_out     | -0.0298     |
+------------------------------------------------------------
+---------------------------------------------------------
+| perf/mse  | 0.012085 |
+| time-step | 200      |
+| y_out     | 0.0483   |
+---------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint 200
+-------------------------------------------------------------
+| perf/mse  | 0.0121003315 |
+| time-step | 201          |
+| y_out     | -0.0602      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01250341 |
+| time-step | 202        |
+| y_out     | -0.0408    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012854552 |
+| time-step | 203         |
+| y_out     | -0.0162     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012434529 |
+| time-step | 204         |
+| y_out     | 0.0407      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012821165 |
+| time-step | 205         |
+| y_out     | -0.0601     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012978372 |
+| time-step | 206         |
+| y_out     | 0.0248      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012783785 |
+| time-step | 207         |
+| y_out     | 0.0886      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012412922 |
+| time-step | 208         |
+| y_out     | -0.00118    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013082026 |
+| time-step | 209         |
+| y_out     | -0.0274     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012463743 |
+| time-step | 210         |
+| y_out     | -0.0222     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0119159315 |
+| time-step | 211          |
+| y_out     | -0.0639      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011504205 |
+| time-step | 212         |
+| y_out     | 0.0993      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011533032 |
+| time-step | 213         |
+| y_out     | -0.0498     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011533143 |
+| time-step | 214         |
+| y_out     | 0.0155      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011066494 |
+| time-step | 215         |
+| y_out     | 0.0158      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011071108 |
+| time-step | 216         |
+| y_out     | 0.139       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011399285 |
+| time-step | 217         |
+| y_out     | -0.0708     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010827815 |
+| time-step | 218         |
+| y_out     | 0.031       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010404508 |
+| time-step | 219         |
+| y_out     | -0.0693     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010643965 |
+| time-step | 220         |
+| y_out     | -0.0219     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010983663 |
+| time-step | 221         |
+| y_out     | -0.0718     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011280661 |
+| time-step | 222         |
+| y_out     | 0.0256      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011255766 |
+| time-step | 223         |
+| y_out     | -0.122      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011315351 |
+| time-step | 224         |
+| y_out     | -0.00652    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010898156 |
+| time-step | 225         |
+| y_out     | -0.0408     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010742856 |
+| time-step | 226         |
+| y_out     | -0.0613     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010361671 |
+| time-step | 227         |
+| y_out     | 0.0815      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010702359 |
+| time-step | 228         |
+| y_out     | -0.0496     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01037641 |
+| time-step | 229        |
+| y_out     | 0.0999     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010638231 |
+| time-step | 230         |
+| y_out     | -0.0963     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011000741 |
+| time-step | 231         |
+| y_out     | -0.0934     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010648107 |
+| time-step | 232         |
+| y_out     | -0.0973     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010665002 |
+| time-step | 233         |
+| y_out     | -0.0437     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010773917 |
+| time-step | 234         |
+| y_out     | 0.0246      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011147271 |
+| time-step | 235         |
+| y_out     | 0.0326      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010947985 |
+| time-step | 236         |
+| y_out     | -0.00875    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011461743 |
+| time-step | 237         |
+| y_out     | -0.00615    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011465353 |
+| time-step | 238         |
+| y_out     | -0.0254     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011548136 |
+| time-step | 239         |
+| y_out     | -0.0362     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011317678 |
+| time-step | 240         |
+| y_out     | 0.145       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01101277 |
+| time-step | 241        |
+| y_out     | 0.0131     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010909235 |
+| time-step | 242         |
+| y_out     | 0.0501      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010884374 |
+| time-step | 243         |
+| y_out     | 0.0618      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010763186 |
+| time-step | 244         |
+| y_out     | 0.0114      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01102829 |
+| time-step | 245        |
+| y_out     | 0.16       |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01089759 |
+| time-step | 246        |
+| y_out     | -0.0675    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010658663 |
+| time-step | 247         |
+| y_out     | -0.103      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010504929 |
+| time-step | 248         |
+| y_out     | -0.0274     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011187745 |
+| time-step | 249         |
+| y_out     | 0.0853      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011144376 |
+| time-step | 250         |
+| y_out     | -0.012      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011077395 |
+| time-step | 251         |
+| y_out     | -0.0414     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011236122 |
+| time-step | 252         |
+| y_out     | -0.0363     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0109797595 |
+| time-step | 253          |
+| y_out     | -0.0717      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010989378 |
+| time-step | 254         |
+| y_out     | -0.0247     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010269151 |
+| time-step | 255         |
+| y_out     | -0.0646     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010586536 |
+| time-step | 256         |
+| y_out     | 0.0245      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01066226 |
+| time-step | 257        |
+| y_out     | 0.105      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010843486 |
+| time-step | 258         |
+| y_out     | -0.0326     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010546822 |
+| time-step | 259         |
+| y_out     | -0.0932     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010866616 |
+| time-step | 260         |
+| y_out     | -0.14       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01089716 |
+| time-step | 261        |
+| y_out     | -0.0657    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01102573 |
+| time-step | 262        |
+| y_out     | -0.0242    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01122058 |
+| time-step | 263        |
+| y_out     | -0.0395    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011261811 |
+| time-step | 264         |
+| y_out     | 0.0155      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011647056 |
+| time-step | 265         |
+| y_out     | 0.106       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011396077 |
+| time-step | 266         |
+| y_out     | 0.0259      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010895356 |
+| time-step | 267         |
+| y_out     | 0.0204      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010594012 |
+| time-step | 268         |
+| y_out     | 0.0821      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009982122 |
+| time-step | 269         |
+| y_out     | 0.0403      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009611089 |
+| time-step | 270         |
+| y_out     | -0.00278    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009687115 |
+| time-step | 271         |
+| y_out     | 0.00855     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009739076 |
+| time-step | 272         |
+| y_out     | 0.00219     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009571998 |
+| time-step | 273         |
+| y_out     | -0.165      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009289256 |
+| time-step | 274         |
+| y_out     | 0.0377      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009065564 |
+| time-step | 275         |
+| y_out     | -0.0623     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008992936 |
+| time-step | 276         |
+| y_out     | 0.0689      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009405516 |
+| time-step | 277         |
+| y_out     | 0.0581      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009802055 |
+| time-step | 278         |
+| y_out     | 0.00191     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010002249 |
+| time-step | 279         |
+| y_out     | 0.000525    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010391736 |
+| time-step | 280         |
+| y_out     | 0.0134      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010695013 |
+| time-step | 281         |
+| y_out     | -0.0934     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010549389 |
+| time-step | 282         |
+| y_out     | 0.0681      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010508211 |
+| time-step | 283         |
+| y_out     | 0.0741      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010766902 |
+| time-step | 284         |
+| y_out     | 0.11        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010791292 |
+| time-step | 285         |
+| y_out     | 0.0532      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011008025 |
+| time-step | 286         |
+| y_out     | 0.125       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010703808 |
+| time-step | 287         |
+| y_out     | 0.0223      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0106153665 |
+| time-step | 288          |
+| y_out     | -0.0939      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010478398 |
+| time-step | 289         |
+| y_out     | 0.183       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009825417 |
+| time-step | 290         |
+| y_out     | 0.0312      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00932461 |
+| time-step | 291        |
+| y_out     | 0.00446    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009172568 |
+| time-step | 292         |
+| y_out     | 0.0557      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009338206 |
+| time-step | 293         |
+| y_out     | -0.0188     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009650408 |
+| time-step | 294         |
+| y_out     | 0.0304      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009822436 |
+| time-step | 295         |
+| y_out     | -0.0491     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009617481 |
+| time-step | 296         |
+| y_out     | -0.146      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009609596 |
+| time-step | 297         |
+| y_out     | -0.159      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009172958 |
+| time-step | 298         |
+| y_out     | 0.0645      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009404348 |
+| time-step | 299         |
+| y_out     | -0.0186     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00974897 |
+| time-step | 300        |
+| y_out     | -0.0148    |
+-----------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint 300
+------------------------------------------------------------
+| perf/mse  | 0.009659214 |
+| time-step | 301         |
+| y_out     | -0.0323     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009650087 |
+| time-step | 302         |
+| y_out     | -0.152      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009558508 |
+| time-step | 303         |
+| y_out     | 0.0425      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008911663 |
+| time-step | 304         |
+| y_out     | 0.0629      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008500017 |
+| time-step | 305         |
+| y_out     | -0.0101     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008463617 |
+| time-step | 306         |
+| y_out     | 0.036       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008380451 |
+| time-step | 307         |
+| y_out     | 0.125       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008560829 |
+| time-step | 308         |
+| y_out     | 0.059       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008684639 |
+| time-step | 309         |
+| y_out     | -0.0111     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008560272 |
+| time-step | 310         |
+| y_out     | -0.0722     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00865485 |
+| time-step | 311        |
+| y_out     | 0.0516     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008592058 |
+| time-step | 312         |
+| y_out     | -0.146      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008361543 |
+| time-step | 313         |
+| y_out     | -0.0754     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0085406955 |
+| time-step | 314          |
+| y_out     | 0.0868       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00899319 |
+| time-step | 315        |
+| y_out     | -0.0808    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009028759 |
+| time-step | 316         |
+| y_out     | -0.00468    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00926211 |
+| time-step | 317        |
+| y_out     | 0.0275     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009524745 |
+| time-step | 318         |
+| y_out     | -0.0286     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0092332205 |
+| time-step | 319          |
+| y_out     | -0.0981      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009254497 |
+| time-step | 320         |
+| y_out     | 0.0511      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008958961 |
+| time-step | 321         |
+| y_out     | 0.124       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008968334 |
+| time-step | 322         |
+| y_out     | 0.0236      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008997877 |
+| time-step | 323         |
+| y_out     | -0.00865    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009054659 |
+| time-step | 324         |
+| y_out     | -0.0329     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008929395 |
+| time-step | 325         |
+| y_out     | -0.035      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008791028 |
+| time-step | 326         |
+| y_out     | 0.0798      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008621386 |
+| time-step | 327         |
+| y_out     | 0.0074      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008276341 |
+| time-step | 328         |
+| y_out     | -0.0311     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00802771 |
+| time-step | 329        |
+| y_out     | 0.0866     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007813783 |
+| time-step | 330         |
+| y_out     | -0.0399     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0082202675 |
+| time-step | 331          |
+| y_out     | -0.02        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008203571 |
+| time-step | 332         |
+| y_out     | 0.0823      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008702431 |
+| time-step | 333         |
+| y_out     | 0.0606      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008423993 |
+| time-step | 334         |
+| y_out     | 0.027       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008752415 |
+| time-step | 335         |
+| y_out     | -0.0573     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008889762 |
+| time-step | 336         |
+| y_out     | 0.0102      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008702272 |
+| time-step | 337         |
+| y_out     | -0.0295     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009433094 |
+| time-step | 338         |
+| y_out     | 0.0704      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009802917 |
+| time-step | 339         |
+| y_out     | -0.0779     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009853077 |
+| time-step | 340         |
+| y_out     | 0.104       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0097324485 |
+| time-step | 341          |
+| y_out     | 0.138        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009619629 |
+| time-step | 342         |
+| y_out     | 0.121       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009217979 |
+| time-step | 343         |
+| y_out     | 0.07        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009695167 |
+| time-step | 344         |
+| y_out     | -0.0978     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009033231 |
+| time-step | 345         |
+| y_out     | -0.174      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008923811 |
+| time-step | 346         |
+| y_out     | 0.0255      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009237295 |
+| time-step | 347         |
+| y_out     | 0.0401      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008707397 |
+| time-step | 348         |
+| y_out     | -0.0165     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008889573 |
+| time-step | 349         |
+| y_out     | -0.123      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009206372 |
+| time-step | 350         |
+| y_out     | -0.0646     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008988985 |
+| time-step | 351         |
+| y_out     | -0.121      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009025356 |
+| time-step | 352         |
+| y_out     | -0.0818     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008861664 |
+| time-step | 353         |
+| y_out     | -0.0533     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008521282 |
+| time-step | 354         |
+| y_out     | 0.178       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008602491 |
+| time-step | 355         |
+| y_out     | -0.0709     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008645901 |
+| time-step | 356         |
+| y_out     | -0.13       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008593101 |
+| time-step | 357         |
+| y_out     | 0.00774     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008487901 |
+| time-step | 358         |
+| y_out     | -0.0525     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008191287 |
+| time-step | 359         |
+| y_out     | 0.011       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008271532 |
+| time-step | 360         |
+| y_out     | 0.0693      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008348331 |
+| time-step | 361         |
+| y_out     | 0.0426      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008694865 |
+| time-step | 362         |
+| y_out     | 0.0647      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008881613 |
+| time-step | 363         |
+| y_out     | -0.00697    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008868958 |
+| time-step | 364         |
+| y_out     | 0.02        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009162227 |
+| time-step | 365         |
+| y_out     | -0.0133     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00931981 |
+| time-step | 366        |
+| y_out     | -0.014     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009015773 |
+| time-step | 367         |
+| y_out     | -0.0538     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008895373 |
+| time-step | 368         |
+| y_out     | 0.00901     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009197822 |
+| time-step | 369         |
+| y_out     | -0.0664     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008726108 |
+| time-step | 370         |
+| y_out     | 0.0625      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008916206 |
+| time-step | 371         |
+| y_out     | -0.0186     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008774358 |
+| time-step | 372         |
+| y_out     | 0.0806      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008648316 |
+| time-step | 373         |
+| y_out     | 0.0213      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008675615 |
+| time-step | 374         |
+| y_out     | -0.0567     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008376681 |
+| time-step | 375         |
+| y_out     | 0.155       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008051926 |
+| time-step | 376         |
+| y_out     | 0.0613      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008303893 |
+| time-step | 377         |
+| y_out     | -0.0217     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008236652 |
+| time-step | 378         |
+| y_out     | -0.000877   |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076520974 |
+| time-step | 379          |
+| y_out     | -0.0459      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007512162 |
+| time-step | 380         |
+| y_out     | -0.0558     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0075943572 |
+| time-step | 381          |
+| y_out     | 0.03         |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007268989 |
+| time-step | 382         |
+| y_out     | -0.0461     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0072541414 |
+| time-step | 383          |
+| y_out     | -0.01        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007113274 |
+| time-step | 384         |
+| y_out     | 0.0198      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074879713 |
+| time-step | 385          |
+| y_out     | -0.0104      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0075280406 |
+| time-step | 386          |
+| y_out     | 0.133        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0077055274 |
+| time-step | 387          |
+| y_out     | -0.0287      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076229894 |
+| time-step | 388          |
+| y_out     | -0.00364     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076871687 |
+| time-step | 389          |
+| y_out     | -0.0797      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007881274 |
+| time-step | 390         |
+| y_out     | -0.125      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007767598 |
+| time-step | 391         |
+| y_out     | 0.0131      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007888826 |
+| time-step | 392         |
+| y_out     | -0.0222     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007940272 |
+| time-step | 393         |
+| y_out     | 0.16        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008078203 |
+| time-step | 394         |
+| y_out     | 0.0122      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076349094 |
+| time-step | 395          |
+| y_out     | 0.0419       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074360506 |
+| time-step | 396          |
+| y_out     | 0.0635       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070463954 |
+| time-step | 397          |
+| y_out     | 0.0965       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0072657345 |
+| time-step | 398          |
+| y_out     | 0.0273       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007447394 |
+| time-step | 399         |
+| y_out     | -0.0995     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073229196 |
+| time-step | 400          |
+| y_out     | 0.12         |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint 400
+------------------------------------------------------------
+| perf/mse  | 0.006974215 |
+| time-step | 401         |
+| y_out     | 0.0628      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067043165 |
+| time-step | 402          |
+| y_out     | -0.0479      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0066258474 |
+| time-step | 403          |
+| y_out     | 0.0632       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006491606 |
+| time-step | 404         |
+| y_out     | 0.0112      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006644386 |
+| time-step | 405         |
+| y_out     | -0.0853     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006644313 |
+| time-step | 406         |
+| y_out     | -0.034      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007075014 |
+| time-step | 407         |
+| y_out     | 0.0623      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067524374 |
+| time-step | 408          |
+| y_out     | 0.0921       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067051286 |
+| time-step | 409          |
+| y_out     | -0.109       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006571252 |
+| time-step | 410         |
+| y_out     | -0.145      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006660252 |
+| time-step | 411         |
+| y_out     | 0.0572      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00707165 |
+| time-step | 412        |
+| y_out     | 0.0905     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007256379 |
+| time-step | 413         |
+| y_out     | 0.0543      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007263222 |
+| time-step | 414         |
+| y_out     | -0.0221     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070702685 |
+| time-step | 415          |
+| y_out     | -0.107       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073101074 |
+| time-step | 416          |
+| y_out     | 0.0428       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070546805 |
+| time-step | 417          |
+| y_out     | -0.0223      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071849586 |
+| time-step | 418          |
+| y_out     | 0.00784      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069580055 |
+| time-step | 419          |
+| y_out     | 0.0682       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007267782 |
+| time-step | 420         |
+| y_out     | 0.0874      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074610235 |
+| time-step | 421          |
+| y_out     | -0.075       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071352487 |
+| time-step | 422          |
+| y_out     | -0.0513      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071086613 |
+| time-step | 423          |
+| y_out     | 0.0339       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070788027 |
+| time-step | 424          |
+| y_out     | 0.0939       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069850706 |
+| time-step | 425          |
+| y_out     | 0.0731       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006988878 |
+| time-step | 426         |
+| y_out     | -0.0837     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069258674 |
+| time-step | 427          |
+| y_out     | -0.00185     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006869568 |
+| time-step | 428         |
+| y_out     | 0.0527      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071738972 |
+| time-step | 429          |
+| y_out     | -0.0106      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007338633 |
+| time-step | 430         |
+| y_out     | -0.00814    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007385929 |
+| time-step | 431         |
+| y_out     | 0.0389      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0075799907 |
+| time-step | 432          |
+| y_out     | 0.0491       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074687875 |
+| time-step | 433          |
+| y_out     | 0.00271      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007415314 |
+| time-step | 434         |
+| y_out     | 0.0816      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007560329 |
+| time-step | 435         |
+| y_out     | 0.0406      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076255873 |
+| time-step | 436          |
+| y_out     | 0.0133       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007551273 |
+| time-step | 437         |
+| y_out     | 0.0795      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076690973 |
+| time-step | 438          |
+| y_out     | -0.0241      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074901045 |
+| time-step | 439          |
+| y_out     | 0.037        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074266596 |
+| time-step | 440          |
+| y_out     | -0.0175      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071635484 |
+| time-step | 441          |
+| y_out     | 0.21         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071878745 |
+| time-step | 442          |
+| y_out     | 0.0588       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071336953 |
+| time-step | 443          |
+| y_out     | -0.105       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071941717 |
+| time-step | 444          |
+| y_out     | 0.0783       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071380264 |
+| time-step | 445          |
+| y_out     | -0.0795      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007155928 |
+| time-step | 446         |
+| y_out     | 0.0126      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071311267 |
+| time-step | 447          |
+| y_out     | 0.0568       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071576396 |
+| time-step | 448          |
+| y_out     | 0.124        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070625073 |
+| time-step | 449          |
+| y_out     | -0.0171      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0068681017 |
+| time-step | 450          |
+| y_out     | 0.0421       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071220687 |
+| time-step | 451          |
+| y_out     | -0.0299      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069928826 |
+| time-step | 452          |
+| y_out     | -0.113       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070566796 |
+| time-step | 453          |
+| y_out     | 0.0422       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071178176 |
+| time-step | 454          |
+| y_out     | -0.0146      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0072293133 |
+| time-step | 455          |
+| y_out     | -0.0123      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069733923 |
+| time-step | 456          |
+| y_out     | -0.0135      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007098145 |
+| time-step | 457         |
+| y_out     | -0.0189     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073137046 |
+| time-step | 458          |
+| y_out     | 0.125        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074471524 |
+| time-step | 459          |
+| y_out     | -0.0421      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007314945 |
+| time-step | 460         |
+| y_out     | 0.0541      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073141293 |
+| time-step | 461          |
+| y_out     | -0.224       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073226253 |
+| time-step | 462          |
+| y_out     | 0.0484       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0075716553 |
+| time-step | 463          |
+| y_out     | 0.0485       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0075203343 |
+| time-step | 464          |
+| y_out     | 0.0995       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007616365 |
+| time-step | 465         |
+| y_out     | 0.145       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007872241 |
+| time-step | 466         |
+| y_out     | 0.0149      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007797871 |
+| time-step | 467         |
+| y_out     | 0.0683      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007348449 |
+| time-step | 468         |
+| y_out     | 0.0471      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007487183 |
+| time-step | 469         |
+| y_out     | -0.0139     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007666313 |
+| time-step | 470         |
+| y_out     | -0.0565     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007621041 |
+| time-step | 471         |
+| y_out     | -0.0338     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007781482 |
+| time-step | 472         |
+| y_out     | -0.0366     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074633197 |
+| time-step | 473          |
+| y_out     | 0.0474       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007517954 |
+| time-step | 474         |
+| y_out     | 0.0802      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007420115 |
+| time-step | 475         |
+| y_out     | -0.0423     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007126314 |
+| time-step | 476         |
+| y_out     | 0.0103      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007138537 |
+| time-step | 477         |
+| y_out     | -0.0584     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007529103 |
+| time-step | 478         |
+| y_out     | 0.0925      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007232096 |
+| time-step | 479         |
+| y_out     | 0.0448      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071658813 |
+| time-step | 480          |
+| y_out     | -0.0666      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006865284 |
+| time-step | 481         |
+| y_out     | -0.0149     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006643592 |
+| time-step | 482         |
+| y_out     | -0.0408     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006437654 |
+| time-step | 483         |
+| y_out     | -0.147      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064374385 |
+| time-step | 484          |
+| y_out     | -0.0888      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061637172 |
+| time-step | 485          |
+| y_out     | 0.014        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006397553 |
+| time-step | 486         |
+| y_out     | 0.0581      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006467645 |
+| time-step | 487         |
+| y_out     | 0.088       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065242136 |
+| time-step | 488          |
+| y_out     | 0.0384       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006476526 |
+| time-step | 489         |
+| y_out     | -0.0303     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00637464 |
+| time-step | 490        |
+| y_out     | -0.091     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006558396 |
+| time-step | 491         |
+| y_out     | 0.122       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006630133 |
+| time-step | 492         |
+| y_out     | -0.0255     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006634014 |
+| time-step | 493         |
+| y_out     | -0.0312     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067515434 |
+| time-step | 494          |
+| y_out     | -0.225       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070284335 |
+| time-step | 495          |
+| y_out     | -0.0133      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0068016658 |
+| time-step | 496          |
+| y_out     | 0.116        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0066306917 |
+| time-step | 497          |
+| y_out     | -0.108       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006383223 |
+| time-step | 498         |
+| y_out     | 0.00924     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064143986 |
+| time-step | 499          |
+| y_out     | -0.0665      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063092476 |
+| time-step | 500          |
+| y_out     | -0.0365      |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint 500
+------------------------------------------------------------
+| perf/mse  | 0.006410853 |
+| time-step | 501         |
+| y_out     | -0.0838     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063125803 |
+| time-step | 502          |
+| y_out     | 0.0414       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006380326 |
+| time-step | 503         |
+| y_out     | 0.0964      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060056667 |
+| time-step | 504          |
+| y_out     | 0.0724       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006038694 |
+| time-step | 505         |
+| y_out     | 0.0232      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060741603 |
+| time-step | 506          |
+| y_out     | -0.0263      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062994985 |
+| time-step | 507          |
+| y_out     | 0.0266       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006276124 |
+| time-step | 508         |
+| y_out     | 0.0096      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061255046 |
+| time-step | 509          |
+| y_out     | 0.0371       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061656605 |
+| time-step | 510          |
+| y_out     | 0.0782       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059149642 |
+| time-step | 511          |
+| y_out     | 0.052        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061497926 |
+| time-step | 512          |
+| y_out     | 0.0293       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006169011 |
+| time-step | 513         |
+| y_out     | 0.0149      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062508285 |
+| time-step | 514          |
+| y_out     | 0.118        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006057213 |
+| time-step | 515         |
+| y_out     | -0.12       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061819674 |
+| time-step | 516          |
+| y_out     | 0.0134       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006344872 |
+| time-step | 517         |
+| y_out     | 0.158       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063840365 |
+| time-step | 518          |
+| y_out     | -0.0158      |
+-------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0064421 |
+| time-step | 519       |
+| y_out     | 0.0606    |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063485084 |
+| time-step | 520          |
+| y_out     | -0.193       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006528827 |
+| time-step | 521         |
+| y_out     | 0.153       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006375099 |
+| time-step | 522         |
+| y_out     | -0.0412     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00626872 |
+| time-step | 523        |
+| y_out     | 0.0229     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062585557 |
+| time-step | 524          |
+| y_out     | 0.0509       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006252048 |
+| time-step | 525         |
+| y_out     | 0.132       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060802856 |
+| time-step | 526          |
+| y_out     | 0.0788       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00571565 |
+| time-step | 527        |
+| y_out     | 0.0177     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057090363 |
+| time-step | 528          |
+| y_out     | -0.156       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005775889 |
+| time-step | 529         |
+| y_out     | -0.0269     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005885468 |
+| time-step | 530         |
+| y_out     | -0.0361     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00562531 |
+| time-step | 531        |
+| y_out     | -0.112     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005705278 |
+| time-step | 532         |
+| y_out     | 0.0279      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005722405 |
+| time-step | 533         |
+| y_out     | 0.0202      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058096964 |
+| time-step | 534          |
+| y_out     | -0.000581    |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058348286 |
+| time-step | 535          |
+| y_out     | -0.0285      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005756849 |
+| time-step | 536         |
+| y_out     | 0.157       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005865877 |
+| time-step | 537         |
+| y_out     | 0.0517      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005779306 |
+| time-step | 538         |
+| y_out     | 0.0519      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057658544 |
+| time-step | 539          |
+| y_out     | 0.0454       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057187616 |
+| time-step | 540          |
+| y_out     | 0.107        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005913873 |
+| time-step | 541         |
+| y_out     | -0.0549     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00569485 |
+| time-step | 542        |
+| y_out     | -0.0338    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006070492 |
+| time-step | 543         |
+| y_out     | -0.0635     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059679695 |
+| time-step | 544          |
+| y_out     | 0.0968       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005937689 |
+| time-step | 545         |
+| y_out     | -0.0914     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058847824 |
+| time-step | 546          |
+| y_out     | 0.0598       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060364204 |
+| time-step | 547          |
+| y_out     | 0.0862       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060583553 |
+| time-step | 548          |
+| y_out     | 0.0244       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006164747 |
+| time-step | 549         |
+| y_out     | -0.0861     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062775738 |
+| time-step | 550          |
+| y_out     | -0.0447      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006435971 |
+| time-step | 551         |
+| y_out     | 0.0184      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064379363 |
+| time-step | 552          |
+| y_out     | -0.0198      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006262321 |
+| time-step | 553         |
+| y_out     | 0.000665    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065218285 |
+| time-step | 554          |
+| y_out     | 0.0464       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0066356524 |
+| time-step | 555          |
+| y_out     | 0.0697       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00690855 |
+| time-step | 556        |
+| y_out     | -0.0769    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065123164 |
+| time-step | 557          |
+| y_out     | 0.0408       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006517934 |
+| time-step | 558         |
+| y_out     | -0.068      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006416404 |
+| time-step | 559         |
+| y_out     | 0.0929      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006382212 |
+| time-step | 560         |
+| y_out     | -0.114      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006172291 |
+| time-step | 561         |
+| y_out     | -0.0213     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006486813 |
+| time-step | 562         |
+| y_out     | -0.188      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063959197 |
+| time-step | 563          |
+| y_out     | -0.0131      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062121847 |
+| time-step | 564          |
+| y_out     | -0.147       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006033571 |
+| time-step | 565         |
+| y_out     | 0.177       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006053632 |
+| time-step | 566         |
+| y_out     | 0.0247      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006283164 |
+| time-step | 567         |
+| y_out     | -0.00537    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062189503 |
+| time-step | 568          |
+| y_out     | 0.00804      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006192473 |
+| time-step | 569         |
+| y_out     | -0.0161     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061711622 |
+| time-step | 570          |
+| y_out     | 0.0464       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00621113 |
+| time-step | 571        |
+| y_out     | -0.084     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062055713 |
+| time-step | 572          |
+| y_out     | -0.137       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006361153 |
+| time-step | 573         |
+| y_out     | 0.00258     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006329463 |
+| time-step | 574         |
+| y_out     | 0.0532      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006423714 |
+| time-step | 575         |
+| y_out     | 0.0369      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062316386 |
+| time-step | 576          |
+| y_out     | -0.0605      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061640413 |
+| time-step | 577          |
+| y_out     | -0.0378      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065376996 |
+| time-step | 578          |
+| y_out     | -0.000645    |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006798501 |
+| time-step | 579         |
+| y_out     | 0.0332      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006835184 |
+| time-step | 580         |
+| y_out     | -0.0839     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067300596 |
+| time-step | 581          |
+| y_out     | 0.0925       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064291903 |
+| time-step | 582          |
+| y_out     | 0.0828       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064151757 |
+| time-step | 583          |
+| y_out     | 0.000887     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006358838 |
+| time-step | 584         |
+| y_out     | -0.0216     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062949844 |
+| time-step | 585          |
+| y_out     | -0.0399      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065687774 |
+| time-step | 586          |
+| y_out     | -0.00623     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006732789 |
+| time-step | 587         |
+| y_out     | -0.0145     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006350413 |
+| time-step | 588         |
+| y_out     | 0.0105      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062813824 |
+| time-step | 589          |
+| y_out     | 0.0775       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006291695 |
+| time-step | 590         |
+| y_out     | -0.121      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062973006 |
+| time-step | 591          |
+| y_out     | 0.0235       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006482204 |
+| time-step | 592         |
+| y_out     | -0.0151     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062797316 |
+| time-step | 593          |
+| y_out     | -0.0338      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060943626 |
+| time-step | 594          |
+| y_out     | -0.12        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006060673 |
+| time-step | 595         |
+| y_out     | 0.0696      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058491575 |
+| time-step | 596          |
+| y_out     | -0.0881      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056013167 |
+| time-step | 597          |
+| y_out     | 0.0555       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056335153 |
+| time-step | 598          |
+| y_out     | -0.0227      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055206358 |
+| time-step | 599          |
+| y_out     | -0.0145      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053234184 |
+| time-step | 600          |
+| y_out     | 0.114        |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint 600
+-------------------------------------------------------------
+| perf/mse  | 0.0053103687 |
+| time-step | 601          |
+| y_out     | 0.131        |
+-------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0052162 |
+| time-step | 602       |
+| y_out     | 0.0133    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005208586 |
+| time-step | 603         |
+| y_out     | -0.0552     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054678055 |
+| time-step | 604          |
+| y_out     | -0.0665      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005652829 |
+| time-step | 605         |
+| y_out     | 0.00538     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005549931 |
+| time-step | 606         |
+| y_out     | 0.00538     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057286425 |
+| time-step | 607          |
+| y_out     | 0.0756       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058101555 |
+| time-step | 608          |
+| y_out     | -0.0335      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058230096 |
+| time-step | 609          |
+| y_out     | 0.0427       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058073634 |
+| time-step | 610          |
+| y_out     | 0.00601      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057664067 |
+| time-step | 611          |
+| y_out     | 0.00625      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005824791 |
+| time-step | 612         |
+| y_out     | 0.219       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005838863 |
+| time-step | 613         |
+| y_out     | -0.11       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005838423 |
+| time-step | 614         |
+| y_out     | -0.00335    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005856662 |
+| time-step | 615         |
+| y_out     | 0.0543      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060580852 |
+| time-step | 616          |
+| y_out     | -0.107       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005655325 |
+| time-step | 617         |
+| y_out     | -0.0295     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054848753 |
+| time-step | 618          |
+| y_out     | -0.0533      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054236734 |
+| time-step | 619          |
+| y_out     | -0.0649      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005423072 |
+| time-step | 620         |
+| y_out     | -0.0353     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005385162 |
+| time-step | 621         |
+| y_out     | 0.0668      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052685244 |
+| time-step | 622          |
+| y_out     | -0.158       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053974665 |
+| time-step | 623          |
+| y_out     | -0.00632     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052526495 |
+| time-step | 624          |
+| y_out     | -0.1         |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004981737 |
+| time-step | 625         |
+| y_out     | 0.0313      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004875469 |
+| time-step | 626         |
+| y_out     | 0.0669      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052874335 |
+| time-step | 627          |
+| y_out     | 0.0941       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005451837 |
+| time-step | 628         |
+| y_out     | 0.0387      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005573095 |
+| time-step | 629         |
+| y_out     | -0.033      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00598256 |
+| time-step | 630        |
+| y_out     | -0.024     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005923933 |
+| time-step | 631         |
+| y_out     | 0.00958     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059836316 |
+| time-step | 632          |
+| y_out     | -0.0457      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060538505 |
+| time-step | 633          |
+| y_out     | -0.0198      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006047846 |
+| time-step | 634         |
+| y_out     | -0.0647     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006080807 |
+| time-step | 635         |
+| y_out     | -0.0401     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00599097 |
+| time-step | 636        |
+| y_out     | 0.0409     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058805295 |
+| time-step | 637          |
+| y_out     | -0.0455      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005881214 |
+| time-step | 638         |
+| y_out     | -0.0284     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058441786 |
+| time-step | 639          |
+| y_out     | -0.0589      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005528598 |
+| time-step | 640         |
+| y_out     | 0.0632      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005639632 |
+| time-step | 641         |
+| y_out     | 0.103       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056080096 |
+| time-step | 642          |
+| y_out     | -0.0126      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005362663 |
+| time-step | 643         |
+| y_out     | 0.0214      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005540789 |
+| time-step | 644         |
+| y_out     | 0.105       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005658085 |
+| time-step | 645         |
+| y_out     | 0.0114      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057937005 |
+| time-step | 646          |
+| y_out     | 0.0177       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005652317 |
+| time-step | 647         |
+| y_out     | 0.0828      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005586999 |
+| time-step | 648         |
+| y_out     | -0.0204     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00555695 |
+| time-step | 649        |
+| y_out     | -0.0327    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055426257 |
+| time-step | 650          |
+| y_out     | 0.0651       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005631237 |
+| time-step | 651         |
+| y_out     | -0.08       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005731188 |
+| time-step | 652         |
+| y_out     | -0.0995     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056848605 |
+| time-step | 653          |
+| y_out     | -0.115       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056562508 |
+| time-step | 654          |
+| y_out     | -0.0168      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005583243 |
+| time-step | 655         |
+| y_out     | 0.0626      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058037057 |
+| time-step | 656          |
+| y_out     | 0.0727       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057565453 |
+| time-step | 657          |
+| y_out     | 0.108        |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00593283 |
+| time-step | 658        |
+| y_out     | 0.0207     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005832375 |
+| time-step | 659         |
+| y_out     | -0.0716     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005958461 |
+| time-step | 660         |
+| y_out     | -0.0481     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00596415 |
+| time-step | 661        |
+| y_out     | -0.0941    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057445727 |
+| time-step | 662          |
+| y_out     | 0.0265       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060045067 |
+| time-step | 663          |
+| y_out     | -0.0915      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060107643 |
+| time-step | 664          |
+| y_out     | -0.0857      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060831867 |
+| time-step | 665          |
+| y_out     | 0.0973       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005690192 |
+| time-step | 666         |
+| y_out     | -0.068      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005793244 |
+| time-step | 667         |
+| y_out     | -0.00557    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055814865 |
+| time-step | 668          |
+| y_out     | 0.0137       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055364585 |
+| time-step | 669          |
+| y_out     | 0.047        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053845188 |
+| time-step | 670          |
+| y_out     | -0.0897      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054389285 |
+| time-step | 671          |
+| y_out     | 0.14         |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005470776 |
+| time-step | 672         |
+| y_out     | 0.0075      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00518021 |
+| time-step | 673        |
+| y_out     | 0.0496     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004929944 |
+| time-step | 674         |
+| y_out     | -0.112      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004721289 |
+| time-step | 675         |
+| y_out     | 0.0465      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004790255 |
+| time-step | 676         |
+| y_out     | -0.0682     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004781442 |
+| time-step | 677         |
+| y_out     | -0.0873     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047044973 |
+| time-step | 678          |
+| y_out     | 0.0715       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004829833 |
+| time-step | 679         |
+| y_out     | 0.0785      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048114187 |
+| time-step | 680          |
+| y_out     | -0.0264      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004737159 |
+| time-step | 681         |
+| y_out     | -0.0207     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004797871 |
+| time-step | 682         |
+| y_out     | 0.0018      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004972216 |
+| time-step | 683         |
+| y_out     | -0.0877     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050873416 |
+| time-step | 684          |
+| y_out     | -0.0309      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052958406 |
+| time-step | 685          |
+| y_out     | -0.0555      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052558226 |
+| time-step | 686          |
+| y_out     | -0.209       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00505994 |
+| time-step | 687        |
+| y_out     | 0.0629     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005301248 |
+| time-step | 688         |
+| y_out     | 0.00828     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005145841 |
+| time-step | 689         |
+| y_out     | 0.078       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005175805 |
+| time-step | 690         |
+| y_out     | 0.022       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005072666 |
+| time-step | 691         |
+| y_out     | 0.0494      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050318367 |
+| time-step | 692          |
+| y_out     | -0.0459      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00495977 |
+| time-step | 693        |
+| y_out     | 0.00521    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049736327 |
+| time-step | 694          |
+| y_out     | 0.0997       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049477825 |
+| time-step | 695          |
+| y_out     | -0.013       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050119488 |
+| time-step | 696          |
+| y_out     | 0.0364       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005259874 |
+| time-step | 697         |
+| y_out     | 0.0678      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005174082 |
+| time-step | 698         |
+| y_out     | -0.0304     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005234071 |
+| time-step | 699         |
+| y_out     | 0.0157      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005318279 |
+| time-step | 700         |
+| y_out     | 0.0458      |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint 700
+-------------------------------------------------------------
+| perf/mse  | 0.0053585297 |
+| time-step | 701          |
+| y_out     | -0.0224      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054472187 |
+| time-step | 702          |
+| y_out     | -0.00393     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054075853 |
+| time-step | 703          |
+| y_out     | -0.0477      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052512554 |
+| time-step | 704          |
+| y_out     | 0.047        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052173245 |
+| time-step | 705          |
+| y_out     | -0.0842      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005176955 |
+| time-step | 706         |
+| y_out     | -0.0647     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005014113 |
+| time-step | 707         |
+| y_out     | -0.0351     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004791107 |
+| time-step | 708         |
+| y_out     | -0.0131     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004639486 |
+| time-step | 709         |
+| y_out     | 0.0091      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046699205 |
+| time-step | 710          |
+| y_out     | 0.00544      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004846975 |
+| time-step | 711         |
+| y_out     | 0.00898     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004852234 |
+| time-step | 712         |
+| y_out     | -0.0955     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049797418 |
+| time-step | 713          |
+| y_out     | -0.0102      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004992086 |
+| time-step | 714         |
+| y_out     | -0.143      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005117629 |
+| time-step | 715         |
+| y_out     | -0.145      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004990969 |
+| time-step | 716         |
+| y_out     | 0.0269      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00531058 |
+| time-step | 717        |
+| y_out     | -0.0319    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005671566 |
+| time-step | 718         |
+| y_out     | -0.0583     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006027546 |
+| time-step | 719         |
+| y_out     | -0.0364     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058292653 |
+| time-step | 720          |
+| y_out     | -0.0857      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055674165 |
+| time-step | 721          |
+| y_out     | 0.11         |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005296134 |
+| time-step | 722         |
+| y_out     | -0.00827    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005272624 |
+| time-step | 723         |
+| y_out     | -0.102      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053456556 |
+| time-step | 724          |
+| y_out     | -0.0631      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053317016 |
+| time-step | 725          |
+| y_out     | 0.185        |
+-------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0054772 |
+| time-step | 726       |
+| y_out     | 0.073     |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052649626 |
+| time-step | 727          |
+| y_out     | -0.0833      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051203216 |
+| time-step | 728          |
+| y_out     | 0.0851       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048611336 |
+| time-step | 729          |
+| y_out     | -0.00561     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048834207 |
+| time-step | 730          |
+| y_out     | 0.168        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004988238 |
+| time-step | 731         |
+| y_out     | 0.21        |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051153335 |
+| time-step | 732          |
+| y_out     | 0.0283       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050399606 |
+| time-step | 733          |
+| y_out     | 0.00764      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052681724 |
+| time-step | 734          |
+| y_out     | 0.0776       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052630017 |
+| time-step | 735          |
+| y_out     | 0.116        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053777313 |
+| time-step | 736          |
+| y_out     | -0.147       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055167703 |
+| time-step | 737          |
+| y_out     | 0.0567       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005421219 |
+| time-step | 738         |
+| y_out     | 0.027       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056234156 |
+| time-step | 739          |
+| y_out     | -0.0568      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005609937 |
+| time-step | 740         |
+| y_out     | 0.0291      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005493781 |
+| time-step | 741         |
+| y_out     | 0.0242      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00550913 |
+| time-step | 742        |
+| y_out     | -0.0933    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054801735 |
+| time-step | 743          |
+| y_out     | -0.0573      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053504272 |
+| time-step | 744          |
+| y_out     | -0.117       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052574836 |
+| time-step | 745          |
+| y_out     | -0.0616      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052500037 |
+| time-step | 746          |
+| y_out     | -0.0717      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049751005 |
+| time-step | 747          |
+| y_out     | -0.0929      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048878053 |
+| time-step | 748          |
+| y_out     | 0.0171       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004856643 |
+| time-step | 749         |
+| y_out     | 0.0459      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004894593 |
+| time-step | 750         |
+| y_out     | 0.00297     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004904653 |
+| time-step | 751         |
+| y_out     | 0.0711      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048743533 |
+| time-step | 752          |
+| y_out     | 0.0382       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004888937 |
+| time-step | 753         |
+| y_out     | 0.115       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050419783 |
+| time-step | 754          |
+| y_out     | -0.107       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004962045 |
+| time-step | 755         |
+| y_out     | -0.0338     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004732796 |
+| time-step | 756         |
+| y_out     | -0.0292     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004706106 |
+| time-step | 757         |
+| y_out     | 0.0919      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00473864 |
+| time-step | 758        |
+| y_out     | 0.0295     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045996015 |
+| time-step | 759          |
+| y_out     | 0.0571       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00475166 |
+| time-step | 760        |
+| y_out     | 0.0135     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0045398 |
+| time-step | 761       |
+| y_out     | -0.0165   |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045748875 |
+| time-step | 762          |
+| y_out     | 0.0233       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004456512 |
+| time-step | 763         |
+| y_out     | 0.0698      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043449975 |
+| time-step | 764          |
+| y_out     | -0.0439      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045670895 |
+| time-step | 765          |
+| y_out     | 0.0399       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045255786 |
+| time-step | 766          |
+| y_out     | 0.0114       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004743767 |
+| time-step | 767         |
+| y_out     | 0.0481      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048627025 |
+| time-step | 768          |
+| y_out     | 0.145        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004834731 |
+| time-step | 769         |
+| y_out     | 0.0108      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047857375 |
+| time-step | 770          |
+| y_out     | 0.0683       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049077547 |
+| time-step | 771          |
+| y_out     | 0.0318       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004852662 |
+| time-step | 772         |
+| y_out     | -0.0183     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048671286 |
+| time-step | 773          |
+| y_out     | -0.0147      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004895644 |
+| time-step | 774         |
+| y_out     | 0.0358      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049077235 |
+| time-step | 775          |
+| y_out     | -0.11        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004886048 |
+| time-step | 776         |
+| y_out     | -0.0125     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004699385 |
+| time-step | 777         |
+| y_out     | -0.00572    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046963403 |
+| time-step | 778          |
+| y_out     | 0.0277       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004736431 |
+| time-step | 779         |
+| y_out     | 0.0441      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045894543 |
+| time-step | 780          |
+| y_out     | -0.0428      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046472945 |
+| time-step | 781          |
+| y_out     | 0.0205       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004546878 |
+| time-step | 782         |
+| y_out     | 0.00329     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004714864 |
+| time-step | 783         |
+| y_out     | -0.033      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045516314 |
+| time-step | 784          |
+| y_out     | 0.0859       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045556026 |
+| time-step | 785          |
+| y_out     | 0.0473       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046186005 |
+| time-step | 786          |
+| y_out     | 0.0597       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004640814 |
+| time-step | 787         |
+| y_out     | 0.00654     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00441129 |
+| time-step | 788        |
+| y_out     | 0.00609    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044520227 |
+| time-step | 789          |
+| y_out     | -0.0741      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004653627 |
+| time-step | 790         |
+| y_out     | -0.0774     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046147592 |
+| time-step | 791          |
+| y_out     | -0.0318      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00479171 |
+| time-step | 792        |
+| y_out     | 0.0496     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046523414 |
+| time-step | 793          |
+| y_out     | -0.155       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004650621 |
+| time-step | 794         |
+| y_out     | -0.0302     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004321754 |
+| time-step | 795         |
+| y_out     | -0.0635     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004386327 |
+| time-step | 796         |
+| y_out     | -0.085      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042695254 |
+| time-step | 797          |
+| y_out     | 0.132        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004391427 |
+| time-step | 798         |
+| y_out     | -0.00564    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004358123 |
+| time-step | 799         |
+| y_out     | 0.0217      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004127129 |
+| time-step | 800         |
+| y_out     | 0.047       |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint 800
+-------------------------------------------------------------
+| perf/mse  | 0.0040562777 |
+| time-step | 801          |
+| y_out     | -0.0566      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003989099 |
+| time-step | 802         |
+| y_out     | -0.0814     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040815724 |
+| time-step | 803          |
+| y_out     | 0.0232       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040360005 |
+| time-step | 804          |
+| y_out     | -0.0839      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004112023 |
+| time-step | 805         |
+| y_out     | -0.0185     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004039911 |
+| time-step | 806         |
+| y_out     | 0.0202      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004311596 |
+| time-step | 807         |
+| y_out     | 0.0175      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004326271 |
+| time-step | 808         |
+| y_out     | -0.0374     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041819457 |
+| time-step | 809          |
+| y_out     | -0.0142      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041624107 |
+| time-step | 810          |
+| y_out     | 0.0548       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004298868 |
+| time-step | 811         |
+| y_out     | -0.0721     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004268541 |
+| time-step | 812         |
+| y_out     | 0.0312      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041068112 |
+| time-step | 813          |
+| y_out     | -0.0756      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004058507 |
+| time-step | 814         |
+| y_out     | -0.0122     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004065884 |
+| time-step | 815         |
+| y_out     | 0.031       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042096553 |
+| time-step | 816          |
+| y_out     | -0.0113      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041722236 |
+| time-step | 817          |
+| y_out     | -0.0432      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040931143 |
+| time-step | 818          |
+| y_out     | 0.0318       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004154513 |
+| time-step | 819         |
+| y_out     | -0.00974    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042210864 |
+| time-step | 820          |
+| y_out     | -0.0265      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041227527 |
+| time-step | 821          |
+| y_out     | -0.0332      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00402163 |
+| time-step | 822        |
+| y_out     | -0.00756   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004214854 |
+| time-step | 823         |
+| y_out     | 0.0223      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042724824 |
+| time-step | 824          |
+| y_out     | -0.0816      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043421034 |
+| time-step | 825          |
+| y_out     | -0.057       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041782237 |
+| time-step | 826          |
+| y_out     | -0.0178      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004040577 |
+| time-step | 827         |
+| y_out     | -0.022      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040065325 |
+| time-step | 828          |
+| y_out     | 0.00573      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003988184 |
+| time-step | 829         |
+| y_out     | 0.04        |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039855205 |
+| time-step | 830          |
+| y_out     | -0.0178      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004029011 |
+| time-step | 831         |
+| y_out     | 0.00744     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004251573 |
+| time-step | 832         |
+| y_out     | 0.0129      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004214334 |
+| time-step | 833         |
+| y_out     | 0.0229      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004192804 |
+| time-step | 834         |
+| y_out     | -0.0378     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003940071 |
+| time-step | 835         |
+| y_out     | 0.0315      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040270844 |
+| time-step | 836          |
+| y_out     | 0.0264       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004037282 |
+| time-step | 837         |
+| y_out     | -0.0518     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004143486 |
+| time-step | 838         |
+| y_out     | 0.022       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004338912 |
+| time-step | 839         |
+| y_out     | 0.0864      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004528165 |
+| time-step | 840         |
+| y_out     | -0.0442     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00444869 |
+| time-step | 841        |
+| y_out     | 0.0989     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004272726 |
+| time-step | 842         |
+| y_out     | -0.0865     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041704476 |
+| time-step | 843          |
+| y_out     | -0.0578      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004118084 |
+| time-step | 844         |
+| y_out     | 0.108       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00413963 |
+| time-step | 845        |
+| y_out     | -0.0741    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039881384 |
+| time-step | 846          |
+| y_out     | 0.033        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040209573 |
+| time-step | 847          |
+| y_out     | 0.148        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039468803 |
+| time-step | 848          |
+| y_out     | -0.149       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037184518 |
+| time-step | 849          |
+| y_out     | 0.0301       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033886537 |
+| time-step | 850          |
+| y_out     | -0.00607     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034263432 |
+| time-step | 851          |
+| y_out     | 0.0504       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034564394 |
+| time-step | 852          |
+| y_out     | 0.00774      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003447393 |
+| time-step | 853         |
+| y_out     | 0.0141      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035165674 |
+| time-step | 854          |
+| y_out     | -0.0472      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038481385 |
+| time-step | 855          |
+| y_out     | 0.0527       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003982555 |
+| time-step | 856         |
+| y_out     | 0.0105      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003939111 |
+| time-step | 857         |
+| y_out     | 0.0963      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039055585 |
+| time-step | 858          |
+| y_out     | -0.0218      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038725447 |
+| time-step | 859          |
+| y_out     | 0.048        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039680605 |
+| time-step | 860          |
+| y_out     | 0.0405       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039801137 |
+| time-step | 861          |
+| y_out     | 0.0649       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041612484 |
+| time-step | 862          |
+| y_out     | -0.024       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042559775 |
+| time-step | 863          |
+| y_out     | 0.00693      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004256179 |
+| time-step | 864         |
+| y_out     | 0.0091      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040335744 |
+| time-step | 865          |
+| y_out     | 0.0925       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040837307 |
+| time-step | 866          |
+| y_out     | -0.0775      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004239221 |
+| time-step | 867         |
+| y_out     | -0.124      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043187523 |
+| time-step | 868          |
+| y_out     | 0.0417       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043789707 |
+| time-step | 869          |
+| y_out     | -0.0864      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044167666 |
+| time-step | 870          |
+| y_out     | 0.0723       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044578053 |
+| time-step | 871          |
+| y_out     | -0.0334      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004390196 |
+| time-step | 872         |
+| y_out     | 0.0261      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004248857 |
+| time-step | 873         |
+| y_out     | -0.0238     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042989356 |
+| time-step | 874          |
+| y_out     | -0.0195      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043008337 |
+| time-step | 875          |
+| y_out     | 0.0218       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004083876 |
+| time-step | 876         |
+| y_out     | -0.047      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040107905 |
+| time-step | 877          |
+| y_out     | 0.0729       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040095556 |
+| time-step | 878          |
+| y_out     | 0.0964       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004087646 |
+| time-step | 879         |
+| y_out     | -0.0751     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041999184 |
+| time-step | 880          |
+| y_out     | 0.0207       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004096242 |
+| time-step | 881         |
+| y_out     | 0.0422      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042469203 |
+| time-step | 882          |
+| y_out     | -0.0618      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042625186 |
+| time-step | 883          |
+| y_out     | 0.00511      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041507035 |
+| time-step | 884          |
+| y_out     | 0.0583       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041474854 |
+| time-step | 885          |
+| y_out     | 0.0729       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004174534 |
+| time-step | 886         |
+| y_out     | -0.0336     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004175606 |
+| time-step | 887         |
+| y_out     | 0.025       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040392242 |
+| time-step | 888          |
+| y_out     | 0.143        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038949153 |
+| time-step | 889          |
+| y_out     | -0.0502      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037763983 |
+| time-step | 890          |
+| y_out     | -0.0126      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003872497 |
+| time-step | 891         |
+| y_out     | -0.0203     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036474157 |
+| time-step | 892          |
+| y_out     | 0.0486       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036936295 |
+| time-step | 893          |
+| y_out     | 0.0156       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036857862 |
+| time-step | 894          |
+| y_out     | 0.0045       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036914926 |
+| time-step | 895          |
+| y_out     | -0.0852      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037500162 |
+| time-step | 896          |
+| y_out     | -0.0477      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037770276 |
+| time-step | 897          |
+| y_out     | 0.0476       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038148519 |
+| time-step | 898          |
+| y_out     | -0.00897     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003841009 |
+| time-step | 899         |
+| y_out     | -0.109      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003945652 |
+| time-step | 900         |
+| y_out     | 0.0224      |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/checkpoint 900
+------------------------------------------------------------
+| perf/mse  | 0.003822885 |
+| time-step | 901         |
+| y_out     | -0.116      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036930875 |
+| time-step | 902          |
+| y_out     | -0.039       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003682067 |
+| time-step | 903         |
+| y_out     | -0.0239     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036827337 |
+| time-step | 904          |
+| y_out     | 0.0545       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038612336 |
+| time-step | 905          |
+| y_out     | 0.0572       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003749943 |
+| time-step | 906         |
+| y_out     | -0.0132     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036443607 |
+| time-step | 907          |
+| y_out     | 0.0296       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035677615 |
+| time-step | 908          |
+| y_out     | 0.0618       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035921622 |
+| time-step | 909          |
+| y_out     | -0.0668      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003497947 |
+| time-step | 910         |
+| y_out     | -0.0217     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036051024 |
+| time-step | 911          |
+| y_out     | 0.127        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036856737 |
+| time-step | 912          |
+| y_out     | 0.0282       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035897132 |
+| time-step | 913          |
+| y_out     | 0.0204       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036784734 |
+| time-step | 914          |
+| y_out     | -0.0147      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036165367 |
+| time-step | 915          |
+| y_out     | 0.0539       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037043053 |
+| time-step | 916          |
+| y_out     | -0.152       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003736385 |
+| time-step | 917         |
+| y_out     | 0.0752      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038468062 |
+| time-step | 918          |
+| y_out     | -0.0412      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003752191 |
+| time-step | 919         |
+| y_out     | -0.0152     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037464662 |
+| time-step | 920          |
+| y_out     | 0.0455       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037775356 |
+| time-step | 921          |
+| y_out     | -0.0364      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037770844 |
+| time-step | 922          |
+| y_out     | -0.0932      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038808458 |
+| time-step | 923          |
+| y_out     | 0.0926       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039042484 |
+| time-step | 924          |
+| y_out     | 0.0228       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003684226 |
+| time-step | 925         |
+| y_out     | -0.0887     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036903638 |
+| time-step | 926          |
+| y_out     | 0.0459       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036559496 |
+| time-step | 927          |
+| y_out     | -0.0969      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035693403 |
+| time-step | 928          |
+| y_out     | -0.0857      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036656198 |
+| time-step | 929          |
+| y_out     | -0.0309      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036183726 |
+| time-step | 930          |
+| y_out     | -0.0369      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035348393 |
+| time-step | 931          |
+| y_out     | 0.00145      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036061138 |
+| time-step | 932          |
+| y_out     | -0.0327      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036647352 |
+| time-step | 933          |
+| y_out     | 0.146        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038364097 |
+| time-step | 934          |
+| y_out     | 0.0103       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038707212 |
+| time-step | 935          |
+| y_out     | -0.068       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039437604 |
+| time-step | 936          |
+| y_out     | 0.0789       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038781285 |
+| time-step | 937          |
+| y_out     | 0.0105       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039777462 |
+| time-step | 938          |
+| y_out     | -0.0559      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039753085 |
+| time-step | 939          |
+| y_out     | 0.0205       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004007493 |
+| time-step | 940         |
+| y_out     | 0.0144      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041054236 |
+| time-step | 941          |
+| y_out     | 0.0663       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040364186 |
+| time-step | 942          |
+| y_out     | 0.106        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003919002 |
+| time-step | 943         |
+| y_out     | -0.0315     |
+------------------------------------------------------------
+-------------------------------------------------------------
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/progress-[||perf--mse||, ||timestep||].csv b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/progress-[||perf--mse||, ||timestep||].csv
new file mode 100644
index 0000000..9ed8e9f
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/progress-[||perf--mse||, ||timestep||].csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/progress.csv b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/progress.csv
new file mode 100644
index 0000000..5757207
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/progress.csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/tb/events/events.out.tfevents.1646141011.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/tb/events/events.out.tfevents.1646141011.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..3bf0661
Binary files /dev/null and b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/tb/events/events.out.tfevents.1646141011.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/warn.txt b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/warn.txt
new file mode 100644
index 0000000..f7e0929
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-23-31-383753 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=888/warn.txt	
@@ -0,0 +1,8 @@
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/backup.txt b/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/backup.txt
new file mode 100644
index 0000000..30333aa
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/backup.txt	
@@ -0,0 +1,7 @@
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.001
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 88
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/log.txt b/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/log.txt
new file mode 100644
index 0000000..09fd7ac
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/log.txt	
@@ -0,0 +1,5048 @@
+Logging to ../log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/
+log dir: ../log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/
+pkl_file: ../archive_tester/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88.pkl
+checkpoint_dir: ../checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/
+results_dir: ../results/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.001
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 88
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.162    |
+---------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 0
+---------------------------------------------------------
+| time-step | 1        |
+| y_out     | 0.0764   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 2        |
+| y_out     | -0.00117 |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 3        |
+| y_out     | -0.0901  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 4        |
+| y_out     | -0.0121  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 5        |
+| y_out     | 0.152    |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 6        |
+| y_out     | -0.0486  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 7        |
+| y_out     | -0.00131 |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 8        |
+| y_out     | 0.075    |
+---------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.4116845 |
+| time-step | 9         |
+| y_out     | -0.023    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.34226435 |
+| time-step | 10         |
+| y_out     | 0.0666     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2865979 |
+| time-step | 11        |
+| y_out     | 0.113     |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22066227 |
+| time-step | 12         |
+| y_out     | 0.0684     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17881593 |
+| time-step | 13         |
+| y_out     | 0.018      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15293343 |
+| time-step | 14         |
+| y_out     | 0.025      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13582486 |
+| time-step | 15         |
+| y_out     | 0.0185     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12673703 |
+| time-step | 16         |
+| y_out     | -0.015     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.118425295 |
+| time-step | 17          |
+| y_out     | 0.0676      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1182645 |
+| time-step | 18        |
+| y_out     | -0.02     |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11723097 |
+| time-step | 19         |
+| y_out     | -0.042     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11428434 |
+| time-step | 20         |
+| y_out     | 0.00202    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.110354364 |
+| time-step | 21          |
+| y_out     | -0.0161     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10520983 |
+| time-step | 22         |
+| y_out     | -0.0112    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09873715 |
+| time-step | 23         |
+| y_out     | 0.0716     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0954464 |
+| time-step | 24        |
+| y_out     | 0.0488    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.090510435 |
+| time-step | 25          |
+| y_out     | -0.0132     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08614574 |
+| time-step | 26         |
+| y_out     | 0.00863    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08214369 |
+| time-step | 27         |
+| y_out     | 0.0444     |
+-----------------------------------------------------------
+---------------------------------------------------------
+| perf/mse  | 0.07739  |
+| time-step | 28       |
+| y_out     | -0.0458  |
+---------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.072461925 |
+| time-step | 29          |
+| y_out     | -0.0655     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07006042 |
+| time-step | 30         |
+| y_out     | 0.02       |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.067246854 |
+| time-step | 31          |
+| y_out     | 0.0098      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.065904096 |
+| time-step | 32          |
+| y_out     | 0.0829      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06531572 |
+| time-step | 33         |
+| y_out     | -0.135     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061929334 |
+| time-step | 34          |
+| y_out     | 0.115       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060432475 |
+| time-step | 35          |
+| y_out     | -0.103      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057160437 |
+| time-step | 36          |
+| y_out     | 0.00449     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05663547 |
+| time-step | 37         |
+| y_out     | 0.129      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05235964 |
+| time-step | 38         |
+| y_out     | -0.0134    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051079948 |
+| time-step | 39          |
+| y_out     | 0.00503     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05068625 |
+| time-step | 40         |
+| y_out     | -0.0426    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04840982 |
+| time-step | 41         |
+| y_out     | -0.00698   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04626112 |
+| time-step | 42         |
+| y_out     | -0.0434    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04484333 |
+| time-step | 43         |
+| y_out     | -0.0343    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046863087 |
+| time-step | 44          |
+| y_out     | 0.0916      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046616856 |
+| time-step | 45          |
+| y_out     | -0.0186     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047773868 |
+| time-step | 46          |
+| y_out     | -0.0539     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04544707 |
+| time-step | 47         |
+| y_out     | 0.0443     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044461053 |
+| time-step | 48          |
+| y_out     | 0.0669      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044983987 |
+| time-step | 49          |
+| y_out     | 0.0983      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04420654 |
+| time-step | 50         |
+| y_out     | 0.00204    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045076367 |
+| time-step | 51          |
+| y_out     | 0.03        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045095697 |
+| time-step | 52          |
+| y_out     | -0.00356    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046461117 |
+| time-step | 53          |
+| y_out     | -0.0288     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04372434 |
+| time-step | 54         |
+| y_out     | -0.0175    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04359477 |
+| time-step | 55         |
+| y_out     | -0.128     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043051086 |
+| time-step | 56          |
+| y_out     | -0.0994     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043757685 |
+| time-step | 57          |
+| y_out     | -0.0302     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046227086 |
+| time-step | 58          |
+| y_out     | -0.0437     |
+------------------------------------------------------------
+---------------------------------------------------------
+| perf/mse  | 0.045824 |
+| time-step | 59       |
+| y_out     | -0.0202  |
+---------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044117607 |
+| time-step | 60          |
+| y_out     | -0.00417    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042604417 |
+| time-step | 61          |
+| y_out     | 0.0341      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043005396 |
+| time-step | 62          |
+| y_out     | -0.00509    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041401844 |
+| time-step | 63          |
+| y_out     | 0.0483      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040821463 |
+| time-step | 64          |
+| y_out     | -0.0328     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039660804 |
+| time-step | 65          |
+| y_out     | -0.0894     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03841579 |
+| time-step | 66         |
+| y_out     | 0.0557     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03781774 |
+| time-step | 67         |
+| y_out     | 0.114      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035546236 |
+| time-step | 68          |
+| y_out     | -0.0366     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03404691 |
+| time-step | 69         |
+| y_out     | -0.0222    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034336008 |
+| time-step | 70          |
+| y_out     | 0.0804      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03648937 |
+| time-step | 71         |
+| y_out     | -0.0365    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03634174 |
+| time-step | 72         |
+| y_out     | 0.057      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034494564 |
+| time-step | 73          |
+| y_out     | 0.00643     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033293024 |
+| time-step | 74          |
+| y_out     | 0.00552     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03323144 |
+| time-step | 75         |
+| y_out     | 0.0468     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034067787 |
+| time-step | 76          |
+| y_out     | -0.0139     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033351593 |
+| time-step | 77          |
+| y_out     | -0.0398     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032609988 |
+| time-step | 78          |
+| y_out     | 0.0358      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032117996 |
+| time-step | 79          |
+| y_out     | -0.0575     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033656016 |
+| time-step | 80          |
+| y_out     | 0.0211      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03157043 |
+| time-step | 81         |
+| y_out     | -0.0187    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030397505 |
+| time-step | 82          |
+| y_out     | 0.147       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031375386 |
+| time-step | 83          |
+| y_out     | -0.0468     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0331585 |
+| time-step | 84        |
+| y_out     | 0.0566    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03305576 |
+| time-step | 85         |
+| y_out     | 0.0877     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032709464 |
+| time-step | 86          |
+| y_out     | 0.0485      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0320201 |
+| time-step | 87        |
+| y_out     | -0.0536   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032831933 |
+| time-step | 88          |
+| y_out     | 0.0773      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033486065 |
+| time-step | 89          |
+| y_out     | 0.126       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032104082 |
+| time-step | 90          |
+| y_out     | -0.0475     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0311484 |
+| time-step | 91        |
+| y_out     | -0.0314   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031639583 |
+| time-step | 92          |
+| y_out     | -0.0322     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030790463 |
+| time-step | 93          |
+| y_out     | 0.0516      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030732488 |
+| time-step | 94          |
+| y_out     | 0.0592      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03033323 |
+| time-step | 95         |
+| y_out     | 0.00317    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030574158 |
+| time-step | 96          |
+| y_out     | 0.133       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030977616 |
+| time-step | 97          |
+| y_out     | 0.035       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029380009 |
+| time-step | 98          |
+| y_out     | 0.0651      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028496768 |
+| time-step | 99          |
+| y_out     | 0.00447     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028147172 |
+| time-step | 100         |
+| y_out     | 0.0809      |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 100
+------------------------------------------------------------
+| perf/mse  | 0.028606955 |
+| time-step | 101         |
+| y_out     | 0.00757     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027945647 |
+| time-step | 102         |
+| y_out     | -0.06       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028185958 |
+| time-step | 103         |
+| y_out     | -0.0608     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026932105 |
+| time-step | 104         |
+| y_out     | -0.192      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027318224 |
+| time-step | 105         |
+| y_out     | -0.0059     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02615488 |
+| time-step | 106        |
+| y_out     | -0.0368    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02680419 |
+| time-step | 107        |
+| y_out     | -0.0188    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026500195 |
+| time-step | 108         |
+| y_out     | -0.0346     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026046822 |
+| time-step | 109         |
+| y_out     | 0.151       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026122168 |
+| time-step | 110         |
+| y_out     | 0.0502      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026296128 |
+| time-step | 111         |
+| y_out     | -0.0766     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025212338 |
+| time-step | 112         |
+| y_out     | 0.11        |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02545307 |
+| time-step | 113        |
+| y_out     | 0.13       |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025583547 |
+| time-step | 114         |
+| y_out     | 0.0302      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024545776 |
+| time-step | 115         |
+| y_out     | -0.0325     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024057273 |
+| time-step | 116         |
+| y_out     | 0.115       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02244277 |
+| time-step | 117        |
+| y_out     | 0.0902     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022560945 |
+| time-step | 118         |
+| y_out     | 0.0403      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02307056 |
+| time-step | 119        |
+| y_out     | 0.157      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022394374 |
+| time-step | 120         |
+| y_out     | -0.0801     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021637758 |
+| time-step | 121         |
+| y_out     | -0.0606     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023614278 |
+| time-step | 122         |
+| y_out     | 0.0233      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022924874 |
+| time-step | 123         |
+| y_out     | 0.0966      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022113372 |
+| time-step | 124         |
+| y_out     | -0.0791     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022329802 |
+| time-step | 125         |
+| y_out     | -0.0896     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022729445 |
+| time-step | 126         |
+| y_out     | 0.0265      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023531575 |
+| time-step | 127         |
+| y_out     | -0.00328    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02450201 |
+| time-step | 128        |
+| y_out     | 0.00183    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0238791 |
+| time-step | 129       |
+| y_out     | 0.0177    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023837082 |
+| time-step | 130         |
+| y_out     | -0.0648     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023029322 |
+| time-step | 131         |
+| y_out     | -0.0334     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021849122 |
+| time-step | 132         |
+| y_out     | 0.0258      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021838527 |
+| time-step | 133         |
+| y_out     | 0.000211    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021615716 |
+| time-step | 134         |
+| y_out     | -0.158      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021900065 |
+| time-step | 135         |
+| y_out     | -0.178      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021565739 |
+| time-step | 136         |
+| y_out     | 0.0959      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021086615 |
+| time-step | 137         |
+| y_out     | 0.078       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019709218 |
+| time-step | 138         |
+| y_out     | 0.00684     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019687956 |
+| time-step | 139         |
+| y_out     | -0.000109   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019365186 |
+| time-step | 140         |
+| y_out     | 0.0968      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019639643 |
+| time-step | 141         |
+| y_out     | 0.042       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018927427 |
+| time-step | 142         |
+| y_out     | -0.00592    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018604886 |
+| time-step | 143         |
+| y_out     | -0.0829     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018862568 |
+| time-step | 144         |
+| y_out     | -0.0535     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018079463 |
+| time-step | 145         |
+| y_out     | 0.0251      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017920785 |
+| time-step | 146         |
+| y_out     | -0.00981    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017506853 |
+| time-step | 147         |
+| y_out     | -0.0881     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017187018 |
+| time-step | 148         |
+| y_out     | 0.00942     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01760382 |
+| time-step | 149        |
+| y_out     | 0.0663     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017503249 |
+| time-step | 150         |
+| y_out     | 0.0516      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017784001 |
+| time-step | 151         |
+| y_out     | -0.0723     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017888974 |
+| time-step | 152         |
+| y_out     | 0.036       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018136583 |
+| time-step | 153         |
+| y_out     | 0.00191     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018050347 |
+| time-step | 154         |
+| y_out     | -0.0105     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017828379 |
+| time-step | 155         |
+| y_out     | 0.0108      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017954323 |
+| time-step | 156         |
+| y_out     | -0.0918     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018049702 |
+| time-step | 157         |
+| y_out     | 0.0948      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018038373 |
+| time-step | 158         |
+| y_out     | -0.0615     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017828844 |
+| time-step | 159         |
+| y_out     | -0.0162     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018320028 |
+| time-step | 160         |
+| y_out     | -0.0668     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018292453 |
+| time-step | 161         |
+| y_out     | -0.00346    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01878645 |
+| time-step | 162        |
+| y_out     | -0.0552    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018440742 |
+| time-step | 163         |
+| y_out     | 0.0539      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018220808 |
+| time-step | 164         |
+| y_out     | 0.177       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018326517 |
+| time-step | 165         |
+| y_out     | 0.0654      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018403286 |
+| time-step | 166         |
+| y_out     | 0.0598      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018378321 |
+| time-step | 167         |
+| y_out     | 0.0508      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018518543 |
+| time-step | 168         |
+| y_out     | 0.13        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017999902 |
+| time-step | 169         |
+| y_out     | 0.143       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018018136 |
+| time-step | 170         |
+| y_out     | 0.0729      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017436014 |
+| time-step | 171         |
+| y_out     | -0.0233     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016493583 |
+| time-step | 172         |
+| y_out     | -0.023      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01667047 |
+| time-step | 173        |
+| y_out     | 0.0148     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016739586 |
+| time-step | 174         |
+| y_out     | 0.0475      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016361233 |
+| time-step | 175         |
+| y_out     | -0.0506     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01617429 |
+| time-step | 176        |
+| y_out     | -0.00511   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015391266 |
+| time-step | 177         |
+| y_out     | 0.0208      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015521039 |
+| time-step | 178         |
+| y_out     | 0.0143      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016098613 |
+| time-step | 179         |
+| y_out     | 0.0399      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015389146 |
+| time-step | 180         |
+| y_out     | 0.102       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01587503 |
+| time-step | 181        |
+| y_out     | 0.109      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015917998 |
+| time-step | 182         |
+| y_out     | -0.109      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015256567 |
+| time-step | 183         |
+| y_out     | 0.0305      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015143004 |
+| time-step | 184         |
+| y_out     | 0.0587      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015454161 |
+| time-step | 185         |
+| y_out     | 0.0947      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015269896 |
+| time-step | 186         |
+| y_out     | 0.129       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015705734 |
+| time-step | 187         |
+| y_out     | 0.0726      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015584086 |
+| time-step | 188         |
+| y_out     | -0.0888     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014840121 |
+| time-step | 189         |
+| y_out     | -0.13       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014894625 |
+| time-step | 190         |
+| y_out     | -0.111      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014608892 |
+| time-step | 191         |
+| y_out     | -0.0322     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014609566 |
+| time-step | 192         |
+| y_out     | 0.0314      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014993951 |
+| time-step | 193         |
+| y_out     | -0.0565     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015000495 |
+| time-step | 194         |
+| y_out     | -0.0133     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014394654 |
+| time-step | 195         |
+| y_out     | -0.0325     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013766756 |
+| time-step | 196         |
+| y_out     | 0.0385      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013613485 |
+| time-step | 197         |
+| y_out     | 0.0137      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013916832 |
+| time-step | 198         |
+| y_out     | 0.0227      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014203673 |
+| time-step | 199         |
+| y_out     | -0.0424     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013562766 |
+| time-step | 200         |
+| y_out     | 0.00657     |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 200
+------------------------------------------------------------
+| perf/mse  | 0.013382072 |
+| time-step | 201         |
+| y_out     | -0.0591     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01315968 |
+| time-step | 202        |
+| y_out     | 0.0126     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012887858 |
+| time-step | 203         |
+| y_out     | 0.0239      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01337339 |
+| time-step | 204        |
+| y_out     | -0.0578    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013425673 |
+| time-step | 205         |
+| y_out     | 0.0176      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014038166 |
+| time-step | 206         |
+| y_out     | 0.00637     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013868433 |
+| time-step | 207         |
+| y_out     | -0.0728     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01329882 |
+| time-step | 208        |
+| y_out     | 0.0671     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013069521 |
+| time-step | 209         |
+| y_out     | 0.0452      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0135223 |
+| time-step | 210       |
+| y_out     | 0.15      |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01441008 |
+| time-step | 211        |
+| y_out     | -0.0789    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014295165 |
+| time-step | 212         |
+| y_out     | -0.082      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01453729 |
+| time-step | 213        |
+| y_out     | -0.0197    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014061342 |
+| time-step | 214         |
+| y_out     | -0.106      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014587182 |
+| time-step | 215         |
+| y_out     | 0.0385      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0144081805 |
+| time-step | 216          |
+| y_out     | -0.0056      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014319906 |
+| time-step | 217         |
+| y_out     | -0.0673     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014193867 |
+| time-step | 218         |
+| y_out     | -0.0344     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014007312 |
+| time-step | 219         |
+| y_out     | 0.0441      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013912772 |
+| time-step | 220         |
+| y_out     | 0.00299     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012988779 |
+| time-step | 221         |
+| y_out     | -0.0634     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012617411 |
+| time-step | 222         |
+| y_out     | -0.0169     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012815865 |
+| time-step | 223         |
+| y_out     | 0.0799      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012343487 |
+| time-step | 224         |
+| y_out     | 0.00233     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011513001 |
+| time-step | 225         |
+| y_out     | 0.0507      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011502563 |
+| time-step | 226         |
+| y_out     | -0.219      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011924874 |
+| time-step | 227         |
+| y_out     | -0.0398     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012385877 |
+| time-step | 228         |
+| y_out     | 0.041       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012254153 |
+| time-step | 229         |
+| y_out     | -0.00562    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012247067 |
+| time-step | 230         |
+| y_out     | -0.0154     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012499314 |
+| time-step | 231         |
+| y_out     | 0.0487      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01362699 |
+| time-step | 232        |
+| y_out     | -0.0415    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013595112 |
+| time-step | 233         |
+| y_out     | 0.0409      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014221516 |
+| time-step | 234         |
+| y_out     | 0.0317      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014418885 |
+| time-step | 235         |
+| y_out     | -0.0367     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014496855 |
+| time-step | 236         |
+| y_out     | -0.137      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014122048 |
+| time-step | 237         |
+| y_out     | -0.0409     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0140051795 |
+| time-step | 238          |
+| y_out     | -0.0658      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014470877 |
+| time-step | 239         |
+| y_out     | -0.0863     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014183926 |
+| time-step | 240         |
+| y_out     | 0.0133      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013880688 |
+| time-step | 241         |
+| y_out     | 0.0122      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01320968 |
+| time-step | 242        |
+| y_out     | 0.0467     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0130267665 |
+| time-step | 243          |
+| y_out     | -0.0753      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01298934 |
+| time-step | 244        |
+| y_out     | -0.0338    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012912792 |
+| time-step | 245         |
+| y_out     | -0.0476     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012643425 |
+| time-step | 246         |
+| y_out     | 0.0185      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012662187 |
+| time-step | 247         |
+| y_out     | 0.0885      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012426092 |
+| time-step | 248         |
+| y_out     | -0.0377     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011928229 |
+| time-step | 249         |
+| y_out     | 0.0316      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011977758 |
+| time-step | 250         |
+| y_out     | 0.0757      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012049417 |
+| time-step | 251         |
+| y_out     | 0.102       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012098239 |
+| time-step | 252         |
+| y_out     | -0.0765     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011614481 |
+| time-step | 253         |
+| y_out     | 0.0093      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011484854 |
+| time-step | 254         |
+| y_out     | 0.0177      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011765086 |
+| time-step | 255         |
+| y_out     | 0.0297      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011991626 |
+| time-step | 256         |
+| y_out     | -0.0132     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0120916385 |
+| time-step | 257          |
+| y_out     | -0.00747     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012188358 |
+| time-step | 258         |
+| y_out     | -0.00225    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0122345975 |
+| time-step | 259          |
+| y_out     | 0.0485       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012064756 |
+| time-step | 260         |
+| y_out     | 0.0117      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011673996 |
+| time-step | 261         |
+| y_out     | -0.027      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011549339 |
+| time-step | 262         |
+| y_out     | -0.0442     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011740644 |
+| time-step | 263         |
+| y_out     | 0.0691      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011079816 |
+| time-step | 264         |
+| y_out     | -0.0513     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010692937 |
+| time-step | 265         |
+| y_out     | -0.103      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010324869 |
+| time-step | 266         |
+| y_out     | 0.0518      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00977801 |
+| time-step | 267        |
+| y_out     | 0.11       |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009445672 |
+| time-step | 268         |
+| y_out     | -0.0976     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009423474 |
+| time-step | 269         |
+| y_out     | -0.0939     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009913127 |
+| time-step | 270         |
+| y_out     | 0.0384      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010573848 |
+| time-step | 271         |
+| y_out     | 0.0124      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010515319 |
+| time-step | 272         |
+| y_out     | 7.88e-05    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010208281 |
+| time-step | 273         |
+| y_out     | -0.134      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010351671 |
+| time-step | 274         |
+| y_out     | -0.031      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010713759 |
+| time-step | 275         |
+| y_out     | -0.0226     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010491295 |
+| time-step | 276         |
+| y_out     | 0.00212     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010827411 |
+| time-step | 277         |
+| y_out     | -0.0396     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010586554 |
+| time-step | 278         |
+| y_out     | -0.0562     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01052269 |
+| time-step | 279        |
+| y_out     | -0.0824    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010579078 |
+| time-step | 280         |
+| y_out     | -0.0348     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010243152 |
+| time-step | 281         |
+| y_out     | -0.14       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010236494 |
+| time-step | 282         |
+| y_out     | 0.0369      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010239321 |
+| time-step | 283         |
+| y_out     | 0.0211      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010472357 |
+| time-step | 284         |
+| y_out     | 0.169       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010008585 |
+| time-step | 285         |
+| y_out     | -0.0163     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010203567 |
+| time-step | 286         |
+| y_out     | -0.0955     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010348397 |
+| time-step | 287         |
+| y_out     | -0.0134     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010345891 |
+| time-step | 288         |
+| y_out     | 0.0673      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010700961 |
+| time-step | 289         |
+| y_out     | 0.0279      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010388944 |
+| time-step | 290         |
+| y_out     | 0.039       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0106908465 |
+| time-step | 291          |
+| y_out     | -0.0646      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010309842 |
+| time-step | 292         |
+| y_out     | -0.00861    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010474081 |
+| time-step | 293         |
+| y_out     | 0.126       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010539789 |
+| time-step | 294         |
+| y_out     | -0.0214     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010585275 |
+| time-step | 295         |
+| y_out     | 0.0815      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010429607 |
+| time-step | 296         |
+| y_out     | 0.0479      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010243757 |
+| time-step | 297         |
+| y_out     | 0.0108      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010742357 |
+| time-step | 298         |
+| y_out     | -0.0382     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010794876 |
+| time-step | 299         |
+| y_out     | 0.00499     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010691104 |
+| time-step | 300         |
+| y_out     | -0.0847     |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 300
+------------------------------------------------------------
+| perf/mse  | 0.010032582 |
+| time-step | 301         |
+| y_out     | 0.104       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010654226 |
+| time-step | 302         |
+| y_out     | -0.146      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010426847 |
+| time-step | 303         |
+| y_out     | -0.0516     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010203677 |
+| time-step | 304         |
+| y_out     | -0.00144    |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0099885 |
+| time-step | 305       |
+| y_out     | -0.0273   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009654961 |
+| time-step | 306         |
+| y_out     | -0.128      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0093472665 |
+| time-step | 307          |
+| y_out     | 0.0159       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009603806 |
+| time-step | 308         |
+| y_out     | 0.0653      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009766078 |
+| time-step | 309         |
+| y_out     | 0.0162      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009610362 |
+| time-step | 310         |
+| y_out     | 0.0457      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009599518 |
+| time-step | 311         |
+| y_out     | 0.0568      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009064664 |
+| time-step | 312         |
+| y_out     | -0.035      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009033918 |
+| time-step | 313         |
+| y_out     | -0.0416     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009256901 |
+| time-step | 314         |
+| y_out     | -0.0177     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009281759 |
+| time-step | 315         |
+| y_out     | 0.0364      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0097410735 |
+| time-step | 316          |
+| y_out     | -0.084       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010113874 |
+| time-step | 317         |
+| y_out     | 0.0209      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009633789 |
+| time-step | 318         |
+| y_out     | -0.0276     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009057928 |
+| time-step | 319         |
+| y_out     | -0.0024     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009432124 |
+| time-step | 320         |
+| y_out     | 0.103       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009712348 |
+| time-step | 321         |
+| y_out     | -0.00902    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009893559 |
+| time-step | 322         |
+| y_out     | 0.0358      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010303207 |
+| time-step | 323         |
+| y_out     | 0.144       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009784044 |
+| time-step | 324         |
+| y_out     | -0.0916     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010053712 |
+| time-step | 325         |
+| y_out     | -0.0956     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00999709 |
+| time-step | 326        |
+| y_out     | -0.0276    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009864601 |
+| time-step | 327         |
+| y_out     | -0.0115     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010140386 |
+| time-step | 328         |
+| y_out     | 0.0272      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010266502 |
+| time-step | 329         |
+| y_out     | 0.0206      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010139539 |
+| time-step | 330         |
+| y_out     | -0.0627     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010079867 |
+| time-step | 331         |
+| y_out     | -0.0129     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0100426935 |
+| time-step | 332          |
+| y_out     | 0.0306       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0098304525 |
+| time-step | 333          |
+| y_out     | 0.137        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009680955 |
+| time-step | 334         |
+| y_out     | 0.00633     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00940544 |
+| time-step | 335        |
+| y_out     | -0.0228    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009379968 |
+| time-step | 336         |
+| y_out     | -0.000607   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009440757 |
+| time-step | 337         |
+| y_out     | 0.0379      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009191423 |
+| time-step | 338         |
+| y_out     | 0.0463      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00887909 |
+| time-step | 339        |
+| y_out     | -0.1       |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008715235 |
+| time-step | 340         |
+| y_out     | -0.0349     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00873994 |
+| time-step | 341        |
+| y_out     | 0.139      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008850156 |
+| time-step | 342         |
+| y_out     | -0.00513    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0087323515 |
+| time-step | 343          |
+| y_out     | 0.172        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008909301 |
+| time-step | 344         |
+| y_out     | 0.0901      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008655539 |
+| time-step | 345         |
+| y_out     | 0.0471      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008628974 |
+| time-step | 346         |
+| y_out     | 0.123       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008353037 |
+| time-step | 347         |
+| y_out     | -0.0302     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008267693 |
+| time-step | 348         |
+| y_out     | 0.12        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008761351 |
+| time-step | 349         |
+| y_out     | -0.0754     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008686851 |
+| time-step | 350         |
+| y_out     | -0.05       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008627215 |
+| time-step | 351         |
+| y_out     | -0.0427     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0084546115 |
+| time-step | 352          |
+| y_out     | 0.0676       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008602088 |
+| time-step | 353         |
+| y_out     | 0.0864      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00847422 |
+| time-step | 354        |
+| y_out     | -0.00837   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008744193 |
+| time-step | 355         |
+| y_out     | -0.0775     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008688221 |
+| time-step | 356         |
+| y_out     | -0.0353     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008734159 |
+| time-step | 357         |
+| y_out     | 0.035       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008668827 |
+| time-step | 358         |
+| y_out     | 0.142       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008293263 |
+| time-step | 359         |
+| y_out     | -0.104      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008320389 |
+| time-step | 360         |
+| y_out     | -0.0417     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008116147 |
+| time-step | 361         |
+| y_out     | 0.0219      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008200083 |
+| time-step | 362         |
+| y_out     | 0.0554      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008153895 |
+| time-step | 363         |
+| y_out     | 0.0382      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0086259 |
+| time-step | 364       |
+| y_out     | -0.143    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008612452 |
+| time-step | 365         |
+| y_out     | 0.0177      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008708006 |
+| time-step | 366         |
+| y_out     | -0.029      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008772651 |
+| time-step | 367         |
+| y_out     | 0.118       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008839598 |
+| time-step | 368         |
+| y_out     | 0.0212      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008819107 |
+| time-step | 369         |
+| y_out     | -0.0621     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009045528 |
+| time-step | 370         |
+| y_out     | 0.0192      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008872421 |
+| time-step | 371         |
+| y_out     | 0.106       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008818625 |
+| time-step | 372         |
+| y_out     | -0.00862    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008557636 |
+| time-step | 373         |
+| y_out     | -0.0271     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008084479 |
+| time-step | 374         |
+| y_out     | -0.15       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0078722015 |
+| time-step | 375          |
+| y_out     | 0.0263       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007573083 |
+| time-step | 376         |
+| y_out     | 0.035       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00774151 |
+| time-step | 377        |
+| y_out     | -0.0983    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007743551 |
+| time-step | 378         |
+| y_out     | -0.0231     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076592416 |
+| time-step | 379          |
+| y_out     | 0.00175      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0075029926 |
+| time-step | 380          |
+| y_out     | 0.00758      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0075995037 |
+| time-step | 381          |
+| y_out     | -0.157       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0075590387 |
+| time-step | 382          |
+| y_out     | -0.0401      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007698643 |
+| time-step | 383         |
+| y_out     | 0.062       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007813014 |
+| time-step | 384         |
+| y_out     | -0.0452     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008237163 |
+| time-step | 385         |
+| y_out     | 0.0324      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008597644 |
+| time-step | 386         |
+| y_out     | 0.0444      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008355604 |
+| time-step | 387         |
+| y_out     | -0.111      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008163278 |
+| time-step | 388         |
+| y_out     | -0.127      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008101468 |
+| time-step | 389         |
+| y_out     | 0.0488      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008072455 |
+| time-step | 390         |
+| y_out     | -0.0146     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008196184 |
+| time-step | 391         |
+| y_out     | -0.00761    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00814244 |
+| time-step | 392        |
+| y_out     | 0.0353     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008040335 |
+| time-step | 393         |
+| y_out     | 0.107       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008026287 |
+| time-step | 394         |
+| y_out     | -0.0415     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007766598 |
+| time-step | 395         |
+| y_out     | 0.0852      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074539334 |
+| time-step | 396          |
+| y_out     | 0.0612       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0076807747 |
+| time-step | 397          |
+| y_out     | 0.134        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007772682 |
+| time-step | 398         |
+| y_out     | -0.0239     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007994756 |
+| time-step | 399         |
+| y_out     | 0.00977     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008108705 |
+| time-step | 400         |
+| y_out     | 0.0322      |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 400
+------------------------------------------------------------
+| perf/mse  | 0.008036339 |
+| time-step | 401         |
+| y_out     | -0.0411     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008008949 |
+| time-step | 402         |
+| y_out     | -0.0747     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00787331 |
+| time-step | 403        |
+| y_out     | 0.0745     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007942772 |
+| time-step | 404         |
+| y_out     | -0.159      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007894406 |
+| time-step | 405         |
+| y_out     | -0.0197     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007802221 |
+| time-step | 406         |
+| y_out     | 0.0337      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007519926 |
+| time-step | 407         |
+| y_out     | 0.0518      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007418646 |
+| time-step | 408         |
+| y_out     | -0.0352     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070824646 |
+| time-step | 409          |
+| y_out     | -0.000199    |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00677785 |
+| time-step | 410        |
+| y_out     | -0.0157    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006938192 |
+| time-step | 411         |
+| y_out     | 0.0549      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067039416 |
+| time-step | 412          |
+| y_out     | 0.098        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006594211 |
+| time-step | 413         |
+| y_out     | 0.106       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064013363 |
+| time-step | 414          |
+| y_out     | 0.0183       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006770623 |
+| time-step | 415         |
+| y_out     | -0.0762     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006823994 |
+| time-step | 416         |
+| y_out     | 0.104       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067184344 |
+| time-step | 417          |
+| y_out     | -0.111       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0066474816 |
+| time-step | 418          |
+| y_out     | 0.0114       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007046877 |
+| time-step | 419         |
+| y_out     | -0.0202     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0072142407 |
+| time-step | 420          |
+| y_out     | 0.0401       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073034638 |
+| time-step | 421          |
+| y_out     | -0.056       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0077653467 |
+| time-step | 422          |
+| y_out     | -0.0614      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008059444 |
+| time-step | 423         |
+| y_out     | -0.105      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008089931 |
+| time-step | 424         |
+| y_out     | -0.0421     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007656876 |
+| time-step | 425         |
+| y_out     | 0.177       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00780701 |
+| time-step | 426        |
+| y_out     | -0.0339    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00797386 |
+| time-step | 427        |
+| y_out     | 0.0874     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008552184 |
+| time-step | 428         |
+| y_out     | 0.0294      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008538587 |
+| time-step | 429         |
+| y_out     | -0.119      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008467974 |
+| time-step | 430         |
+| y_out     | 0.0259      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008256846 |
+| time-step | 431         |
+| y_out     | -0.0598     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008384232 |
+| time-step | 432         |
+| y_out     | -0.0264     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008347852 |
+| time-step | 433         |
+| y_out     | -0.0434     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00868402 |
+| time-step | 434        |
+| y_out     | 0.026      |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0086899875 |
+| time-step | 435          |
+| y_out     | -0.119       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008616216 |
+| time-step | 436         |
+| y_out     | 0.0874      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00865731 |
+| time-step | 437        |
+| y_out     | 0.0563     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007961267 |
+| time-step | 438         |
+| y_out     | -0.0287     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008033151 |
+| time-step | 439         |
+| y_out     | -0.0434     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0082433615 |
+| time-step | 440          |
+| y_out     | -0.0495      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008022939 |
+| time-step | 441         |
+| y_out     | 0.0675      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074998075 |
+| time-step | 442          |
+| y_out     | 0.0991       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073705884 |
+| time-step | 443          |
+| y_out     | 0.0225       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070143277 |
+| time-step | 444          |
+| y_out     | 0.0851       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069089322 |
+| time-step | 445          |
+| y_out     | -0.0629      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006694626 |
+| time-step | 446         |
+| y_out     | 0.0294      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065240427 |
+| time-step | 447          |
+| y_out     | -0.00337     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067109377 |
+| time-step | 448          |
+| y_out     | 0.052        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006611994 |
+| time-step | 449         |
+| y_out     | 0.0148      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062818853 |
+| time-step | 450          |
+| y_out     | -0.0841      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064523383 |
+| time-step | 451          |
+| y_out     | 0.033        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064939125 |
+| time-step | 452          |
+| y_out     | 0.00723      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006648867 |
+| time-step | 453         |
+| y_out     | -0.0725     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006654945 |
+| time-step | 454         |
+| y_out     | -0.000962   |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0068740114 |
+| time-step | 455          |
+| y_out     | -0.0239      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0072521814 |
+| time-step | 456          |
+| y_out     | 0.0407       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007261589 |
+| time-step | 457         |
+| y_out     | -0.132      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0072377757 |
+| time-step | 458          |
+| y_out     | -0.0976      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070746467 |
+| time-step | 459          |
+| y_out     | -0.047       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073849633 |
+| time-step | 460          |
+| y_out     | 0.143        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074552074 |
+| time-step | 461          |
+| y_out     | 0.105        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007544022 |
+| time-step | 462         |
+| y_out     | 0.0904      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007212624 |
+| time-step | 463         |
+| y_out     | 0.0794      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007286919 |
+| time-step | 464         |
+| y_out     | -5.91e-05   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007602378 |
+| time-step | 465         |
+| y_out     | -0.0872     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073095886 |
+| time-step | 466          |
+| y_out     | -0.0568      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007411178 |
+| time-step | 467         |
+| y_out     | 0.0148      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073589766 |
+| time-step | 468          |
+| y_out     | -0.033       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0072832955 |
+| time-step | 469          |
+| y_out     | -0.000327    |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007596711 |
+| time-step | 470         |
+| y_out     | -0.143      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0075078136 |
+| time-step | 471          |
+| y_out     | 0.0379       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0074071614 |
+| time-step | 472          |
+| y_out     | -9.85e-05    |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007423722 |
+| time-step | 473         |
+| y_out     | -0.153      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0073407656 |
+| time-step | 474          |
+| y_out     | 0.073        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069066794 |
+| time-step | 475          |
+| y_out     | -0.0101      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007060787 |
+| time-step | 476         |
+| y_out     | 0.065       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007000332 |
+| time-step | 477         |
+| y_out     | 0.0805      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070274533 |
+| time-step | 478          |
+| y_out     | 0.00416      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007200422 |
+| time-step | 479         |
+| y_out     | 0.0168      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067265322 |
+| time-step | 480          |
+| y_out     | 0.00751      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006689547 |
+| time-step | 481         |
+| y_out     | -0.089      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006638625 |
+| time-step | 482         |
+| y_out     | -0.0729     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0066219317 |
+| time-step | 483          |
+| y_out     | -0.0736      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006606955 |
+| time-step | 484         |
+| y_out     | -0.0124     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006765942 |
+| time-step | 485         |
+| y_out     | -0.049      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065927855 |
+| time-step | 486          |
+| y_out     | 0.184        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006678297 |
+| time-step | 487         |
+| y_out     | 0.103       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0066388734 |
+| time-step | 488          |
+| y_out     | 0.0338       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006275315 |
+| time-step | 489         |
+| y_out     | -0.0526     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061541293 |
+| time-step | 490          |
+| y_out     | -0.0985      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006242218 |
+| time-step | 491         |
+| y_out     | 0.000441    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063144267 |
+| time-step | 492          |
+| y_out     | 0.0538       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006560821 |
+| time-step | 493         |
+| y_out     | -0.028      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006544578 |
+| time-step | 494         |
+| y_out     | -0.0715     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006322493 |
+| time-step | 495         |
+| y_out     | -0.0169     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006461198 |
+| time-step | 496         |
+| y_out     | -0.0957     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062784078 |
+| time-step | 497          |
+| y_out     | 0.0241       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063545927 |
+| time-step | 498          |
+| y_out     | 0.0334       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006499437 |
+| time-step | 499         |
+| y_out     | 0.0615      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064413697 |
+| time-step | 500          |
+| y_out     | 0.109        |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 500
+-----------------------------------------------------------
+| perf/mse  | 0.00621063 |
+| time-step | 501        |
+| y_out     | 0.012      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006231707 |
+| time-step | 502         |
+| y_out     | -0.0184     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063161002 |
+| time-step | 503          |
+| y_out     | -0.053       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064998628 |
+| time-step | 504          |
+| y_out     | 0.00578      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006720625 |
+| time-step | 505         |
+| y_out     | 0.0142      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00659097 |
+| time-step | 506        |
+| y_out     | 0.0112     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064210193 |
+| time-step | 507          |
+| y_out     | -0.0567      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063262633 |
+| time-step | 508          |
+| y_out     | 0.0599       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006205489 |
+| time-step | 509         |
+| y_out     | -0.0121     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0064683952 |
+| time-step | 510          |
+| y_out     | 0.132        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069108633 |
+| time-step | 511          |
+| y_out     | -0.0124      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067900373 |
+| time-step | 512          |
+| y_out     | 0.0542       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00673942 |
+| time-step | 513        |
+| y_out     | -0.0249    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065494156 |
+| time-step | 514          |
+| y_out     | -0.02        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006396496 |
+| time-step | 515         |
+| y_out     | 0.153       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006454207 |
+| time-step | 516         |
+| y_out     | 0.0591      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006593805 |
+| time-step | 517         |
+| y_out     | -0.0731     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006836043 |
+| time-step | 518         |
+| y_out     | -0.00846    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006825322 |
+| time-step | 519         |
+| y_out     | -0.0152     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006663178 |
+| time-step | 520         |
+| y_out     | -0.14       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0066322843 |
+| time-step | 521          |
+| y_out     | 0.0598       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0068622595 |
+| time-step | 522          |
+| y_out     | 0.0236       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067408183 |
+| time-step | 523          |
+| y_out     | -0.0177      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069634477 |
+| time-step | 524          |
+| y_out     | 0.138        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070486367 |
+| time-step | 525          |
+| y_out     | 0.0442       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.007063304 |
+| time-step | 526         |
+| y_out     | -0.107      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0071476838 |
+| time-step | 527          |
+| y_out     | 0.041        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006980233 |
+| time-step | 528         |
+| y_out     | 0.0512      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0069410824 |
+| time-step | 529          |
+| y_out     | 0.0272       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0070783896 |
+| time-step | 530          |
+| y_out     | -0.0168      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0067939656 |
+| time-step | 531          |
+| y_out     | -0.0117      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0065552196 |
+| time-step | 532          |
+| y_out     | 0.118        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006775963 |
+| time-step | 533         |
+| y_out     | 0.00576     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006312805 |
+| time-step | 534         |
+| y_out     | -0.0186     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059630363 |
+| time-step | 535          |
+| y_out     | -0.0408      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006159703 |
+| time-step | 536         |
+| y_out     | -0.011      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062006665 |
+| time-step | 537          |
+| y_out     | 0.0221       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060760686 |
+| time-step | 538          |
+| y_out     | -0.0104      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061911535 |
+| time-step | 539          |
+| y_out     | -0.0475      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006080167 |
+| time-step | 540         |
+| y_out     | -0.086      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00597334 |
+| time-step | 541        |
+| y_out     | 0.146      |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060778647 |
+| time-step | 542          |
+| y_out     | -0.0287      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005795536 |
+| time-step | 543         |
+| y_out     | 0.00836     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061646267 |
+| time-step | 544          |
+| y_out     | 0.0687       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063526086 |
+| time-step | 545          |
+| y_out     | -0.0461      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061190845 |
+| time-step | 546          |
+| y_out     | 0.00884      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058716508 |
+| time-step | 547          |
+| y_out     | -0.0454      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005901403 |
+| time-step | 548         |
+| y_out     | -0.154      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058907983 |
+| time-step | 549          |
+| y_out     | 0.0456       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005808233 |
+| time-step | 550         |
+| y_out     | -0.0928     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058353427 |
+| time-step | 551          |
+| y_out     | 0.02         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057009934 |
+| time-step | 552          |
+| y_out     | 0.0961       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005772243 |
+| time-step | 553         |
+| y_out     | -0.00203    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056734663 |
+| time-step | 554          |
+| y_out     | -0.0552      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005680301 |
+| time-step | 555         |
+| y_out     | -0.0595     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005705866 |
+| time-step | 556         |
+| y_out     | -0.0109     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005871755 |
+| time-step | 557         |
+| y_out     | 0.0127      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005826918 |
+| time-step | 558         |
+| y_out     | -0.0945     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005780345 |
+| time-step | 559         |
+| y_out     | 0.0226      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0060133236 |
+| time-step | 560          |
+| y_out     | 0.111        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006240505 |
+| time-step | 561         |
+| y_out     | 0.0292      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0062749935 |
+| time-step | 562          |
+| y_out     | -0.0935      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061353166 |
+| time-step | 563          |
+| y_out     | 0.0228       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006270499 |
+| time-step | 564         |
+| y_out     | 0.0055      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061956593 |
+| time-step | 565          |
+| y_out     | 0.0216       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00631012 |
+| time-step | 566        |
+| y_out     | 0.0286     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006206002 |
+| time-step | 567         |
+| y_out     | 0.0255      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.006253851 |
+| time-step | 568         |
+| y_out     | -0.09       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0063427994 |
+| time-step | 569          |
+| y_out     | -0.0291      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0059592677 |
+| time-step | 570          |
+| y_out     | 0.0627       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0057757176 |
+| time-step | 571          |
+| y_out     | 0.00575      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00569206 |
+| time-step | 572        |
+| y_out     | -0.151     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005986777 |
+| time-step | 573         |
+| y_out     | -0.0331     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058613303 |
+| time-step | 574          |
+| y_out     | 0.0705       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005958927 |
+| time-step | 575         |
+| y_out     | 0.0258      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005617448 |
+| time-step | 576         |
+| y_out     | 0.0104      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055160574 |
+| time-step | 577          |
+| y_out     | -0.00768     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052953125 |
+| time-step | 578          |
+| y_out     | -0.0603      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052530663 |
+| time-step | 579          |
+| y_out     | 0.00334      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054323934 |
+| time-step | 580          |
+| y_out     | -0.05        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0054982672 |
+| time-step | 581          |
+| y_out     | -0.032       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056226975 |
+| time-step | 582          |
+| y_out     | -0.0671      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055458853 |
+| time-step | 583          |
+| y_out     | -0.0829      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053948304 |
+| time-step | 584          |
+| y_out     | 0.0316       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053327624 |
+| time-step | 585          |
+| y_out     | 0.00731      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005581173 |
+| time-step | 586         |
+| y_out     | 0.0623      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005891134 |
+| time-step | 587         |
+| y_out     | -0.054      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0061695324 |
+| time-step | 588          |
+| y_out     | -0.0583      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005980528 |
+| time-step | 589         |
+| y_out     | -0.0325     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0058547505 |
+| time-step | 590          |
+| y_out     | 0.0358       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0056473007 |
+| time-step | 591          |
+| y_out     | 0.106        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0055733095 |
+| time-step | 592          |
+| y_out     | -0.00866     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005406431 |
+| time-step | 593         |
+| y_out     | -0.045      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00548005 |
+| time-step | 594        |
+| y_out     | 0.108      |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053556636 |
+| time-step | 595          |
+| y_out     | 0.072        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005052309 |
+| time-step | 596         |
+| y_out     | 0.0143      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004848642 |
+| time-step | 597         |
+| y_out     | 0.157       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045913546 |
+| time-step | 598          |
+| y_out     | -0.0766      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047361376 |
+| time-step | 599          |
+| y_out     | 0.0636       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004598742 |
+| time-step | 600         |
+| y_out     | 0.0382      |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 600
+------------------------------------------------------------
+| perf/mse  | 0.004634972 |
+| time-step | 601         |
+| y_out     | 0.0657      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004533014 |
+| time-step | 602         |
+| y_out     | 0.0252      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004535699 |
+| time-step | 603         |
+| y_out     | 0.0401      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044071414 |
+| time-step | 604          |
+| y_out     | 0.0942       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047056647 |
+| time-step | 605          |
+| y_out     | 0.0641       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046403827 |
+| time-step | 606          |
+| y_out     | -0.0326      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004571066 |
+| time-step | 607         |
+| y_out     | -0.0446     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004566402 |
+| time-step | 608         |
+| y_out     | -0.0582     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044177594 |
+| time-step | 609          |
+| y_out     | -0.032       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004487564 |
+| time-step | 610         |
+| y_out     | -0.0521     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00452708 |
+| time-step | 611        |
+| y_out     | 0.0582     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004643608 |
+| time-step | 612         |
+| y_out     | -0.0341     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046588853 |
+| time-step | 613          |
+| y_out     | 0.0828       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048282165 |
+| time-step | 614          |
+| y_out     | 0.0704       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045193206 |
+| time-step | 615          |
+| y_out     | 0.0342       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046170405 |
+| time-step | 616          |
+| y_out     | 0.0454       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004550939 |
+| time-step | 617         |
+| y_out     | 0.0189      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004781705 |
+| time-step | 618         |
+| y_out     | -0.137      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00489563 |
+| time-step | 619        |
+| y_out     | -0.00544   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004941295 |
+| time-step | 620         |
+| y_out     | -0.0303     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049533835 |
+| time-step | 621          |
+| y_out     | 0.0403       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049276003 |
+| time-step | 622          |
+| y_out     | -0.0685      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048876107 |
+| time-step | 623          |
+| y_out     | -0.051       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048700334 |
+| time-step | 624          |
+| y_out     | -0.0602      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050332868 |
+| time-step | 625          |
+| y_out     | 0.069        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051972196 |
+| time-step | 626          |
+| y_out     | 0.00514      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052523883 |
+| time-step | 627          |
+| y_out     | 0.00444      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051567964 |
+| time-step | 628          |
+| y_out     | -0.0668      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005077093 |
+| time-step | 629         |
+| y_out     | 0.0337      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051636947 |
+| time-step | 630          |
+| y_out     | -0.0681      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005091381 |
+| time-step | 631         |
+| y_out     | -0.0209     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050412402 |
+| time-step | 632          |
+| y_out     | -0.0862      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0053023286 |
+| time-step | 633          |
+| y_out     | 0.164        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005147076 |
+| time-step | 634         |
+| y_out     | 0.0776      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005135088 |
+| time-step | 635         |
+| y_out     | 0.0359      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051928936 |
+| time-step | 636          |
+| y_out     | -0.00876     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005089516 |
+| time-step | 637         |
+| y_out     | -0.000341   |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050600795 |
+| time-step | 638          |
+| y_out     | 0.0105       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051478497 |
+| time-step | 639          |
+| y_out     | -0.0707      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005037625 |
+| time-step | 640         |
+| y_out     | -0.0135     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0052268542 |
+| time-step | 641          |
+| y_out     | 0.0146       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0051815296 |
+| time-step | 642          |
+| y_out     | -0.00587     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048808097 |
+| time-step | 643          |
+| y_out     | -0.00185     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004830329 |
+| time-step | 644         |
+| y_out     | 0.0259      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004680804 |
+| time-step | 645         |
+| y_out     | -0.0285     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044285273 |
+| time-step | 646          |
+| y_out     | -0.0135      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0045601004 |
+| time-step | 647          |
+| y_out     | -0.0323      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047163423 |
+| time-step | 648          |
+| y_out     | -0.0383      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004692768 |
+| time-step | 649         |
+| y_out     | 0.0226      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004637492 |
+| time-step | 650         |
+| y_out     | 0.0851      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044860104 |
+| time-step | 651          |
+| y_out     | 0.000304     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004424686 |
+| time-step | 652         |
+| y_out     | -0.019      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004633838 |
+| time-step | 653         |
+| y_out     | -0.0166     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0046405606 |
+| time-step | 654          |
+| y_out     | -0.061       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047650584 |
+| time-step | 655          |
+| y_out     | 0.00763      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049680765 |
+| time-step | 656          |
+| y_out     | 0.0825       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049326844 |
+| time-step | 657          |
+| y_out     | -0.0718      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047585946 |
+| time-step | 658          |
+| y_out     | -0.0537      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047918633 |
+| time-step | 659          |
+| y_out     | -0.00843     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004724721 |
+| time-step | 660         |
+| y_out     | 0.0989      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048890566 |
+| time-step | 661          |
+| y_out     | -0.00455     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004971376 |
+| time-step | 662         |
+| y_out     | 0.105       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004909157 |
+| time-step | 663         |
+| y_out     | -0.0112     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005111843 |
+| time-step | 664         |
+| y_out     | 0.0775      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049361186 |
+| time-step | 665          |
+| y_out     | 0.0258       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047376654 |
+| time-step | 666          |
+| y_out     | 0.0134       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004892788 |
+| time-step | 667         |
+| y_out     | -0.0873     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048651057 |
+| time-step | 668          |
+| y_out     | -0.00773     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004750267 |
+| time-step | 669         |
+| y_out     | 0.182       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049602417 |
+| time-step | 670          |
+| y_out     | 0.0335       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004778258 |
+| time-step | 671         |
+| y_out     | 0.0294      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004826422 |
+| time-step | 672         |
+| y_out     | 0.00771     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004779159 |
+| time-step | 673         |
+| y_out     | -0.118      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004690573 |
+| time-step | 674         |
+| y_out     | 0.0551      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004945249 |
+| time-step | 675         |
+| y_out     | -0.0269     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005086684 |
+| time-step | 676         |
+| y_out     | -0.033      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0048571876 |
+| time-step | 677          |
+| y_out     | -0.0537      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049944143 |
+| time-step | 678          |
+| y_out     | -0.0888      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005010283 |
+| time-step | 679         |
+| y_out     | 0.037       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050083688 |
+| time-step | 680          |
+| y_out     | -0.113       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050245402 |
+| time-step | 681          |
+| y_out     | 0.00984      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0050216233 |
+| time-step | 682          |
+| y_out     | -0.0832      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.005127184 |
+| time-step | 683         |
+| y_out     | 0.0783      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0049683326 |
+| time-step | 684          |
+| y_out     | 0.0219       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0047148704 |
+| time-step | 685          |
+| y_out     | 0.0558       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004610197 |
+| time-step | 686         |
+| y_out     | -0.127      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004741398 |
+| time-step | 687         |
+| y_out     | -0.109      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004597264 |
+| time-step | 688         |
+| y_out     | 0.0303      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004618845 |
+| time-step | 689         |
+| y_out     | 0.0159      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004414019 |
+| time-step | 690         |
+| y_out     | -0.139      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004552943 |
+| time-step | 691         |
+| y_out     | 0.0541      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00442238 |
+| time-step | 692        |
+| y_out     | -0.0234    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041751354 |
+| time-step | 693          |
+| y_out     | 0.053        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004106088 |
+| time-step | 694         |
+| y_out     | -0.0462     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004189212 |
+| time-step | 695         |
+| y_out     | 0.0178      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040491535 |
+| time-step | 696          |
+| y_out     | -0.0328      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039100004 |
+| time-step | 697          |
+| y_out     | 0.0301       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040162345 |
+| time-step | 698          |
+| y_out     | 0.0129       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003942767 |
+| time-step | 699         |
+| y_out     | 0.0221      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040876735 |
+| time-step | 700          |
+| y_out     | 0.0758       |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 700
+------------------------------------------------------------
+| perf/mse  | 0.003729812 |
+| time-step | 701         |
+| y_out     | 0.111       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038686127 |
+| time-step | 702          |
+| y_out     | -0.0156      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00385611 |
+| time-step | 703        |
+| y_out     | -0.0098    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004042896 |
+| time-step | 704         |
+| y_out     | -0.0747     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003986498 |
+| time-step | 705         |
+| y_out     | -0.0667     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004055772 |
+| time-step | 706         |
+| y_out     | 0.0746      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004201124 |
+| time-step | 707         |
+| y_out     | -0.00169    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004111974 |
+| time-step | 708         |
+| y_out     | -0.0592     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043309256 |
+| time-step | 709          |
+| y_out     | 0.054        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043430342 |
+| time-step | 710          |
+| y_out     | -0.104       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004472181 |
+| time-step | 711         |
+| y_out     | 0.042       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043492676 |
+| time-step | 712          |
+| y_out     | -0.0276      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0044110697 |
+| time-step | 713          |
+| y_out     | 0.0146       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042669624 |
+| time-step | 714          |
+| y_out     | -0.0644      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042817146 |
+| time-step | 715          |
+| y_out     | 0.142        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0043626153 |
+| time-step | 716          |
+| y_out     | -0.157       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042219707 |
+| time-step | 717          |
+| y_out     | -0.096       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004112959 |
+| time-step | 718         |
+| y_out     | -0.00464    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004066611 |
+| time-step | 719         |
+| y_out     | 0.0974      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038754623 |
+| time-step | 720          |
+| y_out     | 0.0876       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003815264 |
+| time-step | 721         |
+| y_out     | 0.0584      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003838975 |
+| time-step | 722         |
+| y_out     | 0.0964      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037858554 |
+| time-step | 723          |
+| y_out     | -0.0368      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038625945 |
+| time-step | 724          |
+| y_out     | -0.0693      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037987952 |
+| time-step | 725          |
+| y_out     | 0.127        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003686358 |
+| time-step | 726         |
+| y_out     | 0.0407      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036802702 |
+| time-step | 727          |
+| y_out     | -0.0307      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037022214 |
+| time-step | 728          |
+| y_out     | -0.0429      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003445339 |
+| time-step | 729         |
+| y_out     | -0.0135     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035373773 |
+| time-step | 730          |
+| y_out     | 0.0819       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036810234 |
+| time-step | 731          |
+| y_out     | -0.085       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038107373 |
+| time-step | 732          |
+| y_out     | -0.117       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038043964 |
+| time-step | 733          |
+| y_out     | -0.0358      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003717544 |
+| time-step | 734         |
+| y_out     | 0.0426      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003678637 |
+| time-step | 735         |
+| y_out     | 0.0968      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036115635 |
+| time-step | 736          |
+| y_out     | 0.0993       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036957276 |
+| time-step | 737          |
+| y_out     | 0.0308       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037481033 |
+| time-step | 738          |
+| y_out     | -0.0403      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038508766 |
+| time-step | 739          |
+| y_out     | 0.111        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038630976 |
+| time-step | 740          |
+| y_out     | -0.0389      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037170083 |
+| time-step | 741          |
+| y_out     | -0.0708      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003649521 |
+| time-step | 742         |
+| y_out     | -0.0317     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037201955 |
+| time-step | 743          |
+| y_out     | 0.0467       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00391642 |
+| time-step | 744        |
+| y_out     | -0.0696    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038866703 |
+| time-step | 745          |
+| y_out     | -0.0029      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004041676 |
+| time-step | 746         |
+| y_out     | 0.0321      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004043228 |
+| time-step | 747         |
+| y_out     | 0.113       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004040128 |
+| time-step | 748         |
+| y_out     | 0.0673      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003994991 |
+| time-step | 749         |
+| y_out     | 0.00229     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004052109 |
+| time-step | 750         |
+| y_out     | -0.0143     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004226033 |
+| time-step | 751         |
+| y_out     | -0.06       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041722385 |
+| time-step | 752          |
+| y_out     | -0.1         |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00424968 |
+| time-step | 753        |
+| y_out     | 0.123      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004154115 |
+| time-step | 754         |
+| y_out     | 0.0424      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0042567067 |
+| time-step | 755          |
+| y_out     | 0.0133       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0041675316 |
+| time-step | 756          |
+| y_out     | 0.0103       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004105932 |
+| time-step | 757         |
+| y_out     | 0.045       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.004032029 |
+| time-step | 758         |
+| y_out     | 0.116       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040264954 |
+| time-step | 759          |
+| y_out     | 0.0382       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039516105 |
+| time-step | 760          |
+| y_out     | -0.0111      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037971255 |
+| time-step | 761          |
+| y_out     | 0.0388       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037790202 |
+| time-step | 762          |
+| y_out     | -0.0544      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003778922 |
+| time-step | 763         |
+| y_out     | -0.00411    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036977367 |
+| time-step | 764          |
+| y_out     | -0.0915      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0037087915 |
+| time-step | 765          |
+| y_out     | -0.0319      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039525065 |
+| time-step | 766          |
+| y_out     | -0.0524      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039980244 |
+| time-step | 767          |
+| y_out     | -0.0495      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039825886 |
+| time-step | 768          |
+| y_out     | -0.0386      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0040001534 |
+| time-step | 769          |
+| y_out     | 0.00236      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00383548 |
+| time-step | 770        |
+| y_out     | -0.0372    |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039546015 |
+| time-step | 771          |
+| y_out     | -0.102       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039620395 |
+| time-step | 772          |
+| y_out     | 0.0594       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038548734 |
+| time-step | 773          |
+| y_out     | -0.125       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0039359787 |
+| time-step | 774          |
+| y_out     | 0.0504       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038400274 |
+| time-step | 775          |
+| y_out     | 0.0728       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036323394 |
+| time-step | 776          |
+| y_out     | 0.0928       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035986449 |
+| time-step | 777          |
+| y_out     | -0.00341     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003687563 |
+| time-step | 778         |
+| y_out     | -0.0405     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036622726 |
+| time-step | 779          |
+| y_out     | 0.0192       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0038158644 |
+| time-step | 780          |
+| y_out     | -0.031       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003729894 |
+| time-step | 781         |
+| y_out     | -0.00749    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003683503 |
+| time-step | 782         |
+| y_out     | -0.0168     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035964332 |
+| time-step | 783          |
+| y_out     | -0.0432      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003480843 |
+| time-step | 784         |
+| y_out     | 0.0127      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036128939 |
+| time-step | 785          |
+| y_out     | -0.0379      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036562826 |
+| time-step | 786          |
+| y_out     | 0.096        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035760994 |
+| time-step | 787          |
+| y_out     | -0.0242      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035297046 |
+| time-step | 788          |
+| y_out     | 0.000675     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035434575 |
+| time-step | 789          |
+| y_out     | -0.0173      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034767096 |
+| time-step | 790          |
+| y_out     | -0.0184      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003353361 |
+| time-step | 791         |
+| y_out     | -0.191      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032775328 |
+| time-step | 792          |
+| y_out     | -0.0554      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033687316 |
+| time-step | 793          |
+| y_out     | 0.00598      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033070124 |
+| time-step | 794          |
+| y_out     | 0.0404       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032601536 |
+| time-step | 795          |
+| y_out     | 0.106        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003161159 |
+| time-step | 796         |
+| y_out     | -0.00927    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032265156 |
+| time-step | 797          |
+| y_out     | 0.00169      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031818873 |
+| time-step | 798          |
+| y_out     | 0.0778       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031017424 |
+| time-step | 799          |
+| y_out     | 0.147        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032288763 |
+| time-step | 800          |
+| y_out     | -0.0669      |
+-------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 800
+-------------------------------------------------------------
+| perf/mse  | 0.0032676668 |
+| time-step | 801          |
+| y_out     | 0.114        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033182434 |
+| time-step | 802          |
+| y_out     | -0.0433      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032686964 |
+| time-step | 803          |
+| y_out     | 0.0658       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003436252 |
+| time-step | 804         |
+| y_out     | -0.109      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034474372 |
+| time-step | 805          |
+| y_out     | -0.00904     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034694537 |
+| time-step | 806          |
+| y_out     | 0.0767       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034219064 |
+| time-step | 807          |
+| y_out     | -0.000698    |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035863728 |
+| time-step | 808          |
+| y_out     | -0.0346      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0036884497 |
+| time-step | 809          |
+| y_out     | 0.0605       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034927535 |
+| time-step | 810          |
+| y_out     | -0.132       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003425325 |
+| time-step | 811         |
+| y_out     | 0.057       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035630472 |
+| time-step | 812          |
+| y_out     | -0.0151      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034441266 |
+| time-step | 813          |
+| y_out     | -0.0175      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003489071 |
+| time-step | 814         |
+| y_out     | -0.0665     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003428346 |
+| time-step | 815         |
+| y_out     | -0.119      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033998818 |
+| time-step | 816          |
+| y_out     | -0.0134      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003290704 |
+| time-step | 817         |
+| y_out     | 0.0236      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003076804 |
+| time-step | 818         |
+| y_out     | 0.0842      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003178605 |
+| time-step | 819         |
+| y_out     | -0.064      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032066137 |
+| time-step | 820          |
+| y_out     | -0.138       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032917908 |
+| time-step | 821          |
+| y_out     | 0.056        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003245283 |
+| time-step | 822         |
+| y_out     | 0.0426      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033186455 |
+| time-step | 823          |
+| y_out     | 0.0391       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031323172 |
+| time-step | 824          |
+| y_out     | 0.0479       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031264585 |
+| time-step | 825          |
+| y_out     | 0.102        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003235293 |
+| time-step | 826         |
+| y_out     | 0.0345      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032661506 |
+| time-step | 827          |
+| y_out     | 0.00544      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033029548 |
+| time-step | 828          |
+| y_out     | 0.0194       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032958488 |
+| time-step | 829          |
+| y_out     | -0.0601      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033438976 |
+| time-step | 830          |
+| y_out     | -0.0899      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033375197 |
+| time-step | 831          |
+| y_out     | -0.0959      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033517475 |
+| time-step | 832          |
+| y_out     | 0.161        |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003423817 |
+| time-step | 833         |
+| y_out     | -0.0634     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0035058944 |
+| time-step | 834          |
+| y_out     | 0.0588       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034250028 |
+| time-step | 835          |
+| y_out     | 0.0472       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003330797 |
+| time-step | 836         |
+| y_out     | 0.0698      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033542768 |
+| time-step | 837          |
+| y_out     | -0.181       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034240452 |
+| time-step | 838          |
+| y_out     | 0.0667       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032489195 |
+| time-step | 839          |
+| y_out     | 0.00482      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031635188 |
+| time-step | 840          |
+| y_out     | -0.113       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033099272 |
+| time-step | 841          |
+| y_out     | 0.0471       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033037607 |
+| time-step | 842          |
+| y_out     | -0.0781      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033315886 |
+| time-step | 843          |
+| y_out     | -0.16        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032614009 |
+| time-step | 844          |
+| y_out     | 0.0136       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032734796 |
+| time-step | 845          |
+| y_out     | 0.00552      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032019324 |
+| time-step | 846          |
+| y_out     | -0.0121      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031833663 |
+| time-step | 847          |
+| y_out     | -0.00449     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032005191 |
+| time-step | 848          |
+| y_out     | 0.0857       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032530755 |
+| time-step | 849          |
+| y_out     | 0.0175       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033189058 |
+| time-step | 850          |
+| y_out     | -0.0853      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003115492 |
+| time-step | 851         |
+| y_out     | -0.0657     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029809824 |
+| time-step | 852          |
+| y_out     | 0.0612       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028147802 |
+| time-step | 853          |
+| y_out     | -0.0591      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029157738 |
+| time-step | 854          |
+| y_out     | -0.0541      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0030645095 |
+| time-step | 855          |
+| y_out     | 0.0828       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031741385 |
+| time-step | 856          |
+| y_out     | 0.0197       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003266183 |
+| time-step | 857         |
+| y_out     | 0.0658      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032271645 |
+| time-step | 858          |
+| y_out     | -0.0201      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032051094 |
+| time-step | 859          |
+| y_out     | 0.0748       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00324094 |
+| time-step | 860        |
+| y_out     | 0.0139     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033564512 |
+| time-step | 861          |
+| y_out     | -0.147       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033736713 |
+| time-step | 862          |
+| y_out     | 0.0569       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0034372103 |
+| time-step | 863          |
+| y_out     | 0.0749       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0033273466 |
+| time-step | 864          |
+| y_out     | -0.0514      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003213677 |
+| time-step | 865         |
+| y_out     | 0.0181      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0032043264 |
+| time-step | 866          |
+| y_out     | 0.0158       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003204727 |
+| time-step | 867         |
+| y_out     | 0.0113      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0031503104 |
+| time-step | 868          |
+| y_out     | -0.0446      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.003117843 |
+| time-step | 869         |
+| y_out     | 0.0591      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029591485 |
+| time-step | 870          |
+| y_out     | -0.0343      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002859468 |
+| time-step | 871         |
+| y_out     | 0.101       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028428584 |
+| time-step | 872          |
+| y_out     | -0.0313      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002765915 |
+| time-step | 873         |
+| y_out     | -0.0801     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028484226 |
+| time-step | 874          |
+| y_out     | 0.109        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029216858 |
+| time-step | 875          |
+| y_out     | -0.133       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027983815 |
+| time-step | 876          |
+| y_out     | -0.00207     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027129385 |
+| time-step | 877          |
+| y_out     | 0.086        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026772115 |
+| time-step | 878          |
+| y_out     | 0.0174       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027274308 |
+| time-step | 879          |
+| y_out     | 0.141        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028083853 |
+| time-step | 880          |
+| y_out     | 0.0272       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002793008 |
+| time-step | 881         |
+| y_out     | 0.111       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027967675 |
+| time-step | 882          |
+| y_out     | 0.0984       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027795169 |
+| time-step | 883          |
+| y_out     | -0.0585      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026735994 |
+| time-step | 884          |
+| y_out     | 0.0134       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002630074 |
+| time-step | 885         |
+| y_out     | -0.00269    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002714734 |
+| time-step | 886         |
+| y_out     | -0.0945     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027270503 |
+| time-step | 887          |
+| y_out     | -0.0269      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027634504 |
+| time-step | 888          |
+| y_out     | 0.0368       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002701294 |
+| time-step | 889         |
+| y_out     | -0.0127     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027970974 |
+| time-step | 890          |
+| y_out     | -0.0324      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028218855 |
+| time-step | 891          |
+| y_out     | -0.000696    |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027766651 |
+| time-step | 892          |
+| y_out     | 0.129        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028786203 |
+| time-step | 893          |
+| y_out     | -0.0383      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028952886 |
+| time-step | 894          |
+| y_out     | -0.0595      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0029038605 |
+| time-step | 895          |
+| y_out     | -0.0804      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028753176 |
+| time-step | 896          |
+| y_out     | -0.0276      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002866432 |
+| time-step | 897         |
+| y_out     | -0.0168     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028397955 |
+| time-step | 898          |
+| y_out     | -0.0477      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002851792 |
+| time-step | 899         |
+| y_out     | 0.0174      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002816549 |
+| time-step | 900         |
+| y_out     | 0.0337      |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 900
+-------------------------------------------------------------
+| perf/mse  | 0.0027485967 |
+| time-step | 901          |
+| y_out     | 0.0995       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026748937 |
+| time-step | 902          |
+| y_out     | 0.0378       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025315913 |
+| time-step | 903          |
+| y_out     | -0.0227      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025534085 |
+| time-step | 904          |
+| y_out     | -0.0178      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025399844 |
+| time-step | 905          |
+| y_out     | 0.0125       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025236164 |
+| time-step | 906          |
+| y_out     | 0.0618       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025169034 |
+| time-step | 907          |
+| y_out     | 0.0162       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002507784 |
+| time-step | 908         |
+| y_out     | -0.145      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002504256 |
+| time-step | 909         |
+| y_out     | 0.0571      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002442155 |
+| time-step | 910         |
+| y_out     | -0.0573     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00253605 |
+| time-step | 911        |
+| y_out     | 0.109      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002608821 |
+| time-step | 912         |
+| y_out     | 0.0404      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027101613 |
+| time-step | 913          |
+| y_out     | -0.0124      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027743748 |
+| time-step | 914          |
+| y_out     | 0.052        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027319647 |
+| time-step | 915          |
+| y_out     | 0.0158       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027333172 |
+| time-step | 916          |
+| y_out     | -0.0125      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002764387 |
+| time-step | 917         |
+| y_out     | 0.0578      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028133676 |
+| time-step | 918          |
+| y_out     | 0.0241       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027685028 |
+| time-step | 919          |
+| y_out     | 0.0595       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027501308 |
+| time-step | 920          |
+| y_out     | 0.00205      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027057172 |
+| time-step | 921          |
+| y_out     | -0.0119      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0028365753 |
+| time-step | 922          |
+| y_out     | -0.0493      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027511637 |
+| time-step | 923          |
+| y_out     | 0.0255       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027142898 |
+| time-step | 924          |
+| y_out     | 0.0253       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026565057 |
+| time-step | 925          |
+| y_out     | -0.0836      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027628713 |
+| time-step | 926          |
+| y_out     | 0.113        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026700185 |
+| time-step | 927          |
+| y_out     | -0.12        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025884407 |
+| time-step | 928          |
+| y_out     | -0.000187    |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026282899 |
+| time-step | 929          |
+| y_out     | 0.0964       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027991056 |
+| time-step | 930          |
+| y_out     | 0.0537       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002731581 |
+| time-step | 931         |
+| y_out     | -0.0297     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026563578 |
+| time-step | 932          |
+| y_out     | -0.00933     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026089703 |
+| time-step | 933          |
+| y_out     | 0.0927       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025489016 |
+| time-step | 934          |
+| y_out     | -0.0675      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002604143 |
+| time-step | 935         |
+| y_out     | -0.0109     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025323257 |
+| time-step | 936          |
+| y_out     | -0.0722      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002669424 |
+| time-step | 937         |
+| y_out     | 0.0433      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0027382039 |
+| time-step | 938          |
+| y_out     | 0.0434       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026474565 |
+| time-step | 939          |
+| y_out     | -0.0386      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023901553 |
+| time-step | 940          |
+| y_out     | -0.0667      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025278465 |
+| time-step | 941          |
+| y_out     | -0.0498      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023952548 |
+| time-step | 942          |
+| y_out     | -0.067       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024865908 |
+| time-step | 943          |
+| y_out     | -0.0803      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026007656 |
+| time-step | 944          |
+| y_out     | 0.112        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0026051172 |
+| time-step | 945          |
+| y_out     | -0.0404      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025503978 |
+| time-step | 946          |
+| y_out     | -0.0269      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0025096468 |
+| time-step | 947          |
+| y_out     | -0.085       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024287277 |
+| time-step | 948          |
+| y_out     | 0.027        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024341654 |
+| time-step | 949          |
+| y_out     | 0.0934       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024979038 |
+| time-step | 950          |
+| y_out     | -0.0917      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024164848 |
+| time-step | 951          |
+| y_out     | -0.0763      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024580457 |
+| time-step | 952          |
+| y_out     | -0.0386      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002458097 |
+| time-step | 953         |
+| y_out     | -0.0708     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023210857 |
+| time-step | 954          |
+| y_out     | 0.0113       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022932545 |
+| time-step | 955          |
+| y_out     | -0.0236      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022801147 |
+| time-step | 956          |
+| y_out     | -0.0469      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021593324 |
+| time-step | 957          |
+| y_out     | -0.105       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021776601 |
+| time-step | 958          |
+| y_out     | 0.0189       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021715018 |
+| time-step | 959          |
+| y_out     | -0.0488      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021020514 |
+| time-step | 960          |
+| y_out     | -0.0691      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021458864 |
+| time-step | 961          |
+| y_out     | -0.0284      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021522031 |
+| time-step | 962          |
+| y_out     | 0.0755       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021304737 |
+| time-step | 963          |
+| y_out     | -0.0125      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021381008 |
+| time-step | 964          |
+| y_out     | -0.0685      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021221023 |
+| time-step | 965          |
+| y_out     | 0.116        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0020764007 |
+| time-step | 966          |
+| y_out     | 0.0434       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021562024 |
+| time-step | 967          |
+| y_out     | 0.0153       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021939613 |
+| time-step | 968          |
+| y_out     | -0.11        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022224276 |
+| time-step | 969          |
+| y_out     | 0.139        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023009425 |
+| time-step | 970          |
+| y_out     | 0.0374       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002268298 |
+| time-step | 971         |
+| y_out     | 0.00215     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023580329 |
+| time-step | 972          |
+| y_out     | -0.0989      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023340737 |
+| time-step | 973          |
+| y_out     | 0.0637       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023236922 |
+| time-step | 974          |
+| y_out     | 0.0546       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002295545 |
+| time-step | 975         |
+| y_out     | -0.089      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023230477 |
+| time-step | 976          |
+| y_out     | 0.0349       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023237732 |
+| time-step | 977          |
+| y_out     | 0.0877       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023587295 |
+| time-step | 978          |
+| y_out     | -0.0439      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023891227 |
+| time-step | 979          |
+| y_out     | 0.0198       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0024165357 |
+| time-step | 980          |
+| y_out     | -0.0398      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023942129 |
+| time-step | 981          |
+| y_out     | 0.101        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023297165 |
+| time-step | 982          |
+| y_out     | -0.136       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023099093 |
+| time-step | 983          |
+| y_out     | 0.0213       |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00227584 |
+| time-step | 984        |
+| y_out     | 0.0158     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022622366 |
+| time-step | 985          |
+| y_out     | -0.00373     |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022867525 |
+| time-step | 986          |
+| y_out     | 0.0424       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023754737 |
+| time-step | 987          |
+| y_out     | 0.00235      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022828027 |
+| time-step | 988          |
+| y_out     | -0.0433      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022986343 |
+| time-step | 989          |
+| y_out     | -0.0304      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023213169 |
+| time-step | 990          |
+| y_out     | 0.0673       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022419526 |
+| time-step | 991          |
+| y_out     | -0.0689      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.002177755 |
+| time-step | 992         |
+| y_out     | -0.0748     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022139498 |
+| time-step | 993          |
+| y_out     | -0.0142      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0023084688 |
+| time-step | 994          |
+| y_out     | -0.129       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022994452 |
+| time-step | 995          |
+| y_out     | 0.11         |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0022951874 |
+| time-step | 996          |
+| y_out     | -0.0658      |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021848192 |
+| time-step | 997          |
+| y_out     | 0.0932       |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021798057 |
+| time-step | 998          |
+| y_out     | 0.102        |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0021983192 |
+| time-step | 999          |
+| y_out     | 0.1          |
+-------------------------------------------------------------
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/progress-[||perf--mse||, ||timestep||].csv b/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/progress-[||perf--mse||, ||timestep||].csv
new file mode 100644
index 0000000..fd5b072
--- /dev/null
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/progress.csv b/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/progress.csv
new file mode 100644
index 0000000..c303fb5
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/progress.csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/tb/events/events.out.tfevents.1646141219.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/tb/events/events.out.tfevents.1646141219.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..d95e99f
Binary files /dev/null and b/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/tb/events/events.out.tfevents.1646141219.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/warn.txt b/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/warn.txt
new file mode 100644
index 0000000..f7e0929
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-26-59-653703 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/warn.txt	
@@ -0,0 +1,8 @@
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/backup.txt b/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/backup.txt
new file mode 100644
index 0000000..30333aa
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/backup.txt	
@@ -0,0 +1,7 @@
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.001
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 88
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/log.txt b/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/log.txt
new file mode 100644
index 0000000..2b948cc
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/log.txt	
@@ -0,0 +1,1797 @@
+Logging to ../log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/
+log dir: ../log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/
+pkl_file: ../archive_tester/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88.pkl
+checkpoint_dir: ../checkpoint/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/
+results_dir: ../results/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/
+[BACKUP] 0 : key: env_id, value: Test-v1
+[BACKUP] 0 : key: info, value: default exp info
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.001
+[BACKUP] 0 : key: loaded_date, value: True
+[BACKUP] 0 : key: loaded_task_name, value: 
+[BACKUP] 0 : key: seed, value: 88
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
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+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.162    |
+---------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 0
+---------------------------------------------------------
+| time-step | 1        |
+| y_out     | 0.0764   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 2        |
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+---------------------------------------------------------
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+| time-step | 3        |
+| y_out     | -0.0901  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 4        |
+| y_out     | -0.0121  |
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+---------------------------------------------------------
+| time-step | 5        |
+| y_out     | 0.152    |
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+| time-step | 6        |
+| y_out     | -0.0486  |
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+---------------------------------------------------------
+| time-step | 7        |
+| y_out     | -0.00131 |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 8        |
+| y_out     | 0.075    |
+---------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.4116845 |
+| time-step | 9         |
+| y_out     | -0.023    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.34226435 |
+| time-step | 10         |
+| y_out     | 0.0666     |
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+----------------------------------------------------------
+| perf/mse  | 0.2865979 |
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+-----------------------------------------------------------
+| perf/mse  | 0.22066227 |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17881593 |
+| time-step | 13         |
+| y_out     | 0.018      |
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+-----------------------------------------------------------
+| perf/mse  | 0.15293343 |
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+| perf/mse  | 0.13582486 |
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+| perf/mse  | 0.12673703 |
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+| perf/mse  | 0.118425295 |
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+| perf/mse  | 0.1182645 |
+| time-step | 18        |
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+| perf/mse  | 0.11723097 |
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+| perf/mse  | 0.11428434 |
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+| perf/mse  | 0.110354364 |
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+| perf/mse  | 0.10520983 |
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+| perf/mse  | 0.09873715 |
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+| perf/mse  | 0.0954464 |
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+| perf/mse  | 0.090510435 |
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+| perf/mse  | 0.07739  |
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+| perf/mse  | 0.072461925 |
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+| perf/mse  | 0.07006042 |
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+| perf/mse  | 0.067246854 |
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+| perf/mse  | 0.060432475 |
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+| perf/mse  | 0.057160437 |
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+| perf/mse  | 0.030574158 |
+| time-step | 96          |
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+------------------------------------------------------------
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+| perf/mse  | 0.030977616 |
+| time-step | 97          |
+| y_out     | 0.035       |
+------------------------------------------------------------
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+| perf/mse  | 0.029380009 |
+| time-step | 98          |
+| y_out     | 0.0651      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028496768 |
+| time-step | 99          |
+| y_out     | 0.00447     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028147172 |
+| time-step | 100         |
+| y_out     | 0.0809      |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 100
+------------------------------------------------------------
+| perf/mse  | 0.028606955 |
+| time-step | 101         |
+| y_out     | 0.00757     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027945647 |
+| time-step | 102         |
+| y_out     | -0.06       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028185958 |
+| time-step | 103         |
+| y_out     | -0.0608     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026932105 |
+| time-step | 104         |
+| y_out     | -0.192      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027318224 |
+| time-step | 105         |
+| y_out     | -0.0059     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02615488 |
+| time-step | 106        |
+| y_out     | -0.0368    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02680419 |
+| time-step | 107        |
+| y_out     | -0.0188    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026500195 |
+| time-step | 108         |
+| y_out     | -0.0346     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026046822 |
+| time-step | 109         |
+| y_out     | 0.151       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026122168 |
+| time-step | 110         |
+| y_out     | 0.0502      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026296128 |
+| time-step | 111         |
+| y_out     | -0.0766     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025212338 |
+| time-step | 112         |
+| y_out     | 0.11        |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02545307 |
+| time-step | 113        |
+| y_out     | 0.13       |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025583547 |
+| time-step | 114         |
+| y_out     | 0.0302      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024545776 |
+| time-step | 115         |
+| y_out     | -0.0325     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024057273 |
+| time-step | 116         |
+| y_out     | 0.115       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02244277 |
+| time-step | 117        |
+| y_out     | 0.0902     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022560945 |
+| time-step | 118         |
+| y_out     | 0.0403      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02307056 |
+| time-step | 119        |
+| y_out     | 0.157      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022394374 |
+| time-step | 120         |
+| y_out     | -0.0801     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021637758 |
+| time-step | 121         |
+| y_out     | -0.0606     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023614278 |
+| time-step | 122         |
+| y_out     | 0.0233      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022924874 |
+| time-step | 123         |
+| y_out     | 0.0966      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022113372 |
+| time-step | 124         |
+| y_out     | -0.0791     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022329802 |
+| time-step | 125         |
+| y_out     | -0.0896     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022729445 |
+| time-step | 126         |
+| y_out     | 0.0265      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023531575 |
+| time-step | 127         |
+| y_out     | -0.00328    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02450201 |
+| time-step | 128        |
+| y_out     | 0.00183    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0238791 |
+| time-step | 129       |
+| y_out     | 0.0177    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023837082 |
+| time-step | 130         |
+| y_out     | -0.0648     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023029322 |
+| time-step | 131         |
+| y_out     | -0.0334     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021849122 |
+| time-step | 132         |
+| y_out     | 0.0258      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021838527 |
+| time-step | 133         |
+| y_out     | 0.000211    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021615716 |
+| time-step | 134         |
+| y_out     | -0.158      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021900065 |
+| time-step | 135         |
+| y_out     | -0.178      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021565739 |
+| time-step | 136         |
+| y_out     | 0.0959      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021086615 |
+| time-step | 137         |
+| y_out     | 0.078       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019709218 |
+| time-step | 138         |
+| y_out     | 0.00684     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019687956 |
+| time-step | 139         |
+| y_out     | -0.000109   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019365186 |
+| time-step | 140         |
+| y_out     | 0.0968      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019639643 |
+| time-step | 141         |
+| y_out     | 0.042       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018927427 |
+| time-step | 142         |
+| y_out     | -0.00592    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018604886 |
+| time-step | 143         |
+| y_out     | -0.0829     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018862568 |
+| time-step | 144         |
+| y_out     | -0.0535     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018079463 |
+| time-step | 145         |
+| y_out     | 0.0251      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017920785 |
+| time-step | 146         |
+| y_out     | -0.00981    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017506853 |
+| time-step | 147         |
+| y_out     | -0.0881     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017187018 |
+| time-step | 148         |
+| y_out     | 0.00942     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01760382 |
+| time-step | 149        |
+| y_out     | 0.0663     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017503249 |
+| time-step | 150         |
+| y_out     | 0.0516      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017784001 |
+| time-step | 151         |
+| y_out     | -0.0723     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017888974 |
+| time-step | 152         |
+| y_out     | 0.036       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018136583 |
+| time-step | 153         |
+| y_out     | 0.00191     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018050347 |
+| time-step | 154         |
+| y_out     | -0.0105     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017828379 |
+| time-step | 155         |
+| y_out     | 0.0108      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017954323 |
+| time-step | 156         |
+| y_out     | -0.0918     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018049702 |
+| time-step | 157         |
+| y_out     | 0.0948      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018038373 |
+| time-step | 158         |
+| y_out     | -0.0615     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017828844 |
+| time-step | 159         |
+| y_out     | -0.0162     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018320028 |
+| time-step | 160         |
+| y_out     | -0.0668     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018292453 |
+| time-step | 161         |
+| y_out     | -0.00346    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01878645 |
+| time-step | 162        |
+| y_out     | -0.0552    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018440742 |
+| time-step | 163         |
+| y_out     | 0.0539      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018220808 |
+| time-step | 164         |
+| y_out     | 0.177       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018326517 |
+| time-step | 165         |
+| y_out     | 0.0654      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018403286 |
+| time-step | 166         |
+| y_out     | 0.0598      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018378321 |
+| time-step | 167         |
+| y_out     | 0.0508      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018518543 |
+| time-step | 168         |
+| y_out     | 0.13        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017999902 |
+| time-step | 169         |
+| y_out     | 0.143       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018018136 |
+| time-step | 170         |
+| y_out     | 0.0729      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017436014 |
+| time-step | 171         |
+| y_out     | -0.0233     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016493583 |
+| time-step | 172         |
+| y_out     | -0.023      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01667047 |
+| time-step | 173        |
+| y_out     | 0.0148     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016739586 |
+| time-step | 174         |
+| y_out     | 0.0475      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016361233 |
+| time-step | 175         |
+| y_out     | -0.0506     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01617429 |
+| time-step | 176        |
+| y_out     | -0.00511   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015391266 |
+| time-step | 177         |
+| y_out     | 0.0208      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015521039 |
+| time-step | 178         |
+| y_out     | 0.0143      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016098613 |
+| time-step | 179         |
+| y_out     | 0.0399      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015389146 |
+| time-step | 180         |
+| y_out     | 0.102       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01587503 |
+| time-step | 181        |
+| y_out     | 0.109      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015917998 |
+| time-step | 182         |
+| y_out     | -0.109      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015256567 |
+| time-step | 183         |
+| y_out     | 0.0305      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015143004 |
+| time-step | 184         |
+| y_out     | 0.0587      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015454161 |
+| time-step | 185         |
+| y_out     | 0.0947      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015269896 |
+| time-step | 186         |
+| y_out     | 0.129       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015705734 |
+| time-step | 187         |
+| y_out     | 0.0726      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015584086 |
+| time-step | 188         |
+| y_out     | -0.0888     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014840121 |
+| time-step | 189         |
+| y_out     | -0.13       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014894625 |
+| time-step | 190         |
+| y_out     | -0.111      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014608892 |
+| time-step | 191         |
+| y_out     | -0.0322     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014609566 |
+| time-step | 192         |
+| y_out     | 0.0314      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014993951 |
+| time-step | 193         |
+| y_out     | -0.0565     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015000495 |
+| time-step | 194         |
+| y_out     | -0.0133     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014394654 |
+| time-step | 195         |
+| y_out     | -0.0325     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013766756 |
+| time-step | 196         |
+| y_out     | 0.0385      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013613485 |
+| time-step | 197         |
+| y_out     | 0.0137      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013916832 |
+| time-step | 198         |
+| y_out     | 0.0227      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014203673 |
+| time-step | 199         |
+| y_out     | -0.0424     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013562766 |
+| time-step | 200         |
+| y_out     | 0.00657     |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 200
+------------------------------------------------------------
+| perf/mse  | 0.013382072 |
+| time-step | 201         |
+| y_out     | -0.0591     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01315968 |
+| time-step | 202        |
+| y_out     | 0.0126     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012887858 |
+| time-step | 203         |
+| y_out     | 0.0239      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01337339 |
+| time-step | 204        |
+| y_out     | -0.0578    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013425673 |
+| time-step | 205         |
+| y_out     | 0.0176      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014038166 |
+| time-step | 206         |
+| y_out     | 0.00637     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013868433 |
+| time-step | 207         |
+| y_out     | -0.0728     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01329882 |
+| time-step | 208        |
+| y_out     | 0.0671     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013069521 |
+| time-step | 209         |
+| y_out     | 0.0452      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0135223 |
+| time-step | 210       |
+| y_out     | 0.15      |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01441008 |
+| time-step | 211        |
+| y_out     | -0.0789    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014295165 |
+| time-step | 212         |
+| y_out     | -0.082      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01453729 |
+| time-step | 213        |
+| y_out     | -0.0197    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014061342 |
+| time-step | 214         |
+| y_out     | -0.106      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014587182 |
+| time-step | 215         |
+| y_out     | 0.0385      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0144081805 |
+| time-step | 216          |
+| y_out     | -0.0056      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014319906 |
+| time-step | 217         |
+| y_out     | -0.0673     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014193867 |
+| time-step | 218         |
+| y_out     | -0.0344     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014007312 |
+| time-step | 219         |
+| y_out     | 0.0441      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013912772 |
+| time-step | 220         |
+| y_out     | 0.00299     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012988779 |
+| time-step | 221         |
+| y_out     | -0.0634     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012617411 |
+| time-step | 222         |
+| y_out     | -0.0169     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012815865 |
+| time-step | 223         |
+| y_out     | 0.0799      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012343487 |
+| time-step | 224         |
+| y_out     | 0.00233     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011513001 |
+| time-step | 225         |
+| y_out     | 0.0507      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011502563 |
+| time-step | 226         |
+| y_out     | -0.219      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011924874 |
+| time-step | 227         |
+| y_out     | -0.0398     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012385877 |
+| time-step | 228         |
+| y_out     | 0.041       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012254153 |
+| time-step | 229         |
+| y_out     | -0.00562    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012247067 |
+| time-step | 230         |
+| y_out     | -0.0154     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012499314 |
+| time-step | 231         |
+| y_out     | 0.0487      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01362699 |
+| time-step | 232        |
+| y_out     | -0.0415    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013595112 |
+| time-step | 233         |
+| y_out     | 0.0409      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014221516 |
+| time-step | 234         |
+| y_out     | 0.0317      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014418885 |
+| time-step | 235         |
+| y_out     | -0.0367     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014496855 |
+| time-step | 236         |
+| y_out     | -0.137      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014122048 |
+| time-step | 237         |
+| y_out     | -0.0409     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0140051795 |
+| time-step | 238          |
+| y_out     | -0.0658      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014470877 |
+| time-step | 239         |
+| y_out     | -0.0863     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014183926 |
+| time-step | 240         |
+| y_out     | 0.0133      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.013880688 |
+| time-step | 241         |
+| y_out     | 0.0122      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01320968 |
+| time-step | 242        |
+| y_out     | 0.0467     |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0130267665 |
+| time-step | 243          |
+| y_out     | -0.0753      |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01298934 |
+| time-step | 244        |
+| y_out     | -0.0338    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012912792 |
+| time-step | 245         |
+| y_out     | -0.0476     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012643425 |
+| time-step | 246         |
+| y_out     | 0.0185      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012662187 |
+| time-step | 247         |
+| y_out     | 0.0885      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012426092 |
+| time-step | 248         |
+| y_out     | -0.0377     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011928229 |
+| time-step | 249         |
+| y_out     | 0.0316      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011977758 |
+| time-step | 250         |
+| y_out     | 0.0757      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012049417 |
+| time-step | 251         |
+| y_out     | 0.102       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012098239 |
+| time-step | 252         |
+| y_out     | -0.0765     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011614481 |
+| time-step | 253         |
+| y_out     | 0.0093      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011484854 |
+| time-step | 254         |
+| y_out     | 0.0177      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011765086 |
+| time-step | 255         |
+| y_out     | 0.0297      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011991626 |
+| time-step | 256         |
+| y_out     | -0.0132     |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0120916385 |
+| time-step | 257          |
+| y_out     | -0.00747     |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012188358 |
+| time-step | 258         |
+| y_out     | -0.00225    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0122345975 |
+| time-step | 259          |
+| y_out     | 0.0485       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.012064756 |
+| time-step | 260         |
+| y_out     | 0.0117      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011673996 |
+| time-step | 261         |
+| y_out     | -0.027      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011549339 |
+| time-step | 262         |
+| y_out     | -0.0442     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011740644 |
+| time-step | 263         |
+| y_out     | 0.0691      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.011079816 |
+| time-step | 264         |
+| y_out     | -0.0513     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010692937 |
+| time-step | 265         |
+| y_out     | -0.103      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010324869 |
+| time-step | 266         |
+| y_out     | 0.0518      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00977801 |
+| time-step | 267        |
+| y_out     | 0.11       |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009445672 |
+| time-step | 268         |
+| y_out     | -0.0976     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009423474 |
+| time-step | 269         |
+| y_out     | -0.0939     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009913127 |
+| time-step | 270         |
+| y_out     | 0.0384      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010573848 |
+| time-step | 271         |
+| y_out     | 0.0124      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010515319 |
+| time-step | 272         |
+| y_out     | 7.88e-05    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010208281 |
+| time-step | 273         |
+| y_out     | -0.134      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010351671 |
+| time-step | 274         |
+| y_out     | -0.031      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010713759 |
+| time-step | 275         |
+| y_out     | -0.0226     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010491295 |
+| time-step | 276         |
+| y_out     | 0.00212     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010827411 |
+| time-step | 277         |
+| y_out     | -0.0396     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010586554 |
+| time-step | 278         |
+| y_out     | -0.0562     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01052269 |
+| time-step | 279        |
+| y_out     | -0.0824    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010579078 |
+| time-step | 280         |
+| y_out     | -0.0348     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010243152 |
+| time-step | 281         |
+| y_out     | -0.14       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010236494 |
+| time-step | 282         |
+| y_out     | 0.0369      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010239321 |
+| time-step | 283         |
+| y_out     | 0.0211      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010472357 |
+| time-step | 284         |
+| y_out     | 0.169       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010008585 |
+| time-step | 285         |
+| y_out     | -0.0163     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010203567 |
+| time-step | 286         |
+| y_out     | -0.0955     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010348397 |
+| time-step | 287         |
+| y_out     | -0.0134     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010345891 |
+| time-step | 288         |
+| y_out     | 0.0673      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010700961 |
+| time-step | 289         |
+| y_out     | 0.0279      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010388944 |
+| time-step | 290         |
+| y_out     | 0.039       |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0106908465 |
+| time-step | 291          |
+| y_out     | -0.0646      |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010309842 |
+| time-step | 292         |
+| y_out     | -0.00861    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010474081 |
+| time-step | 293         |
+| y_out     | 0.126       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010539789 |
+| time-step | 294         |
+| y_out     | -0.0214     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010585275 |
+| time-step | 295         |
+| y_out     | 0.0815      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010429607 |
+| time-step | 296         |
+| y_out     | 0.0479      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010243757 |
+| time-step | 297         |
+| y_out     | 0.0108      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010742357 |
+| time-step | 298         |
+| y_out     | -0.0382     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010794876 |
+| time-step | 299         |
+| y_out     | 0.00499     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010691104 |
+| time-step | 300         |
+| y_out     | -0.0847     |
+------------------------------------------------------------
+save checkpoint to  ../checkpoint/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/checkpoint 300
+------------------------------------------------------------
+| perf/mse  | 0.010032582 |
+| time-step | 301         |
+| y_out     | 0.104       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010654226 |
+| time-step | 302         |
+| y_out     | -0.146      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010426847 |
+| time-step | 303         |
+| y_out     | -0.0516     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010203677 |
+| time-step | 304         |
+| y_out     | -0.00144    |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0099885 |
+| time-step | 305       |
+| y_out     | -0.0273   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009654961 |
+| time-step | 306         |
+| y_out     | -0.128      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0093472665 |
+| time-step | 307          |
+| y_out     | 0.0159       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009603806 |
+| time-step | 308         |
+| y_out     | 0.0653      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009766078 |
+| time-step | 309         |
+| y_out     | 0.0162      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009610362 |
+| time-step | 310         |
+| y_out     | 0.0457      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009599518 |
+| time-step | 311         |
+| y_out     | 0.0568      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009064664 |
+| time-step | 312         |
+| y_out     | -0.035      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009033918 |
+| time-step | 313         |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009256901 |
+| time-step | 314         |
+| y_out     | -0.0177     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009281759 |
+| time-step | 315         |
+| y_out     | 0.0364      |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0097410735 |
+| time-step | 316          |
+| y_out     | -0.084       |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010113874 |
+| time-step | 317         |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009633789 |
+| time-step | 318         |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009057928 |
+| time-step | 319         |
+| y_out     | -0.0024     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009432124 |
+| time-step | 320         |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009712348 |
+| time-step | 321         |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009893559 |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010303207 |
+| time-step | 323         |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009784044 |
+| time-step | 324         |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010053712 |
+| time-step | 325         |
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+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00999709 |
+| time-step | 326        |
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+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009864601 |
+| time-step | 327         |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010140386 |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010266502 |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.010139539 |
+| time-step | 330         |
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+------------------------------------------------------------
+| perf/mse  | 0.010079867 |
+| time-step | 331         |
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+-------------------------------------------------------------
+| perf/mse  | 0.0100426935 |
+| time-step | 332          |
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+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0098304525 |
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+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009680955 |
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+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00940544 |
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+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009379968 |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009440757 |
+| time-step | 337         |
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+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.009191423 |
+| time-step | 338         |
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+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00887909 |
+| time-step | 339        |
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+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008715235 |
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+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.00873994 |
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+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008850156 |
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+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0087323515 |
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+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.008909301 |
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+------------------------------------------------------------
+| perf/mse  | 0.008655539 |
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+| perf/mse  | 0.008628974 |
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+------------------------------------------------------------
+| perf/mse  | 0.008353037 |
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+------------------------------------------------------------
+| perf/mse  | 0.008267693 |
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+------------------------------------------------------------
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+------------------------------------------------------------
+| perf/mse  | 0.008686851 |
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+------------------------------------------------------------
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/progress.csv b/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/progress.csv
new file mode 100644
index 0000000..b4ae68e
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/progress.csv	
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diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/tb/events/events.out.tfevents.1646142685.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/tb/events/events.out.tfevents.1646142685.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..eda57c5
Binary files /dev/null and b/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/tb/events/events.out.tfevents.1646142685.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/warn.txt b/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/warn.txt
new file mode 100644
index 0000000..f7e0929
--- /dev/null
+++ b/test/test_data_root/log/demo_task/2022/03/01/21-51-25-574898 11.0.91.89 &env_id=Test-v1&learning_rate=0.001&seed=88/warn.txt	
@@ -0,0 +1,8 @@
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
+[WARN] 0 : meet invalid loader config when use it
+[WARN] 0 : load_date True
+[WARN] 0 : task_name 
+[WARN] 0 : root None
diff --git a/test/test_data_root/log/demo_task/simple_res.pdf b/test/test_data_root/log/demo_task/simple_res.pdf
new file mode 100644
index 0000000..1700b19
Binary files /dev/null and b/test/test_data_root/log/demo_task/simple_res.pdf differ
diff --git a/test/test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/backup.txt b/test/test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/backup.txt
new file mode 100644
index 0000000..7cb516c
--- /dev/null
+++ b/test/test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: hp1, value: 1
+[BACKUP] 0 : key: hp2, value: 2
diff --git a/test/test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/log.txt b/test/test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/log.txt
new file mode 100644
index 0000000..9532f07
--- /dev/null
+++ b/test/test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/log.txt	
@@ -0,0 +1,7 @@
+Logging to ../../test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/
+log dir: ../../test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/
+pkl_file: ../../test_data_root/archive_tester/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/
+results_dir: ../../test_data_root/results/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/
+[BACKUP] 0 : key: hp1, value: 1
+[BACKUP] 0 : key: hp2, value: 2
diff --git a/test/test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/progress.csv b/test/test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/progress.csv
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diff --git a/test/test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/warn.txt b/test/test_data_root/log/test_demo_task/2022/06/21/13-54-13-775042 172.16.0.65 &hp1=1/warn.txt
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index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/backup.txt
new file mode 100644
index 0000000..7cb516c
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/backup.txt	
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+[BACKUP] 0 : key: hp1, value: 1
+[BACKUP] 0 : key: hp2, value: 2
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/log.txt
new file mode 100644
index 0000000..3246249
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/log.txt	
@@ -0,0 +1,7 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/
+[BACKUP] 0 : key: hp1, value: 1
+[BACKUP] 0 : key: hp2, value: 2
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/progress.csv
new file mode 100644
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Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/tb/events/events.out.tfevents.1655790880.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/13-54-38-241150 172.16.0.65 &hp1=1/warn.txt
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/backup.txt
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index 0000000..b833532
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+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 2
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/log.txt
new file mode 100644
index 0000000..e2ddb28
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/log.txt	
@@ -0,0 +1,39 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/
+[BACKUP] 0 : key: input_size, value: 2
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(2, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(2, 64) dtype=float32_ref>
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+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.0614   |
+---------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/checkpoint 0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/progress.csv
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index 0000000..689dd84
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/progress.csv	
@@ -0,0 +1,2 @@
+time-step,y_out
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/tb/events/events.out.tfevents.1655791870.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/tb/events/events.out.tfevents.1655791870.MBP-C02ZL0TNLVCF-2243.local
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-06-996700 172.16.0.65 &input_size=2/warn.txt
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..ef98249
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,4389 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
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+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | -0.0582  |
+---------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint 0
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12832956 |
+| time-step | 81         |
+| y_out     | 0.0901     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13284071 |
+| time-step | 82         |
+| y_out     | -0.0529    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12788132 |
+| time-step | 83         |
+| y_out     | -0.0814    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12010696 |
+| time-step | 84         |
+| y_out     | 2.45e-05   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12400609 |
+| time-step | 85         |
+| y_out     | -0.0042    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12114714 |
+| time-step | 86         |
+| y_out     | 0.0576     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12049351 |
+| time-step | 87         |
+| y_out     | -0.0692    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.117591545 |
+| time-step | 88          |
+| y_out     | 0.0132      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11618533 |
+| time-step | 89         |
+| y_out     | 0.0526     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11202963 |
+| time-step | 90         |
+| y_out     | 0.0595     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10749795 |
+| time-step | 91         |
+| y_out     | 0.0674     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10089743 |
+| time-step | 92         |
+| y_out     | 0.0401     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10151825 |
+| time-step | 93         |
+| y_out     | 0.000541   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10420946 |
+| time-step | 94         |
+| y_out     | -0.0644    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10103506 |
+| time-step | 95         |
+| y_out     | -0.0612    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.097031645 |
+| time-step | 96          |
+| y_out     | -0.0377     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09652333 |
+| time-step | 97         |
+| y_out     | -0.0137    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09672431 |
+| time-step | 98         |
+| y_out     | 0.0254     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09413188 |
+| time-step | 99         |
+| y_out     | -0.0012    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09722036 |
+| time-step | 100        |
+| y_out     | 0.0312     |
+-----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint 100
+-----------------------------------------------------------
+| perf/mse  | 0.09866486 |
+| time-step | 101        |
+| y_out     | 0.0561     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.098303795 |
+| time-step | 102         |
+| y_out     | -0.00522    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09725959 |
+| time-step | 103        |
+| y_out     | -0.0901    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.098537505 |
+| time-step | 104         |
+| y_out     | 0.0605      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09504741 |
+| time-step | 105        |
+| y_out     | 0.1        |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.098326646 |
+| time-step | 106         |
+| y_out     | -0.0117     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09678003 |
+| time-step | 107        |
+| y_out     | 0.0191     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09677333 |
+| time-step | 108        |
+| y_out     | -0.0169    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09431489 |
+| time-step | 109        |
+| y_out     | -0.108     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09145598 |
+| time-step | 110        |
+| y_out     | -0.00448   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08987512 |
+| time-step | 111        |
+| y_out     | -0.0545    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09513543 |
+| time-step | 112        |
+| y_out     | 0.0405     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.093599126 |
+| time-step | 113         |
+| y_out     | 0.0402      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08917842 |
+| time-step | 114        |
+| y_out     | 0.0636     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09043958 |
+| time-step | 115        |
+| y_out     | -0.00276   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08764857 |
+| time-step | 116        |
+| y_out     | -0.0389    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08793067 |
+| time-step | 117        |
+| y_out     | 0.0292     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08886134 |
+| time-step | 118        |
+| y_out     | -0.0553    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08878454 |
+| time-step | 119        |
+| y_out     | 0.0628     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09203519 |
+| time-step | 120        |
+| y_out     | 0.00969    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09488541 |
+| time-step | 121        |
+| y_out     | 0.0494     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0933571 |
+| time-step | 122       |
+| y_out     | 0.0661    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.091652706 |
+| time-step | 123         |
+| y_out     | -0.0185     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09163402 |
+| time-step | 124        |
+| y_out     | 0.00669    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09070408 |
+| time-step | 125        |
+| y_out     | 0.0537     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.089621276 |
+| time-step | 126         |
+| y_out     | 0.0278      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08641612 |
+| time-step | 127        |
+| y_out     | -0.102     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.082999505 |
+| time-step | 128         |
+| y_out     | -0.0432     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08350495 |
+| time-step | 129        |
+| y_out     | 0.0329     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.081158616 |
+| time-step | 130         |
+| y_out     | -0.0468     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.076140806 |
+| time-step | 131         |
+| y_out     | -0.0696     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07255548 |
+| time-step | 132        |
+| y_out     | 0.0575     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07368334 |
+| time-step | 133        |
+| y_out     | -0.0172    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.072576866 |
+| time-step | 134         |
+| y_out     | 0.0724      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07336119 |
+| time-step | 135        |
+| y_out     | 0.0568     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07360689 |
+| time-step | 136        |
+| y_out     | 0.00706    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.074170314 |
+| time-step | 137         |
+| y_out     | 0.0416      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07597777 |
+| time-step | 138        |
+| y_out     | -0.0596    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.075255744 |
+| time-step | 139         |
+| y_out     | -0.0354     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.071989335 |
+| time-step | 140         |
+| y_out     | -0.000725   |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07520542 |
+| time-step | 141        |
+| y_out     | -0.0416    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0753596 |
+| time-step | 142       |
+| y_out     | 0.053     |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07662775 |
+| time-step | 143        |
+| y_out     | -0.0975    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.077184625 |
+| time-step | 144         |
+| y_out     | -0.00893    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07795949 |
+| time-step | 145        |
+| y_out     | -0.019     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07859997 |
+| time-step | 146        |
+| y_out     | 0.00495    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07880659 |
+| time-step | 147        |
+| y_out     | -0.0374    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07766412 |
+| time-step | 148        |
+| y_out     | 0.0841     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07902576 |
+| time-step | 149        |
+| y_out     | -0.0843    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.079748586 |
+| time-step | 150         |
+| y_out     | -0.102      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07685062 |
+| time-step | 151        |
+| y_out     | -0.0854    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07658921 |
+| time-step | 152        |
+| y_out     | -0.0528    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07520914 |
+| time-step | 153        |
+| y_out     | -0.0215    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07570366 |
+| time-step | 154        |
+| y_out     | -0.0467    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07410152 |
+| time-step | 155        |
+| y_out     | 0.00465    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.074178524 |
+| time-step | 156         |
+| y_out     | -0.0319     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07349205 |
+| time-step | 157        |
+| y_out     | -0.127     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.072328135 |
+| time-step | 158         |
+| y_out     | 0.0556      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07030443 |
+| time-step | 159        |
+| y_out     | -0.0276    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06974628 |
+| time-step | 160        |
+| y_out     | -0.0121    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07045316 |
+| time-step | 161        |
+| y_out     | 0.00422    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.070341855 |
+| time-step | 162         |
+| y_out     | 0.0545      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0749472 |
+| time-step | 163       |
+| y_out     | 0.00851   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07200081 |
+| time-step | 164        |
+| y_out     | -0.0752    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.072234765 |
+| time-step | 165         |
+| y_out     | 0.00151     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06968574 |
+| time-step | 166        |
+| y_out     | -0.00486   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06747711 |
+| time-step | 167        |
+| y_out     | -0.0276    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.068632185 |
+| time-step | 168         |
+| y_out     | 0.0412      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07016902 |
+| time-step | 169        |
+| y_out     | -0.169     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06920503 |
+| time-step | 170        |
+| y_out     | 0.00116    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06865578 |
+| time-step | 171        |
+| y_out     | 0.0953     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.068018794 |
+| time-step | 172         |
+| y_out     | 0.00361     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060454153 |
+| time-step | 173         |
+| y_out     | -0.0556     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060338758 |
+| time-step | 174         |
+| y_out     | -0.0851     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05821512 |
+| time-step | 175        |
+| y_out     | 0.0218     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061771907 |
+| time-step | 176         |
+| y_out     | 0.00628     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061037354 |
+| time-step | 177         |
+| y_out     | -0.0296     |
+------------------------------------------------------------
+---------------------------------------------------------
+| perf/mse  | 0.05939  |
+| time-step | 178      |
+| y_out     | 0.0337   |
+---------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058682185 |
+| time-step | 179         |
+| y_out     | -0.0738     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061186206 |
+| time-step | 180         |
+| y_out     | 0.0195      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06249378 |
+| time-step | 181        |
+| y_out     | -0.0391    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062954776 |
+| time-step | 182         |
+| y_out     | 0.008       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.063530326 |
+| time-step | 183         |
+| y_out     | 0.023       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06510935 |
+| time-step | 184        |
+| y_out     | -0.0162    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.066021495 |
+| time-step | 185         |
+| y_out     | -0.0352     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06407362 |
+| time-step | 186        |
+| y_out     | 0.0266     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06525252 |
+| time-step | 187        |
+| y_out     | 0.0358     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06584628 |
+| time-step | 188        |
+| y_out     | -0.0134    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06453097 |
+| time-step | 189        |
+| y_out     | -0.0585    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06360158 |
+| time-step | 190        |
+| y_out     | -0.153     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06385478 |
+| time-step | 191        |
+| y_out     | 0.025      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06277536 |
+| time-step | 192        |
+| y_out     | -0.036     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06564377 |
+| time-step | 193        |
+| y_out     | 0.0823     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0660685 |
+| time-step | 194       |
+| y_out     | 0.0954    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06663888 |
+| time-step | 195        |
+| y_out     | -0.0426    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06857444 |
+| time-step | 196        |
+| y_out     | -0.049     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06884728 |
+| time-step | 197        |
+| y_out     | -0.0081    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06818736 |
+| time-step | 198        |
+| y_out     | 0.00521    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06983349 |
+| time-step | 199        |
+| y_out     | -0.0341    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06811339 |
+| time-step | 200        |
+| y_out     | 0.00414    |
+-----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint 200
+-----------------------------------------------------------
+| perf/mse  | 0.06616482 |
+| time-step | 201        |
+| y_out     | -0.166     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06590567 |
+| time-step | 202        |
+| y_out     | 0.0119     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06592958 |
+| time-step | 203        |
+| y_out     | 0.0667     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06354184 |
+| time-step | 204        |
+| y_out     | -0.0532    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06171415 |
+| time-step | 205        |
+| y_out     | 0.0726     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061320703 |
+| time-step | 206         |
+| y_out     | 0.0626      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059749436 |
+| time-step | 207         |
+| y_out     | -0.0141     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059748657 |
+| time-step | 208         |
+| y_out     | -0.0524     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05967269 |
+| time-step | 209        |
+| y_out     | 0.0352     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0627072 |
+| time-step | 210       |
+| y_out     | 0.0348    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06190454 |
+| time-step | 211        |
+| y_out     | -0.0506    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06425017 |
+| time-step | 212        |
+| y_out     | 0.0993     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06392668 |
+| time-step | 213        |
+| y_out     | 0.217      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06618197 |
+| time-step | 214        |
+| y_out     | -0.000663  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06672396 |
+| time-step | 215        |
+| y_out     | 0.0193     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06532984 |
+| time-step | 216        |
+| y_out     | -0.00299   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.066146486 |
+| time-step | 217         |
+| y_out     | 0.00902     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.064975806 |
+| time-step | 218         |
+| y_out     | 0.0214      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0630003 |
+| time-step | 219       |
+| y_out     | -0.137    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060314745 |
+| time-step | 220         |
+| y_out     | -0.126      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0624517 |
+| time-step | 221       |
+| y_out     | -0.0072   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060216933 |
+| time-step | 222         |
+| y_out     | -0.0461     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05960244 |
+| time-step | 223        |
+| y_out     | 0.0305     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05884903 |
+| time-step | 224        |
+| y_out     | 0.116      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05948279 |
+| time-step | 225        |
+| y_out     | 0.088      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059296004 |
+| time-step | 226         |
+| y_out     | 0.125       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057963468 |
+| time-step | 227         |
+| y_out     | 0.00525     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05948673 |
+| time-step | 228        |
+| y_out     | -0.12      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06030799 |
+| time-step | 229        |
+| y_out     | 0.0231     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06217482 |
+| time-step | 230        |
+| y_out     | -0.0692    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060006768 |
+| time-step | 231         |
+| y_out     | -0.0425     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059678555 |
+| time-step | 232         |
+| y_out     | 0.0274      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056896836 |
+| time-step | 233         |
+| y_out     | 0.0648      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05519135 |
+| time-step | 234        |
+| y_out     | 0.0118     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05357951 |
+| time-step | 235        |
+| y_out     | -0.0334    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.053739198 |
+| time-step | 236         |
+| y_out     | 0.00437     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.054819513 |
+| time-step | 237         |
+| y_out     | 0.0127      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.054516714 |
+| time-step | 238         |
+| y_out     | 0.0353      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.055505436 |
+| time-step | 239         |
+| y_out     | -0.00941    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05397337 |
+| time-step | 240        |
+| y_out     | -0.0962    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.053612806 |
+| time-step | 241         |
+| y_out     | -0.0356     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056304086 |
+| time-step | 242         |
+| y_out     | -0.0703     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05672614 |
+| time-step | 243        |
+| y_out     | 0.00876    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058642082 |
+| time-step | 244         |
+| y_out     | -0.0922     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060372073 |
+| time-step | 245         |
+| y_out     | 0.0919      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06131935 |
+| time-step | 246        |
+| y_out     | -0.0781    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06267763 |
+| time-step | 247        |
+| y_out     | -0.0529    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06262267 |
+| time-step | 248        |
+| y_out     | -0.0251    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06004439 |
+| time-step | 249        |
+| y_out     | 0.0254     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059957266 |
+| time-step | 250         |
+| y_out     | -0.0781     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062695615 |
+| time-step | 251         |
+| y_out     | -0.0278     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06092366 |
+| time-step | 252        |
+| y_out     | -0.0184    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062162317 |
+| time-step | 253         |
+| y_out     | -0.0318     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061132085 |
+| time-step | 254         |
+| y_out     | 0.0799      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05924613 |
+| time-step | 255        |
+| y_out     | 0.0653     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060434055 |
+| time-step | 256         |
+| y_out     | -0.0788     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057571102 |
+| time-step | 257         |
+| y_out     | -0.0524     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05639541 |
+| time-step | 258        |
+| y_out     | 0.00217    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057101797 |
+| time-step | 259         |
+| y_out     | -0.0289     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05618251 |
+| time-step | 260        |
+| y_out     | -0.108     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05464252 |
+| time-step | 261        |
+| y_out     | -0.116     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05428974 |
+| time-step | 262        |
+| y_out     | 0.0337     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.052216798 |
+| time-step | 263         |
+| y_out     | -0.0103     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051262338 |
+| time-step | 264         |
+| y_out     | 0.0165      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050855733 |
+| time-step | 265         |
+| y_out     | 0.104       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04840196 |
+| time-step | 266        |
+| y_out     | -0.0576    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049175605 |
+| time-step | 267         |
+| y_out     | -0.131      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049470197 |
+| time-step | 268         |
+| y_out     | -0.00244    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050518066 |
+| time-step | 269         |
+| y_out     | 0.083       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051476307 |
+| time-step | 270         |
+| y_out     | 0.0592      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051011473 |
+| time-step | 271         |
+| y_out     | -0.0573     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049953796 |
+| time-step | 272         |
+| y_out     | 0.0429      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05097891 |
+| time-step | 273        |
+| y_out     | 0.0567     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05294041 |
+| time-step | 274        |
+| y_out     | -0.216     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05349181 |
+| time-step | 275        |
+| y_out     | -0.0373    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.052047722 |
+| time-step | 276         |
+| y_out     | 0.00351     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051791944 |
+| time-step | 277         |
+| y_out     | -0.147      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051755946 |
+| time-step | 278         |
+| y_out     | 0.0682      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04898694 |
+| time-step | 279        |
+| y_out     | 0.0934     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048101082 |
+| time-step | 280         |
+| y_out     | -0.0504     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048442706 |
+| time-step | 281         |
+| y_out     | 0.0308      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04843736 |
+| time-step | 282        |
+| y_out     | 0.024      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04744278 |
+| time-step | 283        |
+| y_out     | 0.0689     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046123512 |
+| time-step | 284         |
+| y_out     | -0.0299     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044650167 |
+| time-step | 285         |
+| y_out     | 0.0108      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045727085 |
+| time-step | 286         |
+| y_out     | 0.0343      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046573404 |
+| time-step | 287         |
+| y_out     | -0.0485     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045426007 |
+| time-step | 288         |
+| y_out     | -0.127      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046911374 |
+| time-step | 289         |
+| y_out     | -0.0342     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04829799 |
+| time-step | 290        |
+| y_out     | -0.103     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047202215 |
+| time-step | 291         |
+| y_out     | -0.0443     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047759354 |
+| time-step | 292         |
+| y_out     | -0.0464     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04901324 |
+| time-step | 293        |
+| y_out     | -0.0202    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049171332 |
+| time-step | 294         |
+| y_out     | 0.0805      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050798703 |
+| time-step | 295         |
+| y_out     | -0.0214     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050071336 |
+| time-step | 296         |
+| y_out     | -0.0884     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04819747 |
+| time-step | 297        |
+| y_out     | 0.0701     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051176287 |
+| time-step | 298         |
+| y_out     | -0.0205     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050809048 |
+| time-step | 299         |
+| y_out     | -0.0126     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050406896 |
+| time-step | 300         |
+| y_out     | 0.116       |
+------------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint 300
+-----------------------------------------------------------
+| perf/mse  | 0.05174283 |
+| time-step | 301        |
+| y_out     | 0.024      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05258121 |
+| time-step | 302        |
+| y_out     | -0.0303    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0546092 |
+| time-step | 303       |
+| y_out     | -0.0389   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.054850496 |
+| time-step | 304         |
+| y_out     | 0.00659     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05508005 |
+| time-step | 305        |
+| y_out     | -0.0544    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05548368 |
+| time-step | 306        |
+| y_out     | -0.0218    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057017952 |
+| time-step | 307         |
+| y_out     | 0.0167      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05396073 |
+| time-step | 308        |
+| y_out     | -0.0447    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.053436358 |
+| time-step | 309         |
+| y_out     | -0.00739    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.052189868 |
+| time-step | 310         |
+| y_out     | 0.051       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051170953 |
+| time-step | 311         |
+| y_out     | -0.0448     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051373493 |
+| time-step | 312         |
+| y_out     | -0.0193     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051005155 |
+| time-step | 313         |
+| y_out     | -0.0162     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050715476 |
+| time-step | 314         |
+| y_out     | -0.0722     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048216157 |
+| time-step | 315         |
+| y_out     | 0.0666      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046970457 |
+| time-step | 316         |
+| y_out     | 0.0139      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04634898 |
+| time-step | 317        |
+| y_out     | -0.123     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047595352 |
+| time-step | 318         |
+| y_out     | -0.00802    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04812552 |
+| time-step | 319        |
+| y_out     | 0.00408    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048778407 |
+| time-step | 320         |
+| y_out     | 0.0253      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048348833 |
+| time-step | 321         |
+| y_out     | 0.0814      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048585653 |
+| time-step | 322         |
+| y_out     | -0.0666     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047750764 |
+| time-step | 323         |
+| y_out     | 0.0234      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047169484 |
+| time-step | 324         |
+| y_out     | 0.0134      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051051043 |
+| time-step | 325         |
+| y_out     | -0.0697     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050783254 |
+| time-step | 326         |
+| y_out     | -0.113      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05024145 |
+| time-step | 327        |
+| y_out     | -0.0369    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049518358 |
+| time-step | 328         |
+| y_out     | 0.05        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050671153 |
+| time-step | 329         |
+| y_out     | -0.0229     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04976202 |
+| time-step | 330        |
+| y_out     | 0.0191     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04918482 |
+| time-step | 331        |
+| y_out     | 0.0149     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046014633 |
+| time-step | 332         |
+| y_out     | 0.0611      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04446416 |
+| time-step | 333        |
+| y_out     | 0.225      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047265183 |
+| time-step | 334         |
+| y_out     | 0.139       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045637976 |
+| time-step | 335         |
+| y_out     | -0.21       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04610682 |
+| time-step | 336        |
+| y_out     | -0.0574    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04625579 |
+| time-step | 337        |
+| y_out     | 0.00341    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045840107 |
+| time-step | 338         |
+| y_out     | -0.031      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04537605 |
+| time-step | 339        |
+| y_out     | 0.0899     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044669002 |
+| time-step | 340         |
+| y_out     | 0.123       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045253754 |
+| time-step | 341         |
+| y_out     | -0.0432     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045383714 |
+| time-step | 342         |
+| y_out     | -0.0727     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044924565 |
+| time-step | 343         |
+| y_out     | 0.0429      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043362886 |
+| time-step | 344         |
+| y_out     | 0.0401      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041840345 |
+| time-step | 345         |
+| y_out     | -0.153      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043310747 |
+| time-step | 346         |
+| y_out     | -0.0121     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044173524 |
+| time-step | 347         |
+| y_out     | -0.137      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045624156 |
+| time-step | 348         |
+| y_out     | 0.0497      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047848817 |
+| time-step | 349         |
+| y_out     | -0.128      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047799133 |
+| time-step | 350         |
+| y_out     | 0.117       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047366858 |
+| time-step | 351         |
+| y_out     | 0.0389      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04888399 |
+| time-step | 352        |
+| y_out     | 0.0713     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049352866 |
+| time-step | 353         |
+| y_out     | 0.0587      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051599108 |
+| time-step | 354         |
+| y_out     | 0.0556      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05342742 |
+| time-step | 355        |
+| y_out     | -0.0932    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05273967 |
+| time-step | 356        |
+| y_out     | 0.121      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051974278 |
+| time-step | 357         |
+| y_out     | 0.0238      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05059583 |
+| time-step | 358        |
+| y_out     | 0.0447     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04586927 |
+| time-step | 359        |
+| y_out     | -0.0184    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047620047 |
+| time-step | 360         |
+| y_out     | 0.171       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047107298 |
+| time-step | 361         |
+| y_out     | -0.0801     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047237623 |
+| time-step | 362         |
+| y_out     | 0.0878      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048508685 |
+| time-step | 363         |
+| y_out     | 0.076       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045510255 |
+| time-step | 364         |
+| y_out     | -0.0532     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043865245 |
+| time-step | 365         |
+| y_out     | 0.0193      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041885465 |
+| time-step | 366         |
+| y_out     | 0.0736      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04296519 |
+| time-step | 367        |
+| y_out     | -0.0214    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04382933 |
+| time-step | 368        |
+| y_out     | -0.0343    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046098553 |
+| time-step | 369         |
+| y_out     | 0.117       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04480189 |
+| time-step | 370        |
+| y_out     | 0.117      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044245582 |
+| time-step | 371         |
+| y_out     | 0.138       |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0439245 |
+| time-step | 372       |
+| y_out     | 0.0668    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043942112 |
+| time-step | 373         |
+| y_out     | -0.0123     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043682896 |
+| time-step | 374         |
+| y_out     | -0.115      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043958686 |
+| time-step | 375         |
+| y_out     | 0.114       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04454767 |
+| time-step | 376        |
+| y_out     | -0.0614    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04306534 |
+| time-step | 377        |
+| y_out     | 0.00934    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041446973 |
+| time-step | 378         |
+| y_out     | -0.151      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04100363 |
+| time-step | 379        |
+| y_out     | 0.024      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04180368 |
+| time-step | 380        |
+| y_out     | -0.0599    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042992603 |
+| time-step | 381         |
+| y_out     | -0.0548     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043098643 |
+| time-step | 382         |
+| y_out     | -0.0116     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043514136 |
+| time-step | 383         |
+| y_out     | 0.0327      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04369066 |
+| time-step | 384        |
+| y_out     | 0.0623     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04326322 |
+| time-step | 385        |
+| y_out     | -0.096     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044685632 |
+| time-step | 386         |
+| y_out     | 0.0277      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04526978 |
+| time-step | 387        |
+| y_out     | -0.0655    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04619179 |
+| time-step | 388        |
+| y_out     | -0.0376    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04565365 |
+| time-step | 389        |
+| y_out     | -0.0207    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0453593 |
+| time-step | 390       |
+| y_out     | -0.0541   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044717014 |
+| time-step | 391         |
+| y_out     | -0.0988     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04368487 |
+| time-step | 392        |
+| y_out     | -0.0678    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04057418 |
+| time-step | 393        |
+| y_out     | 0.0429     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039496467 |
+| time-step | 394         |
+| y_out     | 0.0154      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041491378 |
+| time-step | 395         |
+| y_out     | -0.0318     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040550705 |
+| time-step | 396         |
+| y_out     | 0.0563      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040151957 |
+| time-step | 397         |
+| y_out     | -0.126      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040751856 |
+| time-step | 398         |
+| y_out     | -0.0782     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041047078 |
+| time-step | 399         |
+| y_out     | -0.113      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040643338 |
+| time-step | 400         |
+| y_out     | 0.0683      |
+------------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint 400
+------------------------------------------------------------
+| perf/mse  | 0.040522076 |
+| time-step | 401         |
+| y_out     | 0.0577      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040857714 |
+| time-step | 402         |
+| y_out     | -0.0516     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041983295 |
+| time-step | 403         |
+| y_out     | 0.0433      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041991167 |
+| time-step | 404         |
+| y_out     | -0.0785     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04240365 |
+| time-step | 405        |
+| y_out     | 0.103      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042375665 |
+| time-step | 406         |
+| y_out     | -0.00437    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043141805 |
+| time-step | 407         |
+| y_out     | -0.0194     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041378472 |
+| time-step | 408         |
+| y_out     | -0.0212     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04113335 |
+| time-step | 409        |
+| y_out     | 0.018      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040811963 |
+| time-step | 410         |
+| y_out     | -0.0737     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041282825 |
+| time-step | 411         |
+| y_out     | 0.118       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042308565 |
+| time-step | 412         |
+| y_out     | -0.00289    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041298993 |
+| time-step | 413         |
+| y_out     | -0.0298     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042077266 |
+| time-step | 414         |
+| y_out     | -0.1        |
+------------------------------------------------------------
+---------------------------------------------------------
+| perf/mse  | 0.039507 |
+| time-step | 415      |
+| y_out     | 0.0715   |
+---------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039639987 |
+| time-step | 416         |
+| y_out     | 0.0893      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03933642 |
+| time-step | 417        |
+| y_out     | -0.0395    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04217129 |
+| time-step | 418        |
+| y_out     | -0.0557    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043428592 |
+| time-step | 419         |
+| y_out     | 0.111       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044488728 |
+| time-step | 420         |
+| y_out     | 0.0178      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044093788 |
+| time-step | 421         |
+| y_out     | -0.067      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043048836 |
+| time-step | 422         |
+| y_out     | 0.0504      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04314921 |
+| time-step | 423        |
+| y_out     | 0.0162     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04349237 |
+| time-step | 424        |
+| y_out     | 0.01       |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04452821 |
+| time-step | 425        |
+| y_out     | 0.00787    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04426225 |
+| time-step | 426        |
+| y_out     | 0.0487     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04360787 |
+| time-step | 427        |
+| y_out     | 0.0032     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043667957 |
+| time-step | 428         |
+| y_out     | -0.0608     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042218983 |
+| time-step | 429         |
+| y_out     | -0.0561     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0419866 |
+| time-step | 430       |
+| y_out     | -0.0284   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041578658 |
+| time-step | 431         |
+| y_out     | 0.0457      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04186579 |
+| time-step | 432        |
+| y_out     | 0.000263   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04387967 |
+| time-step | 433        |
+| y_out     | -0.0321    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04383936 |
+| time-step | 434        |
+| y_out     | -0.0136    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044992246 |
+| time-step | 435         |
+| y_out     | 0.0228      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044400357 |
+| time-step | 436         |
+| y_out     | 0.0347      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045319177 |
+| time-step | 437         |
+| y_out     | 0.0315      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043344233 |
+| time-step | 438         |
+| y_out     | -0.105      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04432697 |
+| time-step | 439        |
+| y_out     | -0.119     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04481327 |
+| time-step | 440        |
+| y_out     | 0.00662    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.045533992 |
+| time-step | 441         |
+| y_out     | -0.104      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043996777 |
+| time-step | 442         |
+| y_out     | 0.00843     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0417896 |
+| time-step | 443       |
+| y_out     | -0.0833   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04127677 |
+| time-step | 444        |
+| y_out     | -0.0714    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038586713 |
+| time-step | 445         |
+| y_out     | 0.0362      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039206453 |
+| time-step | 446         |
+| y_out     | -0.0908     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038004704 |
+| time-step | 447         |
+| y_out     | 0.0358      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03785272 |
+| time-step | 448        |
+| y_out     | 0.0616     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036497425 |
+| time-step | 449         |
+| y_out     | 0.00423     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034876965 |
+| time-step | 450         |
+| y_out     | -0.0286     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034173798 |
+| time-step | 451         |
+| y_out     | -0.00491    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03518618 |
+| time-step | 452        |
+| y_out     | -0.0324    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035749175 |
+| time-step | 453         |
+| y_out     | 0.00848     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03567919 |
+| time-step | 454        |
+| y_out     | -0.0353    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036438446 |
+| time-step | 455         |
+| y_out     | 0.111       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03529737 |
+| time-step | 456        |
+| y_out     | -0.00363   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035979748 |
+| time-step | 457         |
+| y_out     | 0.0314      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036433093 |
+| time-step | 458         |
+| y_out     | -0.012      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03787488 |
+| time-step | 459        |
+| y_out     | 0.00587    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038683094 |
+| time-step | 460         |
+| y_out     | -0.0452     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039051373 |
+| time-step | 461         |
+| y_out     | -0.00751    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039357126 |
+| time-step | 462         |
+| y_out     | 0.0189      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03825305 |
+| time-step | 463        |
+| y_out     | 0.0738     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03832134 |
+| time-step | 464        |
+| y_out     | 0.026      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03921164 |
+| time-step | 465        |
+| y_out     | 0.131      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039929666 |
+| time-step | 466         |
+| y_out     | -0.117      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039560802 |
+| time-step | 467         |
+| y_out     | 0.118       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03878637 |
+| time-step | 468        |
+| y_out     | -0.117     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03827966 |
+| time-step | 469        |
+| y_out     | -0.0765    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037641738 |
+| time-step | 470         |
+| y_out     | -0.023      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038357873 |
+| time-step | 471         |
+| y_out     | -0.0722     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038132764 |
+| time-step | 472         |
+| y_out     | 0.109       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03891701 |
+| time-step | 473        |
+| y_out     | -0.0703    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038103554 |
+| time-step | 474         |
+| y_out     | 0.0692      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03621999 |
+| time-step | 475        |
+| y_out     | -0.000647  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036580242 |
+| time-step | 476         |
+| y_out     | -0.0134     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036139347 |
+| time-step | 477         |
+| y_out     | -0.0238     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03563816 |
+| time-step | 478        |
+| y_out     | 0.0093     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035266772 |
+| time-step | 479         |
+| y_out     | 2.33e-05    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035108875 |
+| time-step | 480         |
+| y_out     | -0.0566     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03506776 |
+| time-step | 481        |
+| y_out     | 0.0127     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034106456 |
+| time-step | 482         |
+| y_out     | 0.124       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033619884 |
+| time-step | 483         |
+| y_out     | 0.0468      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034672335 |
+| time-step | 484         |
+| y_out     | 0.063       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03652858 |
+| time-step | 485        |
+| y_out     | 0.00827    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037528615 |
+| time-step | 486         |
+| y_out     | -0.182      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038261674 |
+| time-step | 487         |
+| y_out     | 0.0356      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039350253 |
+| time-step | 488         |
+| y_out     | 0.0238      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0382124 |
+| time-step | 489       |
+| y_out     | 0.127     |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03865268 |
+| time-step | 490        |
+| y_out     | -0.062     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037496835 |
+| time-step | 491         |
+| y_out     | 0.0473      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03886371 |
+| time-step | 492        |
+| y_out     | 0.0499     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038803898 |
+| time-step | 493         |
+| y_out     | -0.0929     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03859285 |
+| time-step | 494        |
+| y_out     | 0.0994     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037241153 |
+| time-step | 495         |
+| y_out     | 0.0234      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036005683 |
+| time-step | 496         |
+| y_out     | -0.0635     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035168167 |
+| time-step | 497         |
+| y_out     | 0.00639     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034892336 |
+| time-step | 498         |
+| y_out     | -0.0325     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035247844 |
+| time-step | 499         |
+| y_out     | 0.052       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03583549 |
+| time-step | 500        |
+| y_out     | 0.00646    |
+-----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint 500
+-----------------------------------------------------------
+| perf/mse  | 0.03733476 |
+| time-step | 501        |
+| y_out     | -0.0302    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037066884 |
+| time-step | 502         |
+| y_out     | 0.0845      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036854826 |
+| time-step | 503         |
+| y_out     | 0.0376      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037260495 |
+| time-step | 504         |
+| y_out     | 0.0172      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037195526 |
+| time-step | 505         |
+| y_out     | -0.0203     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03681703 |
+| time-step | 506        |
+| y_out     | 0.0952     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03714765 |
+| time-step | 507        |
+| y_out     | 0.144      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03735614 |
+| time-step | 508        |
+| y_out     | -0.0451    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03867663 |
+| time-step | 509        |
+| y_out     | -0.0839    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037212465 |
+| time-step | 510         |
+| y_out     | 0.00484     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03521788 |
+| time-step | 511        |
+| y_out     | 0.00774    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036547348 |
+| time-step | 512         |
+| y_out     | 0.0122      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037850987 |
+| time-step | 513         |
+| y_out     | 0.0956      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03790846 |
+| time-step | 514        |
+| y_out     | 0.0194     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037776686 |
+| time-step | 515         |
+| y_out     | 0.11        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038909797 |
+| time-step | 516         |
+| y_out     | -0.0527     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038011245 |
+| time-step | 517         |
+| y_out     | 0.0574      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038414802 |
+| time-step | 518         |
+| y_out     | -0.0221     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038682684 |
+| time-step | 519         |
+| y_out     | -0.000219   |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03936618 |
+| time-step | 520        |
+| y_out     | 0.0755     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0401329 |
+| time-step | 521       |
+| y_out     | -0.0511   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037935577 |
+| time-step | 522         |
+| y_out     | -0.0776     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037657436 |
+| time-step | 523         |
+| y_out     | -0.0248     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036557578 |
+| time-step | 524         |
+| y_out     | -0.119      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036983322 |
+| time-step | 525         |
+| y_out     | -0.091      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035910983 |
+| time-step | 526         |
+| y_out     | 0.0255      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036941655 |
+| time-step | 527         |
+| y_out     | -0.0308     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036313415 |
+| time-step | 528         |
+| y_out     | -0.0558     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034566753 |
+| time-step | 529         |
+| y_out     | -0.0136     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03446619 |
+| time-step | 530        |
+| y_out     | -0.0846    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034713157 |
+| time-step | 531         |
+| y_out     | 0.0129      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036472194 |
+| time-step | 532         |
+| y_out     | 0.0272      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035231292 |
+| time-step | 533         |
+| y_out     | -0.0406     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035995197 |
+| time-step | 534         |
+| y_out     | -0.0932     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03676777 |
+| time-step | 535        |
+| y_out     | 0.108      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037174717 |
+| time-step | 536         |
+| y_out     | -0.0364     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03749975 |
+| time-step | 537        |
+| y_out     | 0.0197     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037383832 |
+| time-step | 538         |
+| y_out     | -0.0188     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03777265 |
+| time-step | 539        |
+| y_out     | 0.023      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03784097 |
+| time-step | 540        |
+| y_out     | 0.0279     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036639057 |
+| time-step | 541         |
+| y_out     | 0.0781      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035638865 |
+| time-step | 542         |
+| y_out     | -0.117      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036030825 |
+| time-step | 543         |
+| y_out     | -0.0932     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035704833 |
+| time-step | 544         |
+| y_out     | -0.0248     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034461215 |
+| time-step | 545         |
+| y_out     | 0.00317     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034431677 |
+| time-step | 546         |
+| y_out     | 0.0268      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033069186 |
+| time-step | 547         |
+| y_out     | 0.0918      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032823406 |
+| time-step | 548         |
+| y_out     | -0.17       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03383097 |
+| time-step | 549        |
+| y_out     | -0.00244   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03324308 |
+| time-step | 550        |
+| y_out     | -0.016     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03337992 |
+| time-step | 551        |
+| y_out     | 0.0363     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03336678 |
+| time-step | 552        |
+| y_out     | -0.0461    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033796065 |
+| time-step | 553         |
+| y_out     | 0.102       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033875298 |
+| time-step | 554         |
+| y_out     | -0.0256     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033707503 |
+| time-step | 555         |
+| y_out     | -0.0254     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03477437 |
+| time-step | 556        |
+| y_out     | 0.0673     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037721146 |
+| time-step | 557         |
+| y_out     | 0.0961      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037171803 |
+| time-step | 558         |
+| y_out     | 0.0863      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03746583 |
+| time-step | 559        |
+| y_out     | 0.0299     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03680097 |
+| time-step | 560        |
+| y_out     | -0.0652    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037690498 |
+| time-step | 561         |
+| y_out     | 0.0334      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037210356 |
+| time-step | 562         |
+| y_out     | 0.0707      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036322925 |
+| time-step | 563         |
+| y_out     | -0.0154     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037201725 |
+| time-step | 564         |
+| y_out     | -0.0757     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037067797 |
+| time-step | 565         |
+| y_out     | -0.106      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0362378 |
+| time-step | 566       |
+| y_out     | 0.0437    |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034831397 |
+| time-step | 567         |
+| y_out     | -0.0502     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035759993 |
+| time-step | 568         |
+| y_out     | 0.0359      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034532867 |
+| time-step | 569         |
+| y_out     | -0.0246     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03503443 |
+| time-step | 570        |
+| y_out     | 0.0237     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03428251 |
+| time-step | 571        |
+| y_out     | 0.00545    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03483584 |
+| time-step | 572        |
+| y_out     | 0.0163     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03531785 |
+| time-step | 573        |
+| y_out     | -0.0865    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0360641 |
+| time-step | 574       |
+| y_out     | -0.0317   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.036605097 |
+| time-step | 575         |
+| y_out     | -0.00924    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03552351 |
+| time-step | 576        |
+| y_out     | 0.00129    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034539882 |
+| time-step | 577         |
+| y_out     | 0.00987     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033754088 |
+| time-step | 578         |
+| y_out     | -0.0458     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034340445 |
+| time-step | 579         |
+| y_out     | -0.0525     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034343418 |
+| time-step | 580         |
+| y_out     | 0.0191      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033559937 |
+| time-step | 581         |
+| y_out     | -0.0297     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032644056 |
+| time-step | 582         |
+| y_out     | 0.0281      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03298333 |
+| time-step | 583        |
+| y_out     | 0.0182     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03146042 |
+| time-step | 584        |
+| y_out     | -0.0691    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032784816 |
+| time-step | 585         |
+| y_out     | 0.0775      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033799257 |
+| time-step | 586         |
+| y_out     | -0.104      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034174956 |
+| time-step | 587         |
+| y_out     | 0.015       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034603573 |
+| time-step | 588         |
+| y_out     | -0.00166    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03349266 |
+| time-step | 589        |
+| y_out     | 0.0394     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034142423 |
+| time-step | 590         |
+| y_out     | -0.0372     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03479029 |
+| time-step | 591        |
+| y_out     | -0.0133    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03450986 |
+| time-step | 592        |
+| y_out     | 0.0506     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034693528 |
+| time-step | 593         |
+| y_out     | 0.00966     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033304214 |
+| time-step | 594         |
+| y_out     | 0.036       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032109775 |
+| time-step | 595         |
+| y_out     | 0.112       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031864252 |
+| time-step | 596         |
+| y_out     | -0.0726     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030815398 |
+| time-step | 597         |
+| y_out     | -0.0907     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029445121 |
+| time-step | 598         |
+| y_out     | 0.0155      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030023491 |
+| time-step | 599         |
+| y_out     | 0.0474      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029509742 |
+| time-step | 600         |
+| y_out     | -0.0995     |
+------------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint 600
+-----------------------------------------------------------
+| perf/mse  | 0.02955753 |
+| time-step | 601        |
+| y_out     | -0.0466    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029665494 |
+| time-step | 602         |
+| y_out     | -0.0332     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029646825 |
+| time-step | 603         |
+| y_out     | -0.0353     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031129738 |
+| time-step | 604         |
+| y_out     | 0.0409      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030378044 |
+| time-step | 605         |
+| y_out     | -0.0555     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02960462 |
+| time-step | 606        |
+| y_out     | 0.081      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030322623 |
+| time-step | 607         |
+| y_out     | 0.183       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032099858 |
+| time-step | 608         |
+| y_out     | -0.00356    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031497665 |
+| time-step | 609         |
+| y_out     | 0.0442      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03245721 |
+| time-step | 610        |
+| y_out     | -0.027     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03226036 |
+| time-step | 611        |
+| y_out     | -0.0141    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033365626 |
+| time-step | 612         |
+| y_out     | 0.0502      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032781415 |
+| time-step | 613         |
+| y_out     | 0.0341      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032474138 |
+| time-step | 614         |
+| y_out     | -0.0315     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033486746 |
+| time-step | 615         |
+| y_out     | 0.0203      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0338382 |
+| time-step | 616       |
+| y_out     | 0.143     |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033649676 |
+| time-step | 617         |
+| y_out     | 0.0633      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032934792 |
+| time-step | 618         |
+| y_out     | -0.0988     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03315308 |
+| time-step | 619        |
+| y_out     | -0.0178    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03229132 |
+| time-step | 620        |
+| y_out     | -0.0611    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0327541 |
+| time-step | 621       |
+| y_out     | 0.00041   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031698115 |
+| time-step | 622         |
+| y_out     | 0.0749      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032262318 |
+| time-step | 623         |
+| y_out     | -0.063      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03194316 |
+| time-step | 624        |
+| y_out     | 0.0229     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031548526 |
+| time-step | 625         |
+| y_out     | -0.135      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03101631 |
+| time-step | 626        |
+| y_out     | 0.164      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031343438 |
+| time-step | 627         |
+| y_out     | 0.0476      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031631738 |
+| time-step | 628         |
+| y_out     | -0.0989     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031573154 |
+| time-step | 629         |
+| y_out     | 0.00181     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031537663 |
+| time-step | 630         |
+| y_out     | 0.0358      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03145404 |
+| time-step | 631        |
+| y_out     | -0.0157    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032795534 |
+| time-step | 632         |
+| y_out     | -0.0114     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033159014 |
+| time-step | 633         |
+| y_out     | -0.157      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03244157 |
+| time-step | 634        |
+| y_out     | 0.0512     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03138875 |
+| time-step | 635        |
+| y_out     | -0.0156    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03282702 |
+| time-step | 636        |
+| y_out     | 0.0454     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033254016 |
+| time-step | 637         |
+| y_out     | 0.056       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032730367 |
+| time-step | 638         |
+| y_out     | 0.05        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032393552 |
+| time-step | 639         |
+| y_out     | 0.0597      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032702588 |
+| time-step | 640         |
+| y_out     | -0.134      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032438923 |
+| time-step | 641         |
+| y_out     | -0.0474     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03167278 |
+| time-step | 642        |
+| y_out     | -0.00115   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03203782 |
+| time-step | 643        |
+| y_out     | -0.043     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033848215 |
+| time-step | 644         |
+| y_out     | 0.0673      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03518135 |
+| time-step | 645        |
+| y_out     | 0.0419     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034817737 |
+| time-step | 646         |
+| y_out     | -0.132      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03450574 |
+| time-step | 647        |
+| y_out     | 0.0362     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034160025 |
+| time-step | 648         |
+| y_out     | -0.0592     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.033882897 |
+| time-step | 649         |
+| y_out     | 0.0346      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0326714 |
+| time-step | 650       |
+| y_out     | 0.138     |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032752864 |
+| time-step | 651         |
+| y_out     | -0.0257     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.032670703 |
+| time-step | 652         |
+| y_out     | -0.0755     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03110286 |
+| time-step | 653        |
+| y_out     | -0.0117    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030341158 |
+| time-step | 654         |
+| y_out     | 0.0549      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029916978 |
+| time-step | 655         |
+| y_out     | -0.0918     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02941716 |
+| time-step | 656        |
+| y_out     | -0.0771    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02865209 |
+| time-step | 657        |
+| y_out     | 0.00312    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02901752 |
+| time-step | 658        |
+| y_out     | 0.0537     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029792726 |
+| time-step | 659         |
+| y_out     | -0.127      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029873054 |
+| time-step | 660         |
+| y_out     | 0.0515      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030676743 |
+| time-step | 661         |
+| y_out     | 0.0639      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031062389 |
+| time-step | 662         |
+| y_out     | 0.0299      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03166271 |
+| time-step | 663        |
+| y_out     | 0.0591     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031490974 |
+| time-step | 664         |
+| y_out     | -0.0373     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03088668 |
+| time-step | 665        |
+| y_out     | 0.00167    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03148003 |
+| time-step | 666        |
+| y_out     | -0.109     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031402674 |
+| time-step | 667         |
+| y_out     | -0.0581     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031179061 |
+| time-step | 668         |
+| y_out     | 0.0113      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03079604 |
+| time-step | 669        |
+| y_out     | -0.00028   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031088773 |
+| time-step | 670         |
+| y_out     | 0.0094      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030224254 |
+| time-step | 671         |
+| y_out     | -0.0298     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029808868 |
+| time-step | 672         |
+| y_out     | -0.0773     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02891534 |
+| time-step | 673        |
+| y_out     | -0.0166    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028422376 |
+| time-step | 674         |
+| y_out     | -0.127      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030108148 |
+| time-step | 675         |
+| y_out     | 0.0629      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0293192 |
+| time-step | 676       |
+| y_out     | -0.0498   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029605547 |
+| time-step | 677         |
+| y_out     | 0.0293      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02997067 |
+| time-step | 678        |
+| y_out     | -0.0469    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030485094 |
+| time-step | 679         |
+| y_out     | -0.012      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030837243 |
+| time-step | 680         |
+| y_out     | 0.0721      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0318379 |
+| time-step | 681       |
+| y_out     | 0.085     |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03163385 |
+| time-step | 682        |
+| y_out     | -0.0654    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03240539 |
+| time-step | 683        |
+| y_out     | -0.0353    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031963926 |
+| time-step | 684         |
+| y_out     | -0.0702     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03049387 |
+| time-step | 685        |
+| y_out     | -0.0258    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031243065 |
+| time-step | 686         |
+| y_out     | 0.0972      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031507593 |
+| time-step | 687         |
+| y_out     | -0.0353     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031517994 |
+| time-step | 688         |
+| y_out     | 0.121       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030255804 |
+| time-step | 689         |
+| y_out     | 0.042       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030702427 |
+| time-step | 690         |
+| y_out     | 0.0404      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029409861 |
+| time-step | 691         |
+| y_out     | -0.0584     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029511292 |
+| time-step | 692         |
+| y_out     | -0.035      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029403899 |
+| time-step | 693         |
+| y_out     | 0.0333      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029586393 |
+| time-step | 694         |
+| y_out     | 0.0622      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029786084 |
+| time-step | 695         |
+| y_out     | -0.00758    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028874695 |
+| time-step | 696         |
+| y_out     | -0.0866     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029097546 |
+| time-step | 697         |
+| y_out     | 0.021       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028396994 |
+| time-step | 698         |
+| y_out     | 0.077       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028549176 |
+| time-step | 699         |
+| y_out     | -0.00077    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028347254 |
+| time-step | 700         |
+| y_out     | 0.000271    |
+------------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint 700
+------------------------------------------------------------
+| perf/mse  | 0.028963346 |
+| time-step | 701         |
+| y_out     | 0.0125      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029113691 |
+| time-step | 702         |
+| y_out     | -0.0219     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028970605 |
+| time-step | 703         |
+| y_out     | -0.0588     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029578626 |
+| time-step | 704         |
+| y_out     | 0.0224      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029758623 |
+| time-step | 705         |
+| y_out     | -0.0247     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029879963 |
+| time-step | 706         |
+| y_out     | 0.0702      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029372385 |
+| time-step | 707         |
+| y_out     | -3.46e-06   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029560158 |
+| time-step | 708         |
+| y_out     | -0.0569     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030831715 |
+| time-step | 709         |
+| y_out     | -0.122      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030472606 |
+| time-step | 710         |
+| y_out     | -0.0808     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030901596 |
+| time-step | 711         |
+| y_out     | -0.0476     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030884814 |
+| time-step | 712         |
+| y_out     | -0.0847     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030973446 |
+| time-step | 713         |
+| y_out     | -0.0202     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030331686 |
+| time-step | 714         |
+| y_out     | -0.0307     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030228352 |
+| time-step | 715         |
+| y_out     | 0.00411     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030201033 |
+| time-step | 716         |
+| y_out     | -0.0111     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030105922 |
+| time-step | 717         |
+| y_out     | 0.151       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029680336 |
+| time-step | 718         |
+| y_out     | -0.0437     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.029252997 |
+| time-step | 719         |
+| y_out     | 0.119       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028861081 |
+| time-step | 720         |
+| y_out     | 0.0444      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028029975 |
+| time-step | 721         |
+| y_out     | 0.00957     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027789643 |
+| time-step | 722         |
+| y_out     | 0.0701      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02665713 |
+| time-step | 723        |
+| y_out     | 0.0123     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027247768 |
+| time-step | 724         |
+| y_out     | -0.00649    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027117502 |
+| time-step | 725         |
+| y_out     | -0.0738     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026762832 |
+| time-step | 726         |
+| y_out     | 0.0303      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026845485 |
+| time-step | 727         |
+| y_out     | -0.0165     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027942032 |
+| time-step | 728         |
+| y_out     | 0.00801     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027209243 |
+| time-step | 729         |
+| y_out     | 0.0709      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028276464 |
+| time-step | 730         |
+| y_out     | 0.0901      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02874479 |
+| time-step | 731        |
+| y_out     | -0.0892    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028927535 |
+| time-step | 732         |
+| y_out     | 0.00363     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02926426 |
+| time-step | 733        |
+| y_out     | -0.032     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028208151 |
+| time-step | 734         |
+| y_out     | -0.036      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028037066 |
+| time-step | 735         |
+| y_out     | 0.146       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027652774 |
+| time-step | 736         |
+| y_out     | -0.00189    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027336884 |
+| time-step | 737         |
+| y_out     | -0.0274     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027784968 |
+| time-step | 738         |
+| y_out     | 0.11        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027967233 |
+| time-step | 739         |
+| y_out     | -0.0183     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02673966 |
+| time-step | 740        |
+| y_out     | -0.00979   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026887584 |
+| time-step | 741         |
+| y_out     | -0.0746     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02652139 |
+| time-step | 742        |
+| y_out     | -0.023     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027052816 |
+| time-step | 743         |
+| y_out     | -0.103      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026686698 |
+| time-step | 744         |
+| y_out     | -0.104      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026838785 |
+| time-step | 745         |
+| y_out     | 0.00821     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02759608 |
+| time-step | 746        |
+| y_out     | -0.0784    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027862066 |
+| time-step | 747         |
+| y_out     | -0.00514    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026777467 |
+| time-step | 748         |
+| y_out     | 0.00837     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02664693 |
+| time-step | 749        |
+| y_out     | -0.0664    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026810069 |
+| time-step | 750         |
+| y_out     | -0.118      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026764642 |
+| time-step | 751         |
+| y_out     | -0.0163     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02680698 |
+| time-step | 752        |
+| y_out     | 0.0245     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027694654 |
+| time-step | 753         |
+| y_out     | -0.0394     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028346315 |
+| time-step | 754         |
+| y_out     | 0.0515      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028019572 |
+| time-step | 755         |
+| y_out     | -0.101      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027854213 |
+| time-step | 756         |
+| y_out     | -0.0151     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027867887 |
+| time-step | 757         |
+| y_out     | 0.0641      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027472273 |
+| time-step | 758         |
+| y_out     | 0.0381      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028287416 |
+| time-step | 759         |
+| y_out     | -0.0893     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028847864 |
+| time-step | 760         |
+| y_out     | 0.181       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027838284 |
+| time-step | 761         |
+| y_out     | -0.0781     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028204313 |
+| time-step | 762         |
+| y_out     | 0.127       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027740236 |
+| time-step | 763         |
+| y_out     | 0.0608      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027531907 |
+| time-step | 764         |
+| y_out     | 0.00487     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028134564 |
+| time-step | 765         |
+| y_out     | -0.0234     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02755475 |
+| time-step | 766        |
+| y_out     | -0.0358    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026865993 |
+| time-step | 767         |
+| y_out     | -0.0822     |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0271254 |
+| time-step | 768       |
+| y_out     | 0.115     |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026092941 |
+| time-step | 769         |
+| y_out     | 0.0899      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025609557 |
+| time-step | 770         |
+| y_out     | 0.0323      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025536533 |
+| time-step | 771         |
+| y_out     | -0.0574     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024458077 |
+| time-step | 772         |
+| y_out     | -0.0745     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023764443 |
+| time-step | 773         |
+| y_out     | -0.0457     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025078932 |
+| time-step | 774         |
+| y_out     | 0.0521      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023830721 |
+| time-step | 775         |
+| y_out     | -0.017      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024070293 |
+| time-step | 776         |
+| y_out     | 0.0863      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023845429 |
+| time-step | 777         |
+| y_out     | -0.0738     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023561928 |
+| time-step | 778         |
+| y_out     | -0.0779     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023658082 |
+| time-step | 779         |
+| y_out     | -0.0212     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023351675 |
+| time-step | 780         |
+| y_out     | -0.0476     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02370647 |
+| time-step | 781        |
+| y_out     | 0.0912     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023887781 |
+| time-step | 782         |
+| y_out     | -0.0959     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024240369 |
+| time-step | 783         |
+| y_out     | -0.0279     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02382132 |
+| time-step | 784        |
+| y_out     | -0.0924    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024463639 |
+| time-step | 785         |
+| y_out     | 0.0294      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024821848 |
+| time-step | 786         |
+| y_out     | 0.0571      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0258226 |
+| time-step | 787       |
+| y_out     | -0.0561   |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025840987 |
+| time-step | 788         |
+| y_out     | -0.0536     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026194667 |
+| time-step | 789         |
+| y_out     | 0.0338      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02627787 |
+| time-step | 790        |
+| y_out     | 0.107      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026274914 |
+| time-step | 791         |
+| y_out     | 0.104       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026150813 |
+| time-step | 792         |
+| y_out     | -0.119      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026745448 |
+| time-step | 793         |
+| y_out     | 0.061       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02623796 |
+| time-step | 794        |
+| y_out     | 0.0852     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026055738 |
+| time-step | 795         |
+| y_out     | -0.0557     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025376296 |
+| time-step | 796         |
+| y_out     | 0.0213      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024506446 |
+| time-step | 797         |
+| y_out     | 0.0349      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024920318 |
+| time-step | 798         |
+| y_out     | -0.0408     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024427403 |
+| time-step | 799         |
+| y_out     | -0.0512     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025273364 |
+| time-step | 800         |
+| y_out     | -0.149      |
+------------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/checkpoint 800
+------------------------------------------------------------
+| perf/mse  | 0.025099674 |
+| time-step | 801         |
+| y_out     | 0.0211      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026260367 |
+| time-step | 802         |
+| y_out     | 0.141       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024992594 |
+| time-step | 803         |
+| y_out     | 0.0534      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02446195 |
+| time-step | 804        |
+| y_out     | -0.0295    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024987685 |
+| time-step | 805         |
+| y_out     | 0.0492      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024890166 |
+| time-step | 806         |
+| y_out     | -0.022      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024760298 |
+| time-step | 807         |
+| y_out     | -0.0161     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025128577 |
+| time-step | 808         |
+| y_out     | 0.0741      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024979237 |
+| time-step | 809         |
+| y_out     | 0.0119      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02427019 |
+| time-step | 810        |
+| y_out     | 0.113      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024389988 |
+| time-step | 811         |
+| y_out     | -0.0555     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023572337 |
+| time-step | 812         |
+| y_out     | -0.0075     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02399315 |
+| time-step | 813        |
+| y_out     | -0.0834    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023871252 |
+| time-step | 814         |
+| y_out     | -0.0452     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02419267 |
+| time-step | 815        |
+| y_out     | 0.0504     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025749292 |
+| time-step | 816         |
+| y_out     | -0.0135     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02573188 |
+| time-step | 817        |
+| y_out     | 0.0101     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02514931 |
+| time-step | 818        |
+| y_out     | -0.00627   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026384655 |
+| time-step | 819         |
+| y_out     | -0.00669    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02601029 |
+| time-step | 820        |
+| y_out     | 0.0411     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02662946 |
+| time-step | 821        |
+| y_out     | -0.188     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026088092 |
+| time-step | 822         |
+| y_out     | -0.0312     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026078641 |
+| time-step | 823         |
+| y_out     | -0.0212     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026248181 |
+| time-step | 824         |
+| y_out     | 0.0828      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025508676 |
+| time-step | 825         |
+| y_out     | -0.0308     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023745066 |
+| time-step | 826         |
+| y_out     | -0.00913    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023781452 |
+| time-step | 827         |
+| y_out     | -0.0153     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024763431 |
+| time-step | 828         |
+| y_out     | 0.0659      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023531226 |
+| time-step | 829         |
+| y_out     | 0.0554      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023835564 |
+| time-step | 830         |
+| y_out     | -0.0662     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022762444 |
+| time-step | 831         |
+| y_out     | -0.0359     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022678234 |
+| time-step | 832         |
+| y_out     | -0.025      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023148514 |
+| time-step | 833         |
+| y_out     | -0.0478     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02327455 |
+| time-step | 834        |
+| y_out     | 0.0129     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023080409 |
+| time-step | 835         |
+| y_out     | -0.0691     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023373868 |
+| time-step | 836         |
+| y_out     | -0.0119     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023609951 |
+| time-step | 837         |
+| y_out     | -0.0611     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022976378 |
+| time-step | 838         |
+| y_out     | -0.106      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023436032 |
+| time-step | 839         |
+| y_out     | -0.0775     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022931974 |
+| time-step | 840         |
+| y_out     | -0.00599    |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022877287 |
+| time-step | 841         |
+| y_out     | 0.0898      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022882814 |
+| time-step | 842         |
+| y_out     | 0.0251      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022571886 |
+| time-step | 843         |
+| y_out     | 0.0798      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022833318 |
+| time-step | 844         |
+| y_out     | -0.145      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02334775 |
+| time-step | 845        |
+| y_out     | 0.112      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024182564 |
+| time-step | 846         |
+| y_out     | 0.139       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024317006 |
+| time-step | 847         |
+| y_out     | -0.0481     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025027264 |
+| time-step | 848         |
+| y_out     | 0.12        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025446761 |
+| time-step | 849         |
+| y_out     | 0.064       |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02643904 |
+| time-step | 850        |
+| y_out     | 0.0479     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026990298 |
+| time-step | 851         |
+| y_out     | -0.00371    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02730025 |
+| time-step | 852        |
+| y_out     | 0.0267     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027157072 |
+| time-step | 853         |
+| y_out     | 0.0913      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027636636 |
+| time-step | 854         |
+| y_out     | 0.0445      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026894446 |
+| time-step | 855         |
+| y_out     | 0.104       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026994431 |
+| time-step | 856         |
+| y_out     | -0.0141     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027878568 |
+| time-step | 857         |
+| y_out     | -0.0884     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02699315 |
+| time-step | 858        |
+| y_out     | -0.213     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027207363 |
+| time-step | 859         |
+| y_out     | 0.0645      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02662946 |
+| time-step | 860        |
+| y_out     | 0.0625     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026158016 |
+| time-step | 861         |
+| y_out     | -0.086      |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0265468 |
+| time-step | 862       |
+| y_out     | -0.0648   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02697922 |
+| time-step | 863        |
+| y_out     | -0.0319    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026413212 |
+| time-step | 864         |
+| y_out     | -0.0252     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02746493 |
+| time-step | 865        |
+| y_out     | 0.0365     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026794966 |
+| time-step | 866         |
+| y_out     | -0.0217     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025881669 |
+| time-step | 867         |
+| y_out     | -0.0429     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026460737 |
+| time-step | 868         |
+| y_out     | 0.132       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026582247 |
+| time-step | 869         |
+| y_out     | -0.0204     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026968986 |
+| time-step | 870         |
+| y_out     | -0.0666     |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..d4026b0
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/progress.csv	
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655791889.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655791889.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..935ce22
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655791889.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-11-26-523232 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..6892dcf
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,530 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.0366   |
+---------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint 0
+---------------------------------------------------------
+| time-step | 1        |
+| y_out     | -0.0197  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 2        |
+| y_out     | 0.107    |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 3        |
+| y_out     | -0.0614  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 4        |
+| y_out     | 0.115    |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 5        |
+| y_out     | -0.0495  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 6        |
+| y_out     | 0.0165   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 7        |
+| y_out     | 0.0121   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 8        |
+| y_out     | -0.0456  |
+---------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.49403268 |
+| time-step | 9          |
+| y_out     | 0.0458     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.4894978 |
+| time-step | 10        |
+| y_out     | 0.0168    |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.4968284 |
+| time-step | 11        |
+| y_out     | 0.0233    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.48914066 |
+| time-step | 12         |
+| y_out     | 0.058      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.48480886 |
+| time-step | 13         |
+| y_out     | 0.0575     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.46780363 |
+| time-step | 14         |
+| y_out     | -0.0607    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.47146302 |
+| time-step | 15         |
+| y_out     | 0.00982    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.47941655 |
+| time-step | 16         |
+| y_out     | -0.0123    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.4715603 |
+| time-step | 17        |
+| y_out     | 0.023     |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.46140704 |
+| time-step | 18         |
+| y_out     | 0.109      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.44607335 |
+| time-step | 19         |
+| y_out     | -0.146     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.41898474 |
+| time-step | 20         |
+| y_out     | 0.0226     |
+-----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint 20
+-----------------------------------------------------------
+| perf/mse  | 0.40553594 |
+| time-step | 21         |
+| y_out     | -0.0446    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.38011175 |
+| time-step | 22         |
+| y_out     | -0.0546    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.3690272 |
+| time-step | 23        |
+| y_out     | 0.00068   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.36561403 |
+| time-step | 24         |
+| y_out     | 0.0405     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.36073396 |
+| time-step | 25         |
+| y_out     | 0.00284    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.34023866 |
+| time-step | 26         |
+| y_out     | 0.00313    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.33840084 |
+| time-step | 27         |
+| y_out     | -0.0519    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.33017984 |
+| time-step | 28         |
+| y_out     | 0.09       |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.33323592 |
+| time-step | 29         |
+| y_out     | -0.0578    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.34132108 |
+| time-step | 30         |
+| y_out     | 0.0563     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.33152276 |
+| time-step | 31         |
+| y_out     | 0.0499     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.33383963 |
+| time-step | 32         |
+| y_out     | -0.101     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.3257926 |
+| time-step | 33        |
+| y_out     | 0.059     |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.31796092 |
+| time-step | 34         |
+| y_out     | 0.00914    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.29548356 |
+| time-step | 35         |
+| y_out     | -0.0118    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.28696665 |
+| time-step | 36         |
+| y_out     | 0.0368     |
+-----------------------------------------------------------
+---------------------------------------------------------
+| perf/mse  | 0.284748 |
+| time-step | 37       |
+| y_out     | 0.0181   |
+---------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2768833 |
+| time-step | 38        |
+| y_out     | -0.025    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26813883 |
+| time-step | 39         |
+| y_out     | -0.0086    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2566311 |
+| time-step | 40        |
+| y_out     | 0.0696    |
+----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint 40
+----------------------------------------------------------
+| perf/mse  | 0.2552605 |
+| time-step | 41        |
+| y_out     | 0.0604    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24862857 |
+| time-step | 42         |
+| y_out     | -0.00336   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24252716 |
+| time-step | 43         |
+| y_out     | 0.0261     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23195381 |
+| time-step | 44         |
+| y_out     | -0.136     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23078997 |
+| time-step | 45         |
+| y_out     | 0.0794     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22888204 |
+| time-step | 46         |
+| y_out     | -0.00281   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21721879 |
+| time-step | 47         |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21273318 |
+| time-step | 48         |
+| y_out     | 0.112      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20590408 |
+| time-step | 49         |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20267124 |
+| time-step | 50         |
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+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2001859 |
+| time-step | 51        |
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+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18815902 |
+| time-step | 52         |
+| y_out     | 0.051      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18449056 |
+| time-step | 53         |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18254283 |
+| time-step | 54         |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17642108 |
+| time-step | 55         |
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+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1702852 |
+| time-step | 56        |
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+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17152339 |
+| time-step | 57         |
+| y_out     | 0.0475     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1694577 |
+| time-step | 58        |
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+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1656811 |
+| time-step | 59        |
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+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15979862 |
+| time-step | 60         |
+| y_out     | -0.00537   |
+-----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint 60
+-----------------------------------------------------------
+| perf/mse  | 0.16016677 |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16087623 |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15536512 |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15460366 |
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+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1521332 |
+| time-step | 65        |
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+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14761244 |
+| time-step | 66         |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14491746 |
+| time-step | 67         |
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+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1404978 |
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+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13709041 |
+| time-step | 69         |
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+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1354272 |
+| time-step | 70        |
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+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13078097 |
+| time-step | 71         |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13269095 |
+| time-step | 72         |
+| y_out     | -0.0806    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12975289 |
+| time-step | 73         |
+| y_out     | -0.00659   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12594536 |
+| time-step | 74         |
+| y_out     | 0.0398     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1251411 |
+| time-step | 75        |
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+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12612459 |
+| time-step | 76         |
+| y_out     | -0.103     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11437243 |
+| time-step | 77         |
+| y_out     | -0.00116   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11322918 |
+| time-step | 78         |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11711192 |
+| time-step | 79         |
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+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.118624486 |
+| time-step | 80          |
+| y_out     | 0.11        |
+------------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/checkpoint 80
+-----------------------------------------------------------
+| perf/mse  | 0.11390598 |
+| time-step | 81         |
+| y_out     | 0.0285     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11026826 |
+| time-step | 82         |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11003125 |
+| time-step | 83         |
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+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.106977604 |
+| time-step | 84          |
+| y_out     | -0.0497     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.101296246 |
+| time-step | 85          |
+| y_out     | -0.0153     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.102011845 |
+| time-step | 86          |
+| y_out     | 0.0206      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.106472656 |
+| time-step | 87          |
+| y_out     | -0.146      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10791086 |
+| time-step | 88         |
+| y_out     | 0.113      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10235711 |
+| time-step | 89         |
+| y_out     | 0.0908     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09590799 |
+| time-step | 90         |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09457566 |
+| time-step | 91         |
+| y_out     | -0.146     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.094312415 |
+| time-step | 92          |
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+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0955606 |
+| time-step | 93        |
+| y_out     | -0.16     |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.095177166 |
+| time-step | 94          |
+| y_out     | 0.0102      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09933234 |
+| time-step | 95         |
+| y_out     | 0.0434     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.094790295 |
+| time-step | 96          |
+| y_out     | -0.0222     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.093192235 |
+| time-step | 97          |
+| y_out     | -0.181      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.090805866 |
+| time-step | 98          |
+| y_out     | -0.0191     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09010676 |
+| time-step | 99         |
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+-----------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..9d10036
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/progress.csv	
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655791940.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655791940.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..421cd61
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655791940.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/21/14-12-17-152036 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..219e9e8
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,7 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-49-22-503248 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..05df47f
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,7 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655887984.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655887984.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..f7e3819
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655887984.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-04-365976 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..964eecf
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,7 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888016.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888016.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..0dc7df8
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888016.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-53-36-896124 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..df45e41
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,7 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888066.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888066.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..e3f62c2
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888066.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-54-26-397068 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..1d80563
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,7 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888111.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888111.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..fce08bd
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888111.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-55-09-724998 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..de07490
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,7 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888235.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888235.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..fac9fa9
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888235.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-15-617475 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..3c83f73
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,498 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+--------------------------------------------------------------
+| time-step | 0             |
+| y_out     | -0.0064428225 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 1          |
+| y_out     | 0.04930454 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 2            |
+| y_out     | -0.048635148 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 3           |
+| y_out     | -0.06222611 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 4          |
+| y_out     | 0.04662519 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 5            |
+| y_out     | -0.084921524 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 6           |
+| y_out     | 0.014047012 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 7          |
+| y_out     | 0.08320155 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 8          |
+| y_out     | -0.0630525 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.29133323   |
+| time-step | 9            |
+| y_out     | 0.0035740389 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.27894574   |
+| time-step | 10           |
+| y_out     | -0.007007368 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2813764   |
+| time-step | 11          |
+| y_out     | -0.11833355 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26360077  |
+| time-step | 12          |
+| y_out     | -0.12807743 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.25464076   |
+| time-step | 13           |
+| y_out     | -0.028712781 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24274907  |
+| time-step | 14          |
+| y_out     | 0.058383036 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22590907  |
+| time-step | 15          |
+| y_out     | 0.025737397 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21498057 |
+| time-step | 16         |
+| y_out     | 0.05553948 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21055989 |
+| time-step | 17         |
+| y_out     | 0.09865355 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2046382    |
+| time-step | 18           |
+| y_out     | -0.056044675 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19731417  |
+| time-step | 19          |
+| y_out     | -0.06431604 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.18899626    |
+| time-step | 20            |
+| y_out     | 0.00017598644 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17280959   |
+| time-step | 21           |
+| y_out     | -0.015044624 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17008579   |
+| time-step | 22           |
+| y_out     | -0.120227665 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15447819  |
+| time-step | 23          |
+| y_out     | 0.045261197 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14958924 |
+| time-step | 24         |
+| y_out     | -0.1116478 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14299273  |
+| time-step | 25          |
+| y_out     | -0.06657992 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13934568 |
+| time-step | 26         |
+| y_out     | 0.0758459  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13117568 |
+| time-step | 27         |
+| y_out     | 0.03616636 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12322526  |
+| time-step | 28          |
+| y_out     | -0.12514639 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11577682  |
+| time-step | 29          |
+| y_out     | 0.013798259 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11055682   |
+| time-step | 30           |
+| y_out     | -0.006620895 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.105241254 |
+| time-step | 31          |
+| y_out     | 0.032227896 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09665185  |
+| time-step | 32          |
+| y_out     | -0.12665775 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09769772 |
+| time-step | 33         |
+| y_out     | 0.09552529 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09115288   |
+| time-step | 34           |
+| y_out     | -0.011207107 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08656562 |
+| time-step | 35         |
+| y_out     | 0.11896068 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.080835894 |
+| time-step | 36          |
+| y_out     | -0.02810283 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.077515565 |
+| time-step | 37          |
+| y_out     | 0.027897896 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07102182 |
+| time-step | 38         |
+| y_out     | 0.03881716 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06643866 |
+| time-step | 39         |
+| y_out     | 0.18280944 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06325918   |
+| time-step | 40           |
+| y_out     | -0.104515135 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058979046 |
+| time-step | 41          |
+| y_out     | 0.07623912  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.054667987  |
+| time-step | 42           |
+| y_out     | -0.024063682 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0485512    |
+| time-step | 43           |
+| y_out     | -0.010029323 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044089053 |
+| time-step | 44          |
+| y_out     | -0.052924   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041207846 |
+| time-step | 45          |
+| y_out     | 0.024971895 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038332544 |
+| time-step | 46          |
+| y_out     | 0.041212946 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034918096 |
+| time-step | 47          |
+| y_out     | 0.027984723 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.03451334  |
+| time-step | 48          |
+| y_out     | -0.09394095 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034174122 |
+| time-step | 49          |
+| y_out     | -0.10102393 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031722546 |
+| time-step | 50          |
+| y_out     | 0.031330526 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.030013701  |
+| time-step | 51           |
+| y_out     | -0.019851547 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.028856004 |
+| time-step | 52          |
+| y_out     | 0.04313557  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.028829789  |
+| time-step | 53           |
+| y_out     | 0.0014160275 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.027576694  |
+| time-step | 54           |
+| y_out     | -0.026711214 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.026937027  |
+| time-step | 55           |
+| y_out     | -0.034319915 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027412336 |
+| time-step | 56          |
+| y_out     | 0.1675624   |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.02752158   |
+| time-step | 57           |
+| y_out     | -0.057806626 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026498616 |
+| time-step | 58          |
+| y_out     | -0.05137684 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026166972 |
+| time-step | 59          |
+| y_out     | 0.032132994 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025562996 |
+| time-step | 60          |
+| y_out     | -0.14915752 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025676519 |
+| time-step | 61          |
+| y_out     | 0.025327248 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0259326    |
+| time-step | 62           |
+| y_out     | -0.113018334 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.02654914  |
+| time-step | 63          |
+| y_out     | 0.021685034 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.027167043  |
+| time-step | 64           |
+| y_out     | -0.013650859 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.026266694  |
+| time-step | 65           |
+| y_out     | -0.026286652 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.025439609  |
+| time-step | 66           |
+| y_out     | -0.054252688 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024236321 |
+| time-step | 67          |
+| y_out     | 0.059082113 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.02555487    |
+| time-step | 68            |
+| y_out     | -0.0026954138 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025181275 |
+| time-step | 69          |
+| y_out     | 0.039880753 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024809182 |
+| time-step | 70          |
+| y_out     | 0.0552663   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024856912 |
+| time-step | 71          |
+| y_out     | 0.02660775  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023902219 |
+| time-step | 72          |
+| y_out     | 0.1204779   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023632662 |
+| time-step | 73          |
+| y_out     | 0.09516594  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.023425533  |
+| time-step | 74           |
+| y_out     | -0.016393162 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.02346071  |
+| time-step | 75          |
+| y_out     | 0.011047229 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023574155 |
+| time-step | 76          |
+| y_out     | 0.056450292 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023553343 |
+| time-step | 77          |
+| y_out     | -0.06861526 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.022610638  |
+| time-step | 78           |
+| y_out     | -0.002044309 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022073971 |
+| time-step | 79          |
+| y_out     | -0.07065364 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021807877 |
+| time-step | 80          |
+| y_out     | 0.07240825  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020889336 |
+| time-step | 81          |
+| y_out     | -0.10459086 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02054435 |
+| time-step | 82         |
+| y_out     | 0.06702097 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019613013 |
+| time-step | 83          |
+| y_out     | -0.06102384 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.01904675   |
+| time-step | 84           |
+| y_out     | -0.044619527 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01880647 |
+| time-step | 85         |
+| y_out     | 0.06340268 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018841162 |
+| time-step | 86          |
+| y_out     | 0.0633072   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018668605 |
+| time-step | 87          |
+| y_out     | 0.05473823  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018587312 |
+| time-step | 88          |
+| y_out     | 0.13797875  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.01905866   |
+| time-step | 89           |
+| y_out     | -0.047649544 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019086238 |
+| time-step | 90          |
+| y_out     | -0.01102992 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019504623 |
+| time-step | 91          |
+| y_out     | -0.08427615 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.020322874  |
+| time-step | 92           |
+| y_out     | -0.014050335 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020055218 |
+| time-step | 93          |
+| y_out     | 0.05293078  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020239636 |
+| time-step | 94          |
+| y_out     | 0.034203973 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.02017063   |
+| time-step | 95           |
+| y_out     | -0.092263706 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020424994 |
+| time-step | 96          |
+| y_out     | -0.08548976 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020271648 |
+| time-step | 97          |
+| y_out     | -0.09652927 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019375183 |
+| time-step | 98          |
+| y_out     | 0.11304718  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019581884 |
+| time-step | 99          |
+| y_out     | -0.04089286 |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..96b20a1
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/progress.csv	
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
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+1,0.04930454,
+2,-0.048635148,
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+4,0.04662519,
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888275.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888275.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..b64c3be
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888275.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-57-55-580105 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..e6a736b
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,7 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888324.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888324.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..287e5e9
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888324.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-58-44-981476 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..5af34bb
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,498 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+-------------------------------------------------------------
+| time-step | 0            |
+| y_out     | -0.005473565 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | 0.037161335 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 2           |
+| y_out     | -0.06501998 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 3          |
+| y_out     | 0.02455449 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 4           |
+| y_out     | 0.016860068 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| time-step | 5             |
+| y_out     | -0.0013134629 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 6          |
+| y_out     | 0.07593635 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 7            |
+| y_out     | -0.047730934 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 8            |
+| y_out     | -0.017584614 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20596051   |
+| time-step | 9            |
+| y_out     | -0.016245855 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1983426   |
+| time-step | 10          |
+| y_out     | -0.02822299 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19022427  |
+| time-step | 11          |
+| y_out     | 0.038943972 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17713091  |
+| time-step | 12          |
+| y_out     | 0.013786145 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16516349   |
+| time-step | 13           |
+| y_out     | -0.022224277 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15760548   |
+| time-step | 14           |
+| y_out     | -0.061533578 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14492801  |
+| time-step | 15          |
+| y_out     | 0.015459763 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13808446  |
+| time-step | 16          |
+| y_out     | -0.05239613 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12876703 |
+| time-step | 17         |
+| y_out     | -0.1377917 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.118549004  |
+| time-step | 18           |
+| y_out     | -0.013552089 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10873952   |
+| time-step | 19           |
+| y_out     | -0.020164963 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.099672124 |
+| time-step | 20          |
+| y_out     | 0.15565994  |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.09186481    |
+| time-step | 21            |
+| y_out     | -0.0012581944 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08677749 |
+| time-step | 22         |
+| y_out     | 0.08709692 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08198102   |
+| time-step | 23           |
+| y_out     | 0.0042557605 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07401527  |
+| time-step | 24          |
+| y_out     | -0.08879388 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07035558  |
+| time-step | 25          |
+| y_out     | -0.10587391 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.06332065  |
+| time-step | 26          |
+| y_out     | -0.06505518 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057802893 |
+| time-step | 27          |
+| y_out     | 0.06938778  |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05258289 |
+| time-step | 28         |
+| y_out     | 0.03206784 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048649915 |
+| time-step | 29          |
+| y_out     | 0.00843237  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.044406664  |
+| time-step | 30           |
+| y_out     | -0.012660939 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039477445 |
+| time-step | 31          |
+| y_out     | -0.03434177 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03696218 |
+| time-step | 32         |
+| y_out     | 0.18581337 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.03362057  |
+| time-step | 33          |
+| y_out     | 0.015094504 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03253196 |
+| time-step | 34         |
+| y_out     | 0.06294083 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.030211482 |
+| time-step | 35          |
+| y_out     | 0.06399681  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027539875 |
+| time-step | 36          |
+| y_out     | -0.11869191 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.026552344 |
+| time-step | 37          |
+| y_out     | 0.071648516 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.025075441 |
+| time-step | 38          |
+| y_out     | 0.008632027 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024470594 |
+| time-step | 39          |
+| y_out     | 0.008459779 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023603637 |
+| time-step | 40          |
+| y_out     | 0.043336518 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023671884 |
+| time-step | 41          |
+| y_out     | 0.025458638 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022012163 |
+| time-step | 42          |
+| y_out     | 0.033911526 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.02118719   |
+| time-step | 43           |
+| y_out     | -0.050353497 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02015956 |
+| time-step | 44         |
+| y_out     | 0.06563209 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.01974195   |
+| time-step | 45           |
+| y_out     | -0.008575788 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.01976085  |
+| time-step | 46          |
+| y_out     | -0.11232753 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019648965 |
+| time-step | 47          |
+| y_out     | 0.07302568  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020105738 |
+| time-step | 48          |
+| y_out     | -0.09043187 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.01956648  |
+| time-step | 49          |
+| y_out     | -0.15734011 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.01925085  |
+| time-step | 50          |
+| y_out     | 0.023605345 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018437555 |
+| time-step | 51          |
+| y_out     | 0.09408584  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018691478 |
+| time-step | 52          |
+| y_out     | 0.004220199 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018367965 |
+| time-step | 53          |
+| y_out     | 0.072774686 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.01875906    |
+| time-step | 54            |
+| y_out     | -0.0072975922 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018593822 |
+| time-step | 55          |
+| y_out     | -0.16808549 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018000465 |
+| time-step | 56          |
+| y_out     | 0.0628224   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017623067 |
+| time-step | 57          |
+| y_out     | 0.02042722  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017264683 |
+| time-step | 58          |
+| y_out     | 0.010324523 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017649133 |
+| time-step | 59          |
+| y_out     | -0.06365155 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.017347679  |
+| time-step | 60           |
+| y_out     | -0.043357205 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017707491 |
+| time-step | 61          |
+| y_out     | 0.08306569  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017761473 |
+| time-step | 62          |
+| y_out     | 0.04287427  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018195068 |
+| time-step | 63          |
+| y_out     | 0.005065769 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.017402736  |
+| time-step | 64           |
+| y_out     | 0.0020838492 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.01723497  |
+| time-step | 65          |
+| y_out     | 0.024589136 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017324278 |
+| time-step | 66          |
+| y_out     | 0.01420309  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.017844828  |
+| time-step | 67           |
+| y_out     | -0.020027153 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017454367 |
+| time-step | 68          |
+| y_out     | 0.04631119  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017562112 |
+| time-step | 69          |
+| y_out     | 0.07272993  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017063253 |
+| time-step | 70          |
+| y_out     | 0.09062946  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016762652 |
+| time-step | 71          |
+| y_out     | -0.08141197 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016286848 |
+| time-step | 72          |
+| y_out     | 0.09429902  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.015961373  |
+| time-step | 73           |
+| y_out     | 0.0024138968 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0159879   |
+| time-step | 74          |
+| y_out     | -0.03504914 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015947768 |
+| time-step | 75          |
+| y_out     | 0.024914254 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016579084 |
+| time-step | 76          |
+| y_out     | 0.015899505 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015885588 |
+| time-step | 77          |
+| y_out     | 0.099190734 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.016451225  |
+| time-step | 78           |
+| y_out     | -0.049516447 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017161198 |
+| time-step | 79          |
+| y_out     | -0.07021698 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.01718121  |
+| time-step | 80          |
+| y_out     | 0.055576485 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016699292 |
+| time-step | 81          |
+| y_out     | 0.02899292  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.016740771  |
+| time-step | 82           |
+| y_out     | -0.027441718 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016899575 |
+| time-step | 83          |
+| y_out     | -0.08274985 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.017276365  |
+| time-step | 84           |
+| y_out     | -0.004885547 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016859176 |
+| time-step | 85          |
+| y_out     | 0.02487176  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016363474 |
+| time-step | 86          |
+| y_out     | 0.0667801   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017056113 |
+| time-step | 87          |
+| y_out     | 0.12462481  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0166123   |
+| time-step | 88          |
+| y_out     | 0.025200315 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015631525 |
+| time-step | 89          |
+| y_out     | 0.078795835 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.01611927   |
+| time-step | 90           |
+| y_out     | -0.025031948 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.015861897  |
+| time-step | 91           |
+| y_out     | -0.027052294 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015694102 |
+| time-step | 92          |
+| y_out     | 0.025734896 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.015410246  |
+| time-step | 93           |
+| y_out     | -0.034757383 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015077102 |
+| time-step | 94          |
+| y_out     | -0.03568291 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015482746 |
+| time-step | 95          |
+| y_out     | -0.0721453  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015650433 |
+| time-step | 96          |
+| y_out     | 0.1916132   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015549061 |
+| time-step | 97          |
+| y_out     | 0.010469772 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016091758 |
+| time-step | 98          |
+| y_out     | 0.05833529  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016369835 |
+| time-step | 99          |
+| y_out     | 0.06953283  |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..9c593ed
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/progress.csv	
@@ -0,0 +1,101 @@
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888345.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888345.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..bb31c69
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888345.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/16-59-05-730246 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..b33a7d3
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,502 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+-----------------------------------------------------------
+| time-step | 0          |
+| y_out     | 0.07279381 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | 0.016407933 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 2           |
+| y_out     | 0.030823339 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 3           |
+| y_out     | -0.05517689 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 4           |
+| y_out     | 0.021586128 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 5            |
+| y_out     | -0.046217807 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 6           |
+| y_out     | -0.12933695 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 7           |
+| y_out     | 0.012045614 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 8           |
+| y_out     | 0.013853498 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24178982  |
+| time-step | 9           |
+| y_out     | 0.010162152 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23038404  |
+| time-step | 10          |
+| y_out     | 0.064647555 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21727113 |
+| time-step | 11         |
+| y_out     | 0.06701864 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20887287 |
+| time-step | 12         |
+| y_out     | 0.06932201 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20712237  |
+| time-step | 13          |
+| y_out     | -0.11365074 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2005409  |
+| time-step | 14         |
+| y_out     | 0.04380514 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.190465     |
+| time-step | 15           |
+| y_out     | -0.043151986 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18227546  |
+| time-step | 16          |
+| y_out     | -0.02161495 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17294268 |
+| time-step | 17         |
+| y_out     | 0.06570023 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16243476  |
+| time-step | 18          |
+| y_out     | 0.016364321 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15330079  |
+| time-step | 19          |
+| y_out     | -0.14136022 |
+------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/checkpoint-0.pt
+-----------------------------------------------------------
+| perf/mse  | 0.149192   |
+| time-step | 20         |
+| y_out     | 0.04076246 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14368239   |
+| time-step | 21           |
+| y_out     | -0.052496735 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13556024   |
+| time-step | 22           |
+| y_out     | -0.035266906 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12911704   |
+| time-step | 23           |
+| y_out     | -0.027849458 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12171614  |
+| time-step | 24          |
+| y_out     | 0.004145846 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.114637315 |
+| time-step | 25          |
+| y_out     | 0.06806973  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10895638  |
+| time-step | 26          |
+| y_out     | 0.008567557 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.102058515 |
+| time-step | 27          |
+| y_out     | 0.005292589 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09645398   |
+| time-step | 28           |
+| y_out     | -0.031402167 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09077955   |
+| time-step | 29           |
+| y_out     | -0.036856443 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08288847  |
+| time-step | 30          |
+| y_out     | 0.025261046 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07590139  |
+| time-step | 31          |
+| y_out     | 0.081667185 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07156151   |
+| time-step | 32           |
+| y_out     | -0.063061595 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.064142026  |
+| time-step | 33           |
+| y_out     | -0.114371285 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059245337 |
+| time-step | 34          |
+| y_out     | 0.11874589  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.053592853 |
+| time-step | 35          |
+| y_out     | 0.07476449  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050328273 |
+| time-step | 36          |
+| y_out     | -0.07528393 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04653665 |
+| time-step | 37         |
+| y_out     | 0.07118043 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0425642    |
+| time-step | 38           |
+| y_out     | -0.038810585 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039804365 |
+| time-step | 39          |
+| y_out     | 0.020143636 |
+------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/checkpoint-20.pt
+-------------------------------------------------------------
+| perf/mse  | 0.03781651   |
+| time-step | 40           |
+| y_out     | -0.037968837 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.035222754 |
+| time-step | 41          |
+| y_out     | 0.033496387 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.03347584  |
+| time-step | 42          |
+| y_out     | -0.08550003 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.03172861  |
+| time-step | 43          |
+| y_out     | -0.09291358 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.029718965  |
+| time-step | 44           |
+| y_out     | -0.041650806 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027937254 |
+| time-step | 45          |
+| y_out     | 0.029188737 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.02514835   |
+| time-step | 46           |
+| y_out     | -0.017454088 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023741255 |
+| time-step | 47          |
+| y_out     | -0.04588217 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022447888 |
+| time-step | 48          |
+| y_out     | 0.09554884  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022077713 |
+| time-step | 49          |
+| y_out     | 0.07444046  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.021678055  |
+| time-step | 50           |
+| y_out     | -0.009035926 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020789377 |
+| time-step | 51          |
+| y_out     | -0.11907022 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019876149 |
+| time-step | 52          |
+| y_out     | 0.014456674 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019399725 |
+| time-step | 53          |
+| y_out     | 0.003938131 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.019473663  |
+| time-step | 54           |
+| y_out     | -0.031840276 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.019636925  |
+| time-step | 55           |
+| y_out     | -0.106645085 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020411646 |
+| time-step | 56          |
+| y_out     | 0.03093068  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020351171 |
+| time-step | 57          |
+| y_out     | 0.08167804  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019507673 |
+| time-step | 58          |
+| y_out     | 0.14236084  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019191384 |
+| time-step | 59          |
+| y_out     | 0.098991334 |
+------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/checkpoint-40.pt
+------------------------------------------------------------
+| perf/mse  | 0.019433819 |
+| time-step | 60          |
+| y_out     | 0.003626912 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.020030247  |
+| time-step | 61           |
+| y_out     | -0.013288043 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019782554 |
+| time-step | 62          |
+| y_out     | 0.00843547  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019979792 |
+| time-step | 63          |
+| y_out     | 0.05561374  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.02011007  |
+| time-step | 64          |
+| y_out     | 0.033915535 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020591635 |
+| time-step | 65          |
+| y_out     | -0.11207235 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01957751 |
+| time-step | 66         |
+| y_out     | 0.10875211 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019132525 |
+| time-step | 67          |
+| y_out     | 0.04658449  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019545222 |
+| time-step | 68          |
+| y_out     | 0.13410303  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019172223 |
+| time-step | 69          |
+| y_out     | 0.063849166 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.018842606  |
+| time-step | 70           |
+| y_out     | -0.078800924 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018472468 |
+| time-step | 71          |
+| y_out     | 0.106782466 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018635945 |
+| time-step | 72          |
+| y_out     | 0.036723495 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.01847647    |
+| time-step | 73            |
+| y_out     | -0.0051542968 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.01780137  |
+| time-step | 74          |
+| y_out     | -0.07984257 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.017668264  |
+| time-step | 75           |
+| y_out     | -0.029827865 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018351024 |
+| time-step | 76          |
+| y_out     | 0.00810023  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.018584447 |
+| time-step | 77          |
+| y_out     | 0.025480442 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.018464508  |
+| time-step | 78           |
+| y_out     | -0.057140388 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019051054 |
+| time-step | 79          |
+| y_out     | -0.06368552 |
+------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/checkpoint-60.pt
+------------------------------------------------------------
+| perf/mse  | 0.018274114 |
+| time-step | 80          |
+| y_out     | -0.10692927 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017903024 |
+| time-step | 81          |
+| y_out     | -0.09355062 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.017162113 |
+| time-step | 82          |
+| y_out     | 0.02996993  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.016157245  |
+| time-step | 83           |
+| y_out     | -0.010486515 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016092764 |
+| time-step | 84          |
+| y_out     | -0.04537561 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015794305 |
+| time-step | 85          |
+| y_out     | 0.005767662 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015520091 |
+| time-step | 86          |
+| y_out     | -0.1132562  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015216676 |
+| time-step | 87          |
+| y_out     | 0.04056326  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015180096 |
+| time-step | 88          |
+| y_out     | 0.04121325  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.014909798 |
+| time-step | 89          |
+| y_out     | 0.01921847  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.01525404   |
+| time-step | 90           |
+| y_out     | -0.012239035 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.015950542  |
+| time-step | 91           |
+| y_out     | -0.003934607 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016551826 |
+| time-step | 92          |
+| y_out     | -0.11800195 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016773727 |
+| time-step | 93          |
+| y_out     | -0.09133028 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.016667794 |
+| time-step | 94          |
+| y_out     | 0.057103887 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.016231196  |
+| time-step | 95           |
+| y_out     | -0.023417514 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.015902657 |
+| time-step | 96          |
+| y_out     | -0.03142748 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.016366422  |
+| time-step | 97           |
+| y_out     | -0.047940217 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.016391257  |
+| time-step | 98           |
+| y_out     | 0.0074154343 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.01644944  |
+| time-step | 99          |
+| y_out     | 0.048710704 |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..cd37a85
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/progress.csv	
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,0.07279381,
+1,0.016407933,
+2,0.030823339,
+3,-0.05517689,
+4,0.021586128,
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+7,0.012045614,
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+11,0.06701864,0.21727113
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+14,0.04380514,0.2005409
+15,-0.043151986,0.190465
+16,-0.02161495,0.18227546
+17,0.06570023,0.17294268
+18,0.016364321,0.16243476
+19,-0.14136022,0.15330079
+20,0.04076246,0.149192
+21,-0.052496735,0.14368239
+22,-0.035266906,0.13556024
+23,-0.027849458,0.12911704
+24,0.004145846,0.12171614
+25,0.06806973,0.114637315
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+27,0.005292589,0.102058515
+28,-0.031402167,0.09645398
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+32,-0.063061595,0.07156151
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888552.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888552.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..1f909da
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888552.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-02-32-834758 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..90dd79e
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,539 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint 0
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.0557   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 1        |
+| y_out     | 0.00311  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 2        |
+| y_out     | -0.00972 |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 3        |
+| y_out     | -0.0394  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 4        |
+| y_out     | 0.0799   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 5        |
+| y_out     | 0.0236   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 6        |
+| y_out     | 0.0181   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 7        |
+| y_out     | -0.0238  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 8        |
+| y_out     | 0.0564   |
+---------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2824137 |
+| time-step | 9         |
+| y_out     | -0.0636   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26877004 |
+| time-step | 10         |
+| y_out     | 0.041      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26120767 |
+| time-step | 11         |
+| y_out     | 0.0837     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25373966 |
+| time-step | 12         |
+| y_out     | 0.00844    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24007268 |
+| time-step | 13         |
+| y_out     | 0.0228     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22862843 |
+| time-step | 14         |
+| y_out     | 0.0461     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21315126 |
+| time-step | 15         |
+| y_out     | -0.000337  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19482127 |
+| time-step | 16         |
+| y_out     | 0.127      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18501805 |
+| time-step | 17         |
+| y_out     | -0.0379    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17624693 |
+| time-step | 18         |
+| y_out     | 0.0539     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16434239 |
+| time-step | 19         |
+| y_out     | 0.00209    |
+-----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint 20
+-----------------------------------------------------------
+| perf/mse  | 0.16034372 |
+| time-step | 20         |
+| y_out     | 0.067      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15379915 |
+| time-step | 21         |
+| y_out     | 0.00211    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15112928 |
+| time-step | 22         |
+| y_out     | -0.0897    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15160899 |
+| time-step | 23         |
+| y_out     | -0.0249    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15943816 |
+| time-step | 24         |
+| y_out     | 0.0399     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15208103 |
+| time-step | 25         |
+| y_out     | -0.0188    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1531156 |
+| time-step | 26        |
+| y_out     | -0.0426   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15138006 |
+| time-step | 27         |
+| y_out     | -0.114     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14564332 |
+| time-step | 28         |
+| y_out     | 0.0787     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13915098 |
+| time-step | 29         |
+| y_out     | 0.0277     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13891086 |
+| time-step | 30         |
+| y_out     | -0.0153    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13712609 |
+| time-step | 31         |
+| y_out     | 0.0084     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1331963 |
+| time-step | 32        |
+| y_out     | 0.111     |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12931305 |
+| time-step | 33         |
+| y_out     | -0.0187    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12120056 |
+| time-step | 34         |
+| y_out     | 0.0564     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1188722 |
+| time-step | 35        |
+| y_out     | 0.072     |
+----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.111206576 |
+| time-step | 36          |
+| y_out     | -0.042      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10626761 |
+| time-step | 37         |
+| y_out     | -0.0223    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10403565 |
+| time-step | 38         |
+| y_out     | -0.0883    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.103952095 |
+| time-step | 39          |
+| y_out     | -0.0187     |
+------------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint 40
+-----------------------------------------------------------
+| perf/mse  | 0.09815784 |
+| time-step | 40         |
+| y_out     | 0.0714     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09511639 |
+| time-step | 41         |
+| y_out     | 0.049      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09398939 |
+| time-step | 42         |
+| y_out     | -0.0433    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09029162 |
+| time-step | 43         |
+| y_out     | -0.0766    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.084561855 |
+| time-step | 44          |
+| y_out     | 0.101       |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0814759 |
+| time-step | 45        |
+| y_out     | 0.0232    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08089705 |
+| time-step | 46         |
+| y_out     | 0.0814     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.081518576 |
+| time-step | 47          |
+| y_out     | -0.0746     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08026893 |
+| time-step | 48         |
+| y_out     | -0.0222    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07917204 |
+| time-step | 49         |
+| y_out     | 9.28e-05   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08026324 |
+| time-step | 50         |
+| y_out     | 0.0327     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0819345 |
+| time-step | 51        |
+| y_out     | -0.0615   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07790412 |
+| time-step | 52         |
+| y_out     | 0.0811     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07704113 |
+| time-step | 53         |
+| y_out     | 0.0199     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07529639 |
+| time-step | 54         |
+| y_out     | 0.0364     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07730509 |
+| time-step | 55         |
+| y_out     | 0.0567     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07693772 |
+| time-step | 56         |
+| y_out     | -0.000945  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07645309 |
+| time-step | 57         |
+| y_out     | 0.038      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07780546 |
+| time-step | 58         |
+| y_out     | -0.0185    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.074930444 |
+| time-step | 59          |
+| y_out     | -0.103      |
+------------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint 60
+-----------------------------------------------------------
+| perf/mse  | 0.07481378 |
+| time-step | 60         |
+| y_out     | 0.0543     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07123813 |
+| time-step | 61         |
+| y_out     | -0.0412    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07271855 |
+| time-step | 62         |
+| y_out     | -0.108     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07212392 |
+| time-step | 63         |
+| y_out     | -0.0221    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07006066 |
+| time-step | 64         |
+| y_out     | 0.0472     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07049903 |
+| time-step | 65         |
+| y_out     | 0.148      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.068563774 |
+| time-step | 66          |
+| y_out     | 0.00878     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06625454 |
+| time-step | 67         |
+| y_out     | -0.00605   |
+-----------------------------------------------------------
+------------------------------------------------------------
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+| time-step | 68          |
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+------------------------------------------------------------
+-----------------------------------------------------------
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+| time-step | 69         |
+| y_out     | 0.0277     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.064507075 |
+| time-step | 70          |
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+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06513302 |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06433549 |
+| time-step | 72         |
+| y_out     | 0.000885   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06534477 |
+| time-step | 73         |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06580062 |
+| time-step | 74         |
+| y_out     | -0.0925    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0654123 |
+| time-step | 75        |
+| y_out     | 0.0607    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06752658 |
+| time-step | 76         |
+| y_out     | -0.042     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06766648 |
+| time-step | 77         |
+| y_out     | 0.0176     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06584333 |
+| time-step | 78         |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06658207 |
+| time-step | 79         |
+| y_out     | 0.0705     |
+-----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/checkpoint 80
+-----------------------------------------------------------
+| perf/mse  | 0.06621905 |
+| time-step | 80         |
+| y_out     | -0.00689   |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0690847 |
+| time-step | 81        |
+| y_out     | 0.0217    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07065071 |
+| time-step | 82         |
+| y_out     | -0.106     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.0691512 |
+| time-step | 83        |
+| y_out     | -0.017    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06870882 |
+| time-step | 84         |
+| y_out     | -0.000422  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06563704 |
+| time-step | 85         |
+| y_out     | 0.0624     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06447594 |
+| time-step | 86         |
+| y_out     | -0.0196    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.064637914 |
+| time-step | 87          |
+| y_out     | -0.0142     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06384728 |
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+| y_out     | -0.102     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.063596085 |
+| time-step | 89          |
+| y_out     | 0.076       |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062437594 |
+| time-step | 90          |
+| y_out     | -0.0418     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057804354 |
+| time-step | 91          |
+| y_out     | 0.07        |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.054329365 |
+| time-step | 92          |
+| y_out     | -0.0554     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.053510975 |
+| time-step | 93          |
+| y_out     | -0.017      |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.054172236 |
+| time-step | 94          |
+| y_out     | -0.00754    |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05565558 |
+| time-step | 95         |
+| y_out     | 0.0206     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05405866 |
+| time-step | 96         |
+| y_out     | 0.0516     |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.054431044 |
+| time-step | 97          |
+| y_out     | 0.0704      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05424893 |
+| time-step | 98         |
+| y_out     | -0.000502  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.052439857 |
+| time-step | 99          |
+| y_out     | -0.0132     |
+------------------------------------------------------------
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_data_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-06-22 17:07:08.049304
+store file ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..50f11ca
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/progress.csv	
@@ -0,0 +1,101 @@
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+66,0.008779305385893804,0.068563774
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+80,-0.006890077633954109,0.06621905
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+83,-0.017016093134943774,0.0691512
+84,-0.0004215635315197516,0.06870882
+85,0.06244310226656889,0.06563704
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+91,0.07003583846804937,0.057804354
+92,-0.0554057999036348,0.054329365
+93,-0.01703987144837913,0.053510975
+94,-0.0075392844750989535,0.054172236
+95,0.02055403178792403,0.05565558
+96,0.051555529518656225,0.05405866
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+99,-0.01324407370724084,0.052439857
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888825.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888825.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..82de279
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655888825.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-01-032600 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..844e362
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..d122118
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/log.txt	
@@ -0,0 +1,511 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
+------------------------------------------------------------
+| perf/mse  | 0.082159676 |
+| time-step | 0           |
+| y_out     | -0.03930173 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10689151 |
+| time-step | 1          |
+| y_out     | 0.02850845 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13199139  |
+| time-step | 2           |
+| y_out     | 0.021799369 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15028876 |
+| time-step | 3          |
+| y_out     | 0.06596703 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1723384    |
+| time-step | 4            |
+| y_out     | -0.024830323 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19386157 |
+| time-step | 5          |
+| y_out     | 0.0264886  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21414629  |
+| time-step | 6           |
+| y_out     | 0.032664597 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23261961   |
+| time-step | 7            |
+| y_out     | -0.015842723 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2511875    |
+| time-step | 8            |
+| y_out     | -0.025599772 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2701317   |
+| time-step | 9           |
+| y_out     | -0.06609582 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25786114 |
+| time-step | 10         |
+| y_out     | 0.02130657 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2460159   |
+| time-step | 11          |
+| y_out     | 0.015463426 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2368942   |
+| time-step | 12          |
+| y_out     | 0.067268506 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23354551 |
+| time-step | 13         |
+| y_out     | -0.0446322 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22740078  |
+| time-step | 14          |
+| y_out     | 0.028773785 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21880226  |
+| time-step | 15          |
+| y_out     | 0.020305729 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20967004 |
+| time-step | 16         |
+| y_out     | 0.06252025 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20231946   |
+| time-step | 17           |
+| y_out     | -0.051750395 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19456811   |
+| time-step | 18           |
+| y_out     | -0.058056142 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1851129  |
+| time-step | 19         |
+| y_out     | 0.09042649 |
+-----------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/checkpoint-0.pt
+------------------------------------------------------------
+| perf/mse  | 0.18099524  |
+| time-step | 20          |
+| y_out     | -0.08248055 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18066162  |
+| time-step | 21          |
+| y_out     | -0.06541039 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.17478596    |
+| time-step | 22            |
+| y_out     | -0.0108490605 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17030677  |
+| time-step | 23          |
+| y_out     | 0.026811648 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15957513  |
+| time-step | 24          |
+| y_out     | 0.102516875 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.15299655    |
+| time-step | 25            |
+| y_out     | -0.0004074918 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14584634  |
+| time-step | 26          |
+| y_out     | -0.13715005 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13843277   |
+| time-step | 27           |
+| y_out     | -0.044480074 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13337915  |
+| time-step | 28          |
+| y_out     | 0.032456663 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13008326 |
+| time-step | 29         |
+| y_out     | 0.03787488 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12148585  |
+| time-step | 30          |
+| y_out     | -0.06639095 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11285533   |
+| time-step | 31           |
+| y_out     | -0.043552965 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.102641724 |
+| time-step | 32          |
+| y_out     | 0.021582067 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09392013 |
+| time-step | 33         |
+| y_out     | 0.09746757 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.087975316 |
+| time-step | 34          |
+| y_out     | 0.14024463  |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08144392 |
+| time-step | 35         |
+| y_out     | 0.1354674  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.079324216  |
+| time-step | 36           |
+| y_out     | -0.036317833 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.076084815 |
+| time-step | 37          |
+| y_out     | 0.022237705 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07042156  |
+| time-step | 38          |
+| y_out     | 0.109902345 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06364162   |
+| time-step | 39           |
+| y_out     | 0.0052000284 |
+-------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/checkpoint-20.pt
+------------------------------------------------------------
+| perf/mse  | 0.057735633 |
+| time-step | 40          |
+| y_out     | 0.09849411  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0540107    |
+| time-step | 41           |
+| y_out     | -0.032637317 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.052619852  |
+| time-step | 42           |
+| y_out     | 0.0101779625 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.050582606 |
+| time-step | 43          |
+| y_out     | -0.0856691  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.049143482  |
+| time-step | 44           |
+| y_out     | 0.0050652325 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.046722174 |
+| time-step | 45          |
+| y_out     | -0.07875314 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043319725 |
+| time-step | 46          |
+| y_out     | 0.08581701  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.040866245 |
+| time-step | 47          |
+| y_out     | -0.13341105 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.03938549 |
+| time-step | 48         |
+| y_out     | 0.07693946 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.038137376 |
+| time-step | 49          |
+| y_out     | 0.06260439  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.037482426 |
+| time-step | 50          |
+| y_out     | 0.01828127  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.034452222 |
+| time-step | 51          |
+| y_out     | 0.050183885 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0340418    |
+| time-step | 52           |
+| y_out     | -0.016950876 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.032532267  |
+| time-step | 53           |
+| y_out     | -0.059085928 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.03138741   |
+| time-step | 54           |
+| y_out     | -0.035430178 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.031528994 |
+| time-step | 55          |
+| y_out     | 0.01941596  |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.030791204   |
+| time-step | 56            |
+| y_out     | -0.0015537776 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.02904976   |
+| time-step | 57           |
+| y_out     | -0.055623204 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027501712 |
+| time-step | 58          |
+| y_out     | 0.039701626 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.027352462 |
+| time-step | 59          |
+| y_out     | 0.004891701 |
+------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/checkpoint-40.pt
+------------------------------------------------------------
+| perf/mse  | 0.027269706 |
+| time-step | 60          |
+| y_out     | -0.07709161 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.02755053  |
+| time-step | 61          |
+| y_out     | -0.04931378 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.026377078  |
+| time-step | 62           |
+| y_out     | -0.022023788 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.026741069  |
+| time-step | 63           |
+| y_out     | -0.014538588 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.0260139  |
+| time-step | 64         |
+| y_out     | 0.02340484 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.02451438  |
+| time-step | 65          |
+| y_out     | 0.010321413 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02392684 |
+| time-step | 66         |
+| y_out     | 0.01862641 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.024274122 |
+| time-step | 67          |
+| y_out     | -0.0616763  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.023776192 |
+| time-step | 68          |
+| y_out     | 0.028239498 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.023010768  |
+| time-step | 69           |
+| y_out     | 0.0131763965 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021358164 |
+| time-step | 70          |
+| y_out     | 0.06939075  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021077689 |
+| time-step | 71          |
+| y_out     | 0.06996262  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.021447627  |
+| time-step | 72           |
+| y_out     | -0.026706778 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.020928541  |
+| time-step | 73           |
+| y_out     | -0.054852653 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021486502 |
+| time-step | 74          |
+| y_out     | 0.0470647   |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.021607533  |
+| time-step | 75           |
+| y_out     | -0.037036818 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021896724 |
+| time-step | 76          |
+| y_out     | 0.14692566  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021102814 |
+| time-step | 77          |
+| y_out     | 0.052704632 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021197226 |
+| time-step | 78          |
+| y_out     | 0.050001167 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022202285 |
+| time-step | 79          |
+| y_out     | 0.023533972 |
+------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/checkpoint-60.pt
+--------------------------------------------------------------
+| perf/mse  | 0.022815336   |
+| time-step | 80            |
+| y_out     | -0.0052834023 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022917341 |
+| time-step | 81          |
+| y_out     | -0.11241844 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022611864 |
+| time-step | 82          |
+| y_out     | 0.07016237  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.022152316 |
+| time-step | 83          |
+| y_out     | 0.028525785 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.021349275   |
+| time-step | 84            |
+| y_out     | 0.00071961805 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020752352 |
+| time-step | 85          |
+| y_out     | 0.10788396  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020431867 |
+| time-step | 86          |
+| y_out     | 0.04132746  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.020160146  |
+| time-step | 87           |
+| y_out     | -0.006474506 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.02040934  |
+| time-step | 88          |
+| y_out     | -0.06577372 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.01908116  |
+| time-step | 89          |
+| y_out     | 0.010509009 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019838309 |
+| time-step | 90          |
+| y_out     | 0.13234864  |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.01960422 |
+| time-step | 91         |
+| y_out     | 0.02000757 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.019332642 |
+| time-step | 92          |
+| y_out     | 0.05553767  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.019244079  |
+| time-step | 93           |
+| y_out     | -0.058784105 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02050903 |
+| time-step | 94         |
+| y_out     | 0.13793725 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.02093038 |
+| time-step | 95         |
+| y_out     | 0.09889053 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.020613886 |
+| time-step | 96          |
+| y_out     | 0.005451856 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.021272965  |
+| time-step | 97           |
+| y_out     | -0.018620625 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021137979 |
+| time-step | 98          |
+| y_out     | 0.042610936 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.021202374 |
+| time-step | 99          |
+| y_out     | 0.087554194 |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..72356b0
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/progress.csv	
@@ -0,0 +1,101 @@
+perf/mse,time-step,y_out
+0.082159676,0,-0.03930173
+0.10689151,1,0.02850845
+0.13199139,2,0.021799369
+0.15028876,3,0.06596703
+0.1723384,4,-0.024830323
+0.19386157,5,0.0264886
+0.21414629,6,0.032664597
+0.23261961,7,-0.015842723
+0.2511875,8,-0.025599772
+0.2701317,9,-0.06609582
+0.25786114,10,0.02130657
+0.2460159,11,0.015463426
+0.2368942,12,0.067268506
+0.23354551,13,-0.0446322
+0.22740078,14,0.028773785
+0.21880226,15,0.020305729
+0.20967004,16,0.06252025
+0.20231946,17,-0.051750395
+0.19456811,18,-0.058056142
+0.1851129,19,0.09042649
+0.18099524,20,-0.08248055
+0.18066162,21,-0.06541039
+0.17478596,22,-0.0108490605
+0.17030677,23,0.026811648
+0.15957513,24,0.102516875
+0.15299655,25,-0.0004074918
+0.14584634,26,-0.13715005
+0.13843277,27,-0.044480074
+0.13337915,28,0.032456663
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/tb/events/events.out.tfevents.1655888828.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/tb/events/events.out.tfevents.1655888828.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..9ac69c9
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/tb/events/events.out.tfevents.1655888828.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/17-07-08-049304 172.16.0.65 &input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-04-34-774593 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-04-34-774593 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-04-34-774593 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-04-34-774593 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..345a3e1
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-04-34-774593 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-04-34-774593 172.16.0.65 &input_size=16/
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-04-34-774593 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-04-34-774593 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-04-34-774593 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-04-34-774593 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..84432bf
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,530 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
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+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
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+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/checkpoint 0
+---------------------------------------------------------
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+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/checkpoint 20
+-----------------------------------------------------------
+| perf/mse  | 0.21686974 |
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+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/checkpoint 40
+-----------------------------------------------------------
+| perf/mse  | 0.12668276 |
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+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/checkpoint 60
+-----------------------------------------------------------
+| perf/mse  | 0.08724648 |
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+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/checkpoint 80
+-----------------------------------------------------------
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07353509 |
+| time-step | 84         |
+| y_out     | 0.042      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07226142 |
+| time-step | 85         |
+| y_out     | 0.16       |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07140441 |
+| time-step | 86         |
+| y_out     | -0.0781    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06855675 |
+| time-step | 87         |
+| y_out     | -0.0993    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06724599 |
+| time-step | 88         |
+| y_out     | -0.0557    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06538289 |
+| time-step | 89         |
+| y_out     | 0.00633    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.063114144 |
+| time-step | 90          |
+| y_out     | -0.0257     |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.064367115 |
+| time-step | 91          |
+| y_out     | -0.0769     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06278794 |
+| time-step | 92         |
+| y_out     | -0.079     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06280473 |
+| time-step | 93         |
+| y_out     | 0.0729     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06374542 |
+| time-step | 94         |
+| y_out     | 0.152      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.063437276 |
+| time-step | 95          |
+| y_out     | -0.135      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06374855 |
+| time-step | 96         |
+| y_out     | -0.0204    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06362433 |
+| time-step | 97         |
+| y_out     | 0.0678     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06546237 |
+| time-step | 98         |
+| y_out     | -0.0841    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062775396 |
+| time-step | 99          |
+| y_out     | -0.115      |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..bdddc68
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/progress.csv	
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892349.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892349.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..1246623
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892349.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-05-46-724107 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..3c007ba
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,502 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+-----------------------------------------------------------
+| time-step | 0          |
+| y_out     | 0.02543969 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 1            |
+| y_out     | -0.083156124 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 2          |
+| y_out     | 0.03420055 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 3          |
+| y_out     | 0.04972852 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 4          |
+| y_out     | 0.07526794 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 5           |
+| y_out     | 0.009961855 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 6            |
+| y_out     | -0.033058282 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 7           |
+| y_out     | -0.06825027 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| time-step | 8             |
+| y_out     | -0.0013000146 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27382952  |
+| time-step | 9           |
+| y_out     | 0.034190312 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2729284   |
+| time-step | 10          |
+| y_out     | 0.092157975 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26507714  |
+| time-step | 11          |
+| y_out     | -0.08565105 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26247218  |
+| time-step | 12          |
+| y_out     | -0.11915968 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.25251848   |
+| time-step | 13           |
+| y_out     | -0.038766995 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25614166  |
+| time-step | 14          |
+| y_out     | -0.12407726 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25389183  |
+| time-step | 15          |
+| y_out     | 0.022983458 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.2577769     |
+| time-step | 16            |
+| y_out     | -0.0105117755 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26272285 |
+| time-step | 17         |
+| y_out     | 0.03392309 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25935462  |
+| time-step | 18          |
+| y_out     | -0.10291268 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2546948   |
+| time-step | 19          |
+| y_out     | -0.10690492 |
+------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/checkpoint-0.pt
+------------------------------------------------------------
+| perf/mse  | 0.2576353   |
+| time-step | 20          |
+| y_out     | 0.024831727 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2595777    |
+| time-step | 21           |
+| y_out     | 0.0025707819 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.25984186   |
+| time-step | 22           |
+| y_out     | 0.0033757165 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.25955763   |
+| time-step | 23           |
+| y_out     | -0.018356264 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.252552    |
+| time-step | 24          |
+| y_out     | -0.10053063 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.24597804   |
+| time-step | 25           |
+| y_out     | 0.0016568508 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23575306  |
+| time-step | 26          |
+| y_out     | -0.04441566 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22787392  |
+| time-step | 27          |
+| y_out     | -0.06055769 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2271574   |
+| time-step | 28          |
+| y_out     | 0.045346983 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2238466   |
+| time-step | 29          |
+| y_out     | 0.010689497 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21453214   |
+| time-step | 30           |
+| y_out     | -0.044969164 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20582466 |
+| time-step | 31         |
+| y_out     | 0.01673904 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20355399 |
+| time-step | 32         |
+| y_out     | 0.07496953 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20363176  |
+| time-step | 33          |
+| y_out     | 0.080068186 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20447294  |
+| time-step | 34          |
+| y_out     | 0.108327955 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20085938 |
+| time-step | 35         |
+| y_out     | 0.1127682  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19800802 |
+| time-step | 36         |
+| y_out     | 0.06506223 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1952958   |
+| time-step | 37          |
+| y_out     | -0.06782767 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1970061    |
+| time-step | 38           |
+| y_out     | -0.021291915 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19322568 |
+| time-step | 39         |
+| y_out     | 0.06708385 |
+-----------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/checkpoint-20.pt
+------------------------------------------------------------
+| perf/mse  | 0.19063005  |
+| time-step | 40          |
+| y_out     | 0.043319844 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19219904  |
+| time-step | 41          |
+| y_out     | 0.115307316 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18779841  |
+| time-step | 42          |
+| y_out     | 0.034356117 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18291819  |
+| time-step | 43          |
+| y_out     | -0.04928518 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17355475 |
+| time-step | 44         |
+| y_out     | 0.093631   |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16952237   |
+| time-step | 45           |
+| y_out     | -0.011238851 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16762704  |
+| time-step | 46          |
+| y_out     | -0.09669346 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16526394 |
+| time-step | 47         |
+| y_out     | 0.08606402 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15910259  |
+| time-step | 48          |
+| y_out     | -0.05098465 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15864669   |
+| time-step | 49           |
+| y_out     | -0.027917232 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15668479 |
+| time-step | 50         |
+| y_out     | 0.08319316 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15292951  |
+| time-step | 51          |
+| y_out     | 0.042520244 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14714378  |
+| time-step | 52          |
+| y_out     | -0.12763633 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14700839  |
+| time-step | 53          |
+| y_out     | 0.012783881 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14750826 |
+| time-step | 54         |
+| y_out     | 0.10585403 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14890778   |
+| time-step | 55           |
+| y_out     | -0.009761127 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1456365    |
+| time-step | 56           |
+| y_out     | -0.004182103 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1402092    |
+| time-step | 57           |
+| y_out     | -0.018213697 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13912843   |
+| time-step | 58           |
+| y_out     | -0.046308033 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13373114  |
+| time-step | 59          |
+| y_out     | 0.048111834 |
+------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/checkpoint-40.pt
+------------------------------------------------------------
+| perf/mse  | 0.13297889  |
+| time-step | 60          |
+| y_out     | -0.04215897 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13264537   |
+| time-step | 61           |
+| y_out     | -0.104556434 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1329548    |
+| time-step | 62           |
+| y_out     | -0.078657895 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1275852    |
+| time-step | 63           |
+| y_out     | -0.027588226 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1285917   |
+| time-step | 64          |
+| y_out     | 0.021769771 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12751028   |
+| time-step | 65           |
+| y_out     | -0.091332346 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.12586856    |
+| time-step | 66            |
+| y_out     | -0.0025204439 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12516212  |
+| time-step | 67          |
+| y_out     | 0.043982074 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.119775236 |
+| time-step | 68          |
+| y_out     | 0.093740515 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11929594 |
+| time-step | 69         |
+| y_out     | 0.0866701  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.116655156 |
+| time-step | 70          |
+| y_out     | 0.02714248  |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11493404 |
+| time-step | 71         |
+| y_out     | 0.09758279 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11613421  |
+| time-step | 72          |
+| y_out     | -0.13671179 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11574344  |
+| time-step | 73          |
+| y_out     | 0.020447982 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11047717   |
+| time-step | 74           |
+| y_out     | -0.005614616 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10763832   |
+| time-step | 75           |
+| y_out     | 0.0036862139 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.108061016  |
+| time-step | 76           |
+| y_out     | -0.008496618 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1072425   |
+| time-step | 77          |
+| y_out     | 0.021117546 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10854429  |
+| time-step | 78          |
+| y_out     | 0.001479405 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.106473565 |
+| time-step | 79          |
+| y_out     | 0.07581104  |
+------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/checkpoint-60.pt
+------------------------------------------------------------
+| perf/mse  | 0.10553888  |
+| time-step | 80          |
+| y_out     | -0.06063995 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.103933826   |
+| time-step | 81            |
+| y_out     | -0.0016014669 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10093144  |
+| time-step | 82          |
+| y_out     | -0.06682074 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09985522 |
+| time-step | 83         |
+| y_out     | -0.0502005 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.100739755  |
+| time-step | 84           |
+| y_out     | -0.023290092 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.098885074  |
+| time-step | 85           |
+| y_out     | -0.055518515 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.0943647  |
+| time-step | 86         |
+| y_out     | 0.05756878 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09271155   |
+| time-step | 87           |
+| y_out     | -0.028779313 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.090505436  |
+| time-step | 88           |
+| y_out     | -0.011349151 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.089886     |
+| time-step | 89           |
+| y_out     | -0.036808457 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.08787341    |
+| time-step | 90            |
+| y_out     | -0.0058836862 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.085306264 |
+| time-step | 91          |
+| y_out     | 0.024026938 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08266204   |
+| time-step | 92           |
+| y_out     | -0.027944604 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.080433235 |
+| time-step | 93          |
+| y_out     | 0.08344623  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07664002   |
+| time-step | 94           |
+| y_out     | -0.031623993 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.073959455 |
+| time-step | 95          |
+| y_out     | 0.04589785  |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.0735512  |
+| time-step | 96         |
+| y_out     | 0.08757552 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07293037 |
+| time-step | 97         |
+| y_out     | 0.10187426 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.072427385 |
+| time-step | 98          |
+| y_out     | 0.00885449  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.070463374 |
+| time-step | 99          |
+| y_out     | 0.05307331  |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..dd171f7
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/progress.csv	
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,0.02543969,
+1,-0.083156124,
+2,0.03420055,
+3,0.04972852,
+4,0.07526794,
+5,0.009961855,
+6,-0.033058282,
+7,-0.06825027,
+8,-0.0013000146,
+9,0.034190312,0.27382952
+10,0.092157975,0.2729284
+11,-0.08565105,0.26507714
+12,-0.11915968,0.26247218
+13,-0.038766995,0.25251848
+14,-0.12407726,0.25614166
+15,0.022983458,0.25389183
+16,-0.0105117755,0.2577769
+17,0.03392309,0.26272285
+18,-0.10291268,0.25935462
+19,-0.10690492,0.2546948
+20,0.024831727,0.2576353
+21,0.0025707819,0.2595777
+22,0.0033757165,0.25984186
+23,-0.018356264,0.25955763
+24,-0.10053063,0.252552
+25,0.0016568508,0.24597804
+26,-0.04441566,0.23575306
+27,-0.06055769,0.22787392
+28,0.045346983,0.2271574
+29,0.010689497,0.2238466
+30,-0.044969164,0.21453214
+31,0.01673904,0.20582466
+32,0.07496953,0.20355399
+33,0.080068186,0.20363176
+34,0.108327955,0.20447294
+35,0.1127682,0.20085938
+36,0.06506223,0.19800802
+37,-0.06782767,0.1952958
+38,-0.021291915,0.1970061
+39,0.06708385,0.19322568
+40,0.043319844,0.19063005
+41,0.115307316,0.19219904
+42,0.034356117,0.18779841
+43,-0.04928518,0.18291819
+44,0.093631,0.17355475
+45,-0.011238851,0.16952237
+46,-0.09669346,0.16762704
+47,0.08606402,0.16526394
+48,-0.05098465,0.15910259
+49,-0.027917232,0.15864669
+50,0.08319316,0.15668479
+51,0.042520244,0.15292951
+52,-0.12763633,0.14714378
+53,0.012783881,0.14700839
+54,0.10585403,0.14750826
+55,-0.009761127,0.14890778
+56,-0.004182103,0.1456365
+57,-0.018213697,0.1402092
+58,-0.046308033,0.13912843
+59,0.048111834,0.13373114
+60,-0.04215897,0.13297889
+61,-0.104556434,0.13264537
+62,-0.078657895,0.1329548
+63,-0.027588226,0.1275852
+64,0.021769771,0.1285917
+65,-0.091332346,0.12751028
+66,-0.0025204439,0.12586856
+67,0.043982074,0.12516212
+68,0.093740515,0.119775236
+69,0.0866701,0.11929594
+70,0.02714248,0.116655156
+71,0.09758279,0.11493404
+72,-0.13671179,0.11613421
+73,0.020447982,0.11574344
+74,-0.005614616,0.11047717
+75,0.0036862139,0.10763832
+76,-0.008496618,0.108061016
+77,0.021117546,0.1072425
+78,0.001479405,0.10854429
+79,0.07581104,0.106473565
+80,-0.06063995,0.10553888
+81,-0.0016014669,0.103933826
+82,-0.06682074,0.10093144
+83,-0.0502005,0.09985522
+84,-0.023290092,0.100739755
+85,-0.055518515,0.098885074
+86,0.05756878,0.0943647
+87,-0.028779313,0.09271155
+88,-0.011349151,0.090505436
+89,-0.036808457,0.089886
+90,-0.0058836862,0.08787341
+91,0.024026938,0.085306264
+92,-0.027944604,0.08266204
+93,0.08344623,0.080433235
+94,-0.031623993,0.07664002
+95,0.04589785,0.073959455
+96,0.08757552,0.0735512
+97,0.10187426,0.07293037
+98,0.00885449,0.072427385
+99,0.05307331,0.070463374
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892385.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892385.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..14ee9f6
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892385.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-06-25-002746 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..de028fa
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,12 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/progress.csv	
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892957.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892957.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..9513f17
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892957.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..1f18179
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-15-57-636377 172.16.0.65 &input_size=16/warn.txt	
@@ -0,0 +1 @@
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..f40deb9
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,12 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/progress.csv	
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892966.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892966.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..36a664e
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655892966.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..1f18179
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-16-06-904673 172.16.0.65 &input_size=16/warn.txt	
@@ -0,0 +1 @@
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..6723e85
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,12 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/progress.csv	
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655893157.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655893157.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..dfe1b4a
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655893157.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..1f18179
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-19-17-522841 172.16.0.65 &input_size=16/warn.txt	
@@ -0,0 +1 @@
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..6cd45d2
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,12 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/progress.csv	
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655893501.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655893501.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..3bcd498
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655893501.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..1f18179
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-25-01-451923 172.16.0.65 &input_size=16/warn.txt	
@@ -0,0 +1 @@
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/backup.txt	
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/log.txt
new file mode 100644
index 0000000..009152b
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/log.txt	
@@ -0,0 +1,12 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/progress.csv	
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655893651.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655893651.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..2480abd
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/tb/events/events.out.tfevents.1655893651.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/warn.txt
new file mode 100644
index 0000000..1f18179
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/18-27-31-330327 172.16.0.65 &input_size=16/warn.txt	
@@ -0,0 +1 @@
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/log.txt
new file mode 100644
index 0000000..e288008
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/log.txt
@@ -0,0 +1,530 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/checkpoint 0
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | -0.0426  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 1        |
+| y_out     | -0.136   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 2        |
+| y_out     | 0.042    |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 3        |
+| y_out     | -0.053   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 4        |
+| y_out     | 0.11     |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 5        |
+| y_out     | 0.0432   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 6        |
+| y_out     | 0.153    |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 7        |
+| y_out     | -0.061   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 8        |
+| y_out     | -0.0457  |
+---------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.47477263 |
+| time-step | 9          |
+| y_out     | 0.0632     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.46034032 |
+| time-step | 10         |
+| y_out     | -0.0555    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.4469831 |
+| time-step | 11        |
+| y_out     | 0.0276    |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.4499182 |
+| time-step | 12        |
+| y_out     | -0.0611   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.43114018 |
+| time-step | 13         |
+| y_out     | -0.0573    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.42561874 |
+| time-step | 14         |
+| y_out     | -0.0623    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.4123823 |
+| time-step | 15        |
+| y_out     | -0.0437   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.41393858 |
+| time-step | 16         |
+| y_out     | -0.0507    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.40838248 |
+| time-step | 17         |
+| y_out     | -0.114     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.39053327 |
+| time-step | 18         |
+| y_out     | 0.0427     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.38163424 |
+| time-step | 19         |
+| y_out     | 0.0313     |
+-----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/checkpoint 20
+-----------------------------------------------------------
+| perf/mse  | 0.38190514 |
+| time-step | 20         |
+| y_out     | 0.02       |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.36398634 |
+| time-step | 21         |
+| y_out     | 0.0423     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.35278568 |
+| time-step | 22         |
+| y_out     | 0.123      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.35168636 |
+| time-step | 23         |
+| y_out     | 0.111      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.33598837 |
+| time-step | 24         |
+| y_out     | 0.0468     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.31792706 |
+| time-step | 25         |
+| y_out     | -0.037     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.30697304 |
+| time-step | 26         |
+| y_out     | -0.0581    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.29171485 |
+| time-step | 27         |
+| y_out     | 0.0902     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.29094085 |
+| time-step | 28         |
+| y_out     | 0.0852     |
+-----------------------------------------------------------
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+| perf/mse  | 0.27945215 |
+| time-step | 29         |
+| y_out     | 0.063      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.27446556 |
+| time-step | 30         |
+| y_out     | -0.0777    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.27430564 |
+| time-step | 31         |
+| y_out     | 0.159      |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2673028 |
+| time-step | 32        |
+| y_out     | 0.0657    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25983655 |
+| time-step | 33         |
+| y_out     | 0.0482     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25814977 |
+| time-step | 34         |
+| y_out     | -0.0711    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2645093 |
+| time-step | 35        |
+| y_out     | 0.0489    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25751668 |
+| time-step | 36         |
+| y_out     | 0.0856     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25217295 |
+| time-step | 37         |
+| y_out     | 0.09       |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24525543 |
+| time-step | 38         |
+| y_out     | 0.0475     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23599634 |
+| time-step | 39         |
+| y_out     | 0.0469     |
+-----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/checkpoint 40
+-----------------------------------------------------------
+| perf/mse  | 0.22994074 |
+| time-step | 40         |
+| y_out     | -0.0452    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22271113 |
+| time-step | 41         |
+| y_out     | 0.0525     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21314354 |
+| time-step | 42         |
+| y_out     | -0.0414    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21123913 |
+| time-step | 43         |
+| y_out     | -0.073     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20096512 |
+| time-step | 44         |
+| y_out     | -0.0477    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19233385 |
+| time-step | 45         |
+| y_out     | 0.0364     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1896142 |
+| time-step | 46        |
+| y_out     | 0.0637    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19023702 |
+| time-step | 47         |
+| y_out     | 0.102      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18690595 |
+| time-step | 48         |
+| y_out     | 0.0722     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18166336 |
+| time-step | 49         |
+| y_out     | 0.0122     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17107615 |
+| time-step | 50         |
+| y_out     | 0.0455     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1711011 |
+| time-step | 51        |
+| y_out     | 0.13      |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17241149 |
+| time-step | 52         |
+| y_out     | 0.0373     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16246584 |
+| time-step | 53         |
+| y_out     | 0.086      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16844432 |
+| time-step | 54         |
+| y_out     | 0.0906     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16454713 |
+| time-step | 55         |
+| y_out     | 0.104      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15855086 |
+| time-step | 56         |
+| y_out     | -0.0461    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15971772 |
+| time-step | 57         |
+| y_out     | -0.000737  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16151166 |
+| time-step | 58         |
+| y_out     | 0.0436     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16324154 |
+| time-step | 59         |
+| y_out     | -0.0676    |
+-----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/checkpoint 60
+-----------------------------------------------------------
+| perf/mse  | 0.17227364 |
+| time-step | 60         |
+| y_out     | -0.141     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16597103 |
+| time-step | 61         |
+| y_out     | 0.0897     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15995072 |
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+| y_out     | 0.0748     |
+-----------------------------------------------------------
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+| perf/mse  | 0.16068172 |
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+| y_out     | -0.0467    |
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+| perf/mse  | 0.15676652 |
+| time-step | 64         |
+| y_out     | 0.0949     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15169835 |
+| time-step | 65         |
+| y_out     | -0.000441  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15456648 |
+| time-step | 66         |
+| y_out     | 0.0306     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15129139 |
+| time-step | 67         |
+| y_out     | 0.0788     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14592138 |
+| time-step | 68         |
+| y_out     | -0.0254    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14131382 |
+| time-step | 69         |
+| y_out     | -0.0318    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13168713 |
+| time-step | 70         |
+| y_out     | 0.00188    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13277394 |
+| time-step | 71         |
+| y_out     | 0.0349     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13121217 |
+| time-step | 72         |
+| y_out     | 0.00193    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12806149 |
+| time-step | 73         |
+| y_out     | 0.00503    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12577616 |
+| time-step | 74         |
+| y_out     | 0.00166    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12997623 |
+| time-step | 75         |
+| y_out     | -0.0776    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12527299 |
+| time-step | 76         |
+| y_out     | 0.121      |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.121158935 |
+| time-step | 77          |
+| y_out     | -0.0168     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12085513 |
+| time-step | 78         |
+| y_out     | 0.00893    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11750875 |
+| time-step | 79         |
+| y_out     | -0.0219    |
+-----------------------------------------------------------
+save checkpoint to  ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/checkpoint 80
+------------------------------------------------------------
+| perf/mse  | 0.119886376 |
+| time-step | 80          |
+| y_out     | 0.0832      |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11578877 |
+| time-step | 81         |
+| y_out     | 0.0612     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11291431 |
+| time-step | 82         |
+| y_out     | -0.107     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11140708 |
+| time-step | 83         |
+| y_out     | 0.149      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10805426 |
+| time-step | 84         |
+| y_out     | 0.0316     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10308941 |
+| time-step | 85         |
+| y_out     | 0.0146     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09938879 |
+| time-step | 86         |
+| y_out     | 0.0442     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09633983 |
+| time-step | 87         |
+| y_out     | 0.043      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09636699 |
+| time-step | 88         |
+| y_out     | 0.0303     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10237183 |
+| time-step | 89         |
+| y_out     | -0.00178   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10292266 |
+| time-step | 90         |
+| y_out     | -0.0718    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10133407 |
+| time-step | 91         |
+| y_out     | 0.0412     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10371248 |
+| time-step | 92         |
+| y_out     | 0.0781     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10306176 |
+| time-step | 93         |
+| y_out     | -0.0071    |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.103057325 |
+| time-step | 94          |
+| y_out     | -0.0368     |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10298441 |
+| time-step | 95         |
+| y_out     | -0.0482    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10385045 |
+| time-step | 96         |
+| y_out     | -0.0303    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10305275 |
+| time-step | 97         |
+| y_out     | 0.00739    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10142896 |
+| time-step | 98         |
+| y_out     | 0.0643     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09574478 |
+| time-step | 99         |
+| y_out     | -0.126     |
+-----------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/progress.csv
new file mode 100644
index 0000000..a4af214
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,-0.042631271423554745,
+1,-0.1364577378250604,
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/tb/events/events.out.tfevents.1655899799.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/tb/events/events.out.tfevents.1655899799.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..16988c8
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/tb/events/events.out.tfevents.1655899799.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-09-55-341226_172.16.0.65_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/log.txt
new file mode 100644
index 0000000..0d4754e
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/log.txt
@@ -0,0 +1,502 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+----------------------------------------------------------
+| time-step | 0         |
+| y_out     | 0.1270025 |
+----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 1            |
+| y_out     | -0.033143442 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 2          |
+| y_out     | 0.04316023 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 3            |
+| y_out     | 0.0074920505 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 4           |
+| y_out     | -0.07922106 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 5           |
+| y_out     | -0.06868453 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 6           |
+| y_out     | 0.056429192 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 7          |
+| y_out     | 0.03839404 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 8          |
+| y_out     | 0.08945827 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24455085  |
+| time-step | 9           |
+| y_out     | -0.07164294 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23955028  |
+| time-step | 10          |
+| y_out     | -0.11064705 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23086555 |
+| time-step | 11         |
+| y_out     | -0.059792  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.22925691   |
+| time-step | 12           |
+| y_out     | -0.035293307 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22662637  |
+| time-step | 13          |
+| y_out     | 0.088819206 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22157809  |
+| time-step | 14          |
+| y_out     | -0.09565427 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20833018  |
+| time-step | 15          |
+| y_out     | -0.09092255 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20849486  |
+| time-step | 16          |
+| y_out     | 0.021676589 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20898747   |
+| time-step | 17           |
+| y_out     | 0.0142172985 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20411983  |
+| time-step | 18          |
+| y_out     | -0.03821886 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20476095 |
+| time-step | 19         |
+| y_out     | -0.0538266 |
+-----------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/checkpoint-0.pt
+------------------------------------------------------------
+| perf/mse  | 0.19967778  |
+| time-step | 20          |
+| y_out     | 0.060248084 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20475204  |
+| time-step | 21          |
+| y_out     | -0.06632933 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19850641   |
+| time-step | 22           |
+| y_out     | -0.032221988 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19360936  |
+| time-step | 23          |
+| y_out     | -0.17311391 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19145238  |
+| time-step | 24          |
+| y_out     | -0.03579962 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19604379 |
+| time-step | 25         |
+| y_out     | 0.06489918 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19490609 |
+| time-step | 26         |
+| y_out     | 0.09866177 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19261387 |
+| time-step | 27         |
+| y_out     | 0.10104892 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18767723  |
+| time-step | 28          |
+| y_out     | -0.10728069 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18340199 |
+| time-step | 29         |
+| y_out     | 0.1305145  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18586314   |
+| time-step | 30           |
+| y_out     | -0.024374193 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18150596   |
+| time-step | 31           |
+| y_out     | 0.0003626207 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17714699   |
+| time-step | 32           |
+| y_out     | -0.038588032 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1739293   |
+| time-step | 33          |
+| y_out     | 0.017144665 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17054138  |
+| time-step | 34          |
+| y_out     | -0.05243077 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16789882  |
+| time-step | 35          |
+| y_out     | -0.12129211 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.162882    |
+| time-step | 36          |
+| y_out     | -0.02741717 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16063163   |
+| time-step | 37           |
+| y_out     | -0.038193766 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1594989    |
+| time-step | 38           |
+| y_out     | 0.0040047728 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15355758   |
+| time-step | 39           |
+| y_out     | -0.027278213 |
+-------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/checkpoint-20.pt
+-----------------------------------------------------------
+| perf/mse  | 0.14723027 |
+| time-step | 40         |
+| y_out     | 0.05641021 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1416758   |
+| time-step | 41          |
+| y_out     | 0.012135993 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13919118  |
+| time-step | 42          |
+| y_out     | 0.080893956 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14177832   |
+| time-step | 43           |
+| y_out     | -0.051185153 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13908552   |
+| time-step | 44           |
+| y_out     | -0.117702864 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1300364   |
+| time-step | 45          |
+| y_out     | -0.04133995 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1272886   |
+| time-step | 46          |
+| y_out     | 0.008002132 |
+------------------------------------------------------------
+---------------------------------------------------------------
+| perf/mse  | 0.124234155    |
+| time-step | 47             |
+| y_out     | -0.00082183536 |
+---------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12085317   |
+| time-step | 48           |
+| y_out     | -0.020323765 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11976151  |
+| time-step | 49          |
+| y_out     | -0.07246548 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11697744  |
+| time-step | 50          |
+| y_out     | -0.04953236 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11347904 |
+| time-step | 51         |
+| y_out     | 0.06752398 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11415629  |
+| time-step | 52          |
+| y_out     | -0.06853134 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1067762   |
+| time-step | 53          |
+| y_out     | 0.012878716 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10410388   |
+| time-step | 54           |
+| y_out     | -0.038085572 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1046227   |
+| time-step | 55          |
+| y_out     | 0.045601353 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10415797  |
+| time-step | 56          |
+| y_out     | 0.037767366 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10001101 |
+| time-step | 57         |
+| y_out     | 0.05764051 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09861631 |
+| time-step | 58         |
+| y_out     | -0.1096278 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0992529    |
+| time-step | 59           |
+| y_out     | -0.012828685 |
+-------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/checkpoint-40.pt
+------------------------------------------------------------
+| perf/mse  | 0.09712757  |
+| time-step | 60          |
+| y_out     | 0.122913696 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09613137   |
+| time-step | 61           |
+| y_out     | -0.067309484 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09078523  |
+| time-step | 62          |
+| y_out     | 0.034743674 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09090859   |
+| time-step | 63           |
+| y_out     | -0.017664798 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08863268 |
+| time-step | 64         |
+| y_out     | 0.04270229 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.086019516   |
+| time-step | 65            |
+| y_out     | -0.0034357123 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.081322595 |
+| time-step | 66          |
+| y_out     | 0.06502881  |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08055607 |
+| time-step | 67         |
+| y_out     | 0.03288244 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08155529  |
+| time-step | 68          |
+| y_out     | -0.04664415 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07779691  |
+| time-step | 69          |
+| y_out     | 0.008411724 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0756353   |
+| time-step | 70          |
+| y_out     | 0.031317182 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07521866  |
+| time-step | 71          |
+| y_out     | -0.05401724 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07443766   |
+| time-step | 72           |
+| y_out     | -0.040979695 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07535409  |
+| time-step | 73          |
+| y_out     | -0.08224071 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07581641 |
+| time-step | 74         |
+| y_out     | 0.01346373 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.074816994 |
+| time-step | 75          |
+| y_out     | -0.07526079 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07569193 |
+| time-step | 76         |
+| y_out     | 0.04082101 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07390078  |
+| time-step | 77          |
+| y_out     | -0.05559425 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.069573805  |
+| time-step | 78           |
+| y_out     | -0.018094297 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.068470836 |
+| time-step | 79          |
+| y_out     | 0.11093522  |
+------------------------------------------------------------
+rm the older checkpoint ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/checkpoint-60.pt
+------------------------------------------------------------
+| perf/mse  | 0.06682734  |
+| time-step | 80          |
+| y_out     | 0.024800558 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.064113766  |
+| time-step | 81           |
+| y_out     | -0.059818994 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06313278   |
+| time-step | 82           |
+| y_out     | -0.030326419 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056976516 |
+| time-step | 83          |
+| y_out     | 0.05496442  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.053131253 |
+| time-step | 84          |
+| y_out     | -0.19041705 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.052117765 |
+| time-step | 85          |
+| y_out     | 0.093179375 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.04999153  |
+| time-step | 86          |
+| y_out     | 0.074907884 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.049099077 |
+| time-step | 87          |
+| y_out     | -0.05020786 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048349667 |
+| time-step | 88          |
+| y_out     | -0.06092811 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.047243617 |
+| time-step | 89          |
+| y_out     | -0.03692233 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04564277 |
+| time-step | 90         |
+| y_out     | 0.1223818  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044437572 |
+| time-step | 91          |
+| y_out     | 0.08561091  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.04359993   |
+| time-step | 92           |
+| y_out     | -0.044740327 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.043888208 |
+| time-step | 93          |
+| y_out     | -0.059321   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.044506133 |
+| time-step | 94          |
+| y_out     | -0.06644118 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.04357945  |
+| time-step | 95          |
+| y_out     | -0.04814718 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042611774 |
+| time-step | 96          |
+| y_out     | -0.03791592 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.041191537 |
+| time-step | 97          |
+| y_out     | 0.08317861  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.039872684 |
+| time-step | 98          |
+| y_out     | 0.0685949   |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.038431402  |
+| time-step | 99           |
+| y_out     | -0.032349944 |
+-------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/progress.csv
new file mode 100644
index 0000000..ca4aa87
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,0.1270025,
+1,-0.033143442,
+2,0.04316023,
+3,0.0074920505,
+4,-0.07922106,
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/tb/events/events.out.tfevents.1655899885.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/tb/events/events.out.tfevents.1655899885.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..f83e76b
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/tb/events/events.out.tfevents.1655899885.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-11-25-796643_172.16.0.65_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/log.txt
new file mode 100644
index 0000000..9635ecb
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/log.txt
@@ -0,0 +1,12 @@
+Logging to ../../test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/
+log dir: ../../test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/
+pkl_file: ../../test_data_root/archive_tester/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16.pkl
+checkpoint_dir: ../../test_data_root/checkpoint/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/
+results_dir: ../../test_data_root/results/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/progress.csv
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/tb/events/events.out.tfevents.1655899958.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/tb/events/events.out.tfevents.1655899958.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..42df690
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/tb/events/events.out.tfevents.1655899958.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/warn.txt
new file mode 100644
index 0000000..1f18179
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/22/20-12-38-834938_172.16.0.65_&input_size=16/warn.txt
@@ -0,0 +1 @@
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/log.txt
new file mode 100644
index 0000000..ba3ef3a
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/log.txt
@@ -0,0 +1,539 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32_ref>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32_ref>
+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/checkpoint 0
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.0145   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 1        |
+| y_out     | 0.0012   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 2        |
+| y_out     | 0.0199   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 3        |
+| y_out     | -0.124   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 4        |
+| y_out     | -0.034   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 5        |
+| y_out     | -0.0102  |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 6        |
+| y_out     | 0.114    |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 7        |
+| y_out     | 0.0567   |
+---------------------------------------------------------
+---------------------------------------------------------
+| time-step | 8        |
+| y_out     | 0.0309   |
+---------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.40850583 |
+| time-step | 9          |
+| y_out     | 0.006      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.38869593 |
+| time-step | 10         |
+| y_out     | 0.111      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.36860225 |
+| time-step | 11         |
+| y_out     | 0.139      |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.34584624 |
+| time-step | 12         |
+| y_out     | -0.0508    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.32611674 |
+| time-step | 13         |
+| y_out     | -0.00365   |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.31833556 |
+| time-step | 14         |
+| y_out     | -0.0405    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.32321334 |
+| time-step | 15         |
+| y_out     | -0.0225    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.31403857 |
+| time-step | 16         |
+| y_out     | -0.0346    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.3043001 |
+| time-step | 17        |
+| y_out     | -0.063    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.29597905 |
+| time-step | 18         |
+| y_out     | 0.0388     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2834611 |
+| time-step | 19        |
+| y_out     | -0.0785   |
+----------------------------------------------------------
+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/checkpoint 20
+-----------------------------------------------------------
+| perf/mse  | 0.28650934 |
+| time-step | 20         |
+| y_out     | -0.0719    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.28245777 |
+| time-step | 21         |
+| y_out     | 0.0275     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.27612627 |
+| time-step | 22         |
+| y_out     | -0.0757    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.28076744 |
+| time-step | 23         |
+| y_out     | 0.0549     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.26953632 |
+| time-step | 24         |
+| y_out     | -0.107     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2545963 |
+| time-step | 25        |
+| y_out     | 0.129     |
+----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2434386 |
+| time-step | 26        |
+| y_out     | 0.00946   |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23191626 |
+| time-step | 27         |
+| y_out     | -0.0125    |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2256171 |
+| time-step | 28        |
+| y_out     | 0.0591    |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22047305 |
+| time-step | 29         |
+| y_out     | 0.0286     |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2102662 |
+| time-step | 30        |
+| y_out     | 0.0831    |
+----------------------------------------------------------
+---------------------------------------------------------
+| perf/mse  | 0.20084  |
+| time-step | 31       |
+| y_out     | 0.0852   |
+---------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20053959 |
+| time-step | 32         |
+| y_out     | -0.0129    |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18661442 |
+| time-step | 33         |
+| y_out     | -0.0325    |
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+-----------------------------------------------------------
+| perf/mse  | 0.17677857 |
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+| y_out     | 0.0221     |
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+-----------------------------------------------------------
+| perf/mse  | 0.16968016 |
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+| y_out     | -0.0597    |
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+-----------------------------------------------------------
+| perf/mse  | 0.16578284 |
+| time-step | 36         |
+| y_out     | 0.0974     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16460271 |
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+| y_out     | 0.0889     |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15855713 |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15664673 |
+| time-step | 39         |
+| y_out     | -0.0312    |
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+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/checkpoint 40
+-----------------------------------------------------------
+| perf/mse  | 0.15285398 |
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+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15155785 |
+| time-step | 41         |
+| y_out     | -0.0889    |
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+| perf/mse  | 0.14486167 |
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+| perf/mse  | 0.14351904 |
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+-----------------------------------------------------------
+| perf/mse  | 0.14487626 |
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+| perf/mse  | 0.13120037 |
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+-----------------------------------------------------------
+| perf/mse  | 0.12494274 |
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+------------------------------------------------------------
+| perf/mse  | 0.116961956 |
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+-----------------------------------------------------------
+| perf/mse  | 0.11480044 |
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+-----------------------------------------------------------
+| perf/mse  | 0.10895107 |
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+-----------------------------------------------------------
+| perf/mse  | 0.10444629 |
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+| perf/mse  | 0.100612834 |
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+| perf/mse  | 0.09450734 |
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+-----------------------------------------------------------
+| perf/mse  | 0.09515741 |
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+-----------------------------------------------------------
+| perf/mse  | 0.08946009 |
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+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/checkpoint 60
+------------------------------------------------------------
+| perf/mse  | 0.088738285 |
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+-----------------------------------------------------------
+| perf/mse  | 0.08860503 |
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+------------------------------------------------------------
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+| perf/mse  | 0.06846817 |
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+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/checkpoint 80
+-----------------------------------------------------------
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+------------------------------------------------------------
+| perf/mse  | 0.060555946 |
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+| perf/mse  | 0.05849896 |
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+| perf/mse  | 0.056794304 |
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+| perf/mse  | 0.05700473 |
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+| perf/mse  | 0.05604604 |
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+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_data_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-06-23 00:07:41.475293
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/progress.csv
new file mode 100644
index 0000000..dddbbac
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/progress.csv
@@ -0,0 +1,101 @@
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new file mode 100644
index 0000000..47d8a4a
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/tb/events/events.out.tfevents.1655914058.MBP-C02ZL0TNLVCF-2243.lan differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-34-677795_192.168.0.192_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..844e362
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..38e4973
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,520 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
+-------------------------------------------------------------
+| perf/mse  | 0.08086397   |
+| time-step | 0            |
+| y_out     | -0.060775932 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11326595   |
+| time-step | 1            |
+| y_out     | -0.098857775 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1440413   |
+| time-step | 2           |
+| y_out     | -0.12534066 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16652903 |
+| time-step | 3          |
+| y_out     | 0.10364883 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18477741  |
+| time-step | 4           |
+| y_out     | 0.057952125 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2126642   |
+| time-step | 5           |
+| y_out     | 0.061784014 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23810646  |
+| time-step | 6           |
+| y_out     | -0.00905833 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2703305    |
+| time-step | 7            |
+| y_out     | -0.033508603 |
+-------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2924213 |
+| time-step | 8         |
+| y_out     | 0.1422859 |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.3157735    |
+| time-step | 9            |
+| y_out     | 0.0023035463 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.3109005    |
+| time-step | 10           |
+| y_out     | -0.024482833 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.30299672  |
+| time-step | 11          |
+| y_out     | -0.19498286 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.29535595   |
+| time-step | 12           |
+| y_out     | -0.006084269 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.29753268   |
+| time-step | 13           |
+| y_out     | -0.064735174 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2941874  |
+| time-step | 14         |
+| y_out     | 0.06912308 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.287302    |
+| time-step | 15          |
+| y_out     | -0.08943631 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.28102845 |
+| time-step | 16         |
+| y_out     | 0.0575895  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2689882   |
+| time-step | 17          |
+| y_out     | 0.011021223 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26968527   |
+| time-step | 18           |
+| y_out     | -0.012696346 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26560968  |
+| time-step | 19          |
+| y_out     | 0.053878043 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/checkpoint-0.pt
+--------------------------------------------------------------
+| perf/mse  | 0.26472914    |
+| time-step | 20            |
+| y_out     | -0.0045364797 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2610011   |
+| time-step | 21          |
+| y_out     | -0.04398114 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25779694  |
+| time-step | 22          |
+| y_out     | -0.03241006 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2536784   |
+| time-step | 23          |
+| y_out     | -0.17368199 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.25576162   |
+| time-step | 24           |
+| y_out     | -0.007032506 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2545869   |
+| time-step | 25          |
+| y_out     | -0.01269643 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2525714    |
+| time-step | 26           |
+| y_out     | -0.025027081 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25287986  |
+| time-step | 27          |
+| y_out     | -0.06325437 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24515364  |
+| time-step | 28          |
+| y_out     | 0.002111489 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24310362 |
+| time-step | 29         |
+| y_out     | 0.06493594 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.23889408    |
+| time-step | 30            |
+| y_out     | -0.0033895932 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23463514  |
+| time-step | 31          |
+| y_out     | 0.051739953 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23258886  |
+| time-step | 32          |
+| y_out     | 0.053889763 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23103246  |
+| time-step | 33          |
+| y_out     | 0.036222875 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22865133  |
+| time-step | 34          |
+| y_out     | 0.021899525 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.21666253    |
+| time-step | 35            |
+| y_out     | -0.0028024297 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20886478 |
+| time-step | 36         |
+| y_out     | 0.04389424 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19953814 |
+| time-step | 37         |
+| y_out     | 0.14929605 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1978017   |
+| time-step | 38          |
+| y_out     | -0.09870275 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19450215 |
+| time-step | 39         |
+| y_out     | 0.09254952 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/checkpoint-20.pt
+------------------------------------------------------------
+| perf/mse  | 0.19200218  |
+| time-step | 40          |
+| y_out     | -0.08111617 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1853044  |
+| time-step | 41         |
+| y_out     | 0.10265785 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18119583  |
+| time-step | 42          |
+| y_out     | 0.011370021 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17199756  |
+| time-step | 43          |
+| y_out     | -0.15405786 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.16842757    |
+| time-step | 44            |
+| y_out     | -0.0053060576 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16981554   |
+| time-step | 45           |
+| y_out     | -0.055502255 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.17296484    |
+| time-step | 46            |
+| y_out     | -0.0039895475 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17410694  |
+| time-step | 47          |
+| y_out     | 0.045056652 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17302658  |
+| time-step | 48          |
+| y_out     | 0.044080053 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16895546 |
+| time-step | 49         |
+| y_out     | 0.07082256 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16726239   |
+| time-step | 50           |
+| y_out     | -0.012292229 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16608213   |
+| time-step | 51           |
+| y_out     | -0.020920776 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16149223  |
+| time-step | 52          |
+| y_out     | 0.042997006 |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1612885 |
+| time-step | 53        |
+| y_out     | 0.0936604 |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1621683    |
+| time-step | 54           |
+| y_out     | -0.025829734 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.1656476     |
+| time-step | 55            |
+| y_out     | -0.0070162676 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16365585 |
+| time-step | 56         |
+| y_out     | 0.06207158 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16027749   |
+| time-step | 57           |
+| y_out     | -0.033068907 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15534171   |
+| time-step | 58           |
+| y_out     | -0.017985152 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1554647    |
+| time-step | 59           |
+| y_out     | -0.028211128 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/checkpoint-40.pt
+------------------------------------------------------------
+| perf/mse  | 0.15105654  |
+| time-step | 60          |
+| y_out     | 0.048252396 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14680314 |
+| time-step | 61         |
+| y_out     | 0.05389127 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14446786  |
+| time-step | 62          |
+| y_out     | 0.083108835 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13845295  |
+| time-step | 63          |
+| y_out     | 0.021797137 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13519627  |
+| time-step | 64          |
+| y_out     | -0.07907666 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12817772 |
+| time-step | 65         |
+| y_out     | 0.07884962 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12238495  |
+| time-step | 66          |
+| y_out     | -0.08350316 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12178098 |
+| time-step | 67         |
+| y_out     | 0.05329544 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.117303595 |
+| time-step | 68          |
+| y_out     | 0.07631386  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11532998   |
+| time-step | 69           |
+| y_out     | -0.035889294 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11208247 |
+| time-step | 70         |
+| y_out     | 0.04623005 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11237923  |
+| time-step | 71          |
+| y_out     | 0.015025133 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11122583  |
+| time-step | 72          |
+| y_out     | -0.08200727 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10800805  |
+| time-step | 73          |
+| y_out     | 0.049507555 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.103816785 |
+| time-step | 74          |
+| y_out     | 0.08936402  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10298413   |
+| time-step | 75           |
+| y_out     | -0.063348114 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10041833   |
+| time-step | 76           |
+| y_out     | -0.035932854 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.097644985 |
+| time-step | 77          |
+| y_out     | 0.009807184 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0961328   |
+| time-step | 78          |
+| y_out     | 0.022201583 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.091510914 |
+| time-step | 79          |
+| y_out     | 0.015230041 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/checkpoint-60.pt
+------------------------------------------------------------
+| perf/mse  | 0.09009916  |
+| time-step | 80          |
+| y_out     | 0.019628862 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.087541334 |
+| time-step | 81          |
+| y_out     | -0.07286345 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.085722275 |
+| time-step | 82          |
+| y_out     | 0.061438806 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08762278 |
+| time-step | 83         |
+| y_out     | 0.03654339 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.085277066 |
+| time-step | 84          |
+| y_out     | 0.094111115 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08328184  |
+| time-step | 85          |
+| y_out     | -0.04229196 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08116603   |
+| time-step | 86           |
+| y_out     | -0.019397281 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07752233  |
+| time-step | 87          |
+| y_out     | 0.037506748 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07598601  |
+| time-step | 88          |
+| y_out     | 0.077352926 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07466737  |
+| time-step | 89          |
+| y_out     | 0.006841588 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07378708  |
+| time-step | 90          |
+| y_out     | 0.047354504 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07126787  |
+| time-step | 91          |
+| y_out     | 0.026697468 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06749016   |
+| time-step | 92           |
+| y_out     | -0.066405706 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.065078974 |
+| time-step | 93          |
+| y_out     | -0.04045777 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.06379058  |
+| time-step | 94          |
+| y_out     | 0.072029635 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.06264709    |
+| time-step | 95            |
+| y_out     | -0.0064311735 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.062091827  |
+| time-step | 96           |
+| y_out     | -0.036961287 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05994628 |
+| time-step | 97         |
+| y_out     | 0.10637689 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.05957383  |
+| time-step | 98          |
+| y_out     | 0.025962997 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057080995 |
+| time-step | 99          |
+| y_out     | 0.10434675  |
+------------------------------------------------------------
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: True
+k: REMOTE_SETTING, v: {'ftp_server': '127.0.0.1', 'username': 'hadoop-peisongpa', 'password': 'wangli78', 'remote_data_root': '/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root', 'file_transfer_protocol': 'ftp'}
+gen log files for record date : 2022-06-23 00:07:43.969899
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..c94f7e0
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+perf/mse,time-step,y_out
+0.08086397,0,-0.060775932
+0.11326595,1,-0.098857775
+0.1440413,2,-0.12534066
+0.16652903,3,0.10364883
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+0.28102845,16,0.0575895
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+0.062091827,96,-0.036961287
+0.05994628,97,0.10637689
+0.05957383,98,0.025962997
+0.057080995,99,0.10434675
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/tb/events/events.out.tfevents.1655914061.MBP-C02ZL0TNLVCF-2243.lan b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/tb/events/events.out.tfevents.1655914061.MBP-C02ZL0TNLVCF-2243.lan
new file mode 100644
index 0000000..f3ead10
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/tb/events/events.out.tfevents.1655914061.MBP-C02ZL0TNLVCF-2243.lan differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-41-475293_192.168.0.192_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..844e362
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..5264165
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/log.txt
@@ -0,0 +1,12 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1655914063.MBP-C02ZL0TNLVCF-2243.lan b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1655914063.MBP-C02ZL0TNLVCF-2243.lan
new file mode 100644
index 0000000..38914c3
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1655914063.MBP-C02ZL0TNLVCF-2243.lan differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..1f18179
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/06/23/00-07-43-969899_192.168.0.192_&input_size=16&input_size=16&input_size=16/warn.txt
@@ -0,0 +1 @@
+[WARN] 0 : sync: start
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-20-55-585209_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-20-55-585209_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..a5d91bd
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,502 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+------------------------------------------------------------
+| time-step | 0           |
+| y_out     | 0.030518502 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 1            |
+| y_out     | -0.033393197 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 2            |
+| y_out     | -0.007742934 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 3           |
+| y_out     | -0.01596877 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 4           |
+| y_out     | 0.030884877 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| time-step | 5             |
+| y_out     | -0.0045458004 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 6           |
+| y_out     | 0.016914047 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 7           |
+| y_out     | 0.058739226 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 8            |
+| y_out     | -0.041022867 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29336125  |
+| time-step | 9           |
+| y_out     | 0.060652874 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.29164058 |
+| time-step | 10         |
+| y_out     | 0.1274339  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.29000726 |
+| time-step | 11         |
+| y_out     | 0.08134254 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29072076  |
+| time-step | 12          |
+| y_out     | 0.013485372 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2932368   |
+| time-step | 13          |
+| y_out     | 0.028466063 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.29551354 |
+| time-step | 14         |
+| y_out     | 0.13443753 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29252884  |
+| time-step | 15          |
+| y_out     | -0.11275613 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.28717178 |
+| time-step | 16         |
+| y_out     | 0.05639878 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.28659546   |
+| time-step | 17           |
+| y_out     | -0.099924065 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2871242  |
+| time-step | 18         |
+| y_out     | 0.03242412 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.28402168  |
+| time-step | 19          |
+| y_out     | -0.03110949 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+-----------------------------------------------------------
+| perf/mse  | 0.27066618 |
+| time-step | 20         |
+| y_out     | 0.05875358 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2627713    |
+| time-step | 21           |
+| y_out     | -0.033607032 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2584219    |
+| time-step | 22           |
+| y_out     | -0.015130712 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25236186 |
+| time-step | 23         |
+| y_out     | 0.06476693 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24022844 |
+| time-step | 24         |
+| y_out     | -0.0767336 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23550415   |
+| time-step | 25           |
+| y_out     | -0.020046847 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23107167 |
+| time-step | 26         |
+| y_out     | -0.0467745 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22907063 |
+| time-step | 27         |
+| y_out     | -0.0748537 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2248693    |
+| time-step | 28           |
+| y_out     | -0.046716034 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.22197667   |
+| time-step | 29           |
+| y_out     | -0.011378339 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21780598 |
+| time-step | 30         |
+| y_out     | 0.01005647 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21327953  |
+| time-step | 31          |
+| y_out     | -0.04633833 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2074606    |
+| time-step | 32           |
+| y_out     | -0.094527245 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19884282   |
+| time-step | 33           |
+| y_out     | -0.009742521 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19805658   |
+| time-step | 34           |
+| y_out     | -0.011344131 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19739105  |
+| time-step | 35          |
+| y_out     | 0.112566374 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19731256  |
+| time-step | 36          |
+| y_out     | 0.042248897 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19531193 |
+| time-step | 37         |
+| y_out     | 0.06522882 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19959511 |
+| time-step | 38         |
+| y_out     | 0.07620217 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19854103  |
+| time-step | 39          |
+| y_out     | 0.122585274 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+-------------------------------------------------------------
+| perf/mse  | 0.19372174   |
+| time-step | 40           |
+| y_out     | -0.002960991 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19408385  |
+| time-step | 41          |
+| y_out     | -0.07097729 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19348522  |
+| time-step | 42          |
+| y_out     | -0.06189739 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19414565 |
+| time-step | 43         |
+| y_out     | 0.05028359 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19138949   |
+| time-step | 44           |
+| y_out     | -0.013175547 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18713829  |
+| time-step | 45          |
+| y_out     | 0.005442504 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18565945  |
+| time-step | 46          |
+| y_out     | 0.011106882 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1825132  |
+| time-step | 47         |
+| y_out     | 0.07664857 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17298117  |
+| time-step | 48          |
+| y_out     | -0.08635352 |
+------------------------------------------------------------
+---------------------------------------------------------------
+| perf/mse  | 0.16509537     |
+| time-step | 49             |
+| y_out     | -0.00039370358 |
+---------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.16305159    |
+| time-step | 50            |
+| y_out     | -0.0042265356 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16104548 |
+| time-step | 51         |
+| y_out     | 0.0999485  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15964627   |
+| time-step | 52           |
+| y_out     | -0.085398875 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15983029  |
+| time-step | 53          |
+| y_out     | 0.061859988 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1572111  |
+| time-step | 54         |
+| y_out     | 0.08364103 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15895776  |
+| time-step | 55          |
+| y_out     | 0.073263586 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15445712 |
+| time-step | 56         |
+| y_out     | 0.08226094 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14806417   |
+| time-step | 57           |
+| y_out     | -0.026149854 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14399093   |
+| time-step | 58           |
+| y_out     | -0.021103658 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14528339  |
+| time-step | 59          |
+| y_out     | 0.114434406 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-----------------------------------------------------------
+| perf/mse  | 0.14390735 |
+| time-step | 60         |
+| y_out     | 0.05787298 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1384615    |
+| time-step | 61           |
+| y_out     | -0.048766248 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13404873   |
+| time-step | 62           |
+| y_out     | -0.025257852 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12611364  |
+| time-step | 63          |
+| y_out     | 0.025775127 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.123898864  |
+| time-step | 64           |
+| y_out     | -0.011621762 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11848967  |
+| time-step | 65          |
+| y_out     | 0.102711074 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11929195 |
+| time-step | 66         |
+| y_out     | 0.0966217  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1183205    |
+| time-step | 67           |
+| y_out     | -0.011137182 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11833509   |
+| time-step | 68           |
+| y_out     | -0.023848323 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11444267  |
+| time-step | 69          |
+| y_out     | 0.029762471 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.111152634 |
+| time-step | 70          |
+| y_out     | 0.005436614 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11039521  |
+| time-step | 71          |
+| y_out     | 0.109175235 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10853287  |
+| time-step | 72          |
+| y_out     | 0.053973574 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10635086   |
+| time-step | 73           |
+| y_out     | -0.027243197 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.104277655 |
+| time-step | 74          |
+| y_out     | 0.08149922  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.099329695 |
+| time-step | 75          |
+| y_out     | -0.05871947 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09659836 |
+| time-step | 76         |
+| y_out     | 0.14336532 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0948668    |
+| time-step | 77           |
+| y_out     | -0.064054884 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09229497  |
+| time-step | 78          |
+| y_out     | -0.13617772 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09069107  |
+| time-step | 79          |
+| y_out     | -0.08961238 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+-------------------------------------------------------------
+| perf/mse  | 0.08794165   |
+| time-step | 80           |
+| y_out     | -0.106694825 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08590981   |
+| time-step | 81           |
+| y_out     | -0.011173539 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08408039  |
+| time-step | 82          |
+| y_out     | 0.011744916 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.083149396 |
+| time-step | 83          |
+| y_out     | 0.05604577  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08320341  |
+| time-step | 84          |
+| y_out     | 0.031677395 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.083048105 |
+| time-step | 85          |
+| y_out     | 0.024229718 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07676857   |
+| time-step | 86           |
+| y_out     | -0.107886605 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.07520245    |
+| time-step | 87            |
+| y_out     | -0.0012855167 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07464133  |
+| time-step | 88          |
+| y_out     | -0.05295017 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07394824   |
+| time-step | 89           |
+| y_out     | -0.032654583 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.073337205 |
+| time-step | 90          |
+| y_out     | -0.07996622 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07082166  |
+| time-step | 91          |
+| y_out     | 0.016168121 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.06771897    |
+| time-step | 92            |
+| y_out     | -0.0076470375 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06530517 |
+| time-step | 93         |
+| y_out     | 0.05407875 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.061540745  |
+| time-step | 94           |
+| y_out     | -0.025275212 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059738267 |
+| time-step | 95          |
+| y_out     | 0.01421193  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.058516156  |
+| time-step | 96           |
+| y_out     | 0.0045911297 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.05771967   |
+| time-step | 97           |
+| y_out     | -0.022478176 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056225486 |
+| time-step | 98          |
+| y_out     | 0.060206324 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.053921174  |
+| time-step | 99           |
+| y_out     | -0.030874588 |
+-------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..0577574
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,0.030518502,
+1,-0.033393197,
+2,-0.007742934,
+3,-0.01596877,
+4,0.030884877,
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+35,0.112566374,0.19739105
+36,0.042248897,0.19731256
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+44,-0.013175547,0.19138949
+45,0.005442504,0.18713829
+46,0.011106882,0.18565945
+47,0.07664857,0.1825132
+48,-0.08635352,0.17298117
+49,-0.00039370358,0.16509537
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+51,0.0999485,0.16104548
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+58,-0.021103658,0.14399093
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+97,-0.022478176,0.05771967
+98,0.060206324,0.056225486
+99,-0.030874588,0.053921174
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658902863.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658902863.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..b48cfaa
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658902863.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-21-03-593883_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-21-206798_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-21-206798_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..85327d8
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,502 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+----------------------------------------------------------
+| time-step | 0         |
+| y_out     | -0.053382 |
+----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | 0.045650437 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 2          |
+| y_out     | 0.05339278 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 3          |
+| y_out     | -0.0775353 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 4            |
+| y_out     | -0.028167328 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 5            |
+| y_out     | -0.005900942 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 6          |
+| y_out     | 0.04798329 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 7            |
+| y_out     | -0.003344886 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 8            |
+| y_out     | -0.047462747 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.30027503  |
+| time-step | 9           |
+| y_out     | 0.015829083 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2934212   |
+| time-step | 10          |
+| y_out     | 0.059881542 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2987965    |
+| time-step | 11           |
+| y_out     | -0.009272702 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.30101544  |
+| time-step | 12          |
+| y_out     | 0.049603462 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29380262  |
+| time-step | 13          |
+| y_out     | 0.032425106 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.28500587   |
+| time-step | 14           |
+| y_out     | -0.012344694 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.28136602  |
+| time-step | 15          |
+| y_out     | 0.020169392 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27763042  |
+| time-step | 16          |
+| y_out     | -0.08705211 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27304006  |
+| time-step | 17          |
+| y_out     | -0.05159188 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26849842   |
+| time-step | 18           |
+| y_out     | -0.037068456 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27657306  |
+| time-step | 19          |
+| y_out     | -0.09358351 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+-------------------------------------------------------------
+| perf/mse  | 0.2796866    |
+| time-step | 20           |
+| y_out     | -0.019539729 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26990002   |
+| time-step | 21           |
+| y_out     | -0.021972729 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2620574   |
+| time-step | 22          |
+| y_out     | 0.013085313 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25789478  |
+| time-step | 23          |
+| y_out     | -0.05457952 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25782084  |
+| time-step | 24          |
+| y_out     | 0.010717595 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25618452  |
+| time-step | 25          |
+| y_out     | -0.08636694 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2565369  |
+| time-step | 26         |
+| y_out     | 0.08772636 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25761914 |
+| time-step | 27         |
+| y_out     | 0.01202143 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.2564744     |
+| time-step | 28            |
+| y_out     | -0.0054021887 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25179884 |
+| time-step | 29         |
+| y_out     | 0.04661929 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24300678  |
+| time-step | 30          |
+| y_out     | 0.056212734 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.24799411   |
+| time-step | 31           |
+| y_out     | -0.070440024 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24533696  |
+| time-step | 32          |
+| y_out     | -0.04130919 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.24483752   |
+| time-step | 33           |
+| y_out     | -0.037757628 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24374942 |
+| time-step | 34         |
+| y_out     | -0.0726663 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23920837  |
+| time-step | 35          |
+| y_out     | 0.043340236 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23480263  |
+| time-step | 36          |
+| y_out     | 0.010692209 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22914653  |
+| time-step | 37          |
+| y_out     | 0.067840025 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22515745  |
+| time-step | 38          |
+| y_out     | 0.015340995 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.22165266   |
+| time-step | 39           |
+| y_out     | -0.009656817 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+------------------------------------------------------------
+| perf/mse  | 0.22295702  |
+| time-step | 40          |
+| y_out     | 0.081051625 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2144696    |
+| time-step | 41           |
+| y_out     | -0.044671327 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21309254  |
+| time-step | 42          |
+| y_out     | 0.027942155 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20530844  |
+| time-step | 43          |
+| y_out     | 0.046437092 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20235494   |
+| time-step | 44           |
+| y_out     | -0.067849025 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20303479   |
+| time-step | 45           |
+| y_out     | -0.026950927 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19950695   |
+| time-step | 46           |
+| y_out     | -0.026469968 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19953376  |
+| time-step | 47          |
+| y_out     | 0.015367106 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19243315   |
+| time-step | 48           |
+| y_out     | -0.084312245 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1869043  |
+| time-step | 49         |
+| y_out     | 0.02860079 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18260653   |
+| time-step | 50           |
+| y_out     | -0.027572392 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18116048  |
+| time-step | 51          |
+| y_out     | -0.03007172 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17711356  |
+| time-step | 52          |
+| y_out     | 0.014593486 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17881139  |
+| time-step | 53          |
+| y_out     | 0.014698405 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17330831  |
+| time-step | 54          |
+| y_out     | 0.049259778 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17041369   |
+| time-step | 55           |
+| y_out     | -0.038264092 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16834478   |
+| time-step | 56           |
+| y_out     | -0.010332934 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16850647  |
+| time-step | 57          |
+| y_out     | -0.07512813 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16840746  |
+| time-step | 58          |
+| y_out     | 0.060619228 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16718045  |
+| time-step | 59          |
+| y_out     | 0.057964023 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+------------------------------------------------------------
+| perf/mse  | 0.1628822   |
+| time-step | 60          |
+| y_out     | 0.081102565 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1623713   |
+| time-step | 61          |
+| y_out     | 0.058298975 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16278872  |
+| time-step | 62          |
+| y_out     | -0.07974974 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16038886  |
+| time-step | 63          |
+| y_out     | 0.059639234 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15992345  |
+| time-step | 64          |
+| y_out     | 0.023764707 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15720807   |
+| time-step | 65           |
+| y_out     | -0.004144271 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15393522  |
+| time-step | 66          |
+| y_out     | -0.12932287 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14937198   |
+| time-step | 67           |
+| y_out     | -0.047974072 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14536922  |
+| time-step | 68          |
+| y_out     | 0.075095564 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14232582 |
+| time-step | 69         |
+| y_out     | 0.0908618  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14242804   |
+| time-step | 70           |
+| y_out     | 0.0032887235 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13628778   |
+| time-step | 71           |
+| y_out     | -0.058934826 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13088018  |
+| time-step | 72          |
+| y_out     | 0.011505894 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12944071  |
+| time-step | 73          |
+| y_out     | 0.034937255 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1223379   |
+| time-step | 74          |
+| y_out     | 0.025602994 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12083119  |
+| time-step | 75          |
+| y_out     | 0.019581316 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12093387  |
+| time-step | 76          |
+| y_out     | -0.01329973 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11675312 |
+| time-step | 77         |
+| y_out     | 0.18749917 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.114287    |
+| time-step | 78          |
+| y_out     | 0.048819073 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11663623 |
+| time-step | 79         |
+| y_out     | 0.06098143 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+-------------------------------------------------------------
+| perf/mse  | 0.1124592    |
+| time-step | 80           |
+| y_out     | -0.037882295 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11000128 |
+| time-step | 81         |
+| y_out     | 0.10433042 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1113008   |
+| time-step | 82          |
+| y_out     | -0.09154537 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.109730735 |
+| time-step | 83          |
+| y_out     | 0.13421288  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11305592  |
+| time-step | 84          |
+| y_out     | -0.04876012 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10756342  |
+| time-step | 85          |
+| y_out     | 0.004025042 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.102371275 |
+| time-step | 86          |
+| y_out     | -0.06008514 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10352768   |
+| time-step | 87           |
+| y_out     | -0.030559605 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10222266  |
+| time-step | 88          |
+| y_out     | 0.042064793 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0963234   |
+| time-step | 89          |
+| y_out     | 0.032981325 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0953534   |
+| time-step | 90          |
+| y_out     | -0.09218843 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.095036656 |
+| time-step | 91          |
+| y_out     | 0.1213803   |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09131355 |
+| time-step | 92         |
+| y_out     | 0.04805747 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08774273  |
+| time-step | 93          |
+| y_out     | 0.028270936 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08384989   |
+| time-step | 94           |
+| y_out     | -0.078458674 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08542852   |
+| time-step | 95           |
+| y_out     | -0.031158429 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08604957   |
+| time-step | 96           |
+| y_out     | -0.054345027 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.079967625  |
+| time-step | 97           |
+| y_out     | -0.020558354 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.079209276 |
+| time-step | 98          |
+| y_out     | 0.044112902 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0761402    |
+| time-step | 99           |
+| y_out     | -0.005410416 |
+-------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..b977c8a
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,-0.053382,
+1,0.045650437,
+2,0.05339278,
+3,-0.0775353,
+4,-0.028167328,
+5,-0.005900942,
+6,0.04798329,
+7,-0.003344886,
+8,-0.047462747,
+9,0.015829083,0.30027503
+10,0.059881542,0.2934212
+11,-0.009272702,0.2987965
+12,0.049603462,0.30101544
+13,0.032425106,0.29380262
+14,-0.012344694,0.28500587
+15,0.020169392,0.28136602
+16,-0.08705211,0.27763042
+17,-0.05159188,0.27304006
+18,-0.037068456,0.26849842
+19,-0.09358351,0.27657306
+20,-0.019539729,0.2796866
+21,-0.021972729,0.26990002
+22,0.013085313,0.2620574
+23,-0.05457952,0.25789478
+24,0.010717595,0.25782084
+25,-0.08636694,0.25618452
+26,0.08772636,0.2565369
+27,0.01202143,0.25761914
+28,-0.0054021887,0.2564744
+29,0.04661929,0.25179884
+30,0.056212734,0.24300678
+31,-0.070440024,0.24799411
+32,-0.04130919,0.24533696
+33,-0.037757628,0.24483752
+34,-0.0726663,0.24374942
+35,0.043340236,0.23920837
+36,0.010692209,0.23480263
+37,0.067840025,0.22914653
+38,0.015340995,0.22515745
+39,-0.009656817,0.22165266
+40,0.081051625,0.22295702
+41,-0.044671327,0.2144696
+42,0.027942155,0.21309254
+43,0.046437092,0.20530844
+44,-0.067849025,0.20235494
+45,-0.026950927,0.20303479
+46,-0.026469968,0.19950695
+47,0.015367106,0.19953376
+48,-0.084312245,0.19243315
+49,0.02860079,0.1869043
+50,-0.027572392,0.18260653
+51,-0.03007172,0.18116048
+52,0.014593486,0.17711356
+53,0.014698405,0.17881139
+54,0.049259778,0.17330831
+55,-0.038264092,0.17041369
+56,-0.010332934,0.16834478
+57,-0.07512813,0.16850647
+58,0.060619228,0.16840746
+59,0.057964023,0.16718045
+60,0.081102565,0.1628822
+61,0.058298975,0.1623713
+62,-0.07974974,0.16278872
+63,0.059639234,0.16038886
+64,0.023764707,0.15992345
+65,-0.004144271,0.15720807
+66,-0.12932287,0.15393522
+67,-0.047974072,0.14937198
+68,0.075095564,0.14536922
+69,0.0908618,0.14232582
+70,0.0032887235,0.14242804
+71,-0.058934826,0.13628778
+72,0.011505894,0.13088018
+73,0.034937255,0.12944071
+74,0.025602994,0.1223379
+75,0.019581316,0.12083119
+76,-0.01329973,0.12093387
+77,0.18749917,0.11675312
+78,0.048819073,0.114287
+79,0.06098143,0.11663623
+80,-0.037882295,0.1124592
+81,0.10433042,0.11000128
+82,-0.09154537,0.1113008
+83,0.13421288,0.109730735
+84,-0.04876012,0.11305592
+85,0.004025042,0.10756342
+86,-0.06008514,0.102371275
+87,-0.030559605,0.10352768
+88,0.042064793,0.10222266
+89,0.032981325,0.0963234
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+91,0.1213803,0.095036656
+92,0.04805747,0.09131355
+93,0.028270936,0.08774273
+94,-0.078458674,0.08384989
+95,-0.031158429,0.08542852
+96,-0.054345027,0.08604957
+97,-0.020558354,0.079967625
+98,0.044112902,0.079209276
+99,-0.005410416,0.0761402
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903126.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903126.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..ed0b5e3
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903126.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-25-26-804960_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-37-503423_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-37-503423_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..f2134c4
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,10 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:27:50.996776
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-45-544539_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..9c188be
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,502 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+------------------------------------------------------------
+| time-step | 0           |
+| y_out     | 0.057954267 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | -0.16365862 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 2            |
+| y_out     | -0.019681245 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 3           |
+| y_out     | 0.022561982 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 4           |
+| y_out     | -0.13416053 |
+------------------------------------------------------------
+----------------------------------------------------------
+| time-step | 5         |
+| y_out     | 0.1339738 |
+----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 6            |
+| y_out     | -0.018874194 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 7           |
+| y_out     | 0.031827815 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 8           |
+| y_out     | 0.061661948 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.33197737 |
+| time-step | 9          |
+| y_out     | 0.21720701 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.32195234  |
+| time-step | 10          |
+| y_out     | 0.099746495 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.31700128  |
+| time-step | 11          |
+| y_out     | 0.007828675 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.3201444    |
+| time-step | 12           |
+| y_out     | 0.0036695674 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.3129303   |
+| time-step | 13          |
+| y_out     | 0.021058306 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.3050651  |
+| time-step | 14         |
+| y_out     | 0.06313879 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.30064285  |
+| time-step | 15          |
+| y_out     | 0.039653137 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2960209   |
+| time-step | 16          |
+| y_out     | 0.022164203 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.29025063 |
+| time-step | 17         |
+| y_out     | -0.0839839 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.27905178 |
+| time-step | 18         |
+| y_out     | 0.07471995 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2759032    |
+| time-step | 19           |
+| y_out     | -0.025503304 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+------------------------------------------------------------
+| perf/mse  | 0.26989073  |
+| time-step | 20          |
+| y_out     | 0.019835057 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26826188   |
+| time-step | 21           |
+| y_out     | -0.062677786 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2584748    |
+| time-step | 22           |
+| y_out     | -0.021318281 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2537949   |
+| time-step | 23          |
+| y_out     | 0.024209933 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25071117 |
+| time-step | 24         |
+| y_out     | 0.13914189 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.24852414   |
+| time-step | 25           |
+| y_out     | -0.020597862 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23885508   |
+| time-step | 26           |
+| y_out     | -0.014543334 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23584354   |
+| time-step | 27           |
+| y_out     | -0.031824157 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2396863   |
+| time-step | 28          |
+| y_out     | -0.04671384 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2309604  |
+| time-step | 29         |
+| y_out     | 0.05610097 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23297271  |
+| time-step | 30          |
+| y_out     | 0.002615407 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23129126  |
+| time-step | 31          |
+| y_out     | 0.070476025 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22723958  |
+| time-step | 32          |
+| y_out     | 0.040641565 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22739749  |
+| time-step | 33          |
+| y_out     | 0.059984356 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.22543204    |
+| time-step | 34            |
+| y_out     | -0.0091008935 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22175045  |
+| time-step | 35          |
+| y_out     | 0.062491737 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21429336   |
+| time-step | 36           |
+| y_out     | -0.024435958 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21352763  |
+| time-step | 37          |
+| y_out     | 0.060051534 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21116233  |
+| time-step | 38          |
+| y_out     | -0.04342635 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.208745     |
+| time-step | 39           |
+| y_out     | 0.0084026465 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+-------------------------------------------------------------
+| perf/mse  | 0.20916812   |
+| time-step | 40           |
+| y_out     | -0.040181134 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20767811   |
+| time-step | 41           |
+| y_out     | -0.024507655 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20549591   |
+| time-step | 42           |
+| y_out     | -0.018106269 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20485921 |
+| time-step | 43         |
+| y_out     | 0.12870893 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20350036 |
+| time-step | 44         |
+| y_out     | -0.0233492 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.201682   |
+| time-step | 45         |
+| y_out     | 0.08209593 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20042662  |
+| time-step | 46          |
+| y_out     | -0.04075575 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19709313  |
+| time-step | 47          |
+| y_out     | -0.08544053 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19168925  |
+| time-step | 48          |
+| y_out     | -0.11520279 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19063959  |
+| time-step | 49          |
+| y_out     | 0.038674615 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17864     |
+| time-step | 50          |
+| y_out     | -0.08781865 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1745716   |
+| time-step | 51          |
+| y_out     | 0.077561215 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17233422  |
+| time-step | 52          |
+| y_out     | -0.06579357 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16532573  |
+| time-step | 53          |
+| y_out     | -0.08304539 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15915759  |
+| time-step | 54          |
+| y_out     | 0.055884387 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16082762 |
+| time-step | 55         |
+| y_out     | 0.09783222 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1613141   |
+| time-step | 56          |
+| y_out     | 0.070208915 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15792848 |
+| time-step | 57         |
+| y_out     | 0.04157433 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15796022 |
+| time-step | 58         |
+| y_out     | 0.03665974 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15588588  |
+| time-step | 59          |
+| y_out     | 0.060706176 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-------------------------------------------------------------
+| perf/mse  | 0.15432505   |
+| time-step | 60           |
+| y_out     | -0.022992976 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15025382  |
+| time-step | 61          |
+| y_out     | -0.08773433 |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.147721  |
+| time-step | 62        |
+| y_out     | 0.1040368 |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14614102   |
+| time-step | 63           |
+| y_out     | 0.0151649155 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14765063   |
+| time-step | 64           |
+| y_out     | -0.043442275 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13919443 |
+| time-step | 65         |
+| y_out     | 0.0847801  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1354896  |
+| time-step | 66         |
+| y_out     | 0.10740591 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13247067  |
+| time-step | 67          |
+| y_out     | -0.10419522 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12644862   |
+| time-step | 68           |
+| y_out     | 0.0026753806 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.1225603     |
+| time-step | 69            |
+| y_out     | 0.00036436133 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12176937  |
+| time-step | 70          |
+| y_out     | 0.056387626 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11758195  |
+| time-step | 71          |
+| y_out     | -0.05721138 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11434059 |
+| time-step | 72         |
+| y_out     | 0.0855271  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11158688   |
+| time-step | 73           |
+| y_out     | -0.028326496 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10592683  |
+| time-step | 74          |
+| y_out     | -0.10301969 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.105455264   |
+| time-step | 75            |
+| y_out     | -0.0090772975 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10311691  |
+| time-step | 76          |
+| y_out     | -0.07309828 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.100159466 |
+| time-step | 77          |
+| y_out     | 0.082424045 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.099643275 |
+| time-step | 78          |
+| y_out     | 0.092503965 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09551458 |
+| time-step | 79         |
+| y_out     | 0.04183206 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+------------------------------------------------------------
+| perf/mse  | 0.09425654  |
+| time-step | 80          |
+| y_out     | 0.033711683 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.093546    |
+| time-step | 81          |
+| y_out     | -0.12882873 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09417549  |
+| time-step | 82          |
+| y_out     | -0.10583155 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09087784   |
+| time-step | 83           |
+| y_out     | -0.008717222 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08957593 |
+| time-step | 84         |
+| y_out     | -0.0442772 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08598084 |
+| time-step | 85         |
+| y_out     | 0.07148127 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.084720545 |
+| time-step | 86          |
+| y_out     | 0.017134115 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.082614586 |
+| time-step | 87          |
+| y_out     | -0.03950606 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08096035   |
+| time-step | 88           |
+| y_out     | -0.029261012 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.080296814 |
+| time-step | 89          |
+| y_out     | 0.057066243 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07668127 |
+| time-step | 90         |
+| y_out     | 0.07098338 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07465087  |
+| time-step | 91          |
+| y_out     | -0.06907131 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07311099  |
+| time-step | 92          |
+| y_out     | -0.05171191 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0725683    |
+| time-step | 93           |
+| y_out     | -0.110601015 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.071776435  |
+| time-step | 94           |
+| y_out     | -0.026199345 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.070866324   |
+| time-step | 95            |
+| y_out     | -6.589107e-07 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07088388  |
+| time-step | 96          |
+| y_out     | 0.024847204 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.0711927     |
+| time-step | 97            |
+| y_out     | 0.00088718906 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07020186  |
+| time-step | 98          |
+| y_out     | 0.042646974 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.070564345 |
+| time-step | 99          |
+| y_out     | -0.15401098 |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..4fb093f
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,0.057954267,
+1,-0.16365862,
+2,-0.019681245,
+3,0.022561982,
+4,-0.13416053,
+5,0.1339738,
+6,-0.018874194,
+7,0.031827815,
+8,0.061661948,
+9,0.21720701,0.33197737
+10,0.099746495,0.32195234
+11,0.007828675,0.31700128
+12,0.0036695674,0.3201444
+13,0.021058306,0.3129303
+14,0.06313879,0.3050651
+15,0.039653137,0.30064285
+16,0.022164203,0.2960209
+17,-0.0839839,0.29025063
+18,0.07471995,0.27905178
+19,-0.025503304,0.2759032
+20,0.019835057,0.26989073
+21,-0.062677786,0.26826188
+22,-0.021318281,0.2584748
+23,0.024209933,0.2537949
+24,0.13914189,0.25071117
+25,-0.020597862,0.24852414
+26,-0.014543334,0.23885508
+27,-0.031824157,0.23584354
+28,-0.04671384,0.2396863
+29,0.05610097,0.2309604
+30,0.002615407,0.23297271
+31,0.070476025,0.23129126
+32,0.040641565,0.22723958
+33,0.059984356,0.22739749
+34,-0.0091008935,0.22543204
+35,0.062491737,0.22175045
+36,-0.024435958,0.21429336
+37,0.060051534,0.21352763
+38,-0.04342635,0.21116233
+39,0.0084026465,0.208745
+40,-0.040181134,0.20916812
+41,-0.024507655,0.20767811
+42,-0.018106269,0.20549591
+43,0.12870893,0.20485921
+44,-0.0233492,0.20350036
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+46,-0.04075575,0.20042662
+47,-0.08544053,0.19709313
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+51,0.077561215,0.1745716
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+93,-0.110601015,0.0725683
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+95,-6.589107e-07,0.070866324
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903271.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903271.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..38dd9dc
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903271.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-27-50-996776_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..b324047
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,10 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:30:28.185124
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-22-366311_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..af16e75
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,502 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+------------------------------------------------------------
+| time-step | 0           |
+| y_out     | 0.026434839 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 1            |
+| y_out     | -0.049877807 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 2            |
+| y_out     | -0.051599193 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 3           |
+| y_out     | -0.11694549 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 4           |
+| y_out     | -0.03727347 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 5           |
+| y_out     | 0.018849868 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 6          |
+| y_out     | 0.07576369 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 7            |
+| y_out     | -0.002556907 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 8            |
+| y_out     | -0.008848466 |
+-------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2629329 |
+| time-step | 9         |
+| y_out     | 0.0791229 |
+----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.25715083    |
+| time-step | 10            |
+| y_out     | -0.0076752566 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25968364  |
+| time-step | 11          |
+| y_out     | 0.100417346 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2597984  |
+| time-step | 12         |
+| y_out     | -0.0365184 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2601893   |
+| time-step | 13          |
+| y_out     | 0.102495074 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2584832    |
+| time-step | 14           |
+| y_out     | -0.037428394 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2552052   |
+| time-step | 15          |
+| y_out     | -0.02633594 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.24659066   |
+| time-step | 16           |
+| y_out     | -0.021568293 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24230392  |
+| time-step | 17          |
+| y_out     | 0.033726517 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23718652  |
+| time-step | 18          |
+| y_out     | 0.036447246 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24159941  |
+| time-step | 19          |
+| y_out     | 0.054382086 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+------------------------------------------------------------
+| perf/mse  | 0.24128008  |
+| time-step | 20          |
+| y_out     | -0.20376685 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23691003   |
+| time-step | 21           |
+| y_out     | -0.002836587 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23388354 |
+| time-step | 22         |
+| y_out     | 0.14707932 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22938702  |
+| time-step | 23          |
+| y_out     | -0.07003376 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22859931 |
+| time-step | 24         |
+| y_out     | 0.10550484 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.22521248    |
+| time-step | 25            |
+| y_out     | -0.0007207263 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22448471 |
+| time-step | 26         |
+| y_out     | 0.05728255 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21967049  |
+| time-step | 27          |
+| y_out     | -0.12098433 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22097278  |
+| time-step | 28          |
+| y_out     | 0.041676782 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21653202 |
+| time-step | 29         |
+| y_out     | 0.10850445 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21655026 |
+| time-step | 30         |
+| y_out     | -0.0720776 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21372685 |
+| time-step | 31         |
+| y_out     | 0.10683118 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20395346   |
+| time-step | 32           |
+| y_out     | 0.0021613948 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19785853  |
+| time-step | 33          |
+| y_out     | -0.01774867 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19639128  |
+| time-step | 34          |
+| y_out     | 0.010545963 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19553241 |
+| time-step | 35         |
+| y_out     | 0.10162185 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19496362 |
+| time-step | 36         |
+| y_out     | 0.09500998 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19576713  |
+| time-step | 37          |
+| y_out     | 0.050578058 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19411947  |
+| time-step | 38          |
+| y_out     | -0.07401246 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18606022   |
+| time-step | 39           |
+| y_out     | -0.006828841 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+--------------------------------------------------------------
+| perf/mse  | 0.1827048     |
+| time-step | 40            |
+| y_out     | -0.0027708597 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18040423  |
+| time-step | 41          |
+| y_out     | 0.090719774 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.18015209    |
+| time-step | 42            |
+| y_out     | -0.0057016797 |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.18449727    |
+| time-step | 43            |
+| y_out     | -0.0033132434 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18158019  |
+| time-step | 44          |
+| y_out     | 0.014757775 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17702779 |
+| time-step | 45         |
+| y_out     | 0.15497266 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17318092   |
+| time-step | 46           |
+| y_out     | -0.090124115 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17348106 |
+| time-step | 47         |
+| y_out     | 0.04543226 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16878992 |
+| time-step | 48         |
+| y_out     | 0.05971071 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16956994  |
+| time-step | 49          |
+| y_out     | 0.081655964 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16808951  |
+| time-step | 50          |
+| y_out     | 0.016992945 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1661295    |
+| time-step | 51           |
+| y_out     | -0.013463862 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1629425   |
+| time-step | 52          |
+| y_out     | 0.048871465 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15583196 |
+| time-step | 53         |
+| y_out     | -0.0736928 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.15354384    |
+| time-step | 54            |
+| y_out     | -0.0049589537 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.152821    |
+| time-step | 55          |
+| y_out     | 0.073062815 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15098399  |
+| time-step | 56          |
+| y_out     | 0.003733769 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14273873  |
+| time-step | 57          |
+| y_out     | 0.008314427 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14075644 |
+| time-step | 58         |
+| y_out     | 0.03463362 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14012197  |
+| time-step | 59          |
+| y_out     | 0.018198382 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-----------------------------------------------------------
+| perf/mse  | 0.13740602 |
+| time-step | 60         |
+| y_out     | 0.08692987 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13576336 |
+| time-step | 61         |
+| y_out     | 0.06653725 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13330787   |
+| time-step | 62           |
+| y_out     | -0.011243273 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13249408   |
+| time-step | 63           |
+| y_out     | -0.022268822 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13094437 |
+| time-step | 64         |
+| y_out     | 0.07237807 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13024911   |
+| time-step | 65           |
+| y_out     | -0.027079508 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12680063  |
+| time-step | 66          |
+| y_out     | -0.09452046 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12755033  |
+| time-step | 67          |
+| y_out     | 0.026418112 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12472048  |
+| time-step | 68          |
+| y_out     | 0.018874599 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1259586   |
+| time-step | 69          |
+| y_out     | 0.012553807 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12604675 |
+| time-step | 70         |
+| y_out     | -0.0870841 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12311997  |
+| time-step | 71          |
+| y_out     | 0.020395912 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1252727  |
+| time-step | 72         |
+| y_out     | 0.07525701 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12483032  |
+| time-step | 73          |
+| y_out     | 0.123660356 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12204591  |
+| time-step | 74          |
+| y_out     | -0.06134852 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.115969636   |
+| time-step | 75            |
+| y_out     | -0.0070918836 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11444948   |
+| time-step | 76           |
+| y_out     | -0.012263858 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11058269   |
+| time-step | 77           |
+| y_out     | -0.124589935 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.110158205 |
+| time-step | 78          |
+| y_out     | -0.06762721 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10824226   |
+| time-step | 79           |
+| y_out     | -0.053701974 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+------------------------------------------------------------
+| perf/mse  | 0.10289679  |
+| time-step | 80          |
+| y_out     | 0.027580366 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10414962   |
+| time-step | 81           |
+| y_out     | -0.049639516 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09778084 |
+| time-step | 82         |
+| y_out     | -0.0870422 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.095247485 |
+| time-step | 83          |
+| y_out     | 0.011361558 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09319492 |
+| time-step | 84         |
+| y_out     | 0.03490648 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0938939    |
+| time-step | 85           |
+| y_out     | -0.118292816 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09501687 |
+| time-step | 86         |
+| y_out     | 0.21377105 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.094822444   |
+| time-step | 87            |
+| y_out     | -0.0048013926 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.093332246 |
+| time-step | 88          |
+| y_out     | -0.09708738 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09160007 |
+| time-step | 89         |
+| y_out     | 0.03655528 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.08857501    |
+| time-step | 90            |
+| y_out     | -0.0052816644 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08409279   |
+| time-step | 91           |
+| y_out     | -0.008492868 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.0848667     |
+| time-step | 92            |
+| y_out     | -0.0069826636 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08365972  |
+| time-step | 93          |
+| y_out     | 0.005212657 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08360197 |
+| time-step | 94         |
+| y_out     | 0.03449457 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.081063546 |
+| time-step | 95          |
+| y_out     | -0.08511153 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.077025235 |
+| time-step | 96          |
+| y_out     | 0.03232435  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07563747  |
+| time-step | 97          |
+| y_out     | -0.08866757 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07169694  |
+| time-step | 98          |
+| y_out     | 0.022935567 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06994214 |
+| time-step | 99         |
+| y_out     | 0.0716854  |
+-----------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..fdb091f
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,0.026434839,
+1,-0.049877807,
+2,-0.051599193,
+3,-0.11694549,
+4,-0.03727347,
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+6,0.07576369,
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+17,0.033726517,0.24230392
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+19,0.054382086,0.24159941
+20,-0.20376685,0.24128008
+21,-0.002836587,0.23691003
+22,0.14707932,0.23388354
+23,-0.07003376,0.22938702
+24,0.10550484,0.22859931
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+26,0.05728255,0.22448471
+27,-0.12098433,0.21967049
+28,0.041676782,0.22097278
+29,0.10850445,0.21653202
+30,-0.0720776,0.21655026
+31,0.10683118,0.21372685
+32,0.0021613948,0.20395346
+33,-0.01774867,0.19785853
+34,0.010545963,0.19639128
+35,0.10162185,0.19553241
+36,0.09500998,0.19496362
+37,0.050578058,0.19576713
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+41,0.090719774,0.18040423
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+43,-0.0033132434,0.18449727
+44,0.014757775,0.18158019
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903428.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903428.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..8717960
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903428.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-30-28-185124_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..f97c13b
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,10 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:31:04.861007
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-00-050367_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..a1643b6
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,502 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+------------------------------------------------------------
+| time-step | 0           |
+| y_out     | -0.12848262 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | -0.06785326 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 2           |
+| y_out     | 0.056364033 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 3           |
+| y_out     | -0.06977393 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 4           |
+| y_out     | 0.026289541 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 5          |
+| y_out     | 0.06534855 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 6          |
+| y_out     | 0.07898515 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 7           |
+| y_out     | -0.03676845 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 8           |
+| y_out     | -0.04125478 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.28131896  |
+| time-step | 9           |
+| y_out     | -0.10180305 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2845005   |
+| time-step | 10          |
+| y_out     | -0.06926264 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2961555   |
+| time-step | 11          |
+| y_out     | 0.016874395 |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2993807 |
+| time-step | 12        |
+| y_out     | 0.0704311 |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.30471697   |
+| time-step | 13           |
+| y_out     | -0.115119845 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2984702   |
+| time-step | 14          |
+| y_out     | -0.04216931 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.28849787   |
+| time-step | 15           |
+| y_out     | -0.102340564 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2775429   |
+| time-step | 16          |
+| y_out     | 0.008078432 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2799391    |
+| time-step | 17           |
+| y_out     | -0.056683853 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.27361232   |
+| time-step | 18           |
+| y_out     | -0.003003575 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26992846   |
+| time-step | 19           |
+| y_out     | -0.027532296 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+-----------------------------------------------------------
+| perf/mse  | 0.26263076 |
+| time-step | 20         |
+| y_out     | -0.1308051 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25778192 |
+| time-step | 21         |
+| y_out     | 0.01261748 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2550023   |
+| time-step | 22          |
+| y_out     | 0.060839813 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24725428  |
+| time-step | 23          |
+| y_out     | 0.020614378 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2404072  |
+| time-step | 24         |
+| y_out     | 0.06309541 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24450545 |
+| time-step | 25         |
+| y_out     | 0.15437658 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24970475  |
+| time-step | 26          |
+| y_out     | -0.04059537 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.24829873    |
+| time-step | 27            |
+| y_out     | -0.0012246734 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24437237 |
+| time-step | 28         |
+| y_out     | 0.08326321 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24151751 |
+| time-step | 29         |
+| y_out     | -0.0534989 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2395076    |
+| time-step | 30           |
+| y_out     | -0.061537247 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23253353 |
+| time-step | 31         |
+| y_out     | 0.01949121 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2286851  |
+| time-step | 32         |
+| y_out     | 0.10312063 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.22495718   |
+| time-step | 33           |
+| y_out     | -0.024884624 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22227582 |
+| time-step | 34         |
+| y_out     | 0.11812688 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22148518  |
+| time-step | 35          |
+| y_out     | 0.049848102 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21244541   |
+| time-step | 36           |
+| y_out     | -0.026208945 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20624682  |
+| time-step | 37          |
+| y_out     | 0.031267077 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20560734  |
+| time-step | 38          |
+| y_out     | 0.035062835 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20381801 |
+| time-step | 39         |
+| y_out     | 0.04963902 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+------------------------------------------------------------
+| perf/mse  | 0.20314369  |
+| time-step | 40          |
+| y_out     | -0.10657357 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20263147  |
+| time-step | 41          |
+| y_out     | 0.007259734 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19718528 |
+| time-step | 42         |
+| y_out     | 0.04854442 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.19953935    |
+| time-step | 43            |
+| y_out     | -0.0074305087 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20247555  |
+| time-step | 44          |
+| y_out     | -0.10489513 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19384983   |
+| time-step | 45           |
+| y_out     | -0.046378914 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19292247  |
+| time-step | 46          |
+| y_out     | -0.04852566 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19074494 |
+| time-step | 47         |
+| y_out     | 0.07670242 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18742517 |
+| time-step | 48         |
+| y_out     | -0.0854886 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18292496   |
+| time-step | 49           |
+| y_out     | -0.036247395 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18134008   |
+| time-step | 50           |
+| y_out     | -0.019006701 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17857876   |
+| time-step | 51           |
+| y_out     | -0.045955583 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17352554 |
+| time-step | 52         |
+| y_out     | 0.07759726 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1663309    |
+| time-step | 53           |
+| y_out     | -0.042124037 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16061138  |
+| time-step | 54          |
+| y_out     | -0.04385255 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1631932   |
+| time-step | 55          |
+| y_out     | 0.030629797 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16492057  |
+| time-step | 56          |
+| y_out     | 0.104431905 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16375723   |
+| time-step | 57           |
+| y_out     | -0.021231502 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1643896   |
+| time-step | 58          |
+| y_out     | -0.02668121 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16420206   |
+| time-step | 59           |
+| y_out     | -0.050844155 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-------------------------------------------------------------
+| perf/mse  | 0.15982455   |
+| time-step | 60           |
+| y_out     | -0.109118775 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1592373  |
+| time-step | 61         |
+| y_out     | 0.14085618 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15920554   |
+| time-step | 62           |
+| y_out     | -0.068694405 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15803203  |
+| time-step | 63          |
+| y_out     | 0.016491776 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15461087  |
+| time-step | 64          |
+| y_out     | 0.011561893 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14906377  |
+| time-step | 65          |
+| y_out     | 0.019716704 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.14067526    |
+| time-step | 66            |
+| y_out     | -0.0041298475 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1378821  |
+| time-step | 67         |
+| y_out     | 0.09931937 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13882685  |
+| time-step | 68          |
+| y_out     | -0.02012933 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13802008   |
+| time-step | 69           |
+| y_out     | 0.0067736655 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1412672   |
+| time-step | 70          |
+| y_out     | -0.09335388 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13644913 |
+| time-step | 71         |
+| y_out     | 0.060213   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13665584  |
+| time-step | 72          |
+| y_out     | 0.057307165 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1345866   |
+| time-step | 73          |
+| y_out     | 0.018420655 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13356753   |
+| time-step | 74           |
+| y_out     | -0.064063594 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13328837 |
+| time-step | 75         |
+| y_out     | 0.14076695 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1337829   |
+| time-step | 76          |
+| y_out     | 0.053789414 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13318875 |
+| time-step | 77         |
+| y_out     | 0.06966139 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12555793   |
+| time-step | 78           |
+| y_out     | -0.025302805 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1225148   |
+| time-step | 79          |
+| y_out     | 0.096397564 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+-----------------------------------------------------------
+| perf/mse  | 0.11754751 |
+| time-step | 80         |
+| y_out     | 0.07205976 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11828484 |
+| time-step | 81         |
+| y_out     | 0.08357977 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11674298  |
+| time-step | 82          |
+| y_out     | 0.040213317 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11335896 |
+| time-step | 83         |
+| y_out     | -0.0534849 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11405946   |
+| time-step | 84           |
+| y_out     | -0.018771097 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.112875685 |
+| time-step | 85          |
+| y_out     | 0.039481785 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11183598  |
+| time-step | 86          |
+| y_out     | 0.059101067 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.109916486 |
+| time-step | 87          |
+| y_out     | 0.006536618 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11134766   |
+| time-step | 88           |
+| y_out     | -0.025049953 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10776442  |
+| time-step | 89          |
+| y_out     | -0.10301233 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.10673557    |
+| time-step | 90            |
+| y_out     | 0.00020663626 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.102664806 |
+| time-step | 91          |
+| y_out     | 0.0886412   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.102406874 |
+| time-step | 92          |
+| y_out     | 0.07497756  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10305681  |
+| time-step | 93          |
+| y_out     | 0.048273362 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09958337 |
+| time-step | 94         |
+| y_out     | 0.03789698 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.095691    |
+| time-step | 95          |
+| y_out     | 0.037954226 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09506022  |
+| time-step | 96          |
+| y_out     | 0.069947265 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09147383  |
+| time-step | 97          |
+| y_out     | 0.077812806 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08942681  |
+| time-step | 98          |
+| y_out     | -0.13509692 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.089082934 |
+| time-step | 99          |
+| y_out     | 0.040448934 |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..1dabd2f
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/progress.csv
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903464.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903464.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..4b94863
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903464.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-31-04-861007_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..9501473
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,10 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:36:31.125348
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-25-708670_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..2d93f85
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,502 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+-----------------------------------------------------------
+| time-step | 0          |
+| y_out     | 0.11568607 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | 0.024448898 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 2          |
+| y_out     | 0.07994281 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 3           |
+| y_out     | 0.065330446 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 4            |
+| y_out     | -0.065069176 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 5            |
+| y_out     | -0.055503707 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| time-step | 6             |
+| y_out     | -0.0043033026 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 7            |
+| y_out     | -0.020655476 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 8           |
+| y_out     | 0.020889513 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.34083837 |
+| time-step | 9          |
+| y_out     | -0.0831574 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.33512047  |
+| time-step | 10          |
+| y_out     | -0.14079991 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.3284237   |
+| time-step | 11          |
+| y_out     | 0.005779678 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.32368138 |
+| time-step | 12         |
+| y_out     | 0.0825783  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.32029843   |
+| time-step | 13           |
+| y_out     | -0.029824995 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.3246012    |
+| time-step | 14           |
+| y_out     | -0.059920803 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.30610698 |
+| time-step | 15         |
+| y_out     | 0.12507735 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.3093604  |
+| time-step | 16         |
+| y_out     | 0.03319899 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.3117656  |
+| time-step | 17         |
+| y_out     | 0.03404002 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2997157  |
+| time-step | 18         |
+| y_out     | 0.10663411 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29453066  |
+| time-step | 19          |
+| y_out     | 0.056197204 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+------------------------------------------------------------
+| perf/mse  | 0.29965302  |
+| time-step | 20          |
+| y_out     | 0.044020534 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.30040467   |
+| time-step | 21           |
+| y_out     | -0.049835414 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2990193   |
+| time-step | 22          |
+| y_out     | -0.07995278 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.291678   |
+| time-step | 23         |
+| y_out     | 0.06804639 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27905765  |
+| time-step | 24          |
+| y_out     | 0.055526786 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2798091   |
+| time-step | 25          |
+| y_out     | -0.10353845 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2728362   |
+| time-step | 26          |
+| y_out     | 0.058165353 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26660055  |
+| time-step | 27          |
+| y_out     | 0.007903464 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26873395   |
+| time-step | 28           |
+| y_out     | -0.046058483 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2711562  |
+| time-step | 29         |
+| y_out     | 0.04439366 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.25874427   |
+| time-step | 30           |
+| y_out     | -0.007603422 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25821948 |
+| time-step | 31         |
+| y_out     | 0.04068803 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24839608  |
+| time-step | 32          |
+| y_out     | -0.08952403 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24346241  |
+| time-step | 33          |
+| y_out     | 0.018602455 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24797666  |
+| time-step | 34          |
+| y_out     | 0.057587057 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23956609 |
+| time-step | 35         |
+| y_out     | 0.03022397 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23921323 |
+| time-step | 36         |
+| y_out     | 0.09063111 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23493099  |
+| time-step | 37          |
+| y_out     | -0.14581057 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22942455  |
+| time-step | 38          |
+| y_out     | -0.04749298 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22233236 |
+| time-step | 39         |
+| y_out     | 0.03781242 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+-----------------------------------------------------------
+| perf/mse  | 0.21607849 |
+| time-step | 40         |
+| y_out     | 0.07351149 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2160447  |
+| time-step | 41         |
+| y_out     | 0.15035543 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21876101   |
+| time-step | 42           |
+| y_out     | -0.008501947 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21954279 |
+| time-step | 43         |
+| y_out     | 0.06866093 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21557808   |
+| time-step | 44           |
+| y_out     | -0.030033905 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21365714   |
+| time-step | 45           |
+| y_out     | -0.031098548 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20586053   |
+| time-step | 46           |
+| y_out     | -0.011046587 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20076784  |
+| time-step | 47          |
+| y_out     | 0.027415186 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20090649  |
+| time-step | 48          |
+| y_out     | 0.050115347 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20178871  |
+| time-step | 49          |
+| y_out     | 0.013391181 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20747896   |
+| time-step | 50           |
+| y_out     | -0.022861479 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20180874   |
+| time-step | 51           |
+| y_out     | 0.0034740195 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19736132   |
+| time-step | 52           |
+| y_out     | 0.0048667844 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19801924  |
+| time-step | 53          |
+| y_out     | 0.009927493 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19459048 |
+| time-step | 54         |
+| y_out     | -0.0700113 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19268462   |
+| time-step | 55           |
+| y_out     | -0.024671154 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19511083 |
+| time-step | 56         |
+| y_out     | 0.10287392 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1952046    |
+| time-step | 57           |
+| y_out     | -0.023748968 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19519237 |
+| time-step | 58         |
+| y_out     | -0.067934  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18978043   |
+| time-step | 59           |
+| y_out     | -0.105003476 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-----------------------------------------------------------
+| perf/mse  | 0.18429948 |
+| time-step | 60         |
+| y_out     | 0.10323535 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17685309   |
+| time-step | 61           |
+| y_out     | -0.118296154 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17099223   |
+| time-step | 62           |
+| y_out     | -0.005616281 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1664906  |
+| time-step | 63         |
+| y_out     | -0.0467896 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16072266  |
+| time-step | 64          |
+| y_out     | -0.06876407 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16197017  |
+| time-step | 65          |
+| y_out     | 0.020221781 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15954879 |
+| time-step | 66         |
+| y_out     | -0.0321968 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15583923   |
+| time-step | 67           |
+| y_out     | -0.009179525 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15168397   |
+| time-step | 68           |
+| y_out     | 0.0067217276 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1501498    |
+| time-step | 69           |
+| y_out     | -0.069230914 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14686692  |
+| time-step | 70          |
+| y_out     | -0.06980356 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14396118 |
+| time-step | 71         |
+| y_out     | 0.11500215 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14670427 |
+| time-step | 72         |
+| y_out     | 0.07772617 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14395751  |
+| time-step | 73          |
+| y_out     | 0.086199656 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14086244   |
+| time-step | 74           |
+| y_out     | -0.022459432 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13966832  |
+| time-step | 75          |
+| y_out     | 0.055353336 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13636823 |
+| time-step | 76         |
+| y_out     | 0.13095704 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13621512  |
+| time-step | 77          |
+| y_out     | -0.04325234 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1293233    |
+| time-step | 78           |
+| y_out     | -0.048939314 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12594667  |
+| time-step | 79          |
+| y_out     | 0.029397279 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+------------------------------------------------------------
+| perf/mse  | 0.123431124 |
+| time-step | 80          |
+| y_out     | 0.12984914  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12356553   |
+| time-step | 81           |
+| y_out     | -0.008728355 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11678207  |
+| time-step | 82          |
+| y_out     | 0.030475125 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11351766 |
+| time-step | 83         |
+| y_out     | 0.06359854 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11347699   |
+| time-step | 84           |
+| y_out     | -0.053080194 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10818969   |
+| time-step | 85           |
+| y_out     | -0.033353634 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.104881726 |
+| time-step | 86          |
+| y_out     | 0.06948737  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09959717   |
+| time-step | 87           |
+| y_out     | -0.043395516 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.098885275  |
+| time-step | 88           |
+| y_out     | -0.037993625 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09477951 |
+| time-step | 89         |
+| y_out     | 0.05911024 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09523795 |
+| time-step | 90         |
+| y_out     | 0.07319937 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09300127 |
+| time-step | 91         |
+| y_out     | 0.12034185 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09218057 |
+| time-step | 92         |
+| y_out     | 0.065578   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08910888  |
+| time-step | 93          |
+| y_out     | -0.05742941 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.086649925  |
+| time-step | 94           |
+| y_out     | 0.0039783856 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08526725 |
+| time-step | 95         |
+| y_out     | 0.11973506 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.082164004  |
+| time-step | 96           |
+| y_out     | -0.035955284 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08162555  |
+| time-step | 97          |
+| y_out     | 0.042112093 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07871069  |
+| time-step | 98          |
+| y_out     | -0.06176394 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0782552   |
+| time-step | 99          |
+| y_out     | 0.059998848 |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..955b294
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,0.11568607,
+1,0.024448898,
+2,0.07994281,
+3,0.065330446,
+4,-0.065069176,
+5,-0.055503707,
+6,-0.0043033026,
+7,-0.020655476,
+8,0.020889513,
+9,-0.0831574,0.34083837
+10,-0.14079991,0.33512047
+11,0.005779678,0.3284237
+12,0.0825783,0.32368138
+13,-0.029824995,0.32029843
+14,-0.059920803,0.3246012
+15,0.12507735,0.30610698
+16,0.03319899,0.3093604
+17,0.03404002,0.3117656
+18,0.10663411,0.2997157
+19,0.056197204,0.29453066
+20,0.044020534,0.29965302
+21,-0.049835414,0.30040467
+22,-0.07995278,0.2990193
+23,0.06804639,0.291678
+24,0.055526786,0.27905765
+25,-0.10353845,0.2798091
+26,0.058165353,0.2728362
+27,0.007903464,0.26660055
+28,-0.046058483,0.26873395
+29,0.04439366,0.2711562
+30,-0.007603422,0.25874427
+31,0.04068803,0.25821948
+32,-0.08952403,0.24839608
+33,0.018602455,0.24346241
+34,0.057587057,0.24797666
+35,0.03022397,0.23956609
+36,0.09063111,0.23921323
+37,-0.14581057,0.23493099
+38,-0.04749298,0.22942455
+39,0.03781242,0.22233236
+40,0.07351149,0.21607849
+41,0.15035543,0.2160447
+42,-0.008501947,0.21876101
+43,0.06866093,0.21954279
+44,-0.030033905,0.21557808
+45,-0.031098548,0.21365714
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+97,0.042112093,0.08162555
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+99,0.059998848,0.0782552
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903791.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903791.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..5f1800c
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903791.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-31-125348_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..4fde4d1
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,10 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:37:01.642717
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-36-57-026857_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..52ca7b7
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,502 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+-----------------------------------------------------------
+| time-step | 0          |
+| y_out     | -0.0621269 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | -0.08904585 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 2            |
+| y_out     | -0.040738642 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 3          |
+| y_out     | 0.10097246 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 4            |
+| y_out     | -0.030524489 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 5          |
+| y_out     | -0.0893484 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 6           |
+| y_out     | 0.042008303 |
+------------------------------------------------------------
+---------------------------------------------------------------
+| time-step | 7              |
+| y_out     | -0.00035213493 |
+---------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 8          |
+| y_out     | 0.15984905 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.27014396   |
+| time-step | 9            |
+| y_out     | -0.030297758 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26762146  |
+| time-step | 10          |
+| y_out     | -0.06882575 |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2681088 |
+| time-step | 11        |
+| y_out     | 0.0799767 |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.27443463   |
+| time-step | 12           |
+| y_out     | -0.049950756 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2711161  |
+| time-step | 13         |
+| y_out     | 0.10139241 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.27013952   |
+| time-step | 14           |
+| y_out     | -0.025325254 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2738251    |
+| time-step | 15           |
+| y_out     | -0.037008815 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2678367   |
+| time-step | 16          |
+| y_out     | -0.07290481 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26659995   |
+| time-step | 17           |
+| y_out     | -0.045389514 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25868112  |
+| time-step | 18          |
+| y_out     | -0.03218077 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24854521 |
+| time-step | 19         |
+| y_out     | 0.07168842 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+-------------------------------------------------------------
+| perf/mse  | 0.24950843   |
+| time-step | 20           |
+| y_out     | 0.0018358855 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.24245      |
+| time-step | 21           |
+| y_out     | -0.054730162 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23498109   |
+| time-step | 22           |
+| y_out     | -0.021847177 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2269868  |
+| time-step | 23         |
+| y_out     | 0.00582619 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21829061 |
+| time-step | 24         |
+| y_out     | 0.10414045 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21394917  |
+| time-step | 25          |
+| y_out     | 0.027192216 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21326227 |
+| time-step | 26         |
+| y_out     | 0.15096214 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20832542   |
+| time-step | 27           |
+| y_out     | -0.012955276 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20061156  |
+| time-step | 28          |
+| y_out     | 0.026791058 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1989101  |
+| time-step | 29         |
+| y_out     | 0.12048819 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19219145  |
+| time-step | 30          |
+| y_out     | 0.005813662 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19144766  |
+| time-step | 31          |
+| y_out     | -0.01813798 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18863854  |
+| time-step | 32          |
+| y_out     | -0.16067702 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18760523   |
+| time-step | 33           |
+| y_out     | -0.012649797 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18688269  |
+| time-step | 34          |
+| y_out     | -0.06185216 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1806612   |
+| time-step | 35          |
+| y_out     | -0.04907435 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18013017   |
+| time-step | 36           |
+| y_out     | -0.030103154 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1801342   |
+| time-step | 37          |
+| y_out     | 0.080157936 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.177956   |
+| time-step | 38         |
+| y_out     | -0.1273037 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1755193    |
+| time-step | 39           |
+| y_out     | 0.0035625994 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+------------------------------------------------------------
+| perf/mse  | 0.17271763  |
+| time-step | 40          |
+| y_out     | 0.008754678 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1674488    |
+| time-step | 41           |
+| y_out     | -0.035720248 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16094092   |
+| time-step | 42           |
+| y_out     | -0.079138145 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15709676  |
+| time-step | 43          |
+| y_out     | -0.07686725 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15263228  |
+| time-step | 44          |
+| y_out     | 0.029497273 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14997931  |
+| time-step | 45          |
+| y_out     | 0.061288655 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14675936   |
+| time-step | 46           |
+| y_out     | -0.009561023 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14187942   |
+| time-step | 47           |
+| y_out     | -0.013016967 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14220299  |
+| time-step | 48          |
+| y_out     | -0.16383211 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13895491 |
+| time-step | 49         |
+| y_out     | 0.06317976 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13796854  |
+| time-step | 50          |
+| y_out     | 0.017956998 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13369314   |
+| time-step | 51           |
+| y_out     | -0.037002932 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13515207   |
+| time-step | 52           |
+| y_out     | -0.009299619 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13565762 |
+| time-step | 53         |
+| y_out     | 0.12379622 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1345194  |
+| time-step | 54         |
+| y_out     | 0.15038662 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13385251  |
+| time-step | 55          |
+| y_out     | 0.023144066 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13122652  |
+| time-step | 56          |
+| y_out     | 0.034012124 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12971863  |
+| time-step | 57          |
+| y_out     | 0.030073725 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12640987  |
+| time-step | 58          |
+| y_out     | -0.05199236 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12471638   |
+| time-step | 59           |
+| y_out     | 0.0001204256 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-------------------------------------------------------------
+| perf/mse  | 0.12016161   |
+| time-step | 60           |
+| y_out     | -0.033366647 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12022146  |
+| time-step | 61          |
+| y_out     | 0.049675383 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11734482 |
+| time-step | 62         |
+| y_out     | 0.09312133 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11408396   |
+| time-step | 63           |
+| y_out     | -0.061545502 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.112718895 |
+| time-step | 64          |
+| y_out     | -0.08500222 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11047077  |
+| time-step | 65          |
+| y_out     | 0.030663803 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10604449   |
+| time-step | 66           |
+| y_out     | -0.032404877 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10367557  |
+| time-step | 67          |
+| y_out     | -0.00931688 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10038223  |
+| time-step | 68          |
+| y_out     | -0.03682807 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0953555   |
+| time-step | 69          |
+| y_out     | -0.15280788 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.093144156 |
+| time-step | 70          |
+| y_out     | 0.020758972 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09255326   |
+| time-step | 71           |
+| y_out     | -0.026524495 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08982011  |
+| time-step | 72          |
+| y_out     | 0.011075083 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08700745   |
+| time-step | 73           |
+| y_out     | -0.038536236 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.083809964 |
+| time-step | 74          |
+| y_out     | 0.17802127  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07953285   |
+| time-step | 75           |
+| y_out     | -0.032739554 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.079190776 |
+| time-step | 76          |
+| y_out     | 0.06450374  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07883298  |
+| time-step | 77          |
+| y_out     | 0.050905712 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07809413 |
+| time-step | 78         |
+| y_out     | 0.00936804 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07796538  |
+| time-step | 79          |
+| y_out     | -0.03812491 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+-----------------------------------------------------------
+| perf/mse  | 0.07717803 |
+| time-step | 80         |
+| y_out     | 0.08966558 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0735263   |
+| time-step | 81          |
+| y_out     | 0.021206941 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07124361  |
+| time-step | 82          |
+| y_out     | 0.011411548 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06886458 |
+| time-step | 83         |
+| y_out     | 0.08673853 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06727667 |
+| time-step | 84         |
+| y_out     | -0.0899993 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.065209836 |
+| time-step | 85          |
+| y_out     | 0.091698796 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060889266 |
+| time-step | 86          |
+| y_out     | -0.17304748 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.055962823 |
+| time-step | 87          |
+| y_out     | -0.11198948 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.052630685  |
+| time-step | 88           |
+| y_out     | -0.030381948 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051134743 |
+| time-step | 89          |
+| y_out     | 0.03683728  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.048947368 |
+| time-step | 90          |
+| y_out     | -0.02167064 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.0470057  |
+| time-step | 91         |
+| y_out     | 0.05283567 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.045351554  |
+| time-step | 92           |
+| y_out     | -0.009969771 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.044354785   |
+| time-step | 93            |
+| y_out     | -0.0117721595 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.043437295  |
+| time-step | 94           |
+| y_out     | 0.0135597475 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.042724147 |
+| time-step | 95          |
+| y_out     | 0.080062404 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.04364051  |
+| time-step | 96          |
+| y_out     | -0.13086124 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.04296896 |
+| time-step | 97         |
+| y_out     | 0.07677312 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.0423295  |
+| time-step | 98         |
+| y_out     | 0.08360295 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0419715    |
+| time-step | 99           |
+| y_out     | -0.029844306 |
+-------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..85daace
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,-0.0621269,
+1,-0.08904585,
+2,-0.040738642,
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+48,-0.16383211,0.14220299
+49,0.06317976,0.13895491
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+59,0.0001204256,0.12471638
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903821.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903821.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..e214520
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903821.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-37-01-642717_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-24-736729_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-24-736729_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..3ccd93a
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,502 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+--------------------------------------------------------------
+| time-step | 0             |
+| y_out     | -0.0046378504 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 1            |
+| y_out     | -0.019780923 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| time-step | 2             |
+| y_out     | -0.0070655495 |
+--------------------------------------------------------------
+----------------------------------------------------------
+| time-step | 3         |
+| y_out     | 0.0657551 |
+----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 4            |
+| y_out     | -0.034142777 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 5          |
+| y_out     | 0.04903492 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 6          |
+| y_out     | 0.04962659 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 7           |
+| y_out     | 0.017461505 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 8          |
+| y_out     | 0.04159434 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.29978654   |
+| time-step | 9            |
+| y_out     | 0.0042816475 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29667485  |
+| time-step | 10          |
+| y_out     | 0.074249215 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.291982     |
+| time-step | 11           |
+| y_out     | 0.0029530004 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.28456914  |
+| time-step | 12          |
+| y_out     | -0.05108078 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2897091    |
+| time-step | 13           |
+| y_out     | -0.043344524 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.28548     |
+| time-step | 14          |
+| y_out     | 0.091172814 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.28956905   |
+| time-step | 15           |
+| y_out     | -0.020786721 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.28326967  |
+| time-step | 16          |
+| y_out     | 0.028366314 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2832108   |
+| time-step | 17          |
+| y_out     | 0.086222485 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2903796    |
+| time-step | 18           |
+| y_out     | 0.0075109825 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2924717   |
+| time-step | 19          |
+| y_out     | 0.021292415 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+------------------------------------------------------------
+| perf/mse  | 0.2852872   |
+| time-step | 20          |
+| y_out     | -0.11340672 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2838872    |
+| time-step | 21           |
+| y_out     | -0.044309147 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2824472   |
+| time-step | 22          |
+| y_out     | 0.067185596 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27756673  |
+| time-step | 23          |
+| y_out     | 0.046435833 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.27773649   |
+| time-step | 24           |
+| y_out     | -0.109382235 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2792964  |
+| time-step | 25         |
+| y_out     | 0.09562312 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.28030592   |
+| time-step | 26           |
+| y_out     | 0.0029435847 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2834482    |
+| time-step | 27           |
+| y_out     | -0.013542404 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.28170207   |
+| time-step | 28           |
+| y_out     | -0.036044773 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2687548   |
+| time-step | 29          |
+| y_out     | 0.055070605 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27080005  |
+| time-step | 30          |
+| y_out     | -0.10904081 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2736729   |
+| time-step | 31          |
+| y_out     | -0.03141955 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27018654  |
+| time-step | 32          |
+| y_out     | -0.10581304 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2687921   |
+| time-step | 33          |
+| y_out     | -0.06341444 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26593333  |
+| time-step | 34          |
+| y_out     | 0.045539115 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26198485   |
+| time-step | 35           |
+| y_out     | -0.048909955 |
+-------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.2605738 |
+| time-step | 36        |
+| y_out     | 0.0534742 |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.25747317   |
+| time-step | 37           |
+| y_out     | -0.084831566 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25592855  |
+| time-step | 38          |
+| y_out     | -0.10834672 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25625658  |
+| time-step | 39          |
+| y_out     | -0.03102902 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+-----------------------------------------------------------
+| perf/mse  | 0.24967524 |
+| time-step | 40         |
+| y_out     | 0.02746974 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2409862   |
+| time-step | 41          |
+| y_out     | 0.020448007 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23902924   |
+| time-step | 42           |
+| y_out     | -0.048272196 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23963729   |
+| time-step | 43           |
+| y_out     | -0.013156783 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23855102 |
+| time-step | 44         |
+| y_out     | -0.1118365 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23346658  |
+| time-step | 45          |
+| y_out     | 0.021053031 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22867557  |
+| time-step | 46          |
+| y_out     | 0.033833794 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.22439222   |
+| time-step | 47           |
+| y_out     | -0.013502828 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21852723  |
+| time-step | 48          |
+| y_out     | 0.055553257 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21861239   |
+| time-step | 49           |
+| y_out     | -0.059772808 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21561305  |
+| time-step | 50          |
+| y_out     | -0.05008673 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21553454   |
+| time-step | 51           |
+| y_out     | -0.053572692 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21503648   |
+| time-step | 52           |
+| y_out     | 0.0124488175 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21054289  |
+| time-step | 53          |
+| y_out     | 0.084663644 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20429361  |
+| time-step | 54          |
+| y_out     | 0.013787143 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2013059  |
+| time-step | 55         |
+| y_out     | 0.04534495 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19729407  |
+| time-step | 56          |
+| y_out     | 0.035005704 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19500044 |
+| time-step | 57         |
+| y_out     | 0.07441234 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19030493  |
+| time-step | 58          |
+| y_out     | -0.06444406 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18369582  |
+| time-step | 59          |
+| y_out     | 0.065821186 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-----------------------------------------------------------
+| perf/mse  | 0.18220358 |
+| time-step | 60         |
+| y_out     | 0.11941984 |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.1825698 |
+| time-step | 61        |
+| y_out     | 0.217374  |
+----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17879888 |
+| time-step | 62         |
+| y_out     | 0.11610821 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.177703    |
+| time-step | 63          |
+| y_out     | -0.11051995 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17994754  |
+| time-step | 64          |
+| y_out     | 0.060198493 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17712244   |
+| time-step | 65           |
+| y_out     | -0.008350709 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17622112  |
+| time-step | 66          |
+| y_out     | -0.14892708 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.172757    |
+| time-step | 67          |
+| y_out     | 0.059383616 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17068549   |
+| time-step | 68           |
+| y_out     | -0.045595337 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17353849  |
+| time-step | 69          |
+| y_out     | 0.044763714 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17411649 |
+| time-step | 70         |
+| y_out     | 0.10367422 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16869567   |
+| time-step | 71           |
+| y_out     | -0.073005036 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16490106  |
+| time-step | 72          |
+| y_out     | -0.15519685 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16293886 |
+| time-step | 73         |
+| y_out     | 0.04658693 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.15933776    |
+| time-step | 74            |
+| y_out     | -0.0026289667 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15781987  |
+| time-step | 75          |
+| y_out     | 0.009125866 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15998429  |
+| time-step | 76          |
+| y_out     | 0.090858184 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15858783  |
+| time-step | 77          |
+| y_out     | 0.055897456 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1558363    |
+| time-step | 78           |
+| y_out     | -0.006013179 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14932892  |
+| time-step | 79          |
+| y_out     | 0.008336421 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+-----------------------------------------------------------
+| perf/mse  | 0.1454597  |
+| time-step | 80         |
+| y_out     | 0.06488421 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14192283  |
+| time-step | 81          |
+| y_out     | -0.15547666 |
+------------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.141622  |
+| time-step | 82        |
+| y_out     | 0.0321649 |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13555433   |
+| time-step | 83           |
+| y_out     | -0.092173114 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1327245   |
+| time-step | 84          |
+| y_out     | 0.029784244 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13349934  |
+| time-step | 85          |
+| y_out     | 0.020547299 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12695877   |
+| time-step | 86           |
+| y_out     | -0.043564305 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12617497 |
+| time-step | 87         |
+| y_out     | 0.05373463 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12654239  |
+| time-step | 88          |
+| y_out     | 0.034082748 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.124902084 |
+| time-step | 89          |
+| y_out     | 0.021392135 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12492652 |
+| time-step | 90         |
+| y_out     | 0.00950554 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1242015  |
+| time-step | 91         |
+| y_out     | 0.09904311 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12388209  |
+| time-step | 92          |
+| y_out     | 0.031926278 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12582809 |
+| time-step | 93         |
+| y_out     | 0.04071804 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12649883 |
+| time-step | 94         |
+| y_out     | 0.0339303  |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.12155074    |
+| time-step | 95            |
+| y_out     | -0.0153811565 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12203358  |
+| time-step | 96          |
+| y_out     | 0.022575766 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11893443 |
+| time-step | 97         |
+| y_out     | 0.07947445 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11564918  |
+| time-step | 98          |
+| y_out     | 0.040852822 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11602974  |
+| time-step | 99          |
+| y_out     | 0.025933117 |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..15cfdbb
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,-0.0046378504,
+1,-0.019780923,
+2,-0.0070655495,
+3,0.0657551,
+4,-0.034142777,
+5,0.04903492,
+6,0.04962659,
+7,0.017461505,
+8,0.04159434,
+9,0.0042816475,0.29978654
+10,0.074249215,0.29667485
+11,0.0029530004,0.291982
+12,-0.05108078,0.28456914
+13,-0.043344524,0.2897091
+14,0.091172814,0.28548
+15,-0.020786721,0.28956905
+16,0.028366314,0.28326967
+17,0.086222485,0.2832108
+18,0.0075109825,0.2903796
+19,0.021292415,0.2924717
+20,-0.11340672,0.2852872
+21,-0.044309147,0.2838872
+22,0.067185596,0.2824472
+23,0.046435833,0.27756673
+24,-0.109382235,0.27773649
+25,0.09562312,0.2792964
+26,0.0029435847,0.28030592
+27,-0.013542404,0.2834482
+28,-0.036044773,0.28170207
+29,0.055070605,0.2687548
+30,-0.10904081,0.27080005
+31,-0.03141955,0.2736729
+32,-0.10581304,0.27018654
+33,-0.06341444,0.2687921
+34,0.045539115,0.26593333
+35,-0.048909955,0.26198485
+36,0.0534742,0.2605738
+37,-0.084831566,0.25747317
+38,-0.10834672,0.25592855
+39,-0.03102902,0.25625658
+40,0.02746974,0.24967524
+41,0.020448007,0.2409862
+42,-0.048272196,0.23902924
+43,-0.013156783,0.23963729
+44,-0.1118365,0.23855102
+45,0.021053031,0.23346658
+46,0.033833794,0.22867557
+47,-0.013502828,0.22439222
+48,0.055553257,0.21852723
+49,-0.059772808,0.21861239
+50,-0.05008673,0.21561305
+51,-0.053572692,0.21553454
+52,0.0124488175,0.21503648
+53,0.084663644,0.21054289
+54,0.013787143,0.20429361
+55,0.04534495,0.2013059
+56,0.035005704,0.19729407
+57,0.07441234,0.19500044
+58,-0.06444406,0.19030493
+59,0.065821186,0.18369582
+60,0.11941984,0.18220358
+61,0.217374,0.1825698
+62,0.11610821,0.17879888
+63,-0.11051995,0.177703
+64,0.060198493,0.17994754
+65,-0.008350709,0.17712244
+66,-0.14892708,0.17622112
+67,0.059383616,0.172757
+68,-0.045595337,0.17068549
+69,0.044763714,0.17353849
+70,0.10367422,0.17411649
+71,-0.073005036,0.16869567
+72,-0.15519685,0.16490106
+73,0.04658693,0.16293886
+74,-0.0026289667,0.15933776
+75,0.009125866,0.15781987
+76,0.090858184,0.15998429
+77,0.055897456,0.15858783
+78,-0.006013179,0.1558363
+79,0.008336421,0.14932892
+80,0.06488421,0.1454597
+81,-0.15547666,0.14192283
+82,0.0321649,0.141622
+83,-0.092173114,0.13555433
+84,0.029784244,0.1327245
+85,0.020547299,0.13349934
+86,-0.043564305,0.12695877
+87,0.05373463,0.12617497
+88,0.034082748,0.12654239
+89,0.021392135,0.124902084
+90,0.00950554,0.12492652
+91,0.09904311,0.1242015
+92,0.031926278,0.12388209
+93,0.04071804,0.12582809
+94,0.0339303,0.12649883
+95,-0.0153811565,0.12155074
+96,0.022575766,0.12203358
+97,0.07947445,0.11893443
+98,0.040852822,0.11564918
+99,0.025933117,0.11602974
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903910.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903910.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..d08cf5c
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903910.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-30-288562_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..857ce54
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,10 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:38:55.598593
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-50-017170_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..11da6f3
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,502 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+------------------------------------------------------------
+| time-step | 0           |
+| y_out     | 0.057942018 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | 0.009299394 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 2           |
+| y_out     | -0.06578922 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 3           |
+| y_out     | 0.033402458 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 4            |
+| y_out     | -0.008838907 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 5            |
+| y_out     | -0.019880205 |
+-------------------------------------------------------------
+----------------------------------------------------------
+| time-step | 6         |
+| y_out     | 0.0695615 |
+----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 7           |
+| y_out     | -0.15606663 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 8            |
+| y_out     | -0.120173976 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29013506  |
+| time-step | 9           |
+| y_out     | -0.09202558 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2898221    |
+| time-step | 10           |
+| y_out     | -0.011703955 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29263645  |
+| time-step | 11          |
+| y_out     | 0.060920823 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2994182  |
+| time-step | 12         |
+| y_out     | 0.05944415 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29352215  |
+| time-step | 13          |
+| y_out     | -0.07763812 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29221824  |
+| time-step | 14          |
+| y_out     | 0.062170967 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.28828755  |
+| time-step | 15          |
+| y_out     | 0.005492449 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.28035492  |
+| time-step | 16          |
+| y_out     | -0.04847223 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.28646168 |
+| time-step | 17         |
+| y_out     | 0.09182331 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.2871818     |
+| time-step | 18            |
+| y_out     | -0.0064124763 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.29379267 |
+| time-step | 19         |
+| y_out     | 0.07605833 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+------------------------------------------------------------
+| perf/mse  | 0.28982177  |
+| time-step | 20          |
+| y_out     | 0.039001852 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2831092   |
+| time-step | 21          |
+| y_out     | 0.052194037 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27635688  |
+| time-step | 22          |
+| y_out     | -0.01785094 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.27692428   |
+| time-step | 23           |
+| y_out     | -0.036689077 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27918747  |
+| time-step | 24          |
+| y_out     | -0.06535956 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27827042  |
+| time-step | 25          |
+| y_out     | -0.08896898 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2757553    |
+| time-step | 26           |
+| y_out     | -0.011511827 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26713935  |
+| time-step | 27          |
+| y_out     | -0.08342426 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26379174  |
+| time-step | 28          |
+| y_out     | -0.10034735 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.255338    |
+| time-step | 29          |
+| y_out     | 0.045106143 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25280485 |
+| time-step | 30         |
+| y_out     | 0.02738909 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24792795  |
+| time-step | 31          |
+| y_out     | 0.028256068 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24373856  |
+| time-step | 32          |
+| y_out     | -0.08389827 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23841174  |
+| time-step | 33          |
+| y_out     | -0.08655572 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.228147    |
+| time-step | 34          |
+| y_out     | 0.045826375 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22488031  |
+| time-step | 35          |
+| y_out     | -0.05299262 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.22154784   |
+| time-step | 36           |
+| y_out     | -0.045899488 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22064455  |
+| time-step | 37          |
+| y_out     | 0.091645986 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.21981803    |
+| time-step | 38            |
+| y_out     | -0.0012162775 |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.22147079    |
+| time-step | 39            |
+| y_out     | -0.0020208508 |
+--------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+-----------------------------------------------------------
+| perf/mse  | 0.22047107 |
+| time-step | 40         |
+| y_out     | 0.12591708 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21899696 |
+| time-step | 41         |
+| y_out     | 0.06925544 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.2210439     |
+| time-step | 42            |
+| y_out     | -0.0070464527 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21959372 |
+| time-step | 43         |
+| y_out     | 0.04973857 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.21936026 |
+| time-step | 44         |
+| y_out     | 0.0725247  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21597585  |
+| time-step | 45          |
+| y_out     | -0.08740508 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21786436  |
+| time-step | 46          |
+| y_out     | 0.032536753 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21506211   |
+| time-step | 47           |
+| y_out     | -0.018441036 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20968458 |
+| time-step | 48         |
+| y_out     | 0.01401718 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2043614    |
+| time-step | 49           |
+| y_out     | -0.001424443 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20206785  |
+| time-step | 50          |
+| y_out     | 0.093531415 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19606623   |
+| time-step | 51           |
+| y_out     | -0.049265433 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19042379   |
+| time-step | 52           |
+| y_out     | -0.068183996 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19138004 |
+| time-step | 53         |
+| y_out     | 0.08574159 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18789084  |
+| time-step | 54          |
+| y_out     | 0.051714674 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19176857 |
+| time-step | 55         |
+| y_out     | 0.08073051 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19088508 |
+| time-step | 56         |
+| y_out     | 0.04505066 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18906093   |
+| time-step | 57           |
+| y_out     | -0.035414148 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18482353  |
+| time-step | 58          |
+| y_out     | 0.061528713 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18541625 |
+| time-step | 59         |
+| y_out     | 0.04952935 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-------------------------------------------------------------
+| perf/mse  | 0.18640448   |
+| time-step | 60           |
+| y_out     | -0.025017155 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18628386  |
+| time-step | 61          |
+| y_out     | 0.014099959 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18729475 |
+| time-step | 62         |
+| y_out     | 0.10357347 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18219523   |
+| time-step | 63           |
+| y_out     | -0.031111814 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17747343 |
+| time-step | 64         |
+| y_out     | 0.03368728 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16973583  |
+| time-step | 65          |
+| y_out     | -0.06781593 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16947493 |
+| time-step | 66         |
+| y_out     | 0.06488516 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16342677 |
+| time-step | 67         |
+| y_out     | 0.07582743 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1654574   |
+| time-step | 68          |
+| y_out     | 0.121700674 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16094437   |
+| time-step | 69           |
+| y_out     | -0.005937267 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.15429744    |
+| time-step | 70            |
+| y_out     | -0.0047529433 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15381148  |
+| time-step | 71          |
+| y_out     | 0.085287094 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15143766  |
+| time-step | 72          |
+| y_out     | 0.054112375 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14919044  |
+| time-step | 73          |
+| y_out     | -0.05339911 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1462567    |
+| time-step | 74           |
+| y_out     | -0.026360363 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14412466  |
+| time-step | 75          |
+| y_out     | 0.008482582 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13650522  |
+| time-step | 76          |
+| y_out     | 0.034575976 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13743336   |
+| time-step | 77           |
+| y_out     | -0.104855455 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13176571  |
+| time-step | 78          |
+| y_out     | -0.04841756 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13497354 |
+| time-step | 79         |
+| y_out     | 0.12414725 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+-----------------------------------------------------------
+| perf/mse  | 0.1362513  |
+| time-step | 80         |
+| y_out     | 0.05217046 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13253215  |
+| time-step | 81          |
+| y_out     | 0.047094554 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12771477   |
+| time-step | 82           |
+| y_out     | 0.0010832641 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1263187  |
+| time-step | 83         |
+| y_out     | 0.12661284 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1275441  |
+| time-step | 84         |
+| y_out     | -0.0670744 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12602034 |
+| time-step | 85         |
+| y_out     | -0.0892787 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12460587 |
+| time-step | 86         |
+| y_out     | 0.1963427  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12172502  |
+| time-step | 87          |
+| y_out     | -0.08841949 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11772132  |
+| time-step | 88          |
+| y_out     | -0.08645722 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.110573865 |
+| time-step | 89          |
+| y_out     | -0.03570364 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10658022   |
+| time-step | 90           |
+| y_out     | -0.003956847 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.104933955 |
+| time-step | 91          |
+| y_out     | 0.16213527  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10195045   |
+| time-step | 92           |
+| y_out     | -0.053512476 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.100434124 |
+| time-step | 93          |
+| y_out     | -0.04065678 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09605147  |
+| time-step | 94          |
+| y_out     | -0.07144512 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09713894  |
+| time-step | 95          |
+| y_out     | -0.04366717 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09636678   |
+| time-step | 96           |
+| y_out     | 0.0022101104 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09568291  |
+| time-step | 97          |
+| y_out     | 0.060549203 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09589204   |
+| time-step | 98           |
+| y_out     | -0.015122104 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.093296304 |
+| time-step | 99          |
+| y_out     | 0.03837713  |
+------------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..c4bba46
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,0.057942018,
+1,0.009299394,
+2,-0.06578922,
+3,0.033402458,
+4,-0.008838907,
+5,-0.019880205,
+6,0.0695615,
+7,-0.15606663,
+8,-0.120173976,
+9,-0.09202558,0.29013506
+10,-0.011703955,0.2898221
+11,0.060920823,0.29263645
+12,0.05944415,0.2994182
+13,-0.07763812,0.29352215
+14,0.062170967,0.29221824
+15,0.005492449,0.28828755
+16,-0.04847223,0.28035492
+17,0.09182331,0.28646168
+18,-0.0064124763,0.2871818
+19,0.07605833,0.29379267
+20,0.039001852,0.28982177
+21,0.052194037,0.2831092
+22,-0.01785094,0.27635688
+23,-0.036689077,0.27692428
+24,-0.06535956,0.27918747
+25,-0.08896898,0.27827042
+26,-0.011511827,0.2757553
+27,-0.08342426,0.26713935
+28,-0.10034735,0.26379174
+29,0.045106143,0.255338
+30,0.02738909,0.25280485
+31,0.028256068,0.24792795
+32,-0.08389827,0.24373856
+33,-0.08655572,0.23841174
+34,0.045826375,0.228147
+35,-0.05299262,0.22488031
+36,-0.045899488,0.22154784
+37,0.091645986,0.22064455
+38,-0.0012162775,0.21981803
+39,-0.0020208508,0.22147079
+40,0.12591708,0.22047107
+41,0.06925544,0.21899696
+42,-0.0070464527,0.2210439
+43,0.04973857,0.21959372
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+46,0.032536753,0.21786436
+47,-0.018441036,0.21506211
+48,0.01401718,0.20968458
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+53,0.08574159,0.19138004
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+59,0.04952935,0.18541625
+60,-0.025017155,0.18640448
+61,0.014099959,0.18628386
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+64,0.03368728,0.17747343
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+68,0.121700674,0.1654574
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903935.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903935.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..ff9d4eb
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658903935.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-38-55-598593_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..21624e4
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-40-00-059094_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..3c584da
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,16 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:44:46.961910
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904286.MBP-C02ZL0TNLVCF-2243.local.39594.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904286.MBP-C02ZL0TNLVCF-2243.local.39594.0.v2
new file mode 100644
index 0000000..94bded7
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904286.MBP-C02ZL0TNLVCF-2243.local.39594.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-38-762656_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..559c7f3
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,502 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+------------------------------------------------------------
+| time-step | 0           |
+| y_out     | 0.022702988 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 1            |
+| y_out     | -0.017002156 |
+-------------------------------------------------------------
+----------------------------------------------------------
+| time-step | 2         |
+| y_out     | 0.1148881 |
+----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 3           |
+| y_out     | 0.018713145 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 4          |
+| y_out     | 0.10800394 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 5          |
+| y_out     | 0.13352066 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 6          |
+| y_out     | 0.09261498 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 7           |
+| y_out     | 0.070487976 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 8            |
+| y_out     | 0.0034726262 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20676251   |
+| time-step | 9            |
+| y_out     | -0.024782188 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20375006  |
+| time-step | 10          |
+| y_out     | 0.012341602 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2003847   |
+| time-step | 11          |
+| y_out     | 0.118284464 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19646353  |
+| time-step | 12          |
+| y_out     | -0.12245024 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19765395   |
+| time-step | 13           |
+| y_out     | -0.028663557 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1914563  |
+| time-step | 14         |
+| y_out     | 0.06015191 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19105707   |
+| time-step | 15           |
+| y_out     | -0.023616312 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18889634   |
+| time-step | 16           |
+| y_out     | -0.053860288 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18714097  |
+| time-step | 17          |
+| y_out     | 0.042223383 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18335158  |
+| time-step | 18          |
+| y_out     | -0.09224718 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18203624 |
+| time-step | 19         |
+| y_out     | 0.05748921 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+-----------------------------------------------------------
+| perf/mse  | 0.17891905 |
+| time-step | 20         |
+| y_out     | 0.07090413 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17500168  |
+| time-step | 21          |
+| y_out     | 0.058406778 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1732279  |
+| time-step | 22         |
+| y_out     | -0.0425646 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17345682   |
+| time-step | 23           |
+| y_out     | -0.084708735 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1719772   |
+| time-step | 24          |
+| y_out     | 0.012224443 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17263499  |
+| time-step | 25          |
+| y_out     | -0.15004756 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.17019968    |
+| time-step | 26            |
+| y_out     | -0.0041401098 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16950327 |
+| time-step | 27         |
+| y_out     | 0.04353801 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17004529 |
+| time-step | 28         |
+| y_out     | 0.08072722 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17395177  |
+| time-step | 29          |
+| y_out     | -0.21787646 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17608349  |
+| time-step | 30          |
+| y_out     | 0.064083666 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17232533 |
+| time-step | 31         |
+| y_out     | 0.0520883  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1712596   |
+| time-step | 32          |
+| y_out     | -0.04616505 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16554484 |
+| time-step | 33         |
+| y_out     | 0.13314453 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16521707 |
+| time-step | 34         |
+| y_out     | 0.08606917 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16352694  |
+| time-step | 35          |
+| y_out     | 0.019153027 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.16038877    |
+| time-step | 36            |
+| y_out     | -6.889086e-05 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15991873  |
+| time-step | 37          |
+| y_out     | 0.076216206 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15920115  |
+| time-step | 38          |
+| y_out     | 0.016891625 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15079188 |
+| time-step | 39         |
+| y_out     | 0.10500196 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+------------------------------------------------------------
+| perf/mse  | 0.14485654  |
+| time-step | 40          |
+| y_out     | 0.028267363 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14569023   |
+| time-step | 41           |
+| y_out     | -0.027418725 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14208612  |
+| time-step | 42          |
+| y_out     | 0.016029691 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14089325  |
+| time-step | 43          |
+| y_out     | -0.07110735 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13935316 |
+| time-step | 44         |
+| y_out     | 0.04972038 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13481855  |
+| time-step | 45          |
+| y_out     | -0.15003915 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1314193    |
+| time-step | 46           |
+| y_out     | -0.014611766 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12870397  |
+| time-step | 47          |
+| y_out     | -0.03103176 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1261075   |
+| time-step | 48          |
+| y_out     | -0.12114393 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12520637 |
+| time-step | 49         |
+| y_out     | 0.08632986 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12521854 |
+| time-step | 50         |
+| y_out     | 0.07628282 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12212239   |
+| time-step | 51           |
+| y_out     | -0.019916533 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.123067    |
+| time-step | 52          |
+| y_out     | -0.10336205 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12052349 |
+| time-step | 53         |
+| y_out     | 0.04098618 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.118042566 |
+| time-step | 54          |
+| y_out     | 0.01792001  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11405585  |
+| time-step | 55          |
+| y_out     | 0.020878412 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.111297965  |
+| time-step | 56           |
+| y_out     | -0.046118136 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11107669   |
+| time-step | 57           |
+| y_out     | -0.017437482 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10915808  |
+| time-step | 58          |
+| y_out     | -0.10367048 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1103778   |
+| time-step | 59          |
+| y_out     | -0.08319569 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-------------------------------------------------------------
+| perf/mse  | 0.10846764   |
+| time-step | 60           |
+| y_out     | -0.033527665 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10446519 |
+| time-step | 61         |
+| y_out     | 0.06583307 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09865031   |
+| time-step | 62           |
+| y_out     | -0.010796258 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09804019  |
+| time-step | 63          |
+| y_out     | 0.054541998 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09606486  |
+| time-step | 64          |
+| y_out     | 0.083635025 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.096160606 |
+| time-step | 65          |
+| y_out     | 0.06393112  |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09645395 |
+| time-step | 66         |
+| y_out     | 0.09685686 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09096019   |
+| time-step | 67           |
+| y_out     | -0.008387884 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.087977275 |
+| time-step | 68          |
+| y_out     | -0.1556329  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08417563  |
+| time-step | 69          |
+| y_out     | -0.09474763 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.085185975 |
+| time-step | 70          |
+| y_out     | 0.044771932 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08570555 |
+| time-step | 71         |
+| y_out     | 0.02919146 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.084778085 |
+| time-step | 72          |
+| y_out     | 0.062552735 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08186426   |
+| time-step | 73           |
+| y_out     | -0.097900376 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08081846 |
+| time-step | 74         |
+| y_out     | 0.09257191 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07957893  |
+| time-step | 75          |
+| y_out     | -0.06687015 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07718705  |
+| time-step | 76          |
+| y_out     | 0.022992369 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07890172   |
+| time-step | 77           |
+| y_out     | -0.008510668 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.078078024  |
+| time-step | 78           |
+| y_out     | -0.044981137 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07850899 |
+| time-step | 79         |
+| y_out     | 0.15110594 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+-------------------------------------------------------------
+| perf/mse  | 0.07451498   |
+| time-step | 80           |
+| y_out     | -0.049430612 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.072332494 |
+| time-step | 81          |
+| y_out     | 0.07478216  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07137644  |
+| time-step | 82          |
+| y_out     | 0.037567277 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.068764076   |
+| time-step | 83            |
+| y_out     | -0.0051398575 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06762849 |
+| time-step | 84         |
+| y_out     | 0.03978119 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06610838   |
+| time-step | 85           |
+| y_out     | -0.031301286 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.06517443  |
+| time-step | 86          |
+| y_out     | 0.046843477 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.062757954 |
+| time-step | 87          |
+| y_out     | 0.06524126  |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.058896147  |
+| time-step | 88           |
+| y_out     | -0.013494093 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.055766176 |
+| time-step | 89          |
+| y_out     | 0.1054272   |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.05380221  |
+| time-step | 90          |
+| y_out     | -0.06561792 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.052687712 |
+| time-step | 91          |
+| y_out     | -0.14812239 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.054115422 |
+| time-step | 92          |
+| y_out     | 0.051594034 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056054078 |
+| time-step | 93          |
+| y_out     | -0.03900086 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.055606402 |
+| time-step | 94          |
+| y_out     | 0.052066028 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05311832 |
+| time-step | 95         |
+| y_out     | 0.12179959 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051549785 |
+| time-step | 96          |
+| y_out     | 0.009877209 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.051778566 |
+| time-step | 97          |
+| y_out     | 0.01264851  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.05244437  |
+| time-step | 98          |
+| y_out     | 0.012708332 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.05188657 |
+| time-step | 99         |
+| y_out     | 0.10608245 |
+-----------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..d69dc89
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,0.022702988,
+1,-0.017002156,
+2,0.1148881,
+3,0.018713145,
+4,0.10800394,
+5,0.13352066,
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904286.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904286.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..66e3af1
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904286.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-44-46-961910_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..b6d9b74
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,13 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/progress.csv
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904340.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904340.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..cd0a61f
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904340.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..77db1a0
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-40-466622_172.16.0.33_&input_size=16/warn.txt
@@ -0,0 +1,2 @@
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..01a8404
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,16 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:46:01.405349
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904361.MBP-C02ZL0TNLVCF-2243.local.39725.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904361.MBP-C02ZL0TNLVCF-2243.local.39725.0.v2
new file mode 100644
index 0000000..dcc33e6
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904361.MBP-C02ZL0TNLVCF-2243.local.39725.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-45-56-371202_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..9beb172
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,511 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+------------------------------------------------------------
+| time-step | 0           |
+| y_out     | -0.14560433 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | -0.06513713 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 2            |
+| y_out     | -0.020630244 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 3           |
+| y_out     | 0.009697197 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 4           |
+| y_out     | 0.018683396 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 5          |
+| y_out     | 0.01694471 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 6          |
+| y_out     | 0.10436954 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 7            |
+| y_out     | 0.0029534735 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 8           |
+| y_out     | 0.016307706 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26573965   |
+| time-step | 9            |
+| y_out     | -0.039751224 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26299697   |
+| time-step | 10           |
+| y_out     | -0.065591924 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2585093   |
+| time-step | 11          |
+| y_out     | -0.07492362 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2539196    |
+| time-step | 12           |
+| y_out     | -0.054703172 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25131482  |
+| time-step | 13          |
+| y_out     | -0.05829399 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.24880612    |
+| time-step | 14            |
+| y_out     | -0.0073772892 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25248024  |
+| time-step | 15          |
+| y_out     | 0.056837097 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2502787    |
+| time-step | 16           |
+| y_out     | 0.0038454086 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24302766  |
+| time-step | 17          |
+| y_out     | 0.034673154 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24251525  |
+| time-step | 18          |
+| y_out     | -0.05066319 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23768063 |
+| time-step | 19         |
+| y_out     | 0.06751132 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+------------------------------------------------------------
+| perf/mse  | 0.2322143   |
+| time-step | 20          |
+| y_out     | -0.10808982 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23440254   |
+| time-step | 21           |
+| y_out     | -0.010165557 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23532894  |
+| time-step | 22          |
+| y_out     | -0.07003514 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23636031  |
+| time-step | 23          |
+| y_out     | -0.04393044 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23357837  |
+| time-step | 24          |
+| y_out     | 0.037063956 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23239155  |
+| time-step | 25          |
+| y_out     | -0.14662828 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23086023  |
+| time-step | 26          |
+| y_out     | -0.03174086 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2264603    |
+| time-step | 27           |
+| y_out     | -0.031023547 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22046311  |
+| time-step | 28          |
+| y_out     | 0.025960289 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22382681  |
+| time-step | 29          |
+| y_out     | -0.10698375 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22345302 |
+| time-step | 30         |
+| y_out     | 0.03662747 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21883062   |
+| time-step | 31           |
+| y_out     | -0.054751053 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21273652  |
+| time-step | 32          |
+| y_out     | 0.042267404 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20519054  |
+| time-step | 33          |
+| y_out     | 0.037221737 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20252283  |
+| time-step | 34          |
+| y_out     | 0.027008403 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19748199   |
+| time-step | 35           |
+| y_out     | -0.002801055 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19469586   |
+| time-step | 36           |
+| y_out     | -0.061698377 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19168518  |
+| time-step | 37          |
+| y_out     | 0.032713074 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19114956   |
+| time-step | 38           |
+| y_out     | -0.027035635 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18508749 |
+| time-step | 39         |
+| y_out     | 0.09596521 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+------------------------------------------------------------
+| perf/mse  | 0.18348403  |
+| time-step | 40          |
+| y_out     | 0.025767457 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.178829    |
+| time-step | 41          |
+| y_out     | 0.014633112 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17531537 |
+| time-step | 42         |
+| y_out     | 0.03203115 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17724416   |
+| time-step | 43           |
+| y_out     | -0.043964066 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17674805 |
+| time-step | 44         |
+| y_out     | 0.1065344  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17139146  |
+| time-step | 45          |
+| y_out     | 0.061935496 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16945615 |
+| time-step | 46         |
+| y_out     | 0.06501904 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16876878  |
+| time-step | 47          |
+| y_out     | 0.036367737 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16242824  |
+| time-step | 48          |
+| y_out     | -0.09388182 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15941323   |
+| time-step | 49           |
+| y_out     | 0.0013054162 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15589613  |
+| time-step | 50          |
+| y_out     | 0.011224348 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15289064  |
+| time-step | 51          |
+| y_out     | -0.06552475 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15140213 |
+| time-step | 52         |
+| y_out     | 0.05927827 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14266331  |
+| time-step | 53          |
+| y_out     | 0.013135757 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13926843   |
+| time-step | 54           |
+| y_out     | -0.026005328 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14169517   |
+| time-step | 55           |
+| y_out     | -0.094185084 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14027698  |
+| time-step | 56          |
+| y_out     | 0.060039893 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13522825  |
+| time-step | 57          |
+| y_out     | 0.012769325 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1333472  |
+| time-step | 58         |
+| y_out     | 0.12772134 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13342555   |
+| time-step | 59           |
+| y_out     | -0.075296305 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-----------------------------------------------------------
+| perf/mse  | 0.13236885 |
+| time-step | 60         |
+| y_out     | -0.1206451 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13189691  |
+| time-step | 61          |
+| y_out     | -0.02815953 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12892446  |
+| time-step | 62          |
+| y_out     | -0.04399247 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13164395 |
+| time-step | 63         |
+| y_out     | -0.1469033 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13043508   |
+| time-step | 64           |
+| y_out     | -0.041740436 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.124947295 |
+| time-step | 65          |
+| y_out     | 0.141908    |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1222829    |
+| time-step | 66           |
+| y_out     | 0.0065501556 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12409185  |
+| time-step | 67          |
+| y_out     | -0.06010112 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12458752   |
+| time-step | 68           |
+| y_out     | -0.004864428 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.118024305 |
+| time-step | 69          |
+| y_out     | 0.021233585 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.115845405 |
+| time-step | 70          |
+| y_out     | 0.04736811  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11290319  |
+| time-step | 71          |
+| y_out     | 0.026037628 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10960686  |
+| time-step | 72          |
+| y_out     | 0.039905317 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.104767516 |
+| time-step | 73          |
+| y_out     | 0.010208586 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10277714   |
+| time-step | 74           |
+| y_out     | -0.008163075 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.100914575 |
+| time-step | 75          |
+| y_out     | 0.15072459  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10058073  |
+| time-step | 76          |
+| y_out     | -0.02368601 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.094629936 |
+| time-step | 77          |
+| y_out     | 0.026960704 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09073193 |
+| time-step | 78         |
+| y_out     | 0.04082627 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09014411  |
+| time-step | 79          |
+| y_out     | 0.043506704 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+------------------------------------------------------------
+| perf/mse  | 0.08719848  |
+| time-step | 80          |
+| y_out     | 0.038850956 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.08484467    |
+| time-step | 81            |
+| y_out     | -0.0040936954 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.08477596 |
+| time-step | 82         |
+| y_out     | 0.04790334 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08448784  |
+| time-step | 83          |
+| y_out     | -0.08591205 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08184661  |
+| time-step | 84          |
+| y_out     | 0.060198396 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0796378   |
+| time-step | 85          |
+| y_out     | 0.008845447 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0783357   |
+| time-step | 86          |
+| y_out     | -0.15774322 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07943835   |
+| time-step | 87           |
+| y_out     | -0.067813955 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07779012 |
+| time-step | 88         |
+| y_out     | 0.02911041 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0758128    |
+| time-step | 89           |
+| y_out     | -0.072832644 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07405873   |
+| time-step | 90           |
+| y_out     | -0.061000407 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0732895    |
+| time-step | 91           |
+| y_out     | -0.039173678 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07300567  |
+| time-step | 92          |
+| y_out     | -0.10083674 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0707062   |
+| time-step | 93          |
+| y_out     | 0.037584472 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07243727  |
+| time-step | 94          |
+| y_out     | 0.073597886 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.07179787    |
+| time-step | 95            |
+| y_out     | -0.0006955154 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06948742   |
+| time-step | 96           |
+| y_out     | 0.0065700375 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06859206   |
+| time-step | 97           |
+| y_out     | -0.058376383 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06718151   |
+| time-step | 98           |
+| y_out     | -0.019329743 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0675487   |
+| time-step | 99          |
+| y_out     | -0.14970846 |
+------------------------------------------------------------
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: True
+k: REMOTE_SETTING, v: {'ftp_server': '127.0.0.1', 'username': 'hadoop-peisongpa', 'password': 'wangli78', 'remote_log_root': '/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:46:03.816155
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..44e893e
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,-0.14560433,
+1,-0.06513713,
+2,-0.020630244,
+3,0.009697197,
+4,0.018683396,
+5,0.01694471,
+6,0.10436954,
+7,0.0029534735,
+8,0.016307706,
+9,-0.039751224,0.26573965
+10,-0.065591924,0.26299697
+11,-0.07492362,0.2585093
+12,-0.054703172,0.2539196
+13,-0.05829399,0.25131482
+14,-0.0073772892,0.24880612
+15,0.056837097,0.25248024
+16,0.0038454086,0.2502787
+17,0.034673154,0.24302766
+18,-0.05066319,0.24251525
+19,0.06751132,0.23768063
+20,-0.10808982,0.2322143
+21,-0.010165557,0.23440254
+22,-0.07003514,0.23532894
+23,-0.04393044,0.23636031
+24,0.037063956,0.23357837
+25,-0.14662828,0.23239155
+26,-0.03174086,0.23086023
+27,-0.031023547,0.2264603
+28,0.025960289,0.22046311
+29,-0.10698375,0.22382681
+30,0.03662747,0.22345302
+31,-0.054751053,0.21883062
+32,0.042267404,0.21273652
+33,0.037221737,0.20519054
+34,0.027008403,0.20252283
+35,-0.002801055,0.19748199
+36,-0.061698377,0.19469586
+37,0.032713074,0.19168518
+38,-0.027035635,0.19114956
+39,0.09596521,0.18508749
+40,0.025767457,0.18348403
+41,0.014633112,0.178829
+42,0.03203115,0.17531537
+43,-0.043964066,0.17724416
+44,0.1065344,0.17674805
+45,0.061935496,0.17139146
+46,0.06501904,0.16945615
+47,0.036367737,0.16876878
+48,-0.09388182,0.16242824
+49,0.0013054162,0.15941323
+50,0.011224348,0.15589613
+51,-0.06552475,0.15289064
+52,0.05927827,0.15140213
+53,0.013135757,0.14266331
+54,-0.026005328,0.13926843
+55,-0.094185084,0.14169517
+56,0.060039893,0.14027698
+57,0.012769325,0.13522825
+58,0.12772134,0.1333472
+59,-0.075296305,0.13342555
+60,-0.1206451,0.13236885
+61,-0.02815953,0.13189691
+62,-0.04399247,0.12892446
+63,-0.1469033,0.13164395
+64,-0.041740436,0.13043508
+65,0.141908,0.124947295
+66,0.0065501556,0.1222829
+67,-0.06010112,0.12409185
+68,-0.004864428,0.12458752
+69,0.021233585,0.118024305
+70,0.04736811,0.115845405
+71,0.026037628,0.11290319
+72,0.039905317,0.10960686
+73,0.010208586,0.104767516
+74,-0.008163075,0.10277714
+75,0.15072459,0.100914575
+76,-0.02368601,0.10058073
+77,0.026960704,0.094629936
+78,0.04082627,0.09073193
+79,0.043506704,0.09014411
+80,0.038850956,0.08719848
+81,-0.0040936954,0.08484467
+82,0.04790334,0.08477596
+83,-0.08591205,0.08448784
+84,0.060198396,0.08184661
+85,0.008845447,0.0796378
+86,-0.15774322,0.0783357
+87,-0.067813955,0.07943835
+88,0.02911041,0.07779012
+89,-0.072832644,0.0758128
+90,-0.061000407,0.07405873
+91,-0.039173678,0.0732895
+92,-0.10083674,0.07300567
+93,0.037584472,0.0707062
+94,0.073597886,0.07243727
+95,-0.0006955154,0.07179787
+96,0.0065700375,0.06948742
+97,-0.058376383,0.06859206
+98,-0.019329743,0.06718151
+99,-0.14970846,0.0675487
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904361.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904361.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..6be0bc0
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904361.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-01-405349_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..844e362
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..f956b78
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt
@@ -0,0 +1,13 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904363.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904363.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..64c1bcd
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904363.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..77db1a0
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-46-03-816155_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt
@@ -0,0 +1,2 @@
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..bdb6762
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,16 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:47:54.184710
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904473.MBP-C02ZL0TNLVCF-2243.local.39903.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904473.MBP-C02ZL0TNLVCF-2243.local.39903.0.v2
new file mode 100644
index 0000000..620b497
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904473.MBP-C02ZL0TNLVCF-2243.local.39903.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-49-100905_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..9f7e15e
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,511 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+------------------------------------------------------------
+| time-step | 0           |
+| y_out     | -0.03820775 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | 0.040611096 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 2           |
+| y_out     | 0.025441056 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 3          |
+| y_out     | 0.16058132 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 4            |
+| y_out     | -0.016598545 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 5          |
+| y_out     | 0.07641238 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 6           |
+| y_out     | -0.07551894 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 7          |
+| y_out     | 0.03351134 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 8            |
+| y_out     | -0.041779466 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.28662068   |
+| time-step | 9            |
+| y_out     | -0.079788834 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2785755   |
+| time-step | 10          |
+| y_out     | 0.019240221 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27527714  |
+| time-step | 11          |
+| y_out     | -0.02260659 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.27194336   |
+| time-step | 12           |
+| y_out     | -0.030526614 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2717179  |
+| time-step | 13         |
+| y_out     | 0.17291255 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.27107912   |
+| time-step | 14           |
+| y_out     | -0.050088823 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26555118  |
+| time-step | 15          |
+| y_out     | -0.04610773 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26614523  |
+| time-step | 16          |
+| y_out     | 0.055066578 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2642731    |
+| time-step | 17           |
+| y_out     | -0.042952653 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26352698  |
+| time-step | 18          |
+| y_out     | 0.026130028 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2533337   |
+| time-step | 19          |
+| y_out     | 0.048699126 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+-------------------------------------------------------------
+| perf/mse  | 0.25179696   |
+| time-step | 20           |
+| y_out     | -0.048879944 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25797287 |
+| time-step | 21         |
+| y_out     | 0.0717807  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25513315  |
+| time-step | 22          |
+| y_out     | 0.010292314 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.24589486   |
+| time-step | 23           |
+| y_out     | -0.037122063 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23594365   |
+| time-step | 24           |
+| y_out     | -0.099513546 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23766379  |
+| time-step | 25          |
+| y_out     | 0.038253985 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23817877  |
+| time-step | 26          |
+| y_out     | -0.04920322 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24043305 |
+| time-step | 27         |
+| y_out     | 0.0235192  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23979974  |
+| time-step | 28          |
+| y_out     | 0.030376742 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23798533  |
+| time-step | 29          |
+| y_out     | -0.06327821 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23461246 |
+| time-step | 30         |
+| y_out     | 0.04228808 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22924678 |
+| time-step | 31         |
+| y_out     | 0.138761   |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2328656    |
+| time-step | 32           |
+| y_out     | -0.053567633 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.22847092   |
+| time-step | 33           |
+| y_out     | -0.008183638 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.22734192   |
+| time-step | 34           |
+| y_out     | -0.050216384 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.22175503    |
+| time-step | 35            |
+| y_out     | -0.0041360054 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20843819   |
+| time-step | 36           |
+| y_out     | -0.023561798 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.20617564   |
+| time-step | 37           |
+| y_out     | -0.025891704 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20248365 |
+| time-step | 38         |
+| y_out     | 0.08572664 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20625186 |
+| time-step | 39         |
+| y_out     | 0.13170026 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+--------------------------------------------------------------
+| perf/mse  | 0.2009382     |
+| time-step | 40            |
+| y_out     | 0.00094016735 |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.19305591    |
+| time-step | 41            |
+| y_out     | -0.0054951357 |
+--------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.18918183    |
+| time-step | 42            |
+| y_out     | -0.0029169833 |
+--------------------------------------------------------------
+---------------------------------------------------------------
+| perf/mse  | 0.1883713      |
+| time-step | 43             |
+| y_out     | -0.00096143037 |
+---------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1894991    |
+| time-step | 44           |
+| y_out     | -0.042103514 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19099621   |
+| time-step | 45           |
+| y_out     | 0.0067213047 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18904176  |
+| time-step | 46          |
+| y_out     | -0.08686924 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18306838 |
+| time-step | 47         |
+| y_out     | 0.0109926  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17863014   |
+| time-step | 48           |
+| y_out     | -0.027261835 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17096086 |
+| time-step | 49         |
+| y_out     | -0.1585369 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1690323   |
+| time-step | 50          |
+| y_out     | -0.12948984 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17059372  |
+| time-step | 51          |
+| y_out     | -0.11507261 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16556242  |
+| time-step | 52          |
+| y_out     | -0.18199441 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16355076  |
+| time-step | 53          |
+| y_out     | 0.013860084 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16063938   |
+| time-step | 54           |
+| y_out     | -0.019884463 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15646468   |
+| time-step | 55           |
+| y_out     | -0.046609916 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15517999  |
+| time-step | 56          |
+| y_out     | 0.042844754 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15396328  |
+| time-step | 57          |
+| y_out     | -0.03711673 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15167476   |
+| time-step | 58           |
+| y_out     | 0.0007336363 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14713715  |
+| time-step | 59          |
+| y_out     | 0.058555212 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-------------------------------------------------------------
+| perf/mse  | 0.1436713    |
+| time-step | 60           |
+| y_out     | -0.120284855 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1380341    |
+| time-step | 61           |
+| y_out     | -0.016127799 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1368053    |
+| time-step | 62           |
+| y_out     | 0.0054674447 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13425002   |
+| time-step | 63           |
+| y_out     | -0.059960075 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13149393 |
+| time-step | 64         |
+| y_out     | 0.14438856 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1281505   |
+| time-step | 65          |
+| y_out     | 0.011800697 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12969482  |
+| time-step | 66          |
+| y_out     | 0.015669543 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12626994 |
+| time-step | 67         |
+| y_out     | -0.0468843 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12709959 |
+| time-step | 68         |
+| y_out     | 0.03655757 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12752475 |
+| time-step | 69         |
+| y_out     | 0.10038442 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12636891 |
+| time-step | 70         |
+| y_out     | 0.20802715 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12778363 |
+| time-step | 71         |
+| y_out     | 0.21081522 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12447137  |
+| time-step | 72          |
+| y_out     | 0.033980966 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.122810625  |
+| time-step | 73           |
+| y_out     | 0.0037000738 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12064107   |
+| time-step | 74           |
+| y_out     | -0.018477367 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11794927  |
+| time-step | 75          |
+| y_out     | -0.05084198 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11358658  |
+| time-step | 76          |
+| y_out     | -0.05224493 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10999091 |
+| time-step | 77         |
+| y_out     | 0.07490153 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10511788 |
+| time-step | 78         |
+| y_out     | 0.11349448 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1034572    |
+| time-step | 79           |
+| y_out     | -0.102458686 |
+-------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+------------------------------------------------------------
+| perf/mse  | 0.10272972  |
+| time-step | 80          |
+| y_out     | -0.11170855 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10055767  |
+| time-step | 81          |
+| y_out     | 0.048252013 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.098345354 |
+| time-step | 82          |
+| y_out     | 0.06658258  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.094661705 |
+| time-step | 83          |
+| y_out     | 0.032272883 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.0924102  |
+| time-step | 84         |
+| y_out     | -0.0400239 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.08946519   |
+| time-step | 85           |
+| y_out     | -0.017674271 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.087174855 |
+| time-step | 86          |
+| y_out     | 0.123464696 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.0877087  |
+| time-step | 87         |
+| y_out     | 0.06833343 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.086945035 |
+| time-step | 88          |
+| y_out     | 0.059506014 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0832867   |
+| time-step | 89          |
+| y_out     | -0.09729373 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.079513974   |
+| time-step | 90            |
+| y_out     | -0.0049524345 |
+--------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07658987   |
+| time-step | 91           |
+| y_out     | -0.083177544 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07553563   |
+| time-step | 92           |
+| y_out     | -0.101716675 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.075119    |
+| time-step | 93          |
+| y_out     | 0.038038142 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.072031364  |
+| time-step | 94           |
+| y_out     | -0.088198036 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.07277975    |
+| time-step | 95            |
+| y_out     | -0.0100238975 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.071574286 |
+| time-step | 96          |
+| y_out     | 0.023492098 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.06966231 |
+| time-step | 97         |
+| y_out     | 0.07876533 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.06716306  |
+| time-step | 98          |
+| y_out     | 0.040174026 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.06682237  |
+| time-step | 99          |
+| y_out     | 0.021723732 |
+------------------------------------------------------------
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: True
+k: REMOTE_SETTING, v: {'ftp_server': '127.0.0.1', 'username': 'hadoop-peisongpa', 'password': 'wangli78', 'remote_log_root': '/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:47:56.711179
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..a26d382
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
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+98,0.040174026,0.06716306
+99,0.021723732,0.06682237
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904474.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904474.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..c4a299d
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904474.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-54-184710_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..844e362
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..fa8d3ca
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt
@@ -0,0 +1,13 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904476.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904476.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..fc29696
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904476.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..77db1a0
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-47-56-711179_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt
@@ -0,0 +1,2 @@
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-10-845379_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-10-845379_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..304d372
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,44 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32>
+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/checkpoint 0
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:49:27.292976
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904566.MBP-C02ZL0TNLVCF-2243.local.40042.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904566.MBP-C02ZL0TNLVCF-2243.local.40042.0.v2
new file mode 100644
index 0000000..b6ec776
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904566.MBP-C02ZL0TNLVCF-2243.local.40042.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-22-038729_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..abbb470
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,512 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+-----------------------------------------------------------
+| time-step | 0          |
+| y_out     | -0.0020859 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | 0.031980075 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 2           |
+| y_out     | 0.058423344 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 3            |
+| y_out     | -0.021017302 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 4          |
+| y_out     | 0.04096725 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| time-step | 5           |
+| y_out     | -0.03104342 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 6            |
+| y_out     | -0.021475729 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 7           |
+| y_out     | 0.020799316 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.3136826  |
+| time-step | 8          |
+| y_out     | 0.09937596 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2765532  |
+| time-step | 9          |
+| y_out     | 0.08211042 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27130106  |
+| time-step | 10          |
+| y_out     | -0.08581577 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.27047285   |
+| time-step | 11           |
+| y_out     | -0.022412244 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26315245   |
+| time-step | 12           |
+| y_out     | -0.033260807 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26309633  |
+| time-step | 13          |
+| y_out     | 0.021408694 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2643932   |
+| time-step | 14          |
+| y_out     | 0.080548525 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.26666328   |
+| time-step | 15           |
+| y_out     | 0.0031361114 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26297137  |
+| time-step | 16          |
+| y_out     | 0.039657228 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25668985 |
+| time-step | 17         |
+| y_out     | 0.0935407  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.25277033   |
+| time-step | 18           |
+| y_out     | 0.0007869452 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2508176  |
+| time-step | 19         |
+| y_out     | 0.03556549 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+------------------------------------------------------------
+| perf/mse  | 0.24853416  |
+| time-step | 20          |
+| y_out     | 0.052074254 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24961443 |
+| time-step | 21         |
+| y_out     | 0.09314161 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2537471   |
+| time-step | 22          |
+| y_out     | -0.08948156 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24890473  |
+| time-step | 23          |
+| y_out     | 0.067223184 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25423217  |
+| time-step | 24          |
+| y_out     | -0.05216405 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2505821   |
+| time-step | 25          |
+| y_out     | 0.038366858 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.251793    |
+| time-step | 26          |
+| y_out     | 0.023320748 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.24815866   |
+| time-step | 27           |
+| y_out     | -0.039473515 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24943197  |
+| time-step | 28          |
+| y_out     | 0.046177328 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2500406    |
+| time-step | 29           |
+| y_out     | -0.047244754 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24617282  |
+| time-step | 30          |
+| y_out     | -0.07088733 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23653093   |
+| time-step | 31           |
+| y_out     | 0.0065606274 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2312419   |
+| time-step | 32          |
+| y_out     | 0.016723756 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22675581 |
+| time-step | 33         |
+| y_out     | -0.08171   |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21659903  |
+| time-step | 34          |
+| y_out     | -0.09873071 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21555057   |
+| time-step | 35           |
+| y_out     | -0.059757374 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21193357   |
+| time-step | 36           |
+| y_out     | -0.043370374 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.20666695 |
+| time-step | 37         |
+| y_out     | 0.06944886 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19821915 |
+| time-step | 38         |
+| y_out     | 0.0314136  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1921199   |
+| time-step | 39          |
+| y_out     | 0.079995126 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+-------------------------------------------------------------
+| perf/mse  | 0.18942408   |
+| time-step | 40           |
+| y_out     | -0.020918403 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18791789 |
+| time-step | 41         |
+| y_out     | 0.03408087 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.18484168 |
+| time-step | 42         |
+| y_out     | 0.13112184 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18305035  |
+| time-step | 43          |
+| y_out     | -0.07193909 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17997578   |
+| time-step | 44           |
+| y_out     | -0.053086106 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17632674 |
+| time-step | 45         |
+| y_out     | 0.02654153 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16969523   |
+| time-step | 46           |
+| y_out     | -0.010074176 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.17249379    |
+| time-step | 47            |
+| y_out     | 0.00023600366 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17726424  |
+| time-step | 48          |
+| y_out     | -0.16684091 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.17442486   |
+| time-step | 49           |
+| y_out     | -0.003640267 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17429727  |
+| time-step | 50          |
+| y_out     | -0.13533406 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1719754  |
+| time-step | 51         |
+| y_out     | 0.05747471 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17001393 |
+| time-step | 52         |
+| y_out     | 0.07101396 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16817808  |
+| time-step | 53          |
+| y_out     | -0.04298064 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17017992  |
+| time-step | 54          |
+| y_out     | -0.05383315 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16974834   |
+| time-step | 55           |
+| y_out     | -0.046655007 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16824584  |
+| time-step | 56          |
+| y_out     | 0.019567885 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16472867  |
+| time-step | 57          |
+| y_out     | 0.013739444 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15629388  |
+| time-step | 58          |
+| y_out     | 0.030205023 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.15398619 |
+| time-step | 59         |
+| y_out     | 0.07840521 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+------------------------------------------------------------
+| perf/mse  | 0.1515061   |
+| time-step | 60          |
+| y_out     | 0.110973276 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14948806   |
+| time-step | 61           |
+| y_out     | -0.057090495 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14896278  |
+| time-step | 62          |
+| y_out     | -0.09041178 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14663382  |
+| time-step | 63          |
+| y_out     | 0.015157577 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13946098  |
+| time-step | 64          |
+| y_out     | 0.118504554 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13621528   |
+| time-step | 65           |
+| y_out     | -0.034283265 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13328913   |
+| time-step | 66           |
+| y_out     | -0.043218225 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13371885  |
+| time-step | 67          |
+| y_out     | -0.11445462 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13089441   |
+| time-step | 68           |
+| y_out     | -0.024501957 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12804088  |
+| time-step | 69          |
+| y_out     | -0.06171209 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12506881 |
+| time-step | 70         |
+| y_out     | 0.09192617 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.122883536 |
+| time-step | 71          |
+| y_out     | 0.05022981  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11683407  |
+| time-step | 72          |
+| y_out     | 0.049124695 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11559465  |
+| time-step | 73          |
+| y_out     | 0.046233743 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.112678066 |
+| time-step | 74          |
+| y_out     | 0.024492767 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.109632805 |
+| time-step | 75          |
+| y_out     | -0.06153723 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.107513234 |
+| time-step | 76          |
+| y_out     | 0.10439761  |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10170199 |
+| time-step | 77         |
+| y_out     | 0.0247088  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10150097   |
+| time-step | 78           |
+| y_out     | -0.052258004 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10264929  |
+| time-step | 79          |
+| y_out     | 0.095354296 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+------------------------------------------------------------
+| perf/mse  | 0.09799919  |
+| time-step | 80          |
+| y_out     | -0.07185683 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09708913   |
+| time-step | 81           |
+| y_out     | -0.006173962 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.096542954 |
+| time-step | 82          |
+| y_out     | -0.07637519 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09363652 |
+| time-step | 83         |
+| y_out     | 0.28974384 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09218248  |
+| time-step | 84          |
+| y_out     | 0.110396884 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.0892167  |
+| time-step | 85         |
+| y_out     | 0.08827115 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.087160714 |
+| time-step | 86          |
+| y_out     | 0.08718131  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08699484  |
+| time-step | 87          |
+| y_out     | -0.00950573 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08438973  |
+| time-step | 88          |
+| y_out     | -0.14732596 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.079335295 |
+| time-step | 89          |
+| y_out     | 0.08806938  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0774589   |
+| time-step | 90          |
+| y_out     | 0.069696456 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.0744612  |
+| time-step | 91         |
+| y_out     | 0.13969997 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.07252376 |
+| time-step | 92         |
+| y_out     | 0.05327324 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06896646   |
+| time-step | 93           |
+| y_out     | -0.020978473 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06610964   |
+| time-step | 94           |
+| y_out     | -0.034531794 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.064324655  |
+| time-step | 95           |
+| y_out     | -0.050053407 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0629728   |
+| time-step | 96          |
+| y_out     | 0.019987341 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059708018 |
+| time-step | 97          |
+| y_out     | 0.006296722 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.05819304   |
+| time-step | 98           |
+| y_out     | -0.018171167 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.055879563 |
+| time-step | 99          |
+| y_out     | 0.015744347 |
+------------------------------------------------------------
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: True
+k: REMOTE_SETTING, v: {'ftp_server': '127.0.0.1', 'username': 'hadoop-peisongpa', 'password': 'wangli78', 'remote_log_root': '/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 14:49:29.936958
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..4096402
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,-0.0020859,
+1,0.031980075,
+2,0.058423344,
+3,-0.021017302,
+4,0.04096725,
+5,-0.03104342,
+6,-0.021475729,
+7,0.020799316,
+8,0.09937596,0.3136826
+9,0.08211042,0.2765532
+10,-0.08581577,0.27130106
+11,-0.022412244,0.27047285
+12,-0.033260807,0.26315245
+13,0.021408694,0.26309633
+14,0.080548525,0.2643932
+15,0.0031361114,0.26666328
+16,0.039657228,0.26297137
+17,0.0935407,0.25668985
+18,0.0007869452,0.25277033
+19,0.03556549,0.2508176
+20,0.052074254,0.24853416
+21,0.09314161,0.24961443
+22,-0.08948156,0.2537471
+23,0.067223184,0.24890473
+24,-0.05216405,0.25423217
+25,0.038366858,0.2505821
+26,0.023320748,0.251793
+27,-0.039473515,0.24815866
+28,0.046177328,0.24943197
+29,-0.047244754,0.2500406
+30,-0.07088733,0.24617282
+31,0.0065606274,0.23653093
+32,0.016723756,0.2312419
+33,-0.08171,0.22675581
+34,-0.09873071,0.21659903
+35,-0.059757374,0.21555057
+36,-0.043370374,0.21193357
+37,0.06944886,0.20666695
+38,0.0314136,0.19821915
+39,0.079995126,0.1921199
+40,-0.020918403,0.18942408
+41,0.03408087,0.18791789
+42,0.13112184,0.18484168
+43,-0.07193909,0.18305035
+44,-0.053086106,0.17997578
+45,0.02654153,0.17632674
+46,-0.010074176,0.16969523
+47,0.00023600366,0.17249379
+48,-0.16684091,0.17726424
+49,-0.003640267,0.17442486
+50,-0.13533406,0.17429727
+51,0.05747471,0.1719754
+52,0.07101396,0.17001393
+53,-0.04298064,0.16817808
+54,-0.05383315,0.17017992
+55,-0.046655007,0.16974834
+56,0.019567885,0.16824584
+57,0.013739444,0.16472867
+58,0.030205023,0.15629388
+59,0.07840521,0.15398619
+60,0.110973276,0.1515061
+61,-0.057090495,0.14948806
+62,-0.09041178,0.14896278
+63,0.015157577,0.14663382
+64,0.118504554,0.13946098
+65,-0.034283265,0.13621528
+66,-0.043218225,0.13328913
+67,-0.11445462,0.13371885
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+69,-0.06171209,0.12804088
+70,0.09192617,0.12506881
+71,0.05022981,0.122883536
+72,0.049124695,0.11683407
+73,0.046233743,0.11559465
+74,0.024492767,0.112678066
+75,-0.06153723,0.109632805
+76,0.10439761,0.107513234
+77,0.0247088,0.10170199
+78,-0.052258004,0.10150097
+79,0.095354296,0.10264929
+80,-0.07185683,0.09799919
+81,-0.006173962,0.09708913
+82,-0.07637519,0.096542954
+83,0.28974384,0.09363652
+84,0.110396884,0.09218248
+85,0.08827115,0.0892167
+86,0.08718131,0.087160714
+87,-0.00950573,0.08699484
+88,-0.14732596,0.08438973
+89,0.08806938,0.079335295
+90,0.069696456,0.0774589
+91,0.13969997,0.0744612
+92,0.05327324,0.07252376
+93,-0.020978473,0.06896646
+94,-0.034531794,0.06610964
+95,-0.050053407,0.064324655
+96,0.019987341,0.0629728
+97,0.006296722,0.059708018
+98,-0.018171167,0.05819304
+99,0.015744347,0.055879563
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904567.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904567.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..bd69225
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904567.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-27-292976_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..844e362
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..85ce5c2
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt
@@ -0,0 +1,13 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904569.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904569.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..7968509
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658904569.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..77db1a0
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-49-29-936958_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt
@@ -0,0 +1,2 @@
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..6a284c0
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,39 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32>
+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/checkpoint 0
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | -0.0555  |
+---------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904610.MBP-C02ZL0TNLVCF-2243.local.40132.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904610.MBP-C02ZL0TNLVCF-2243.local.40132.0.v2
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Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904610.MBP-C02ZL0TNLVCF-2243.local.40132.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-50-05-587086_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..82da642
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904700.MBP-C02ZL0TNLVCF-2243.local.40253.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904700.MBP-C02ZL0TNLVCF-2243.local.40253.0.v2
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Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904700.MBP-C02ZL0TNLVCF-2243.local.40253.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-51-34-399790_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..da2bdf5
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,39 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32>
+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/checkpoint 0
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | -0.0253  |
+---------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/progress.csv
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index 0000000..e69de29
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Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904842.MBP-C02ZL0TNLVCF-2243.local.40468.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-53-56-865123_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..716a9f5
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,39 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32>
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+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32>
+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/checkpoint 0
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.0605   |
+---------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/progress.csv
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index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904902.MBP-C02ZL0TNLVCF-2243.local.40544.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904902.MBP-C02ZL0TNLVCF-2243.local.40544.0.v2
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diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-54-56-398017_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..07c2622
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,39 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32>
+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/checkpoint 0
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.0448   |
+---------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904924.MBP-C02ZL0TNLVCF-2243.local.40579.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904924.MBP-C02ZL0TNLVCF-2243.local.40579.0.v2
new file mode 100644
index 0000000..a275ba1
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658904924.MBP-C02ZL0TNLVCF-2243.local.40579.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-55-20-143125_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..763e238
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,39 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save variable :
+<tf.Variable 'dense/kernel:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32>
+<tf.Variable 'beta1_power:0' shape=() dtype=float32>
+<tf.Variable 'beta2_power:0' shape=() dtype=float32>
+<tf.Variable 'dense/kernel/Adam:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/kernel/Adam_1:0' shape=(16, 64) dtype=float32>
+<tf.Variable 'dense/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_1/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/kernel/Adam_1:0' shape=(64, 64) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_2/bias/Adam_1:0' shape=(64,) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/kernel/Adam_1:0' shape=(64, 1) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam:0' shape=(1,) dtype=float32>
+<tf.Variable 'dense_3/bias/Adam_1:0' shape=(1,) dtype=float32>
+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/checkpoint 0
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.000222 |
+---------------------------------------------------------
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658905095.MBP-C02ZL0TNLVCF-2243.local.40743.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658905095.MBP-C02ZL0TNLVCF-2243.local.40743.0.v2
new file mode 100644
index 0000000..6d8fa22
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658905095.MBP-C02ZL0TNLVCF-2243.local.40743.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/14-58-10-320236_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..98e19d0
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,8 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/checkpoint 0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658906550.MBP-C02ZL0TNLVCF-2243.local.41997.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658906550.MBP-C02ZL0TNLVCF-2243.local.41997.0.v2
new file mode 100644
index 0000000..942669c
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658906550.MBP-C02ZL0TNLVCF-2243.local.41997.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/15-22-21-681058_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..d9eefcb
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,22 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/checkpoint 0
+[WARN] 0 : call save_checkpoints without passing a model_dict
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.0544   |
+---------------------------------------------------------
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 21:02:31.719700
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658926951.MBP-C02ZL0TNLVCF-2243.local.55585.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658926951.MBP-C02ZL0TNLVCF-2243.local.55585.0.v2
new file mode 100644
index 0000000..e346cf8
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658926951.MBP-C02ZL0TNLVCF-2243.local.55585.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..353772e
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-23-935694_172.16.0.33_&input_size=16/warn.txt
@@ -0,0 +1 @@
+[WARN] 0 : call save_checkpoints without passing a model_dict
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..830ebda
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,512 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+------------------------------------------------------------
+| time-step | 0           |
+| y_out     | -0.08115087 |
+------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | 0.020872777 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 2            |
+| y_out     | -0.015734304 |
+-------------------------------------------------------------
+----------------------------------------------------------
+| time-step | 3         |
+| y_out     | 0.1406791 |
+----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 4          |
+| y_out     | 0.16173068 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| time-step | 5          |
+| y_out     | 0.11281406 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 6            |
+| y_out     | -0.049564213 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 7           |
+| y_out     | -0.06445912 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.29529536 |
+| time-step | 8          |
+| y_out     | -0.1681888 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24866977  |
+| time-step | 9           |
+| y_out     | 0.058863617 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24618325  |
+| time-step | 10          |
+| y_out     | 0.049352225 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24046633 |
+| time-step | 11         |
+| y_out     | 0.13582969 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.23825344   |
+| time-step | 12           |
+| y_out     | -0.008340796 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23578516 |
+| time-step | 13         |
+| y_out     | 0.06682308 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24125294 |
+| time-step | 14         |
+| y_out     | 0.20863393 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24277206  |
+| time-step | 15          |
+| y_out     | -0.07228148 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24027088 |
+| time-step | 16         |
+| y_out     | 0.0084858  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24026754  |
+| time-step | 17          |
+| y_out     | -0.14226297 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24350849  |
+| time-step | 18          |
+| y_out     | 0.048472144 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.24074654 |
+| time-step | 19         |
+| y_out     | 0.1272738  |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+-----------------------------------------------------------
+| perf/mse  | 0.23248856 |
+| time-step | 20         |
+| y_out     | 0.0725357  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23360589  |
+| time-step | 21          |
+| y_out     | -0.06995608 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.2281343     |
+| time-step | 22            |
+| y_out     | -0.0065806583 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22420463  |
+| time-step | 23          |
+| y_out     | -0.13088128 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21965651  |
+| time-step | 24          |
+| y_out     | -0.06589019 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21756205   |
+| time-step | 25           |
+| y_out     | -0.006940536 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2120883  |
+| time-step | 26         |
+| y_out     | 0.03967285 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20946279  |
+| time-step | 27          |
+| y_out     | 0.014822016 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20610818  |
+| time-step | 28          |
+| y_out     | 0.083630525 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.2043434  |
+| time-step | 29         |
+| y_out     | 0.02395604 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20440221  |
+| time-step | 30          |
+| y_out     | 0.079894826 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.19967046    |
+| time-step | 31            |
+| y_out     | -0.0049796496 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19899413  |
+| time-step | 32          |
+| y_out     | -0.05193953 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19576254  |
+| time-step | 33          |
+| y_out     | 0.017952517 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.19560614 |
+| time-step | 34         |
+| y_out     | 0.08105061 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1932191    |
+| time-step | 35           |
+| y_out     | -0.020725098 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19571096   |
+| time-step | 36           |
+| y_out     | 0.0064680083 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.19319808   |
+| time-step | 37           |
+| y_out     | -0.019612994 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1829929   |
+| time-step | 38          |
+| y_out     | 0.028183863 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17908275 |
+| time-step | 39         |
+| y_out     | 0.07364625 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+-----------------------------------------------------------
+| perf/mse  | 0.18083875 |
+| time-step | 40         |
+| y_out     | 0.05398774 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.1808972     |
+| time-step | 41            |
+| y_out     | -0.0150926765 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17694792 |
+| time-step | 42         |
+| y_out     | 0.06865174 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17694503  |
+| time-step | 43          |
+| y_out     | -0.07300543 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17190346  |
+| time-step | 44          |
+| y_out     | 0.047520828 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16814709  |
+| time-step | 45          |
+| y_out     | 0.021530662 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16354777  |
+| time-step | 46          |
+| y_out     | 0.020527724 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16285244   |
+| time-step | 47           |
+| y_out     | -0.090933375 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.16293135  |
+| time-step | 48          |
+| y_out     | 0.039987274 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16114184   |
+| time-step | 49           |
+| y_out     | -0.008168457 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15981683  |
+| time-step | 50          |
+| y_out     | -0.03908532 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15484877  |
+| time-step | 51          |
+| y_out     | 0.084308185 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.15495887  |
+| time-step | 52          |
+| y_out     | 0.093848005 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14964141   |
+| time-step | 53           |
+| y_out     | -0.008494442 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14762269  |
+| time-step | 54          |
+| y_out     | 0.025881805 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14554231   |
+| time-step | 55           |
+| y_out     | -0.046755496 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.14182729   |
+| time-step | 56           |
+| y_out     | -0.018667027 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13691452 |
+| time-step | 57         |
+| y_out     | 0.08797852 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13686784  |
+| time-step | 58          |
+| y_out     | -0.13114515 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13756047  |
+| time-step | 59          |
+| y_out     | 0.027838971 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+------------------------------------------------------------
+| perf/mse  | 0.13312738  |
+| time-step | 60          |
+| y_out     | -0.03731026 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.13448606   |
+| time-step | 61           |
+| y_out     | 0.0034433305 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1331622   |
+| time-step | 62          |
+| y_out     | 0.009407705 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13295387  |
+| time-step | 63          |
+| y_out     | 0.024143502 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.12850334   |
+| time-step | 64           |
+| y_out     | -0.019527981 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12429509 |
+| time-step | 65         |
+| y_out     | 0.12750119 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.123570025 |
+| time-step | 66          |
+| y_out     | 0.022382382 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12333448 |
+| time-step | 67         |
+| y_out     | 0.0916282  |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.11747887   |
+| time-step | 68           |
+| y_out     | -0.043575056 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.112562455 |
+| time-step | 69          |
+| y_out     | 0.088415325 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.107550815 |
+| time-step | 70          |
+| y_out     | -0.08003076 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.10520385   |
+| time-step | 71           |
+| y_out     | -0.060309283 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10021104 |
+| time-step | 72         |
+| y_out     | 0.14613459 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09780532  |
+| time-step | 73          |
+| y_out     | 0.014975958 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.09741179 |
+| time-step | 74         |
+| y_out     | 0.07731803 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09523808  |
+| time-step | 75          |
+| y_out     | -0.06037996 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08982111  |
+| time-step | 76          |
+| y_out     | 0.027954731 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0858041   |
+| time-step | 77          |
+| y_out     | 0.070564516 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.0846568  |
+| time-step | 78         |
+| y_out     | 0.12844825 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.081011295 |
+| time-step | 79          |
+| y_out     | -0.06525979 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+-----------------------------------------------------------
+| perf/mse  | 0.08083167 |
+| time-step | 80         |
+| y_out     | 0.06867719 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.076571204 |
+| time-step | 81          |
+| y_out     | -0.08265691 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07691847   |
+| time-step | 82           |
+| y_out     | -0.049602076 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.074431464 |
+| time-step | 83          |
+| y_out     | -0.13827844 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07270539   |
+| time-step | 84           |
+| y_out     | -0.051012516 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.07022231   |
+| time-step | 85           |
+| y_out     | -0.039824918 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.07045157  |
+| time-step | 86          |
+| y_out     | -0.07199623 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.06725861  |
+| time-step | 87          |
+| y_out     | 0.021302592 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0665552   |
+| time-step | 88          |
+| y_out     | -0.04912517 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06505462   |
+| time-step | 89           |
+| y_out     | -0.080845185 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0629918    |
+| time-step | 90           |
+| y_out     | -0.027250418 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.06251709   |
+| time-step | 91           |
+| y_out     | -0.047542214 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.061991654 |
+| time-step | 92          |
+| y_out     | 0.12417862  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.060264748 |
+| time-step | 93          |
+| y_out     | 0.12456248  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.059348445 |
+| time-step | 94          |
+| y_out     | 0.060872413 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058409035 |
+| time-step | 95          |
+| y_out     | 0.024682853 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057927877 |
+| time-step | 96          |
+| y_out     | 0.14814343  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.058606815 |
+| time-step | 97          |
+| y_out     | 0.021679616 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.057646226 |
+| time-step | 98          |
+| y_out     | -0.06782472 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.056733437 |
+| time-step | 99          |
+| y_out     | 0.12753248  |
+------------------------------------------------------------
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: True
+k: REMOTE_SETTING, v: {'ftp_server': '127.0.0.1', 'username': 'hadoop-peisongpa', 'password': 'wangli78', 'remote_log_root': '/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 21:02:34.400465
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..7343023
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
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+98,-0.06782472,0.057646226
+99,0.12753248,0.056733437
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658926951.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658926951.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..fa865f7
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658926951.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-31-719700_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..844e362
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..dd7f993
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt
@@ -0,0 +1,13 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658926954.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658926954.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..0af38d3
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658926954.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..77db1a0
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-02-34-400465_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt
@@ -0,0 +1,2 @@
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..b2c70fe
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,22 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+save checkpoint to  /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/checkpoint 0
+[WARN] 0 : call save_checkpoints without passing a model_dict
+---------------------------------------------------------
+| time-step | 0        |
+| y_out     | 0.05     |
+---------------------------------------------------------
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: False
+k: REMOTE_SETTING, v: {'ftp_server': '', 'username': '', 'password': '', 'remote_log_root': '', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 21:04:34.397714
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/progress.csv
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658927073.MBP-C02ZL0TNLVCF-2243.local.55726.0.v2 b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658927073.MBP-C02ZL0TNLVCF-2243.local.55726.0.v2
new file mode 100644
index 0000000..578a808
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/tb/events/events.out.tfevents.1658927073.MBP-C02ZL0TNLVCF-2243.local.55726.0.v2 differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/warn.txt
new file mode 100644
index 0000000..353772e
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-28-137106_172.16.0.33_&input_size=16/warn.txt
@@ -0,0 +1 @@
+[WARN] 0 : call save_checkpoints without passing a model_dict
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..4361513
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/log.txt
@@ -0,0 +1,512 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
+-------------------------------------------------------------
+| time-step | 0            |
+| y_out     | -0.051086485 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 1           |
+| y_out     | -0.02698705 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 2            |
+| y_out     | -0.075942576 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 3            |
+| y_out     | -0.075461894 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 4           |
+| y_out     | -0.05449262 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 5            |
+| y_out     | -0.027682275 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| time-step | 6            |
+| y_out     | -0.028784622 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| time-step | 7           |
+| y_out     | 0.019450793 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.34783134  |
+| time-step | 8           |
+| y_out     | -0.04913006 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.30258074  |
+| time-step | 9           |
+| y_out     | 0.030929992 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.3059935     |
+| time-step | 10            |
+| y_out     | -0.0095580015 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.31505838 |
+| time-step | 11         |
+| y_out     | 0.11838068 |
+-----------------------------------------------------------
+----------------------------------------------------------
+| perf/mse  | 0.3080894 |
+| time-step | 12        |
+| y_out     | 0.0626796 |
+----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.30533016   |
+| time-step | 13           |
+| y_out     | -0.047757015 |
+-------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.30789313    |
+| time-step | 14            |
+| y_out     | -0.0049784426 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29572922  |
+| time-step | 15          |
+| y_out     | -0.07652553 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.3000459   |
+| time-step | 16          |
+| y_out     | -0.10563607 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.30273893  |
+| time-step | 17          |
+| y_out     | 0.020040935 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29671016  |
+| time-step | 18          |
+| y_out     | -0.08836341 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.29528487  |
+| time-step | 19          |
+| y_out     | 0.005402619 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/checkpoint-0.pt
+-----------------------------------------------------------
+| perf/mse  | 0.28998893 |
+| time-step | 20         |
+| y_out     | 0.05667349 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.28310436 |
+| time-step | 21         |
+| y_out     | -0.0698992 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.28471208 |
+| time-step | 22         |
+| y_out     | 0.06730319 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2819135   |
+| time-step | 23          |
+| y_out     | -0.07337211 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.27035934 |
+| time-step | 24         |
+| y_out     | 0.1207891  |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27713773  |
+| time-step | 25          |
+| y_out     | 0.074191086 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.27480096  |
+| time-step | 26          |
+| y_out     | 0.018349513 |
+------------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.27242175    |
+| time-step | 27            |
+| y_out     | -0.0045923665 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.26434466  |
+| time-step | 28          |
+| y_out     | -0.14349844 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.25847307   |
+| time-step | 29           |
+| y_out     | -0.049871035 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.25446734  |
+| time-step | 30          |
+| y_out     | 0.041328974 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.25169033 |
+| time-step | 31         |
+| y_out     | 0.06320353 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.24659657  |
+| time-step | 32          |
+| y_out     | 0.036889825 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.23987012  |
+| time-step | 33          |
+| y_out     | 0.048881583 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23878428 |
+| time-step | 34         |
+| y_out     | 0.07505766 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.23188324 |
+| time-step | 35         |
+| y_out     | 0.07418668 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22707498 |
+| time-step | 36         |
+| y_out     | 0.09659318 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.22177002  |
+| time-step | 37          |
+| y_out     | 0.009791079 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2218984   |
+| time-step | 38          |
+| y_out     | -0.12587658 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.22242999 |
+| time-step | 39         |
+| y_out     | 0.10701798 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/checkpoint-20.pt
+------------------------------------------------------------
+| perf/mse  | 0.22375815  |
+| time-step | 40          |
+| y_out     | 0.046524167 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.22064534   |
+| time-step | 41           |
+| y_out     | -0.041583918 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21489826   |
+| time-step | 42           |
+| y_out     | -0.025453927 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.21865828   |
+| time-step | 43           |
+| y_out     | -0.016884673 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.21557887  |
+| time-step | 44          |
+| y_out     | 0.043976948 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2105932    |
+| time-step | 45           |
+| y_out     | 0.0055210292 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.2091937    |
+| time-step | 46           |
+| y_out     | 0.0029936023 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.2047565   |
+| time-step | 47          |
+| y_out     | -0.04349903 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.20395362  |
+| time-step | 48          |
+| y_out     | -0.04279626 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19538729  |
+| time-step | 49          |
+| y_out     | 0.007642824 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.19011329  |
+| time-step | 50          |
+| y_out     | 0.041126788 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.18757898  |
+| time-step | 51          |
+| y_out     | -0.03312173 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.18304998   |
+| time-step | 52           |
+| y_out     | -0.041093938 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1811797    |
+| time-step | 53           |
+| y_out     | -0.037838485 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17855981  |
+| time-step | 54          |
+| y_out     | 0.030452728 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.1771881    |
+| time-step | 55           |
+| y_out     | -0.020946955 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1726879   |
+| time-step | 56          |
+| y_out     | -0.06553401 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.1734949  |
+| time-step | 57         |
+| y_out     | 0.05746308 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.17061904  |
+| time-step | 58          |
+| y_out     | 0.053307086 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.17487395 |
+| time-step | 59         |
+| y_out     | 0.09337895 |
+-----------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/checkpoint-40.pt
+-------------------------------------------------------------
+| perf/mse  | 0.1742812    |
+| time-step | 60           |
+| y_out     | 0.0005245544 |
+-------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16906671 |
+| time-step | 61         |
+| y_out     | 0.03827511 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.16997075 |
+| time-step | 62         |
+| y_out     | 0.04714056 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.16285229   |
+| time-step | 63           |
+| y_out     | -0.047981266 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1590677   |
+| time-step | 64          |
+| y_out     | -0.03883331 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.1596856   |
+| time-step | 65          |
+| y_out     | -0.02228038 |
+------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15854773   |
+| time-step | 66           |
+| y_out     | -0.034229316 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15358314   |
+| time-step | 67           |
+| y_out     | -0.057871602 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.15020625   |
+| time-step | 68           |
+| y_out     | -0.015511872 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14463627  |
+| time-step | 69          |
+| y_out     | -0.09023917 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14228424 |
+| time-step | 70         |
+| y_out     | 0.0177396  |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14222746 |
+| time-step | 71         |
+| y_out     | 0.04570595 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.13919248    |
+| time-step | 72            |
+| y_out     | -0.0044672843 |
+--------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.14174445 |
+| time-step | 73         |
+| y_out     | 0.07797983 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.14174882  |
+| time-step | 74          |
+| y_out     | 0.074832246 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13612661  |
+| time-step | 75          |
+| y_out     | -0.12848346 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.13612518  |
+| time-step | 76          |
+| y_out     | 0.071711086 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.13231376 |
+| time-step | 77         |
+| y_out     | 0.09582132 |
+-----------------------------------------------------------
+--------------------------------------------------------------
+| perf/mse  | 0.12807393    |
+| time-step | 78            |
+| y_out     | -0.0016565807 |
+--------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.12530199  |
+| time-step | 79          |
+| y_out     | 0.062675826 |
+------------------------------------------------------------
+rm the older checkpoint /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/checkpoint-60.pt
+-----------------------------------------------------------
+| perf/mse  | 0.12274021 |
+| time-step | 80         |
+| y_out     | 0.07324555 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12176587 |
+| time-step | 81         |
+| y_out     | 0.11235197 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.12102838 |
+| time-step | 82         |
+| y_out     | 0.12049341 |
+-----------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.116737545 |
+| time-step | 83          |
+| y_out     | 0.044141814 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.11334147  |
+| time-step | 84          |
+| y_out     | -0.07883172 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11261556 |
+| time-step | 85         |
+| y_out     | 0.08469814 |
+-----------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.11019285 |
+| time-step | 86         |
+| y_out     | 0.07633519 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.106559     |
+| time-step | 87           |
+| y_out     | -0.008310407 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.10640468  |
+| time-step | 88          |
+| y_out     | -0.01596049 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.105496526 |
+| time-step | 89          |
+| y_out     | 0.16374026  |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.10310552 |
+| time-step | 90         |
+| y_out     | 0.10466293 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0985758    |
+| time-step | 91           |
+| y_out     | -0.042471107 |
+-------------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.09440977   |
+| time-step | 92           |
+| y_out     | -0.021943454 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.09156682  |
+| time-step | 93          |
+| y_out     | -0.03158443 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.090746835 |
+| time-step | 94          |
+| y_out     | 0.09120302  |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08728173  |
+| time-step | 95          |
+| y_out     | 0.017823607 |
+------------------------------------------------------------
+-----------------------------------------------------------
+| perf/mse  | 0.084431   |
+| time-step | 96         |
+| y_out     | 0.03341708 |
+-----------------------------------------------------------
+-------------------------------------------------------------
+| perf/mse  | 0.0857253    |
+| time-step | 97           |
+| y_out     | -0.121995635 |
+-------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.08396189  |
+| time-step | 98          |
+| y_out     | 0.013786392 |
+------------------------------------------------------------
+------------------------------------------------------------
+| perf/mse  | 0.0797403   |
+| time-step | 99          |
+| y_out     | -0.06786626 |
+------------------------------------------------------------
+private_config: 
+k: PROJECT_TYPE, v: {'backup_code_by': 'source'}
+k: BACKUP_CONFIG, v: {'backup_code_dir': ['proj'], 'lib_dir': './build/lib/'}
+k: LOG_USED, v: ['stdout', 'log', 'tensorboard', 'csv']
+k: DL_FRAMEWORK, v: torch
+k: SEND_LOG_FILE, v: True
+k: REMOTE_SETTING, v: {'ftp_server': '127.0.0.1', 'username': 'hadoop-peisongpa', 'password': 'wangli78', 'remote_log_root': '/mnt/dolphinfs/hdd_pool/docker/user/hadoop-peisongpa/bear/projects/RLAssistant/test/remote_data_root', 'file_transfer_protocol': 'sftp'}
+gen log files for record date : 2022-07-27 21:04:37.020422
+store file /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16.pkl
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..71005bb
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,101 @@
+time-step,y_out,perf/mse
+0,-0.051086485,
+1,-0.02698705,
+2,-0.075942576,
+3,-0.075461894,
+4,-0.05449262,
+5,-0.027682275,
+6,-0.028784622,
+7,0.019450793,
+8,-0.04913006,0.34783134
+9,0.030929992,0.30258074
+10,-0.0095580015,0.3059935
+11,0.11838068,0.31505838
+12,0.0626796,0.3080894
+13,-0.047757015,0.30533016
+14,-0.0049784426,0.30789313
+15,-0.07652553,0.29572922
+16,-0.10563607,0.3000459
+17,0.020040935,0.30273893
+18,-0.08836341,0.29671016
+19,0.005402619,0.29528487
+20,0.05667349,0.28998893
+21,-0.0698992,0.28310436
+22,0.06730319,0.28471208
+23,-0.07337211,0.2819135
+24,0.1207891,0.27035934
+25,0.074191086,0.27713773
+26,0.018349513,0.27480096
+27,-0.0045923665,0.27242175
+28,-0.14349844,0.26434466
+29,-0.049871035,0.25847307
+30,0.041328974,0.25446734
+31,0.06320353,0.25169033
+32,0.036889825,0.24659657
+33,0.048881583,0.23987012
+34,0.07505766,0.23878428
+35,0.07418668,0.23188324
+36,0.09659318,0.22707498
+37,0.009791079,0.22177002
+38,-0.12587658,0.2218984
+39,0.10701798,0.22242999
+40,0.046524167,0.22375815
+41,-0.041583918,0.22064534
+42,-0.025453927,0.21489826
+43,-0.016884673,0.21865828
+44,0.043976948,0.21557887
+45,0.0055210292,0.2105932
+46,0.0029936023,0.2091937
+47,-0.04349903,0.2047565
+48,-0.04279626,0.20395362
+49,0.007642824,0.19538729
+50,0.041126788,0.19011329
+51,-0.03312173,0.18757898
+52,-0.041093938,0.18304998
+53,-0.037838485,0.1811797
+54,0.030452728,0.17855981
+55,-0.020946955,0.1771881
+56,-0.06553401,0.1726879
+57,0.05746308,0.1734949
+58,0.053307086,0.17061904
+59,0.09337895,0.17487395
+60,0.0005245544,0.1742812
+61,0.03827511,0.16906671
+62,0.04714056,0.16997075
+63,-0.047981266,0.16285229
+64,-0.03883331,0.1590677
+65,-0.02228038,0.1596856
+66,-0.034229316,0.15854773
+67,-0.057871602,0.15358314
+68,-0.015511872,0.15020625
+69,-0.09023917,0.14463627
+70,0.0177396,0.14228424
+71,0.04570595,0.14222746
+72,-0.0044672843,0.13919248
+73,0.07797983,0.14174445
+74,0.074832246,0.14174882
+75,-0.12848346,0.13612661
+76,0.071711086,0.13612518
+77,0.09582132,0.13231376
+78,-0.0016565807,0.12807393
+79,0.062675826,0.12530199
+80,0.07324555,0.12274021
+81,0.11235197,0.12176587
+82,0.12049341,0.12102838
+83,0.044141814,0.116737545
+84,-0.07883172,0.11334147
+85,0.08469814,0.11261556
+86,0.07633519,0.11019285
+87,-0.008310407,0.106559
+88,-0.01596049,0.10640468
+89,0.16374026,0.105496526
+90,0.10466293,0.10310552
+91,-0.042471107,0.0985758
+92,-0.021943454,0.09440977
+93,-0.03158443,0.09156682
+94,0.09120302,0.090746835
+95,0.017823607,0.08728173
+96,0.03341708,0.084431
+97,-0.121995635,0.0857253
+98,0.013786392,0.08396189
+99,-0.06786626,0.0797403
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658927074.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658927074.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..0c8864a
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/tb/events/events.out.tfevents.1658927074.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-34-397714_172.16.0.33_&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..e69de29
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt
new file mode 100644
index 0000000..844e362
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt
new file mode 100644
index 0000000..981afe4
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/log.txt
@@ -0,0 +1,13 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/
+[BACKUP] 99 : key: input_size, value: 16
+[BACKUP] 99 : key: learning_rate, value: 0.0001
+--------------------------------------------------
+| i         | 0 |
+| time-step | 0 |
+--------------------------------------------------
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv
new file mode 100644
index 0000000..16ef5df
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/progress.csv
@@ -0,0 +1,2 @@
+i,time-step
+0,0
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658927077.MBP-C02ZL0TNLVCF-2243.local b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658927077.MBP-C02ZL0TNLVCF-2243.local
new file mode 100644
index 0000000..0832362
Binary files /dev/null and b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/tb/events/events.out.tfevents.1658927077.MBP-C02ZL0TNLVCF-2243.local differ
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt
new file mode 100644
index 0000000..77db1a0
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-04-37-020422_172.16.0.33_&input_size=16&input_size=16&input_size=16/warn.txt
@@ -0,0 +1,2 @@
+[WARN] 0 : sync: start
+[WARN] 0 : the parameter remote_log_root will be renamed to remote_data_root in future versions.
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/backup.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/backup.txt
new file mode 100644
index 0000000..b8eb801
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/backup.txt
@@ -0,0 +1,2 @@
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/log.txt b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/log.txt
new file mode 100644
index 0000000..806ac3a
--- /dev/null
+++ b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/log.txt
@@ -0,0 +1,7 @@
+Logging to /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/
+log dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/
+pkl_file: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/archive_tester/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16.pkl
+checkpoint_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/checkpoint/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/
+results_dir: /Users/xionghuichen/projects/RLAssistant/test/test_data_root/results/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/
+[BACKUP] 0 : key: input_size, value: 16
+[BACKUP] 0 : key: learning_rate, value: 0.0001
diff --git a/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/progress.csv b/test/test_data_root/log/test_manger_demo_task/2022/07/27/21-29-35-100821_172.16.0.33_&input_size=16/progress.csv
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diff --git a/test/test_plot.py b/test/test_plot.py
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+++ b/test/test_plot.py
@@ -0,0 +1,26 @@
+# Created by xionghuichen at 2022/8/10
+# Email: chenxh@lamda.nju.edu.cn
+from test._base import BaseTest
+from RLA.easy_log.log_tools import DeleteLogTool, Filter
+from RLA.easy_log.log_tools import ArchiveLogTool, ViewLogTool
+from RLA.easy_log.tester import exp_manager
+
+import os
+
+class ScriptTest(BaseTest):
+    def test_plot(self):
+        from RLA import plot_func
+        data_root = 'test_data_root'
+        task = 'demo_task'
+        regs = [
+            '2022/03/01/21-[12]*'
+        ]
+        _ = plot_func(data_root=data_root, task_table_name=task, regs=regs, split_keys=['learning_rate'],
+                      metrics=['perf/mse'])
+        _ = plot_func(data_root=data_root, task_table_name=task, regs=regs, split_keys=['learning_rate'],
+                      metrics=['perf/mse'], ylim=(0, 0.1))
+        _ = plot_func(data_root=data_root, task_table_name=task, regs=regs, split_keys=['learning_rate'],
+                      metrics=['perf/mse'], ylim=(0, 0.1), xlabel='epochs',  ylabel='reward ratio', )
+        _ = plot_func(data_root=data_root, task_table_name=task, regs=regs, split_keys=['learning_rate'],
+                      metrics=['perf/mse'], ylim=(0, 0.1), xlabel='epochs',  ylabel='reward ratio',
+                      shaded_range=False, pretty=True)