Add the argument tensorboard_freq to control sampling ratio during training.

deepmd/train/trainer.py

@@ -257,6 +257,7 @@ def _init_param(self, jdata):
         self.profiling_file = tr_data.get('profiling_file', 'timeline.json')
         self.tensorboard = self.run_opt.is_chief and tr_data.get('tensorboard', False)
         self.tensorboard_log_dir = tr_data.get('tensorboard_log_dir', 'log')
+        self.tensorboard_freq = tr_data.get('tensorboard_freq', 1)
         # self.sys_probs = tr_data['sys_probs']
         # self.auto_prob_style = tr_data['auto_prob']
         self.useBN = False
@@ -475,11 +476,11 @@ def train (self, train_data = None, valid_data=None) :
             train_feed_dict = self.get_feed_dict(train_batch, is_training=True)
             # use tensorboard to visualize the training of deepmd-kit
             # it will takes some extra execution time to generate the tensorboard data
-            if self.tensorboard :
+            if self.tensorboard and (cur_batch % self.tensorboard_freq == 0):
                 summary, _ = run_sess(self.sess, [summary_merged_op, self.train_op], feed_dict=train_feed_dict,
                                            options=prf_options, run_metadata=prf_run_metadata)
                 tb_train_writer.add_summary(summary, cur_batch)
-            else :
+            else:
                 run_sess(self.sess, [self.train_op], feed_dict=train_feed_dict,
                               options=prf_options, run_metadata=prf_run_metadata)
             if self.timing_in_training: toc = time.time()

deepmd/utils/argcheck.py

@@ -571,6 +571,7 @@ def training_args():  # ! modified by Ziyao: data configuration isolated.
     doc_profiling_file = 'Output file for profiling.'
     doc_tensorboard = 'Enable tensorboard'
     doc_tensorboard_log_dir = 'The log directory of tensorboard outputs'
+    doc_tensorboard_freq = 'The frequency of writing tensorboard events.'
 
     arg_training_data = training_data_args()
     arg_validation_data = validation_data_args()
@@ -591,6 +592,7 @@ def training_args():  # ! modified by Ziyao: data configuration isolated.
         Argument("profiling_file", str, optional=True, default='timeline.json', doc=doc_profiling_file),
         Argument("tensorboard", bool, optional=True, default=False, doc=doc_tensorboard),
         Argument("tensorboard_log_dir", str, optional=True, default='log', doc=doc_tensorboard_log_dir),
+        Argument("tensorboard_freq", int, optional=True, default=1, doc=doc_tensorboard_freq),
     ]
 
     doc_training = 'The training options.'

doc/train-input-auto.rst

@@ -1485,3 +1485,10 @@ training:
 
         The log directory of tensorboard outputs
 
+    .. _`training/tensorboard_freq`: 
+
+    tensorboard_freq: 
+        | type: ``int``, optional, default: ``1``
+        | argument path: ``training/tensorboard_freq``
+
+        The frequency of writing tensorboard events.

doc/train/tensorboard.md

@@ -43,6 +43,7 @@ subsection will enable the tensorboard data analysis. eg. **water_se_a.json**.
 	"time_training":true,
 	"tensorboard":	true,
 	"tensorboard_log_dir":"log",
+	"tensorboard_freq": 1000,
 	"profiling":	false,
 	"profiling_file":"timeline.json",
 	"_comment":	"that's all"
@@ -53,7 +54,7 @@ Once you have event files, run TensorBoard and provide the log directory. This
 should print that TensorBoard has started. Next, connect to http://tensorboard_server_ip:6006.
 
 TensorBoard requires a logdir to read logs from. For info on configuring TensorBoard, run tensorboard --help.
-One can easily change the log name with "tensorboard_log_dir".
+One can easily change the log name with "tensorboard_log_dir" and the sampling frequency with "tensorboard_freq".
 
 ```bash
 tensorboard --logdir path/to/logs