models.diff

diff -uar models-my/official/resnet/cifar10_main.py models/official/resnet/cifar10_main.py
--- models/official/resnet/cifar10_main.py	2019-02-13 12:54:50.943172776 +0300
+++ models/official/resnet/cifar10_main.py	2019-02-13 12:56:06.408195123 +0300
@@ -19,6 +19,8 @@
 from __future__ import print_function
 
 import os
+import sys
+import json
 
 from absl import app as absl_app
 from absl import flags
@@ -29,6 +31,9 @@
 from official.utils.flags import core as flags_core
 from official.utils.logs import logger
 
+from tensorflow.contrib.ignite import IgniteDataset
+import tensorflow.contrib.ignite.python.ops.igfs_ops
+
 HEIGHT = 32
 WIDTH = 32
 NUM_CHANNELS = 3
@@ -68,7 +73,7 @@
 def parse_record(raw_record, is_training, dtype):
   """Parse CIFAR-10 image and label from a raw record."""
   # Convert bytes to a vector of uint8 that is record_bytes long.
-  record_vector = tf.decode_raw(raw_record, tf.uint8)
+  record_vector = raw_record
 
   # The first byte represents the label, which we convert from uint8 to int32
   # and then to one-hot.
@@ -125,8 +130,7 @@
   Returns:
     A dataset that can be used for iteration.
   """
-  filenames = get_filenames(is_training, data_dir)
-  dataset = tf.data.FixedLengthRecordDataset(filenames, _RECORD_BYTES)
+  dataset = IgniteDataset("TEST_DATA", local=True).map(lambda row: row['val'])
 
   return resnet_run_loop.process_record_dataset(
       dataset=dataset,
@@ -230,15 +234,14 @@
       fine_tune=params['fine_tune']
   )
 
-
 def define_cifar_flags():
   resnet_run_loop.define_resnet_flags()
   flags.adopt_module_key_flags(resnet_run_loop)
   flags_core.set_defaults(data_dir='/tmp/cifar10_data/cifar-10-batches-bin',
-                          model_dir='/tmp/cifar10_model',
+                          model_dir='igfs:///tmp/cifar10_model',
                           resnet_size='56',
                           train_epochs=182,
-                          epochs_between_evals=10,
+                          epochs_between_evals=1,
                           batch_size=128,
                           image_bytes_as_serving_input=False)
 
diff -uar models-my/official/resnet/resnet_run_loop.py models/official/resnet/resnet_run_loop.py
--- models/official/resnet/resnet_run_loop.py	2019-02-13 12:54:50.943172776 +0300
+++ models/official/resnet/resnet_run_loop.py	2019-02-13 12:58:10.701746369 +0300
@@ -27,6 +27,7 @@
 import math
 import multiprocessing
 import os
+import json
 
 # pylint: disable=g-bad-import-order
 from absl import flags
@@ -476,7 +477,11 @@
   # Creates a `RunConfig` that checkpoints every 24 hours which essentially
   # results in checkpoints determined only by `epochs_between_evals`.
   run_config = tf.estimator.RunConfig(
-      train_distribute=distribution_strategy,
+      experimental_distribute=tf.contrib.distribute.DistributeConfig(
+        train_distribute=tf.contrib.distribute.CollectiveAllReduceStrategy(),
+        eval_distribute=tf.contrib.distribute.MirroredStrategy(),
+        remote_cluster=json.loads(os.environ['TF_CLUSTER'])
+      ),
       session_config=session_config,
       save_checkpoints_secs=60*60*24)
 
@@ -560,8 +565,11 @@
     tf.logging.info('Starting cycle: %d/%d', cycle_index, int(n_loops))
 
     if num_train_epochs:
-      classifier.train(input_fn=lambda: input_fn_train(num_train_epochs),
-                       hooks=train_hooks, max_steps=flags_obj.max_train_steps)
+      tf.estimator.train_and_evaluate(
+        classifier,
+        train_spec=tf.estimator.TrainSpec(input_fn=lambda: input_fn_train(num_train_epochs), hooks=train_hooks, max_steps=flags_obj.max_train_steps),
+        eval_spec=tf.estimator.EvalSpec(input_fn=input_fn_eval, steps=flags_obj.max_train_steps)
+      )
 
     tf.logging.info('Starting to evaluate.')