Merge remote-tracking branch 'upstream/master'

official/boosted_trees/train_higgs_test.py → official/r1/boosted_trees/train_higgs_test.py

@@ -26,7 +26,7 @@ import pandas as pd
 import tensorflow as tf
 
 # pylint: disable=g-bad-import-order
-from official.boosted_trees import train_higgs
+from official.r1.boosted_trees import train_higgs
 from official.utils.misc import keras_utils
 from official.utils.testing import integration
 
@@ -133,7 +133,7 @@ class BaseTest(tf.test.TestCase):
             "--eval_start", "12",
             "--eval_count", "8",
         ],
-        synth=False, max_train=None)
+        synth=False)
     self.assertTrue(tf.gfile.Exists(os.path.join(model_dir, "checkpoint")))
 
   @unittest.skipIf(keras_utils.is_v2_0(), "TF 1.0 only test.")
@@ -152,7 +152,7 @@ class BaseTest(tf.test.TestCase):
             "--eval_start", "12",
             "--eval_count", "8",
         ],
-        synth=False, max_train=None)
+        synth=False)
     self.assertTrue(tf.gfile.Exists(os.path.join(model_dir, "checkpoint")))
     self.assertTrue(tf.gfile.Exists(os.path.join(export_dir)))
 

official/r1/resnet/cifar10_test.py

@@ -168,13 +168,15 @@ class BaseTest(tf.test.TestCase):
   def test_cifar10_end_to_end_synthetic_v1(self):
     integration.run_synthetic(
         main=cifar10_main.run_cifar, tmp_root=self.get_temp_dir(),
-        extra_flags=['-resnet_version', '1', '-batch_size', '4']
+        extra_flags=['-resnet_version', '1', '-batch_size', '4',
+                     '--max_train_steps', '1']
     )
 
   def test_cifar10_end_to_end_synthetic_v2(self):
     integration.run_synthetic(
         main=cifar10_main.run_cifar, tmp_root=self.get_temp_dir(),
-        extra_flags=['-resnet_version', '2', '-batch_size', '4']
+        extra_flags=['-resnet_version', '2', '-batch_size', '4',
+                     '--max_train_steps', '1']
     )
 
 

official/r1/resnet/imagenet_test.py

@@ -282,41 +282,43 @@ class BaseTest(tf.test.TestCase):
   def test_imagenet_end_to_end_synthetic_v1(self):
     integration.run_synthetic(
         main=imagenet_main.run_imagenet, tmp_root=self.get_temp_dir(),
-        extra_flags=['-resnet_version', '1', '-batch_size', '4']
+        extra_flags=['-resnet_version', '1', '-batch_size', '4',
+                     '--max_train_steps', '1']
     )
 
   def test_imagenet_end_to_end_synthetic_v2(self):
     integration.run_synthetic(
         main=imagenet_main.run_imagenet, tmp_root=self.get_temp_dir(),
-        extra_flags=['-resnet_version', '2', '-batch_size', '4']
+        extra_flags=['-resnet_version', '2', '-batch_size', '4',
+                     '--max_train_steps', '1']
     )
 
   def test_imagenet_end_to_end_synthetic_v1_tiny(self):
     integration.run_synthetic(
         main=imagenet_main.run_imagenet, tmp_root=self.get_temp_dir(),
         extra_flags=['-resnet_version', '1', '-batch_size', '4',
-                     '-resnet_size', '18']
+                     '-resnet_size', '18', '--max_train_steps', '1']
     )
 
   def test_imagenet_end_to_end_synthetic_v2_tiny(self):
     integration.run_synthetic(
         main=imagenet_main.run_imagenet, tmp_root=self.get_temp_dir(),
         extra_flags=['-resnet_version', '2', '-batch_size', '4',
-                     '-resnet_size', '18']
+                     '-resnet_size', '18', '--max_train_steps', '1']
     )
 
   def test_imagenet_end_to_end_synthetic_v1_huge(self):
     integration.run_synthetic(
         main=imagenet_main.run_imagenet, tmp_root=self.get_temp_dir(),
         extra_flags=['-resnet_version', '1', '-batch_size', '4',
-                     '-resnet_size', '200']
+                     '-resnet_size', '200', '--max_train_steps', '1']
     )
 
   def test_imagenet_end_to_end_synthetic_v2_huge(self):
     integration.run_synthetic(
         main=imagenet_main.run_imagenet, tmp_root=self.get_temp_dir(),
         extra_flags=['-resnet_version', '2', '-batch_size', '4',
-                     '-resnet_size', '200']
+                     '-resnet_size', '200', '--max_train_steps', '1']
     )
 
 

official/r1/resnet/resnet_run_loop.py

@@ -33,7 +33,7 @@ import tensorflow as tf
 
 from official.r1.resnet import imagenet_preprocessing
 from official.r1.resnet import resnet_model
-from official.utils.export import export
+from official.r1.utils import export
 from official.utils.flags import core as flags_core
 from official.utils.logs import hooks_helper
 from official.utils.logs import logger
@@ -725,14 +725,18 @@ def define_resnet_flags(resnet_size_choices=None, dynamic_loss_scale=False,
   """Add flags and validators for ResNet."""
   flags_core.define_base()
   flags_core.define_performance(num_parallel_calls=False,
+                                inter_op=True,
+                                intra_op=True,
                                 tf_gpu_thread_mode=True,
                                 datasets_num_private_threads=True,
                                 dynamic_loss_scale=dynamic_loss_scale,
                                 fp16_implementation=fp16_implementation,
                                 loss_scale=True,
-                                tf_data_experimental_slack=True)
+                                tf_data_experimental_slack=True,
+                                max_train_steps=True)
   flags_core.define_image()
   flags_core.define_benchmark()
+  flags_core.define_distribution()
   flags.adopt_module_key_flags(flags_core)
 
   flags.DEFINE_enum(
@@ -768,16 +772,6 @@ def define_resnet_flags(resnet_size_choices=None, dynamic_loss_scale=False,
           'If True, uses `tf.estimator.train_and_evaluate` for the training '
           'and evaluation loop, instead of separate calls to `classifier.train '
           'and `classifier.evaluate`, which is the default behavior.'))
-  flags.DEFINE_string(
-      name='worker_hosts', default=None,
-      help=flags_core.help_wrap(
-          'Comma-separated list of worker ip:port pairs for running '
-          'multi-worker models with DistributionStrategy.  The user would '
-          'start the program on each host with identical value for this flag.'))
-  flags.DEFINE_integer(
-      name='task_index', default=-1,
-      help=flags_core.help_wrap('If multi-worker training, the task_index of '
-                                'this worker.'))
   flags.DEFINE_bool(
       name='enable_lars', default=False,
       help=flags_core.help_wrap(

official/utils/export/export_test.py → official/r1/utils/export_test.py

@@ -20,7 +20,7 @@ from __future__ import print_function
 
 import tensorflow as tf  # pylint: disable=g-bad-import-order
 
-from official.utils.export import export
+from official.r1.utils import export
 
 
 class ExportUtilsTest(tf.test.TestCase):

official/wide_deep/README.md → official/r1/wide_deep/README.md

 # Predicting Income with the Census Income Dataset
+
+Note that, the implementation is based on TF 1.x.
+It is subjected to move to R1 archive folder.
+
 ## Overview
 The [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) contains over 48,000 samples with attributes including age, occupation, education, and income (a binary label, either `>50K` or `<=50K`). The dataset is split into roughly 32,000 training and 16,000 testing samples.
 

official/wide_deep/census_main.py → official/r1/wide_deep/census_main.py

@@ -22,8 +22,8 @@ import tensorflow as tf
 
 from official.utils.flags import core as flags_core
 from official.utils.logs import logger
-from official.wide_deep import census_dataset
-from official.wide_deep import wide_deep_run_loop
+from official.r1.wide_deep import census_dataset
+from official.r1.wide_deep import wide_deep_run_loop
 
 
 def define_census_flags():

official/wide_deep/census_test.py → official/r1/wide_deep/census_test.py

@@ -24,8 +24,8 @@ import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.utils.misc import keras_utils
 from official.utils.testing import integration
-from official.wide_deep import census_dataset
-from official.wide_deep import census_main
+from official.r1.wide_deep import census_dataset
+from official.r1.wide_deep import census_main
 
 tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
 
@@ -139,7 +139,7 @@ class BaseTest(tf.test.TestCase):
             '--model_type', 'wide',
             '--download_if_missing=false'
         ],
-        synth=False, max_train=None)
+        synth=False)
 
   @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_end_to_end_deep(self):
@@ -150,7 +150,7 @@ class BaseTest(tf.test.TestCase):
             '--model_type', 'deep',
             '--download_if_missing=false'
         ],
-        synth=False, max_train=None)
+        synth=False)
 
   @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_end_to_end_wide_deep(self):
@@ -161,7 +161,7 @@ class BaseTest(tf.test.TestCase):
             '--model_type', 'wide_deep',
             '--download_if_missing=false'
         ],
-        synth=False, max_train=None)
+        synth=False)
 
 
 if __name__ == '__main__':

official/wide_deep/movielens_main.py → official/r1/wide_deep/movielens_main.py

@@ -27,8 +27,8 @@ import tensorflow as tf
 from official.datasets import movielens
 from official.utils.flags import core as flags_core
 from official.utils.logs import logger
-from official.wide_deep import movielens_dataset
-from official.wide_deep import wide_deep_run_loop
+from official.r1.wide_deep import movielens_dataset
+from official.r1.wide_deep import wide_deep_run_loop
 
 
 def define_movie_flags():

official/wide_deep/movielens_test.py → official/r1/wide_deep/movielens_test.py

@@ -26,8 +26,8 @@ import tensorflow as tf  # pylint: disable=g-bad-import-order
 from official.datasets import movielens
 from official.utils.misc import keras_utils
 from official.utils.testing import integration
-from official.wide_deep import movielens_dataset
-from official.wide_deep import movielens_main
+from official.r1.wide_deep import movielens_dataset
+from official.r1.wide_deep import movielens_main
 
 tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
 
@@ -112,7 +112,7 @@ class BaseTest(tf.test.TestCase):
             "--train_epochs", "1",
             "--epochs_between_evals", "1"
         ],
-        synth=False, max_train=None)
+        synth=False)
 
 
 if __name__ == "__main__":