Automated rollback of commit 844074c2a8e61b744c3de2718e1c9ea7b1d2edc2

PiperOrigin-RevId: 214693201

2 files changed, 2 insertions, 125 deletions

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index fd62a79c84..eec64ad452 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -1394,35 +1394,6 @@ class Estimator(object):
         # It is expected to have one CheckpointSaverHook. If multiple, we pick
         # up the first one to add listener.
         saver_hooks[0]._listeners.extend(saving_listeners)  # pylint: disable=protected-access
-
-    # Add summary hooks to worker 0 if we are running with a master, to ensure
-    # that summaries are written at correct intervals even with long-running
-    # evaluations.
-    save_summary_steps = self._config.save_summary_steps
-    log_step_count_steps = self._config.log_step_count_steps
-    if run_config.TaskType.MASTER in self._config.cluster_spec.jobs:
-      # Update config values to prevent the default hooks from being created on
-      # the master or other workers.
-      save_summary_steps = 0
-      log_step_count_steps = None
-
-      if (self._config.task_type == run_config.TaskType.WORKER and
-          self._config.task_id == 0):
-        if (self._config.save_summary_steps and
-            self._config.save_summary_steps > 0):
-          worker_hooks.append(
-              training.SummarySaverHook(
-                  save_steps=self._config.save_summary_steps,
-                  output_dir=self._config.model_dir,
-                  scaffold=estimator_spec.scaffold))
-
-        if (self._config.log_step_count_steps and
-            self._config.log_step_count_steps > 0):
-          worker_hooks.append(
-              training.StepCounterHook(
-                  every_n_steps=self._config.log_step_count_steps,
-                  output_dir=self._config.model_dir))
-
     with training.MonitoredTrainingSession(
         master=self._config.master,
         is_chief=self._config.is_chief,
@@ -1432,9 +1403,9 @@ class Estimator(object):
         chief_only_hooks=(
             tuple(chief_hooks) + tuple(estimator_spec.training_chief_hooks)),
         save_checkpoint_secs=0,  # Saving is handled by a hook.
-        save_summaries_steps=save_summary_steps,
+        save_summaries_steps=self._config.save_summary_steps,
         config=self._session_config,
-        log_step_count_steps=log_step_count_steps) as mon_sess:
+        log_step_count_steps=self._config.log_step_count_steps) as mon_sess:
       loss = None
       while not mon_sess.should_stop():
         _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 5962086aad..1ed5e30b0e 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -20,7 +20,6 @@ from __future__ import print_function
 
 import functools
 import glob
-import json
 import os
 import tempfile
 
@@ -970,99 +969,6 @@ class EstimatorTrainTest(test.TestCase):
     with self.assertRaisesRegexp(ValueError, 'train_and_evaluate'):
       est.train(dummy_input_fn, steps=1)
 
-  def test_master_distributed_hooks(self):
-    tf_config = json.dumps({
-        'cluster': {
-            run_config.TaskType.PS: ['localhost:1234'],
-            run_config.TaskType.WORKER: ['localhost:1235'],
-            run_config.TaskType.MASTER: ['localhost:1236']
-        },
-        'task': {
-            'type': run_config.TaskType.MASTER,
-            'index': 0
-        }
-    })
-    with test.mock.patch.dict('os.environ', {'TF_CONFIG': tf_config}):
-      est = estimator.Estimator(
-          model_fn=model_fn_global_step_incrementer,
-          config=run_config.RunConfig())
-
-    with test.mock.patch.object(training,
-                                'MonitoredTrainingSession') as mock_sess:
-      est.train(dummy_input_fn, steps=1)
-      self.assertFalse(
-          any(
-              isinstance(hook, basic_session_run_hooks.SummarySaverHook)
-              for hook in mock_sess.call_args[1]['hooks']))
-      self.assertFalse(
-          any(
-              isinstance(hook, basic_session_run_hooks.StepCounterHook)
-              for hook in mock_sess.call_args[1]['hooks']))
-      self.assertEqual(0, mock_sess.call_args[1]['save_summaries_steps'])
-      self.assertIsNone(mock_sess.call_args[1]['log_step_count_steps'])
-
-  def test_master_distributed_hooks_for_worker_0(self):
-    tf_config = json.dumps({
-        'cluster': {
-            run_config.TaskType.PS: ['localhost:1234'],
-            run_config.TaskType.WORKER: ['localhost:1235'],
-            run_config.TaskType.MASTER: ['localhost:1236']
-        },
-        'task': {
-            'type': run_config.TaskType.WORKER,
-            'index': 0
-        }
-    })
-    with test.mock.patch.dict('os.environ', {'TF_CONFIG': tf_config}):
-      est = estimator.Estimator(
-          model_fn=model_fn_global_step_incrementer,
-          config=run_config.RunConfig())
-
-    with test.mock.patch.object(training,
-                                'MonitoredTrainingSession') as mock_sess:
-      est.train(dummy_input_fn, steps=1)
-      self.assertTrue(
-          any(
-              isinstance(hook, basic_session_run_hooks.SummarySaverHook)
-              for hook in mock_sess.call_args[1]['hooks']))
-      self.assertTrue(
-          any(
-              isinstance(hook, basic_session_run_hooks.StepCounterHook)
-              for hook in mock_sess.call_args[1]['hooks']))
-      self.assertEqual(0, mock_sess.call_args[1]['save_summaries_steps'])
-      self.assertIsNone(mock_sess.call_args[1]['log_step_count_steps'])
-
-  def test_master_distributed_hooks_for_worker_nonzero(self):
-    tf_config = json.dumps({
-        'cluster': {
-            run_config.TaskType.PS: ['localhost:1234'],
-            run_config.TaskType.WORKER: ['localhost:1235', 'localhost:1237'],
-            run_config.TaskType.MASTER: ['localhost:1236']
-        },
-        'task': {
-            'type': run_config.TaskType.WORKER,
-            'index': 1
-        }
-    })
-    with test.mock.patch.dict('os.environ', {'TF_CONFIG': tf_config}):
-      est = estimator.Estimator(
-          model_fn=model_fn_global_step_incrementer,
-          config=run_config.RunConfig())
-
-    with test.mock.patch.object(training,
-                                'MonitoredTrainingSession') as mock_sess:
-      est.train(dummy_input_fn, steps=1)
-      self.assertFalse(
-          any(
-              isinstance(hook, basic_session_run_hooks.SummarySaverHook)
-              for hook in mock_sess.call_args[1]['hooks']))
-      self.assertFalse(
-          any(
-              isinstance(hook, basic_session_run_hooks.StepCounterHook)
-              for hook in mock_sess.call_args[1]['hooks']))
-      self.assertEqual(0, mock_sess.call_args[1]['save_summaries_steps'])
-      self.assertIsNone(mock_sess.call_args[1]['log_step_count_steps'])
-
 
 def _model_fn_with_eval_metric_ops(features, labels, mode, params):
   _, _ = features, labels