Merge branch 'master' into fix_expand_dims

28 files changed, 332 insertions, 169 deletions

diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index d665fc9335..61185f65a9 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -79,16 +79,7 @@ py_library(
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
         "//tensorflow/python:weights_broadcast_ops",
-        "//tensorflow/python/estimator",
         "//tensorflow/python/estimator:estimator_py",
-        "//tensorflow/python/estimator:export_export",
-        "//tensorflow/python/estimator:export_output",
-        "//tensorflow/python/estimator:inputs",
-        "//tensorflow/python/estimator:inputs_queues",
-        "//tensorflow/python/estimator:model_fn",
-        "//tensorflow/python/estimator:numpy_io",
-        "//tensorflow/python/estimator:pandas_io",
-        "//tensorflow/python/estimator:run_config",
         "//tensorflow/python/feature_column",
         "//tensorflow/python/feature_column:feature_column_py",
         "//tensorflow/python/ops/losses",
@@ -117,7 +108,6 @@ py_test(
     size = "small",
     srcs = ["python/learn/learn_io/data_feeder_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["no_windows"],  # TODO: needs investigation on Windows
     deps = [
         ":learn",
         "//tensorflow/python:client_testlib",
@@ -171,9 +161,8 @@ tf_py_test(
         "//tensorflow/python:training",
         "//tensorflow/python:util",
         "//tensorflow/python:variables",
-        "//tensorflow/python/estimator",
+        "//tensorflow/python/estimator:estimator_py",
     ],
-    tags = ["no_windows"],  # TODO: needs investigation on Windows
 )
 
 py_test(
@@ -220,7 +209,7 @@ py_test(
         "//tensorflow/contrib/training:training_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:platform",
-        "//tensorflow/python/estimator:run_config",
+        "//tensorflow/python/estimator:estimator_py",
     ],
 )
 
@@ -245,7 +234,7 @@ py_test(
         "//tensorflow/python:summary",
         "//tensorflow/python:training",
         "//tensorflow/python:variables",
-        "//tensorflow/python/estimator",
+        "//tensorflow/python/estimator:estimator_py",
     ],
 )
 
@@ -259,7 +248,7 @@ py_test(
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:training",
-        "//tensorflow/python/estimator:run_config",
+        "//tensorflow/python/estimator:estimator_py",
     ],
 )
 
@@ -281,7 +270,11 @@ py_test(
     size = "medium",
     srcs = ["python/learn/estimators/estimator_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["manual"],
+    tags = [
+        "manual",
+        "noasan",  # times out
+        "optonly",  # test is flaky without optimization.
+    ],
     deps = [
         ":learn",
         "//tensorflow/contrib/framework:framework_py",
@@ -410,6 +403,7 @@ py_test(
     srcs = ["python/learn/estimators/dnn_test.py"],
     shard_count = 4,
     srcs_version = "PY2AND3",
+    tags = ["notap"],
     deps = [
         ":learn",
         "//tensorflow/contrib/layers:layers_py",
@@ -431,6 +425,7 @@ py_test(
     name = "kmeans_test",
     size = "medium",
     srcs = ["python/learn/estimators/kmeans_test.py"],
+    shard_count = 4,
     srcs_version = "PY2AND3",
     tags = [
         "noasan",  # b/73741358
@@ -482,6 +477,7 @@ py_test(
     name = "state_saving_rnn_estimator_test",
     size = "medium",
     srcs = ["python/learn/estimators/state_saving_rnn_estimator_test.py"],
+    shard_count = 4,
     srcs_version = "PY2AND3",
     tags = ["noasan"],
     deps = [
@@ -594,7 +590,6 @@ py_test(
     size = "small",
     srcs = ["python/learn/learn_io/io_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["no_windows"],  # TODO: needs investigation on Windows
     deps = [
         ":learn",
         "//tensorflow/contrib/learn/python/learn/datasets",
@@ -615,7 +610,7 @@ py_test(
         "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:session",
         "//tensorflow/python:training",
-        "//tensorflow/python/estimator:export_output",
+        "//tensorflow/python/estimator:estimator_py",
         "//tensorflow/python/saved_model:signature_constants",
         "@six_archive//:six",
     ],
@@ -741,7 +736,7 @@ py_test(
 
 tf_py_test(
     name = "graph_io_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/learn/learn_io/graph_io_test.py"],
     additional_deps = [
         ":learn",
diff --git a/tensorflow/contrib/learn/__init__.py b/tensorflow/contrib/learn/__init__.py
index 79bd73faaf..28a6f5aed9 100644
--- a/tensorflow/contrib/learn/__init__.py
+++ b/tensorflow/contrib/learn/__init__.py
@@ -19,7 +19,8 @@ This module and all its submodules are deprecated. See
 [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
 for migration instructions.
 
-See the @{$python/contrib.learn} guide.
+See the [Contrib Learn](https://tensorflow.org/api_guides/python/contrib.learn)
+guide.
 
 @@BaseEstimator
 @@Estimator
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py
index c9a11f27f1..1d8a59281a 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py
@@ -155,7 +155,7 @@ class DynamicRnnEstimatorTest(test.TestCase):
     sequence_input = dynamic_rnn_estimator.build_sequence_input(
         self.GetColumnsToTensors(), self.sequence_feature_columns,
         self.context_feature_columns)
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       sess.run(lookup_ops.tables_initializer())
       sequence_input_val = sess.run(sequence_input)
@@ -330,7 +330,7 @@ class DynamicRnnEstimatorTest(test.TestCase):
     actual_state = dynamic_rnn_estimator.dict_to_state_tuple(state_dict, cell)
     flattened_state = dynamic_rnn_estimator.state_tuple_to_dict(actual_state)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       (state_dict_val, actual_state_val, flattened_state_val) = sess.run(
           [state_dict, actual_state, flattened_state])
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
index 7a026a15e4..c1de42782e 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
@@ -72,6 +72,7 @@ from tensorflow.python.saved_model import builder as saved_model_builder
 from tensorflow.python.saved_model import tag_constants
 from tensorflow.python.summary import summary as core_summary
 from tensorflow.python.training import basic_session_run_hooks
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import device_setter
 from tensorflow.python.training import monitored_session
 from tensorflow.python.training import saver
@@ -891,7 +892,7 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable,
 
     # Check that model has been trained (if nothing has been set explicitly).
     if not checkpoint_path:
-      latest_path = saver.latest_checkpoint(self._model_dir)
+      latest_path = checkpoint_management.latest_checkpoint(self._model_dir)
       if not latest_path:
         raise NotFittedError(
             "Couldn't find trained model at %s." % self._model_dir)
@@ -956,7 +957,7 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable,
                    as_iterable=True,
                    iterate_batches=False):
     # Check that model has been trained.
-    checkpoint_path = saver.latest_checkpoint(self._model_dir)
+    checkpoint_path = checkpoint_management.latest_checkpoint(self._model_dir)
     if not checkpoint_path:
       raise NotFittedError(
           "Couldn't find trained model at %s." % self._model_dir)
@@ -1364,7 +1365,7 @@ class Estimator(BaseEstimator):
 
     if not checkpoint_path:
       # Locate the latest checkpoint
-      checkpoint_path = saver.latest_checkpoint(self._model_dir)
+      checkpoint_path = checkpoint_management.latest_checkpoint(self._model_dir)
     if not checkpoint_path:
       raise NotFittedError(
           "Couldn't find trained model at %s." % self._model_dir)
diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator_test.py b/tensorflow/contrib/learn/python/learn/estimators/estimator_test.py
index d81a534b79..9e5aaf3118 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator_test.py
@@ -715,7 +715,9 @@ class EstimatorTest(test.TestCase):
     ckpt = checkpoint_state_pb2.CheckpointState()
     text_format.Merge(checkpoint_file_content, ckpt)
     self.assertEqual(ckpt.model_checkpoint_path, 'model.ckpt-5')
-    self.assertAllEqual(['model.ckpt-1', 'model.ckpt-5'],
+    # TODO(b/78461127): Please modify tests to not directly rely on names of
+    # checkpoints.
+    self.assertAllEqual(['model.ckpt-0', 'model.ckpt-5'],
                         ckpt.all_model_checkpoint_paths)
 
   def test_train_save_copy_reload(self):
diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py
index 06f4173170..c6f79e00d5 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/head.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/head.py
@@ -777,7 +777,7 @@ class _RegressionHead(_SingleHead):
     key = prediction_key.PredictionKey.SCORES
     with ops.name_scope(None, "predictions", (logits,)):
       if self.logits_dimension == 1:
-        logits = array_ops.squeeze(logits, squeeze_dims=(1,), name=key)
+        logits = array_ops.squeeze(logits, axis=(1,), name=key)
       return {key: self._link_fn(logits)}
 
   def _metrics(self, eval_loss, predictions, labels, weights):
@@ -974,7 +974,7 @@ def _softmax_cross_entropy_loss(labels, logits, weights=None):
     is_squeezed_labels = False
     # TODO(ptucker): This will break for dynamic shapes.
     if len(labels.get_shape()) == 2:
-      labels = array_ops.squeeze(labels, squeeze_dims=(1,))
+      labels = array_ops.squeeze(labels, axis=(1,))
       is_squeezed_labels = True
 
     loss = nn.sparse_softmax_cross_entropy_with_logits(
@@ -1862,12 +1862,12 @@ def _get_arguments(func):
   if hasattr(func, "__code__"):
     # Regular function.
     return tf_inspect.getargspec(func)
-  elif hasattr(func, "__call__"):
-    # Callable object.
-    return _get_arguments(func.__call__)
   elif hasattr(func, "func"):
     # Partial function.
     return _get_arguments(func.func)
+  elif hasattr(func, "__call__"):
+    # Callable object.
+    return _get_arguments(func.__call__)
 
 
 def _verify_loss_fn_args(loss_fn):
diff --git a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py
index 66ebcfd1d8..21f7dcc5e4 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py
@@ -15,9 +15,9 @@
 """Implementation of k-means clustering on top of `Estimator` API (deprecated).
 
 This module is deprecated. Please use
-@{tf.contrib.factorization.KMeansClustering} instead of
-@{tf.contrib.learn.KMeansClustering}. It has a similar interface, but uses the
-@{tf.estimator.Estimator} API instead of @{tf.contrib.learn.Estimator}.
+`tf.contrib.factorization.KMeansClustering` instead of
+`tf.contrib.learn.KMeansClustering`. It has a similar interface, but uses the
+`tf.estimator.Estimator` API instead of `tf.contrib.learn.Estimator`.
 """
 
 from __future__ import absolute_import
diff --git a/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py b/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py
index b28835a809..584556992a 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/kmeans_test.py
@@ -36,7 +36,6 @@ from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
-from tensorflow.python.ops import random_ops
 from tensorflow.python.platform import benchmark
 from tensorflow.python.platform import flags
 from tensorflow.python.platform import test
diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear.py b/tensorflow/contrib/learn/python/learn/estimators/linear.py
index 70b70af98c..e100bc7a1e 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/linear.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/linear.py
@@ -31,7 +31,6 @@ import six
 from tensorflow.contrib import layers
 from tensorflow.contrib.framework import deprecated
 from tensorflow.contrib.framework import deprecated_arg_values
-from tensorflow.python.training import training_util
 from tensorflow.contrib.layers.python.layers import feature_column
 from tensorflow.contrib.learn.python.learn.estimators import estimator
 from tensorflow.contrib.learn.python.learn.estimators import head as head_lib
@@ -51,6 +50,7 @@ from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import session_run_hook
 from tensorflow.python.training import training as train
+from tensorflow.python.training import training_util
 
 
 # The default learning rate of 0.2 is a historical artifact of the initial
@@ -244,7 +244,9 @@ def sdca_model_fn(features, labels, mode, params):
   parent_scope = "linear"
 
   with variable_scope.variable_scope(
-      values=features.values(), name_or_scope=parent_scope) as scope:
+      values=features.values(),
+      name_or_scope=parent_scope,
+      partitioner=optimizer.partitioner) as scope:
     features = features.copy()
     features.update(layers.transform_features(features, feature_columns))
     logits, columns_to_variables, bias = (
diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
index d3bb0fda57..597ca4e86d 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
@@ -43,6 +43,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import partitioned_variables
 from tensorflow.python.platform import test
 from tensorflow.python.training import ftrl
 from tensorflow.python.training import input as input_lib
@@ -863,6 +864,38 @@ class LinearClassifierTest(test.TestCase):
     scores = classifier.evaluate(input_fn=input_fn, steps=1)
     self.assertGreater(scores['accuracy'], 0.9)
 
+  def testSdcaOptimizerWeightedSparseFeaturesOOVWithNoOOVBuckets(self):
+    """LinearClassifier with SDCAOptimizer with OOV features (-1 IDs)."""
+
+    def input_fn():
+      return {
+          'example_id':
+              constant_op.constant(['1', '2', '3']),
+          'price':
+              sparse_tensor.SparseTensor(
+                  values=[2., 3., 1.],
+                  indices=[[0, 0], [1, 0], [2, 0]],
+                  dense_shape=[3, 5]),
+          'country':
+              sparse_tensor.SparseTensor(
+                  # 'GB' is out of the vocabulary.
+                  values=['IT', 'US', 'GB'],
+                  indices=[[0, 0], [1, 0], [2, 0]],
+                  dense_shape=[3, 5])
+      }, constant_op.constant([[1], [0], [1]])
+
+    country = feature_column_lib.sparse_column_with_keys(
+        'country', keys=['US', 'CA', 'MK', 'IT', 'CN'])
+    country_weighted_by_price = feature_column_lib.weighted_sparse_column(
+        country, 'price')
+    sdca_optimizer = sdca_optimizer_lib.SDCAOptimizer(
+        example_id_column='example_id')
+    classifier = linear.LinearClassifier(
+        feature_columns=[country_weighted_by_price], optimizer=sdca_optimizer)
+    classifier.fit(input_fn=input_fn, steps=50)
+    scores = classifier.evaluate(input_fn=input_fn, steps=1)
+    self.assertGreater(scores['accuracy'], 0.9)
+
   def testSdcaOptimizerCrossedFeatures(self):
     """Tests LinearClassifier with SDCAOptimizer and crossed features."""
 
@@ -934,6 +967,63 @@ class LinearClassifierTest(test.TestCase):
     scores = classifier.evaluate(input_fn=input_fn, steps=1)
     self.assertGreater(scores['accuracy'], 0.9)
 
+  def testSdcaOptimizerPartitionedVariables(self):
+    """Tests LinearClassifier with SDCAOptimizer with partitioned variables."""
+
+    def input_fn():
+      return {
+          'example_id':
+              constant_op.constant(['1', '2', '3']),
+          'price':
+              constant_op.constant([[0.6], [0.8], [0.3]]),
+          'sq_footage':
+              constant_op.constant([[900.0], [700.0], [600.0]]),
+          'country':
+              sparse_tensor.SparseTensor(
+                  values=['IT', 'US', 'GB'],
+                  indices=[[0, 0], [1, 3], [2, 1]],
+                  dense_shape=[3, 5]),
+          'weights':
+              constant_op.constant([[3.0], [1.0], [1.0]])
+      }, constant_op.constant([[1], [0], [1]])
+
+    price = feature_column_lib.real_valued_column('price')
+    sq_footage_bucket = feature_column_lib.bucketized_column(
+        feature_column_lib.real_valued_column('sq_footage'),
+        boundaries=[650.0, 800.0])
+    country = feature_column_lib.sparse_column_with_hash_bucket(
+        'country', hash_bucket_size=5)
+    sq_footage_country = feature_column_lib.crossed_column(
+        [sq_footage_bucket, country], hash_bucket_size=10)
+
+    sdca_optimizer = sdca_optimizer_lib.SDCAOptimizer(
+        example_id_column='example_id',
+        partitioner=partitioned_variables.fixed_size_partitioner(
+            num_shards=2, axis=0))
+
+    tf_config = {
+        'cluster': {
+            run_config.TaskType.PS: ['fake_ps_0', 'fake_ps_1']
+        }
+    }
+    with test.mock.patch.dict('os.environ',
+                              {'TF_CONFIG': json.dumps(tf_config)}):
+      config = run_config.RunConfig()
+      # Because we did not start a distributed cluster, we need to pass an
+      # empty ClusterSpec, otherwise the device_setter will look for
+      # distributed jobs, such as "/job:ps" which are not present.
+      config._cluster_spec = server_lib.ClusterSpec({})
+
+    classifier = linear.LinearClassifier(
+        feature_columns=[price, sq_footage_bucket, country, sq_footage_country],
+        weight_column_name='weights',
+        optimizer=sdca_optimizer,
+        config=config)
+    classifier.fit(input_fn=input_fn, steps=50)
+    scores = classifier.evaluate(input_fn=input_fn, steps=1)
+    print('all scores = {}'.format(scores))
+    self.assertGreater(scores['accuracy'], 0.9)
+
   def testEval(self):
     """Tests that eval produces correct metrics.
     """
@@ -1508,6 +1598,60 @@ class LinearRegressorTest(test.TestCase):
     loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
     self.assertLess(loss, 0.05)
 
+  def testSdcaOptimizerPartitionedVariables(self):
+    """Tests LinearRegressor with SDCAOptimizer with partitioned variables."""
+
+    def input_fn():
+      return {
+          'example_id':
+              constant_op.constant(['1', '2', '3']),
+          'price':
+              constant_op.constant([0.6, 0.8, 0.3]),
+          'sq_footage':
+              constant_op.constant([[900.0], [700.0], [600.0]]),
+          'country':
+              sparse_tensor.SparseTensor(
+                  values=['IT', 'US', 'GB'],
+                  indices=[[0, 0], [1, 3], [2, 1]],
+                  dense_shape=[3, 5]),
+          'weights':
+              constant_op.constant([[3.0], [5.0], [7.0]])
+      }, constant_op.constant([[1.55], [-1.25], [-3.0]])
+
+    price = feature_column_lib.real_valued_column('price')
+    sq_footage_bucket = feature_column_lib.bucketized_column(
+        feature_column_lib.real_valued_column('sq_footage'),
+        boundaries=[650.0, 800.0])
+    country = feature_column_lib.sparse_column_with_hash_bucket(
+        'country', hash_bucket_size=5)
+    sq_footage_country = feature_column_lib.crossed_column(
+        [sq_footage_bucket, country], hash_bucket_size=10)
+    sdca_optimizer = sdca_optimizer_lib.SDCAOptimizer(
+        example_id_column='example_id', symmetric_l2_regularization=1.0,
+        partitioner=partitioned_variables.fixed_size_partitioner(
+            num_shards=2, axis=0))
+    tf_config = {
+        'cluster': {
+            run_config.TaskType.PS: ['fake_ps_0', 'fake_ps_1']
+        }
+    }
+    with test.mock.patch.dict('os.environ',
+                              {'TF_CONFIG': json.dumps(tf_config)}):
+      config = run_config.RunConfig()
+      # Because we did not start a distributed cluster, we need to pass an
+      # empty ClusterSpec, otherwise the device_setter will look for
+      # distributed jobs, such as "/job:ps" which are not present.
+      config._cluster_spec = server_lib.ClusterSpec({})
+
+    regressor = linear.LinearRegressor(
+        feature_columns=[price, sq_footage_bucket, country, sq_footage_country],
+        weight_column_name='weights',
+        optimizer=sdca_optimizer,
+        config=config)
+    regressor.fit(input_fn=input_fn, steps=20)
+    loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
+    self.assertLess(loss, 0.05)
+
   def testSdcaOptimizerSparseFeaturesWithL1Reg(self):
     """Tests LinearClassifier with SDCAOptimizer and sparse features."""
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/rnn_common_test.py b/tensorflow/contrib/learn/python/learn/estimators/rnn_common_test.py
index 82563141cc..ebf5f5617d 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/rnn_common_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/rnn_common_test.py
@@ -44,7 +44,7 @@ class RnnCommonTest(test.TestCase):
          constant_op.constant(labels, dtype=dtypes.int32),
          constant_op.constant(sequence_length, dtype=dtypes.int32))
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       activations_masked, labels_masked = sess.run(
           [activations_masked_t, labels_masked_t])
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
index 14ee2ba609..08f23aa223 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
@@ -221,7 +221,7 @@ class ClusterConfig(object):
 class RunConfig(ClusterConfig, core_run_config.RunConfig):
   """This class specifies the configurations for an `Estimator` run.
 
-  This class is a deprecated implementation of @{tf.estimator.RunConfig}
+  This class is a deprecated implementation of `tf.estimator.RunConfig`
   interface.
   """
   _USE_DEFAULT = 0
@@ -240,6 +240,7 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
                keep_checkpoint_max=5,
                keep_checkpoint_every_n_hours=10000,
                log_step_count_steps=100,
+               protocol=None,
                evaluation_master='',
                model_dir=None,
                session_config=None):
@@ -289,6 +290,8 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
       session_config: a ConfigProto used to set session parameters, or None.
         Note - using this argument, it is easy to provide settings which break
         otherwise perfectly good models. Use with care.
+      protocol: An optional argument which specifies the protocol used when
+        starting server. None means default to grpc.
     """
     # Neither parent class calls super().__init__(), so here we have to
     # manually call their __init__() methods.
@@ -299,6 +302,7 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
     # so instead of breaking compatibility with that assumption, we
     # just manually initialize this field:
     self._train_distribute = None
+    self._eval_distribute = None
     self._device_fn = None
 
     gpu_options = config_pb2.GPUOptions(
@@ -313,6 +317,7 @@ class RunConfig(ClusterConfig, core_run_config.RunConfig):
     self._save_summary_steps = save_summary_steps
     self._save_checkpoints_secs = save_checkpoints_secs
     self._log_step_count_steps = log_step_count_steps
+    self._protocol = protocol
     self._session_config = session_config
     if save_checkpoints_secs == RunConfig._USE_DEFAULT:
       if save_checkpoints_steps is None:
diff --git a/tensorflow/contrib/learn/python/learn/estimators/stability_test.py b/tensorflow/contrib/learn/python/learn/estimators/stability_test.py
index 6d04543819..81376c0e2a 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/stability_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/stability_test.py
@@ -68,12 +68,12 @@ class StabilityTest(test.TestCase):
     minval = -0.3333
     maxval = 0.3333
     with ops.Graph().as_default() as g:
-      with self.test_session(graph=g) as session:
+      with self.session(graph=g) as session:
         g.seed = my_seed
         x = random_ops.random_uniform([10, 10], minval=minval, maxval=maxval)
         val1 = session.run(x)
     with ops.Graph().as_default() as g:
-      with self.test_session(graph=g) as session:
+      with self.session(graph=g) as session:
         g.seed = my_seed
         x = random_ops.random_uniform([10, 10], minval=minval, maxval=maxval)
         val2 = session.run(x)
diff --git a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator_test.py b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator_test.py
index 442247409d..06c61554fa 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator_test.py
@@ -53,7 +53,7 @@ class PrepareInputsForRnnTest(test.TestCase):
                                                     sequence_feature_columns,
                                                     num_unroll)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       sess.run(lookup_ops.tables_initializer())
       features_val = sess.run(features_by_time)
@@ -314,7 +314,7 @@ class StateSavingRnnEstimatorTest(test.TestCase):
         else:
           self.assertAllEqual(v, got[k])
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       sess.run(lookup_ops.tables_initializer())
       actual_sequence, actual_context = sess.run(
diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index 3744abd860..4e64efdd95 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -38,19 +38,19 @@ from tensorflow.contrib.learn.python.learn import trainable
 from tensorflow.contrib.learn.python.learn.estimators import run_config
 from tensorflow.contrib.tpu.python.tpu import tpu_estimator
 from tensorflow.python.estimator import estimator as core_estimator
-from tensorflow.python.estimator import util as estimator_util
 from tensorflow.python.framework import ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import basic_session_run_hooks
-from tensorflow.python.training import saver
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import server_lib
 from tensorflow.python.util import compat
+from tensorflow.python.util import function_utils
 
 __all__ = ["Experiment"]
 
 
 def _get_standardized_predicate_fn(predicate_fn):
-  pred_fn_args = estimator_util.fn_args(predicate_fn)
+  pred_fn_args = function_utils.fn_args(predicate_fn)
   if "checkpoint_path" not in pred_fn_args:
     # pylint: disable=unused-argument
     def _pred_fn_wrapper(eval_results, checkpoint_path):
@@ -95,7 +95,7 @@ class _EvalAndExportListener(basic_session_run_hooks.CheckpointSaverListener):
     # Load and cache the path of the most recent checkpoint to avoid duplicate
     # searches on GCS.
     logging.info("Checking for checkpoint in %s", self._model_dir)
-    latest_path = saver.latest_checkpoint(self._model_dir)
+    latest_path = checkpoint_management.latest_checkpoint(self._model_dir)
 
     if not latest_path:
       logging.warning("Skipping evaluation and export since model has not been "
@@ -162,16 +162,16 @@ class Experiment(object):
 
     Args:
       estimator: Object implementing Estimator interface, which could be a
-        combination of @{tf.contrib.learn.Trainable} and
-        @{tf.contrib.learn.Evaluable} (deprecated), or
-        @{tf.estimator.Estimator}.
+        combination of `tf.contrib.learn.Trainable` and
+        `tf.contrib.learn.Evaluable` (deprecated), or
+        `tf.estimator.Estimator`.
       train_input_fn: function, returns features and labels for training.
       eval_input_fn: function, returns features and labels for evaluation. If
         `eval_steps` is `None`, this should be configured only to produce for a
         finite number of batches (generally, 1 epoch over the evaluation data).
       eval_metrics: `dict` of string, metric function. If `None`, default set
         is used. This should be `None` if the `estimator` is
-        @{tf.estimator.Estimator}. If metrics are provided they will be
+        `tf.estimator.Estimator`. If metrics are provided they will be
         *appended* to the default set.
       train_steps: Perform this many steps of training. `None`, the default,
         means train forever.
@@ -468,10 +468,15 @@ class Experiment(object):
         on which that evaluation was based.
         At the beginning of evaluation, the passed `eval_results` will be None
         so it's expected that the predicate function handles that gracefully.
-        When `predicate_fn` is not specified, continuous eval will run in an
-        infinite loop (if `train_steps` is None). or exit once global step
-        reaches `train_steps`.
-
+        Continuous eval behavior under different conditions:
+          * When `predicate_fn` is specified:
+            + if `train_steps` is None, run until `predicate_fn` returns False.
+            + if `train_steps` is specified, run until either global step
+              reaches `train_steps` or `predicate_fn` returns False.
+          * When `predicate_fn` is not specified:
+            + if `train_steps` is None, run in an infinite loop.
+            + if `train_steps` is specified, run until global step reaches
+              `train_steps`.
       export: Whether to export from this step. Default is 'True'.
 
     Raises:
@@ -500,7 +505,7 @@ class Experiment(object):
     eval_result = None
     last_warning_time = 0
     while (not predicate_fn or predicate_fn(
-        eval_result, checkpoint_path=previous_path if eval_result else None)):
+        eval_result, checkpoint_path=previous_path)):
       # Exit if we have already reached number of steps to train.
       if self._has_training_stopped(eval_result):
         logging.info("Exiting continuous eval, global_step=%s >= "
@@ -511,7 +516,8 @@ class Experiment(object):
       start = time.time()
 
       error_msg = None
-      latest_path = saver.latest_checkpoint(self._estimator.model_dir)
+      latest_path = checkpoint_management.latest_checkpoint(
+          self._estimator.model_dir)
       if not latest_path:
         error_msg = ("Estimator is not fitted yet. "
                      "Will start an evaluation when a checkpoint is ready.")
@@ -773,7 +779,8 @@ class Experiment(object):
           saving_listeners=self._saving_listeners)
 
       logging.info("Evaluating model now.")
-      latest_checkpoint = saver.latest_checkpoint(self._estimator.model_dir)
+      latest_checkpoint = checkpoint_management.latest_checkpoint(
+          self._estimator.model_dir)
       eval_result = self._call_evaluate(
           input_fn=self._eval_input_fn,
           steps=self._eval_steps,
diff --git a/tensorflow/contrib/learn/python/learn/experiment_test.py b/tensorflow/contrib/learn/python/learn/experiment_test.py
index d10927a0cd..fb16c94c29 100644
--- a/tensorflow/contrib/learn/python/learn/experiment_test.py
+++ b/tensorflow/contrib/learn/python/learn/experiment_test.py
@@ -500,7 +500,7 @@ class ExperimentTest(test.TestCase):
       noop_hook = _NoopHook()
 
       def _predicate_fn(eval_result, checkpoint_path):
-        self.assertEqual(not eval_result,
+        self.assertEqual(eval_result is None,
                          checkpoint_path is None)
         return est.eval_count < 3  # pylint: disable=cell-var-from-loop
 
diff --git a/tensorflow/contrib/learn/python/learn/graph_actions_test.py b/tensorflow/contrib/learn/python/learn/graph_actions_test.py
index 0d039d593b..33180b778a 100644
--- a/tensorflow/contrib/learn/python/learn/graph_actions_test.py
+++ b/tensorflow/contrib/learn/python/learn/graph_actions_test.py
@@ -35,6 +35,7 @@ from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 from tensorflow.python.summary import summary
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import saver as saver_lib
 
 
@@ -124,7 +125,7 @@ class GraphActionsTest(test.TestCase):
 
   # TODO(ptucker): Test number and contents of checkpoint files.
   def _assert_ckpt(self, output_dir, expected=True):
-    ckpt_state = saver_lib.get_checkpoint_state(output_dir)
+    ckpt_state = checkpoint_management.get_checkpoint_state(output_dir)
     if expected:
       pattern = '%s/model.ckpt-.*' % output_dir
       primary_ckpt_path = ckpt_state.model_checkpoint_path
@@ -174,7 +175,7 @@ class GraphActionsTest(test.TestCase):
     return in0, in1, out
 
   def test_infer(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._assert_ckpt(self._output_dir, False)
       in0, in1, out = self._build_inference_graph()
       self.assertEqual({
@@ -192,7 +193,7 @@ class GraphActionsTest(test.TestCase):
       side_effect=learn.graph_actions.coordinator.Coordinator.request_stop,
       autospec=True)
   def test_coordinator_request_stop_called(self, request_stop):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       in0, in1, out = self._build_inference_graph()
       learn.graph_actions.infer(None, {'a': in0, 'b': in1, 'c': out})
       self.assertTrue(request_stop.called)
@@ -203,7 +204,7 @@ class GraphActionsTest(test.TestCase):
       side_effect=learn.graph_actions.coordinator.Coordinator.request_stop,
       autospec=True)
   def test_run_feeds_iter_cleanup_with_exceptions(self, request_stop):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       in0, in1, out = self._build_inference_graph()
       try:
         for _ in learn.graph_actions.run_feeds_iter({
@@ -233,7 +234,7 @@ class GraphActionsTest(test.TestCase):
         self.assertTrue(test_ops.resource_initialized_op(handle).eval())
 
   def test_infer_different_default_graph(self):
-    with self.test_session():
+    with self.cached_session():
       self._assert_ckpt(self._output_dir, False)
       with ops.Graph().as_default():
         in0, in1, out = self._build_inference_graph()
@@ -248,7 +249,7 @@ class GraphActionsTest(test.TestCase):
       self._assert_ckpt(self._output_dir, False)
 
   def test_infer_invalid_feed(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._assert_ckpt(self._output_dir, False)
       in0, _, _ = self._build_inference_graph()
       with self.assertRaisesRegexp(TypeError, 'Can not convert a NoneType'):
@@ -256,7 +257,7 @@ class GraphActionsTest(test.TestCase):
       self._assert_ckpt(self._output_dir, False)
 
   def test_infer_feed(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._assert_ckpt(self._output_dir, False)
       in0, _, out = self._build_inference_graph()
       self.assertEqual(
@@ -270,7 +271,7 @@ class GraphActionsTest(test.TestCase):
   # TODO(ptucker): Test eval for 1 epoch.
 
   def test_evaluate_invalid_args(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._assert_ckpt(self._output_dir, False)
       with self.assertRaisesRegexp(ValueError, 'utput directory'):
         learn.graph_actions.evaluate(
@@ -287,7 +288,7 @@ class GraphActionsTest(test.TestCase):
       self._assert_ckpt(self._output_dir, False)
 
   def test_evaluate(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       _, _, out = self._build_inference_graph()
       writer = learn.graph_actions.get_summary_writer(self._output_dir)
       self._assert_summaries(self._output_dir, writer, expected_session_logs=[])
@@ -309,7 +310,7 @@ class GraphActionsTest(test.TestCase):
       self._assert_ckpt(self._output_dir, False)
 
   def test_evaluate_ready_for_local_init(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       variables_lib.create_global_step()
       v = variables.Variable(1.0)
       variables.Variable(
@@ -326,7 +327,7 @@ class GraphActionsTest(test.TestCase):
           max_steps=1)
 
   def test_evaluate_feed_fn(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       in0, _, out = self._build_inference_graph()
       writer = learn.graph_actions.get_summary_writer(self._output_dir)
       self._assert_summaries(self._output_dir, writer, expected_session_logs=[])
@@ -351,7 +352,7 @@ class GraphActionsTest(test.TestCase):
       self._assert_ckpt(self._output_dir, False)
 
   def test_evaluate_feed_fn_with_exhaustion(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       in0, _, out = self._build_inference_graph()
       writer = learn.graph_actions.get_summary_writer(self._output_dir)
       self._assert_summaries(self._output_dir, writer, expected_session_logs=[])
@@ -374,7 +375,7 @@ class GraphActionsTest(test.TestCase):
           expected_session_logs=[])
 
   def test_evaluate_with_saver(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       _, _, out = self._build_inference_graph()
       ops.add_to_collection(ops.GraphKeys.SAVERS, saver_lib.Saver())
       writer = learn.graph_actions.get_summary_writer(self._output_dir)
@@ -434,7 +435,7 @@ class GraphActionsTrainTest(test.TestCase):
 
   # TODO(ptucker): Test number and contents of checkpoint files.
   def _assert_ckpt(self, output_dir, expected=True):
-    ckpt_state = saver_lib.get_checkpoint_state(output_dir)
+    ckpt_state = checkpoint_management.get_checkpoint_state(output_dir)
     if expected:
       pattern = '%s/model.ckpt-.*' % output_dir
       primary_ckpt_path = ckpt_state.model_checkpoint_path
@@ -468,7 +469,7 @@ class GraphActionsTrainTest(test.TestCase):
     return in0, in1, out
 
   def test_train_invalid_args(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       train_op = constant_op.constant(1.0)
       loss_op = constant_op.constant(2.0)
       with self.assertRaisesRegexp(ValueError, 'utput directory'):
@@ -502,7 +503,7 @@ class GraphActionsTrainTest(test.TestCase):
   # TODO(ptucker): Mock supervisor, and assert all interactions.
 
   def test_train(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       with ops.control_dependencies(self._build_inference_graph()):
         train_op = state_ops.assign_add(variables_lib.get_global_step(), 1)
       self._assert_summaries(self._output_dir)
@@ -521,7 +522,7 @@ class GraphActionsTrainTest(test.TestCase):
       self._assert_ckpt(self._output_dir, True)
 
   def test_train_steps_is_incremental(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       with ops.control_dependencies(self._build_inference_graph()):
         train_op = state_ops.assign_add(variables_lib.get_global_step(), 1)
       learn.graph_actions.train(
@@ -534,7 +535,7 @@ class GraphActionsTrainTest(test.TestCase):
           self._output_dir, variables_lib.get_global_step().name)
       self.assertEqual(10, step)
 
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       with ops.control_dependencies(self._build_inference_graph()):
         train_op = state_ops.assign_add(variables_lib.get_global_step(), 1)
       learn.graph_actions.train(
@@ -548,7 +549,7 @@ class GraphActionsTrainTest(test.TestCase):
       self.assertEqual(25, step)
 
   def test_train_max_steps_is_not_incremental(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       with ops.control_dependencies(self._build_inference_graph()):
         train_op = state_ops.assign_add(variables_lib.get_global_step(), 1)
       learn.graph_actions.train(
@@ -561,7 +562,7 @@ class GraphActionsTrainTest(test.TestCase):
           self._output_dir, variables_lib.get_global_step().name)
       self.assertEqual(10, step)
 
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       with ops.control_dependencies(self._build_inference_graph()):
         train_op = state_ops.assign_add(variables_lib.get_global_step(), 1)
       learn.graph_actions.train(
@@ -575,7 +576,7 @@ class GraphActionsTrainTest(test.TestCase):
       self.assertEqual(15, step)
 
   def test_train_loss(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       variables_lib.create_global_step()
       loss_var = variables_lib.local_variable(10.0)
       train_op = control_flow_ops.group(
@@ -597,7 +598,7 @@ class GraphActionsTrainTest(test.TestCase):
       self._assert_ckpt(self._output_dir, True)
 
   def test_train_summaries(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       with ops.control_dependencies(self._build_inference_graph()):
         train_op = state_ops.assign_add(variables_lib.get_global_step(), 1)
       loss_op = constant_op.constant(2.0)
@@ -623,7 +624,7 @@ class GraphActionsTrainTest(test.TestCase):
       self._assert_ckpt(self._output_dir, True)
 
   def test_train_chief_monitor(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       with ops.control_dependencies(self._build_inference_graph()):
         train_op = state_ops.assign_add(variables_lib.get_global_step(), 1)
       loss_op = constant_op.constant(2.0)
@@ -662,7 +663,7 @@ class GraphActionsTrainTest(test.TestCase):
       # and the other chief exclusive.
       chief_exclusive_monitor = _BaseMonitorWrapper(False)
       all_workers_monitor = _BaseMonitorWrapper(True)
-      with self.test_session(g):
+      with self.session(g):
         loss = learn.graph_actions.train(
             g,
             output_dir=self._output_dir,
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder_test.py b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder_test.py
index 1f439965da..284a4f45f6 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder_test.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder_test.py
@@ -58,7 +58,7 @@ class DataFeederTest(test.TestCase):
         self.assertEqual(expected_np_dtype, v)
     else:
       self.assertEqual(expected_np_dtype, feeder.input_dtype)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       inp, _ = feeder.input_builder()
       if isinstance(inp, dict):
         for v in list(inp.values()):
@@ -147,7 +147,7 @@ class DataFeederTest(test.TestCase):
   def test_unsupervised(self):
 
     def func(feeder):
-      with self.test_session():
+      with self.cached_session():
         inp, _ = feeder.input_builder()
         feed_dict_fn = feeder.get_feed_dict_fn()
         feed_dict = feed_dict_fn()
@@ -181,7 +181,7 @@ class DataFeederTest(test.TestCase):
   def test_epoch(self):
 
     def func(feeder):
-      with self.test_session():
+      with self.cached_session():
         feeder.input_builder()
         epoch = feeder.make_epoch_variable()
         feed_dict_fn = feeder.get_feed_dict_fn()
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/generator_io_test.py b/tensorflow/contrib/learn/python/learn/learn_io/generator_io_test.py
index 7e81f2b7d9..5e90d1fa20 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/generator_io_test.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/generator_io_test.py
@@ -38,7 +38,7 @@ class GeneratorIoTest(test.TestCase):
             'label': np.ones(1) * index - 32
         }
 
-    with self.test_session() as session:
+    with self.cached_session() as session:
       input_fn = generator_io.generator_input_fn(
           generator,
           target_key='label',
@@ -68,7 +68,7 @@ class GeneratorIoTest(test.TestCase):
       for index in range(2):
         yield {'a': np.ones(1) * index}
 
-    with self.test_session() as session:
+    with self.cached_session() as session:
       input_fn = generator_io.generator_input_fn(
           generator, target_key=None, batch_size=2, shuffle=False, num_epochs=1)
       features = input_fn()
@@ -97,7 +97,7 @@ class GeneratorIoTest(test.TestCase):
             'label2': np.ones(1) * index - 64,
         }
 
-    with self.test_session() as session:
+    with self.cached_session() as session:
       input_fn = generator_io.generator_input_fn(
           generator,
           target_key=['label', 'label2'],
@@ -134,7 +134,7 @@ class GeneratorIoTest(test.TestCase):
             'label': np.ones((3, 3)) * index - 32
         }
 
-    with self.test_session() as session:
+    with self.cached_session() as session:
       input_fn = generator_io.generator_input_fn(
           generator,
           target_key='label',
@@ -162,7 +162,7 @@ class GeneratorIoTest(test.TestCase):
 
   def testGeneratorInputFnWithXAsNonGeneratorFunction(self):
     x = np.arange(32, 36)
-    with self.test_session():
+    with self.cached_session():
       with self.assertRaisesRegexp(TypeError, 'x must be generator function'):
         failing_input_fn = generator_io.generator_input_fn(
             x, batch_size=2, shuffle=False, num_epochs=1)
@@ -173,7 +173,7 @@ class GeneratorIoTest(test.TestCase):
     def generator():
       return np.arange(32, 36)
 
-    with self.test_session():
+    with self.cached_session():
       with self.assertRaisesRegexp(TypeError, 'x\(\) must be generator'):
         failing_input_fn = generator_io.generator_input_fn(
             generator, batch_size=2, shuffle=False, num_epochs=1)
@@ -184,7 +184,7 @@ class GeneratorIoTest(test.TestCase):
     def generator():
       yield np.arange(32, 36)
 
-    with self.test_session():
+    with self.cached_session():
       with self.assertRaisesRegexp(TypeError, 'x\(\) must yield dict'):
         failing_input_fn = generator_io.generator_input_fn(
             generator, batch_size=2, shuffle=False, num_epochs=1)
@@ -201,7 +201,7 @@ class GeneratorIoTest(test.TestCase):
         }
 
     y = np.arange(32, 36)
-    with self.test_session():
+    with self.cached_session():
       with self.assertRaisesRegexp(TypeError, 'target_key must be str or'
                                    ' Container of str'):
         failing_input_fn = generator_io.generator_input_fn(
@@ -219,7 +219,7 @@ class GeneratorIoTest(test.TestCase):
         }
 
     y = ['label', np.arange(10)]
-    with self.test_session():
+    with self.cached_session():
       with self.assertRaisesRegexp(TypeError, 'target_key must be str or'
                                    ' Container of str'):
         failing_input_fn = generator_io.generator_input_fn(
@@ -237,7 +237,7 @@ class GeneratorIoTest(test.TestCase):
         }
 
     y = ['label', 'target']
-    with self.test_session():
+    with self.cached_session():
       with self.assertRaisesRegexp(KeyError, 'target_key not in yielded dict'):
         failing_input_fn = generator_io.generator_input_fn(
             generator, target_key=y, batch_size=2, shuffle=False, num_epochs=1)
@@ -253,7 +253,7 @@ class GeneratorIoTest(test.TestCase):
             'label': np.ones(1) * index - 32
         }
 
-    with self.test_session() as session:
+    with self.cached_session() as session:
       input_fn = generator_io.generator_input_fn(
           generator, target_key=None, batch_size=2, shuffle=False, num_epochs=1)
       features = input_fn()
@@ -283,7 +283,7 @@ class GeneratorIoTest(test.TestCase):
             'label': np.ones(1) * index - 32
         }
 
-    with self.test_session() as session:
+    with self.cached_session() as session:
       input_fn = generator_io.generator_input_fn(
           generator, target_key=None, batch_size=4, shuffle=False, num_epochs=1)
       features = input_fn()
@@ -319,7 +319,7 @@ class GeneratorIoTest(test.TestCase):
           'label': np.ones(1) * index - 32
       }
 
-    with self.test_session() as session:
+    with self.cached_session() as session:
       input_fn = generator_io.generator_input_fn(
           generator, target_key=None, batch_size=2, shuffle=False, num_epochs=1)
       features = input_fn()
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py b/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py
index e11e8b698a..8e68a17e47 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py
@@ -207,7 +207,7 @@ class GraphIOTest(test.TestCase):
             parsing_ops.FixedLenFeature(shape=shape, dtype=dtypes_lib.float32)
     }
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as sess:
+    with ops.Graph().as_default() as g, self.session(graph=g) as sess:
       features = graph_io.read_batch_record_features(
           _VALID_FILE_PATTERN,
           batch_size,
@@ -242,7 +242,7 @@ class GraphIOTest(test.TestCase):
     queue_capacity = 1234
     name = "my_batch"
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as sess:
+    with ops.Graph().as_default() as g, self.session(graph=g) as sess:
       inputs = graph_io.read_batch_examples(
           _VALID_FILE_PATTERN,
           batch_size,
@@ -276,7 +276,7 @@ class GraphIOTest(test.TestCase):
     queue_capacity = 1234
     name = "my_batch"
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as sess:
+    with ops.Graph().as_default() as g, self.session(graph=g) as sess:
       inputs = graph_io.read_batch_examples(
           [_VALID_FILE_PATTERN, _VALID_FILE_PATTERN_2],
           batch_size,
@@ -325,7 +325,7 @@ class GraphIOTest(test.TestCase):
     queue_capacity = 5
     name = "my_batch"
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+    with ops.Graph().as_default() as g, self.session(graph=g) as session:
       inputs = graph_io.read_batch_examples(
           filename,
           batch_size,
@@ -374,7 +374,7 @@ class GraphIOTest(test.TestCase):
 
     features = {"sequence": parsing_ops.FixedLenFeature([], dtypes_lib.string)}
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+    with ops.Graph().as_default() as g, self.session(graph=g) as session:
       keys, result = graph_io.read_keyed_batch_features(
           filename,
           batch_size,
@@ -429,7 +429,7 @@ class GraphIOTest(test.TestCase):
 
     features = {"sequence": parsing_ops.FixedLenFeature([], dtypes_lib.string)}
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+    with ops.Graph().as_default() as g, self.session(graph=g) as session:
       result = graph_io.read_batch_features(
           filename,
           batch_size,
@@ -475,7 +475,7 @@ class GraphIOTest(test.TestCase):
     queue_capacity = 5
     name = "my_batch"
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+    with ops.Graph().as_default() as g, self.session(graph=g) as session:
       inputs = graph_io.read_batch_examples(
           filenames,
           batch_size,
@@ -519,7 +519,7 @@ class GraphIOTest(test.TestCase):
     queue_capacity = 5
     name = "my_batch"
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+    with ops.Graph().as_default() as g, self.session(graph=g) as session:
       keys, inputs = graph_io.read_keyed_batch_examples_shared_queue(
           filenames,
           batch_size,
@@ -640,7 +640,7 @@ class GraphIOTest(test.TestCase):
     queue_capacity = 10
     name = "my_batch"
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+    with ops.Graph().as_default() as g, self.session(graph=g) as session:
       inputs = graph_io.read_batch_examples(
           [filename],
           batch_size,
@@ -672,7 +672,7 @@ class GraphIOTest(test.TestCase):
     queue_capacity = 5
     name = "my_batch"
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+    with ops.Graph().as_default() as g, self.session(graph=g) as session:
       keys, inputs = graph_io.read_keyed_batch_examples(
           filename,
           batch_size,
@@ -714,7 +714,7 @@ class GraphIOTest(test.TestCase):
     queue_capacity = 5
     name = "my_batch"
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+    with ops.Graph().as_default() as g, self.session(graph=g) as session:
       dtypes = {"age": parsing_ops.FixedLenFeature([1], dtypes_lib.int64)}
       parse_fn = lambda example: parsing_ops.parse_single_example(  # pylint: disable=g-long-lambda
           parsing_ops.decode_json_example(example), dtypes)
@@ -773,7 +773,7 @@ class GraphIOTest(test.TestCase):
       examples = parsing_ops.parse_example(serialized, features)
       return math_ops.less(examples["age"], 2)
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+    with ops.Graph().as_default() as g, self.session(graph=g) as session:
       keys, inputs = graph_io._read_keyed_batch_examples_helper(
           filename,
           batch_size,
@@ -812,7 +812,7 @@ class GraphIOTest(test.TestCase):
       coord.join(threads)
 
   def test_queue_parsed_features_single_tensor(self):
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+    with ops.Graph().as_default() as g, self.session(graph=g) as session:
       features = {"test": constant_op.constant([1, 2, 3])}
       _, queued_features = graph_io.queue_parsed_features(features)
       coord = coordinator.Coordinator()
@@ -833,7 +833,7 @@ class GraphIOTest(test.TestCase):
     _, queued_feature = graph_io.read_keyed_batch_features_shared_queue(
         _VALID_FILE_PATTERN, batch_size, feature, reader)
 
-    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+    with ops.Graph().as_default() as g, self.session(graph=g) as session:
       features_result = graph_io.read_batch_features(
           _VALID_FILE_PATTERN, batch_size, feature, reader)
       session.run(variables.local_variables_initializer())
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/pandas_io_test.py b/tensorflow/contrib/learn/python/learn/learn_io/pandas_io_test.py
index c738f0e8f3..396539a76a 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/pandas_io_test.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/pandas_io_test.py
@@ -65,7 +65,7 @@ class PandasIoTest(test.TestCase):
   def testPandasInputFn_ProducesExpectedOutputs(self):
     if not HAS_PANDAS:
       return
-    with self.test_session() as session:
+    with self.cached_session() as session:
       x, y = self.makeTestDataFrame()
       input_fn = pandas_io.pandas_input_fn(
           x, y, batch_size=2, shuffle=False, num_epochs=1)
@@ -79,7 +79,7 @@ class PandasIoTest(test.TestCase):
   def testPandasInputFn_ProducesOutputsForLargeBatchAndMultipleEpochs(self):
     if not HAS_PANDAS:
       return
-    with self.test_session() as session:
+    with self.cached_session() as session:
       index = np.arange(100, 102)
       a = np.arange(2)
       b = np.arange(32, 34)
@@ -107,7 +107,7 @@ class PandasIoTest(test.TestCase):
   def testPandasInputFn_ProducesOutputsWhenDataSizeNotDividedByBatchSize(self):
     if not HAS_PANDAS:
       return
-    with self.test_session() as session:
+    with self.cached_session() as session:
       index = np.arange(100, 105)
       a = np.arange(5)
       b = np.arange(32, 37)
@@ -146,7 +146,7 @@ class PandasIoTest(test.TestCase):
   def testPandasInputFn_OnlyX(self):
     if not HAS_PANDAS:
       return
-    with self.test_session() as session:
+    with self.cached_session() as session:
       x, _ = self.makeTestDataFrame()
       input_fn = pandas_io.pandas_input_fn(
           x, y=None, batch_size=2, shuffle=False, num_epochs=1)
@@ -159,7 +159,7 @@ class PandasIoTest(test.TestCase):
   def testPandasInputFn_ExcludesIndex(self):
     if not HAS_PANDAS:
       return
-    with self.test_session() as session:
+    with self.cached_session() as session:
       x, y = self.makeTestDataFrame()
       input_fn = pandas_io.pandas_input_fn(
           x, y, batch_size=2, shuffle=False, num_epochs=1)
@@ -182,7 +182,7 @@ class PandasIoTest(test.TestCase):
   def testPandasInputFn_RespectsEpoch_NoShuffle(self):
     if not HAS_PANDAS:
       return
-    with self.test_session() as session:
+    with self.cached_session() as session:
       x, y = self.makeTestDataFrame()
       input_fn = pandas_io.pandas_input_fn(
           x, y, batch_size=4, shuffle=False, num_epochs=1)
@@ -192,7 +192,7 @@ class PandasIoTest(test.TestCase):
   def testPandasInputFn_RespectsEpoch_WithShuffle(self):
     if not HAS_PANDAS:
       return
-    with self.test_session() as session:
+    with self.cached_session() as session:
       x, y = self.makeTestDataFrame()
       input_fn = pandas_io.pandas_input_fn(
           x, y, batch_size=4, shuffle=True, num_epochs=1)
@@ -202,7 +202,7 @@ class PandasIoTest(test.TestCase):
   def testPandasInputFn_RespectsEpoch_WithShuffleAutosize(self):
     if not HAS_PANDAS:
       return
-    with self.test_session() as session:
+    with self.cached_session() as session:
       x, y = self.makeTestDataFrame()
       input_fn = pandas_io.pandas_input_fn(
           x, y, batch_size=2, shuffle=True, queue_capacity=None, num_epochs=2)
@@ -213,7 +213,7 @@ class PandasIoTest(test.TestCase):
     if not HAS_PANDAS:
       return
     x, y = self.makeTestDataFrame()
-    with self.test_session() as session:
+    with self.cached_session() as session:
       input_fn = pandas_io.pandas_input_fn(
           x, y, batch_size=3, shuffle=False, num_epochs=1)
 
diff --git a/tensorflow/contrib/learn/python/learn/monitors.py b/tensorflow/contrib/learn/python/learn/monitors.py
index 77f7c73d54..3d691d4340 100644
--- a/tensorflow/contrib/learn/python/learn/monitors.py
+++ b/tensorflow/contrib/learn/python/learn/monitors.py
@@ -51,7 +51,7 @@ from tensorflow.python.estimator import estimator as core_estimator
 from tensorflow.python.framework import ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary import summary as core_summary
-from tensorflow.python.training import saver as saver_lib
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import session_run_hook
 from tensorflow.python.training import training_util
 from tensorflow.python.util import deprecation
@@ -735,7 +735,8 @@ class ValidationMonitor(EveryN):
       return False
     self._last_checkpoint_check_time = current_time
     # Check that we are not running evaluation on the same checkpoint.
-    latest_path = saver_lib.latest_checkpoint(self._estimator.model_dir)
+    latest_path = checkpoint_management.latest_checkpoint(
+        self._estimator.model_dir)
     if latest_path is None:
       logging.debug("Skipping evaluation since model has not been saved yet "
                     "at step %d.", step)
@@ -1059,7 +1060,8 @@ class ExportMonitor(EveryN):
 
   def end(self, session=None):
     super(ExportMonitor, self).end(session=session)
-    latest_path = saver_lib.latest_checkpoint(self._estimator.model_dir)
+    latest_path = checkpoint_management.latest_checkpoint(
+        self._estimator.model_dir)
     if latest_path is None:
       logging.info("Skipping export at the end since model has not been saved "
                    "yet.")
diff --git a/tensorflow/contrib/learn/python/learn/monitors_test.py b/tensorflow/contrib/learn/python/learn/monitors_test.py
index 5c34d0ddb0..83e48a36e7 100644
--- a/tensorflow/contrib/learn/python/learn/monitors_test.py
+++ b/tensorflow/contrib/learn/python/learn/monitors_test.py
@@ -39,9 +39,9 @@ from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary import summary
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import gradient_descent
 from tensorflow.python.training import monitored_session
-from tensorflow.python.training import saver
 from tensorflow.python.training import training_util
 
 
@@ -127,12 +127,12 @@ class MonitorsTest(test.TestCase):
     monitor.end()
 
   def test_base_monitor(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._run_monitor(learn.monitors.BaseMonitor())
 
   def test_every_0(self):
     monitor = _MyEveryN(every_n_steps=0, first_n_steps=-1)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._run_monitor(monitor, num_epochs=3, num_steps_per_epoch=10)
       expected_steps = list(range(30))
       self.assertAllEqual(expected_steps, monitor.steps_begun)
@@ -141,7 +141,7 @@ class MonitorsTest(test.TestCase):
 
   def test_every_1(self):
     monitor = _MyEveryN(every_n_steps=1, first_n_steps=-1)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._run_monitor(monitor, num_epochs=3, num_steps_per_epoch=10)
       expected_steps = list(range(1, 30))
       self.assertEqual(expected_steps, monitor.steps_begun)
@@ -150,7 +150,7 @@ class MonitorsTest(test.TestCase):
 
   def test_every_2(self):
     monitor = _MyEveryN(every_n_steps=2, first_n_steps=-1)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._run_monitor(monitor, num_epochs=3, num_steps_per_epoch=10)
       expected_steps = list(range(2, 29, 2)) + [29]
       self.assertEqual(expected_steps, monitor.steps_begun)
@@ -159,7 +159,7 @@ class MonitorsTest(test.TestCase):
 
   def test_every_8(self):
     monitor = _MyEveryN(every_n_steps=8, first_n_steps=2)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._run_monitor(monitor, num_epochs=3, num_steps_per_epoch=10)
       expected_steps = [0, 1, 2, 10, 18, 26, 29]
       self.assertEqual(expected_steps, monitor.steps_begun)
@@ -168,7 +168,7 @@ class MonitorsTest(test.TestCase):
 
   def test_every_8_no_max_steps(self):
     monitor = _MyEveryN(every_n_steps=8, first_n_steps=2)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._run_monitor(
           monitor, num_epochs=3, num_steps_per_epoch=10, pass_max_steps=False)
       begin_end_steps = [0, 1, 2, 10, 18, 26]
@@ -179,7 +179,7 @@ class MonitorsTest(test.TestCase):
 
   def test_every_8_recovered_after_step_begin(self):
     monitor = _MyEveryN(every_n_steps=8)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       for step in [8, 16]:
         monitor.step_begin(step)
         monitor.step_begin(step)
@@ -192,7 +192,7 @@ class MonitorsTest(test.TestCase):
 
   def test_every_8_recovered_after_step_end(self):
     monitor = _MyEveryN(every_n_steps=8)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       for step in [8, 16]:
         monitor.step_begin(step)
         monitor.step_end(step, output=None)
@@ -207,7 +207,7 @@ class MonitorsTest(test.TestCase):
 
   def test_every_8_call_post_step_at_the_end(self):
     monitor = _MyEveryN(every_n_steps=8)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       monitor.begin()
       for step in [8, 16]:
         monitor.step_begin(step)
@@ -224,7 +224,7 @@ class MonitorsTest(test.TestCase):
 
   def test_every_8_call_post_step_should_not_be_called_twice(self):
     monitor = _MyEveryN(every_n_steps=8)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       monitor.begin()
       for step in [8, 16]:
         monitor.step_begin(step)
@@ -240,13 +240,13 @@ class MonitorsTest(test.TestCase):
       self.assertEqual([8, 16], monitor.post_steps)
 
   def test_print(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       t = constant_op.constant(42.0, name='foo')
       self._run_monitor(learn.monitors.PrintTensor(tensor_names=[t.name]))
       self.assertRegexpMatches(str(self.logged_message), t.name)
 
   def test_logging_trainable(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       var = variables.Variable(constant_op.constant(42.0), name='foo')
       var.initializer.run()
       cof = constant_op.constant(1.0)
@@ -258,7 +258,7 @@ class MonitorsTest(test.TestCase):
       self.assertRegexpMatches(str(self.logged_message), var.name)
 
   def test_summary_saver(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       log_dir = 'log/dir'
       summary_writer = testing.FakeSummaryWriter(log_dir, g)
       var = variables.Variable(0.0)
@@ -312,12 +312,12 @@ class MonitorsTest(test.TestCase):
     monitor = learn.monitors.ValidationMonitor(
         x=constant_op.constant(2.0), every_n_steps=0)
     self._assert_validation_monitor(monitor)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       with self.assertRaisesRegexp(ValueError, 'set_estimator'):
         self._run_monitor(monitor)
 
   @test.mock.patch.object(estimators, 'Estimator', autospec=True)
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor_no_ckpt(self, mock_latest_checkpoint,
                                       mock_estimator_class):
     estimator = mock_estimator_class()
@@ -330,13 +330,13 @@ class MonitorsTest(test.TestCase):
         x=constant_op.constant(2.0), every_n_steps=0)
     self._assert_validation_monitor(monitor)
     monitor.set_estimator(estimator)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._run_monitor(monitor)
       self._assert_validation_monitor(monitor)
       mock_latest_checkpoint.assert_called_with(model_dir)
 
   @test.mock.patch.object(estimators, 'Estimator', autospec=True)
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor_no_early_stopping_rounds(self,
                                                        mock_latest_checkpoint,
                                                        mock_estimator_class):
@@ -351,12 +351,12 @@ class MonitorsTest(test.TestCase):
         x=constant_op.constant(2.0), every_n_steps=0)
     self._assert_validation_monitor(monitor)
     monitor.set_estimator(estimator)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       self._run_monitor(monitor)
       self._assert_validation_monitor(monitor)
 
   @test.mock.patch.object(estimators, 'Estimator', autospec=True)
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor_invalid_metric(self, mock_latest_checkpoint,
                                              mock_estimator_class):
     estimator = mock_estimator_class()
@@ -370,12 +370,12 @@ class MonitorsTest(test.TestCase):
         x=constant_op.constant(2.0), every_n_steps=0, early_stopping_rounds=1)
     self._assert_validation_monitor(monitor)
     monitor.set_estimator(estimator)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       with self.assertRaisesRegexp(ValueError, 'missing from outputs'):
         self._run_monitor(monitor, num_epochs=1, num_steps_per_epoch=1)
 
   @test.mock.patch.object(estimators, 'Estimator', autospec=True)
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor(self, mock_latest_checkpoint,
                               mock_estimator_class):
     estimator = mock_estimator_class()
@@ -392,7 +392,7 @@ class MonitorsTest(test.TestCase):
 
     self._assert_validation_monitor(monitor)
     monitor.set_estimator(estimator)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       monitor.begin(max_steps=100)
       monitor.epoch_begin(epoch=0)
       self.assertEqual(0, estimator.evaluate.call_count)
@@ -464,7 +464,7 @@ class MonitorsTest(test.TestCase):
       monitor.epoch_end(epoch=0)
       monitor.end()
 
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor_with_core_estimator(self, mock_latest_checkpoint):
     estimator = test.mock.Mock(spec=core_estimator.Estimator)
     model_dir = 'model/dir'
@@ -477,7 +477,7 @@ class MonitorsTest(test.TestCase):
         every_n_steps=0, early_stopping_rounds=2)
     self._assert_validation_monitor(monitor)
     monitor.set_estimator(estimator)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       monitor.begin(max_steps=100)
       monitor.epoch_begin(epoch=0)
       self.assertEqual(0, estimator.evaluate.call_count)
@@ -495,7 +495,7 @@ class MonitorsTest(test.TestCase):
           expected_best_metrics={'loss': 42.0, 'auc': 0.5})
       monitor.post_step(step=step, session=None)
 
-  @test.mock.patch.object(saver, 'latest_checkpoint')
+  @test.mock.patch.object(checkpoint_management, 'latest_checkpoint')
   def test_validation_monitor_fail_with_core_estimator_and_metrics(
       self, mock_latest_checkpoint):
     estimator = test.mock.Mock(spec=core_estimator.Estimator)
@@ -509,7 +509,7 @@ class MonitorsTest(test.TestCase):
         metrics=constant_op.constant(2.0),
         every_n_steps=0, early_stopping_rounds=2)
     monitor.set_estimator(estimator)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       monitor.begin(max_steps=100)
       monitor.epoch_begin(epoch=0)
 
@@ -525,7 +525,7 @@ class MonitorsTest(test.TestCase):
   def test_graph_dump(self):
     monitor0 = learn.monitors.GraphDump()
     monitor1 = learn.monitors.GraphDump()
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       const_var = variables.Variable(42.0, name='my_const')
       counter_var = variables.Variable(0.0, name='my_counter')
       assign_add = state_ops.assign_add(counter_var, 1.0, name='my_assign_add')
@@ -568,7 +568,7 @@ class MonitorsTest(test.TestCase):
   def test_capture_variable(self):
     monitor = learn.monitors.CaptureVariable(
         var_name='my_assign_add:0', every_n=8, first_n=2)
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       var = variables.Variable(0.0, name='my_var')
       var.initializer.run()
       state_ops.assign_add(var, 1.0, name='my_assign_add')
diff --git a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
index 92976d1539..9f2cadb017 100644
--- a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
+++ b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
@@ -40,7 +40,7 @@ def mean_squared_error_regressor(tensor_in, labels, weights, biases, name=None):
                       [tensor_in, labels]):
     predictions = nn.xw_plus_b(tensor_in, weights, biases)
     if len(labels.get_shape()) == 1 and len(predictions.get_shape()) == 2:
-      predictions = array_ops_.squeeze(predictions, squeeze_dims=[1])
+      predictions = array_ops_.squeeze(predictions, axis=[1])
     return predictions, losses.mean_squared_error(labels, predictions)
 
 
diff --git a/tensorflow/contrib/learn/python/learn/ops/ops_test.py b/tensorflow/contrib/learn/python/learn/ops/ops_test.py
index 80d4923db3..ff190110c1 100644
--- a/tensorflow/contrib/learn/python/learn/ops/ops_test.py
+++ b/tensorflow/contrib/learn/python/learn/ops/ops_test.py
@@ -33,7 +33,7 @@ class OpsTest(test.TestCase):
   """Ops tests."""
 
   def test_softmax_classifier(self):
-    with self.test_session() as session:
+    with self.cached_session() as session:
       features = array_ops.placeholder(dtypes.float32, [None, 3])
       labels = array_ops.placeholder(dtypes.float32, [None, 2])
       weights = constant_op.constant([[0.1, 0.1], [0.1, 0.1], [0.1, 0.1]])
@@ -52,7 +52,7 @@ class OpsTest(test.TestCase):
     ids_shape = (2, 3, 4)
     embeds = np.random.randn(n_embed, d_embed)
     ids = np.random.randint(0, n_embed, ids_shape)
-    with self.test_session():
+    with self.cached_session():
       embed_np = embeds[ids]
       embed_tf = ops.embedding_lookup(embeds, ids).eval()
     self.assertEqual(embed_np.shape, embed_tf.shape)
@@ -60,7 +60,7 @@ class OpsTest(test.TestCase):
 
   def test_categorical_variable(self):
     random_seed.set_random_seed(42)
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       cat_var_idx = array_ops.placeholder(dtypes.int64, [2, 2])
       embeddings = ops.categorical_variable(
           cat_var_idx, n_classes=5, embedding_size=10, name="my_cat_var")
diff --git a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py
index 95aec61955..5a7e4ebfea 100644
--- a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py
+++ b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py
@@ -31,7 +31,7 @@ class Seq2SeqOpsTest(test.TestCase):
   """Sequence-to-sequence tests."""
 
   def test_sequence_classifier(self):
-    with self.test_session() as session:
+    with self.cached_session() as session:
       decoding = [
           array_ops.placeholder(dtypes.float32, [2, 2]) for _ in range(3)
       ]
@@ -60,7 +60,7 @@ class Seq2SeqOpsTest(test.TestCase):
   def test_seq2seq_inputs(self):
     inp = np.array([[[1, 0], [0, 1], [1, 0]], [[0, 1], [1, 0], [0, 1]]])
     out = np.array([[[0, 1, 0], [1, 0, 0]], [[1, 0, 0], [0, 1, 0]]])
-    with self.test_session() as session:
+    with self.cached_session() as session:
       x = array_ops.placeholder(dtypes.float32, [2, 3, 2])
       y = array_ops.placeholder(dtypes.float32, [2, 2, 3])
       in_x, in_y, out_y = ops.seq2seq_inputs(x, y, 3, 2)
@@ -77,7 +77,7 @@ class Seq2SeqOpsTest(test.TestCase):
                                   [[0, 0, 0], [0, 0, 0]]])
 
   def test_rnn_decoder(self):
-    with self.test_session():
+    with self.cached_session():
       decoder_inputs = [
           array_ops.placeholder(dtypes.float32, [2, 2]) for _ in range(3)
       ]
diff --git a/tensorflow/contrib/learn/python/learn/utils/export.py b/tensorflow/contrib/learn/python/learn/utils/export.py
index 3eacac7a3d..0144b93814 100644
--- a/tensorflow/contrib/learn/python/learn/utils/export.py
+++ b/tensorflow/contrib/learn/python/learn/utils/export.py
@@ -35,6 +35,7 @@ from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import saver as tf_saver
 from tensorflow.python.training import training_util
 
@@ -298,7 +299,8 @@ def _export_estimator(estimator,
 
   # If checkpoint_path is specified, use the specified checkpoint path.
   checkpoint_path = (checkpoint_path or
-                     tf_saver.latest_checkpoint(estimator._model_dir))
+                     checkpoint_management.latest_checkpoint(
+                         estimator._model_dir))
   with ops.Graph().as_default() as g:
     training_util.create_global_step(g)
 
diff --git a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
index c7cdb41312..4f22054af3 100644
--- a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
+++ b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
@@ -55,7 +55,7 @@ from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.saved_model import signature_constants
 from tensorflow.python.saved_model import signature_def_utils
 from tensorflow.python.summary import summary_iterator
-from tensorflow.python.training import saver
+from tensorflow.python.training import checkpoint_management
 from tensorflow.python.util import compat
 from tensorflow.python.util.deprecation import deprecated
 
@@ -343,7 +343,8 @@ def get_temp_export_dir(timestamped_export_dir):
   """
   (dirname, basename) = os.path.split(timestamped_export_dir)
   temp_export_dir = os.path.join(
-      compat.as_bytes(dirname), compat.as_bytes('temp-{}'.format(basename)))
+      compat.as_bytes(dirname),
+      compat.as_bytes('temp-{}'.format(compat.as_text(basename))))
   return temp_export_dir
 
 
@@ -414,7 +415,7 @@ def make_export_strategy(serving_input_fn,
       `InputFnOps`.
     default_output_alternative_key: the name of the head to serve when an
       incoming serving request does not explicitly request a specific head.
-      Must be `None` if the estimator inherits from @{tf.estimator.Estimator}
+      Must be `None` if the estimator inherits from `tf.estimator.Estimator`
       or for single-headed models.
     assets_extra: A dict specifying how to populate the assets.extra directory
       within the exported SavedModel.  Each key should give the destination
@@ -452,7 +453,7 @@ def make_export_strategy(serving_input_fn,
       The string path to the exported directory.
 
     Raises:
-      ValueError: If `estimator` is a @{tf.estimator.Estimator} instance
+      ValueError: If `estimator` is a `tf.estimator.Estimator` instance
         and `default_output_alternative_key` was specified.
     """
     if isinstance(estimator, core_estimator.Estimator):
@@ -503,7 +504,7 @@ def make_parsing_export_strategy(feature_columns,
       that must be provided at serving time (excluding labels!).
     default_output_alternative_key: the name of the head to serve when an
       incoming serving request does not explicitly request a specific head.
-      Must be `None` if the estimator inherits from @{tf.estimator.Estimator}
+      Must be `None` if the estimator inherits from `tf.estimator.Estimator`
       or for single-headed models.
     assets_extra: A dict specifying how to populate the assets.extra directory
       within the exported SavedModel.  Each key should give the destination
@@ -713,7 +714,8 @@ def make_best_model_export_strategy(
       #  as soon as contrib is cleaned up and we can thus be sure that
       #  estimator is a tf.estimator.Estimator and not a
       #  tf.contrib.learn.Estimator
-      checkpoint_path = saver.latest_checkpoint(estimator.model_dir)
+      checkpoint_path = checkpoint_management.latest_checkpoint(
+          estimator.model_dir)
     export_checkpoint_path, export_eval_result = best_model_selector.update(
         checkpoint_path, eval_result)
 
@@ -765,7 +767,7 @@ def extend_export_strategy(base_export_strategy,
       The string path to the SavedModel indicated by post_export_fn.
 
     Raises:
-      ValueError: If `estimator` is a @{tf.estimator.Estimator} instance
+      ValueError: If `estimator` is a `tf.estimator.Estimator` instance
         and `default_output_alternative_key` was specified or if post_export_fn
         does not return a valid directory.
       RuntimeError: If unable to create temporary or final export directory.