Introduce Predictor, an interface for efficient, repeated inference.

PiperOrigin-RevId: 159141010

15 files changed, 1212 insertions, 0 deletions

diff --git a/tensorflow/contrib/predictor/BUILD b/tensorflow/contrib/predictor/BUILD
new file mode 100644
index 0000000000..c4b46551c1
--- /dev/null
+++ b/tensorflow/contrib/predictor/BUILD
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+# `Predictor` classes provide an interface for efficient, repeated inference.
+
+package(default_visibility = ["//third_party/tensroflow/contrib/predictor:__subpackages__"])
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+load("//tensorflow:tensorflow.bzl", "py_test")
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
+
+py_library(
+    name = "predictor",
+    srcs = [
+        "__init__.py",
+    ],
+    srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":predictor_factories",
+    ],
+)
+
+py_library(
+    name = "predictor_factories",
+    srcs = [
+        "predictor_factories.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":contrib_estimator_predictor",
+        ":core_estimator_predictor",
+        ":saved_model_predictor",
+        "//tensorflow/contrib/learn",
+    ],
+)
+
+py_library(
+    name = "base_predictor",
+    srcs = [
+        "predictor.py",
+    ],
+    srcs_version = "PY2AND3",
+)
+
+py_library(
+    name = "saved_model_predictor",
+    srcs = [
+        "saved_model_predictor.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":base_predictor",
+        "//tensorflow/python/tools:saved_model_cli",
+    ],
+)
+
+py_library(
+    name = "core_estimator_predictor",
+    srcs = [
+        "core_estimator_predictor.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":base_predictor",
+        "//tensorflow/contrib/learn",
+    ],
+)
+
+py_library(
+    name = "contrib_estimator_predictor",
+    srcs = [
+        "contrib_estimator_predictor.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":base_predictor",
+        "//tensorflow/contrib/learn",
+    ],
+)
+
+py_library(
+    name = "testing_common",
+    srcs = [
+        "testing_common.py",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+    ],
+)
+
+# Transitive dependencies of this target will be included in the pip package.
+py_library(
+    name = "predictor_pip",
+    visibility = ["//visibility:public"],
+    deps = [
+        ":contrib_estimator_predictor",
+        ":core_estimator_predictor",
+        ":saved_model_predictor",
+    ],
+)
+
+py_test(
+    name = "saved_model_predictor_test",
+    srcs = [
+        "saved_model_predictor_test.py",
+    ],
+    data = [":test_export_dir"],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":saved_model_predictor",
+        ":testing_common",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
+py_test(
+    name = "core_estimator_predictor_test",
+    srcs = [
+        "core_estimator_predictor_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":core_estimator_predictor",
+        ":testing_common",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
+py_test(
+    name = "contrib_estimator_predictor_test",
+    srcs = [
+        "contrib_estimator_predictor_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":contrib_estimator_predictor",
+        ":testing_common",
+        "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
+    ],
+)
+
+filegroup(
+    name = "test_export_dir",
+    srcs = glob(["test_export_dir/**/*"]),
+    tags = ["nopip"],
+)
diff --git a/tensorflow/contrib/predictor/README.md b/tensorflow/contrib/predictor/README.md
new file mode 100644
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--- /dev/null
+++ b/tensorflow/contrib/predictor/README.md
@@ -0,0 +1,96 @@
+# Predictors
+
+The `Predictor` classes provide a simple interface for performing repeated,
+efficient inference. A `Predictor` can be constructed from a `SavedModel` on
+disk, a `tf.Estimator` or a `tf.contrib.Estimator`.
+
+To facilitate the examples below, let's define a trivial `Estimator` that just
+calculates a sum:
+
+```python
+def model_fn(features, labels, mode):
+  z = tf.add(features['x'], features['y'], name='z')
+  return tf.contrib.learn.ModelFnOps(
+      mode, {'z': z}, loss=tf.constant(0.0), train_op=tf.no_op())
+
+estimator = tf.contrib.learn.Estimator(model_fn=model_fn)
+```
+
+We can then construct a `Predictor` in two different ways.
+
+## `Predictor` from a `SavedModel`
+
+Given a trained `Estimator`, we first export a `SavedModel`:
+
+```python
+def serving_input_fn():
+  x = tf.placeholder(dtype=tf.float32, shape=[None], name='x')
+  y = tf.placeholder(dtype=tf.float32, shape=[None], name='y')
+
+  features = {'x': x, 'y': y}
+  return tf.contrib.learn.utils.input_fn_utils.InputFnOps(
+           features, None, default_inputs=features)
+
+saved_model_dir = estimator.export_savedmodel(my_export_dir, serving_input_fn)
+```
+
+We can then construct a `Predictor` as follows:
+
+```python
+saved_model_predictor = predictor.from_saved_model(export_dir='test_export_dir')
+output_dict = saved_model_predictor({'x': [1.0], 'y': [5.2]})
+# output_dict == {'sum': [6.2]}
+```
+
+By specifying a signature definition, we can feed and fetch any `Tensor`s in
+the `Graph`. In this example, we feed and fetch the same `Tensor`, `z`:
+
+```python
+inputs = outputs = {'z': tf.TensorInfo(
+                        name='z:0',
+                        dtype=types_pb2.DT_FLOAT,
+                        tensor_shape=tensor_shape_pb2.TensorShapeProto())}
+
+signature_def = tf.saved_model.signature_def_utils.build_signature_def(
+          inputs=inputs,
+          outputs=outputs,
+          method_name='tensorflow/serving/regress')
+
+trivial_predictor = predictor.from_saved_model(
+          export_dir=saved_model_dir,
+          signature_def=signature_def)
+
+output_dict = trivial_predictor({'z': [32.]})
+# output_dict == {'z': [32.]}
+```
+
+You can also specify input and output `Tensor`s by name using the `input_names`
+and `output_names` keywords:
+
+```python
+saved_model_predictor = predictor.from_saved_model(
+          export_dir=saved_model_dir,
+          input_names={'x': 'x:0', 'y': 'y:0'},
+          outputs={'z': 'z:0'})
+
+output_dict = saved_model_predictor({'x': [6.], 'y': [11.]})
+# output_dict == {'z': [17.]}
+```
+
+This functionality is particularly useful for performing encoding once, but
+doing multiple decoding iterations with e.g. seq2seq models.
+
+## `Predictor` from an `Estimator`
+
+We can also construct a `Predictor` directly from an `Estimator`. Defining
+`serving_input_fn` as above,
+
+```python
+estimator_predictor = predictor.from_contrib_estimator(
+    estimator, serving_input_fn)
+output_dict = sum_predictor({'x': [1., 2.], 'y': [3., 4.]})
+# output_dict == {'z': [4., 6.]}
+```
+
+Construction from a `tf.Estimator` is almost identical.
+
diff --git a/tensorflow/contrib/predictor/__init__.py b/tensorflow/contrib/predictor/__init__.py
new file mode 100644
index 0000000000..d270c3f798
--- /dev/null
+++ b/tensorflow/contrib/predictor/__init__.py
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+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Modules for `Predictor`s."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.predictor import from_contrib_estimator
+from tensorflow.contrib.predictor import from_estimator
+from tensorflow.contrib.predictor import from_saved_model
diff --git a/tensorflow/contrib/predictor/contrib_estimator_predictor.py b/tensorflow/contrib/predictor/contrib_estimator_predictor.py
new file mode 100644
index 0000000000..b7a98c68e2
--- /dev/null
+++ b/tensorflow/contrib/predictor/contrib_estimator_predictor.py
@@ -0,0 +1,74 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A `Predictor constructed from a `tf.contrib.learn.Estimator`."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.learn.python.learn.utils import saved_model_export_utils
+from tensorflow.contrib.predictor import predictor
+from tensorflow.python.framework import ops
+from tensorflow.python.training import monitored_session
+from tensorflow.python.training import saver
+
+
+class ContribEstimatorPredictor(predictor.Predictor):
+  """A `Predictor constructed from a `tf.contrib.learn.Estimator`."""
+
+  def __init__(self,
+               estimator,
+               prediction_input_fn,
+               input_alternative_key=None,
+               output_alternative_key=None,
+               graph=None):
+    """Initialize a `ContribEstimatorPredictor`.
+
+    Args:
+      estimator: an instance of `tf.contrib.learn.Estimator`.
+      prediction_input_fn: a function that takes no arguments and returns an
+        instance of `InputFnOps`.
+      input_alternative_key: Optional. Specify the input alternative used for
+        prediction.
+      output_alternative_key: Specify the output alternative used for
+        prediction. Not needed for single-headed models but required for
+        multi-headed models.
+      graph: Optional. The Tensorflow `graph` in which prediction should be
+        done.
+    """
+    self._graph = graph or ops.Graph()
+    with self._graph.as_default():
+      input_fn_ops = prediction_input_fn()
+      # pylint: disable=protected-access
+      model_fn_ops = estimator._get_predict_ops(input_fn_ops.features)
+      # pylint: enable=protected-access
+      checkpoint_path = saver.latest_checkpoint(estimator.model_dir)
+      self._session = monitored_session.MonitoredSession(
+          session_creator=monitored_session.ChiefSessionCreator(
+              checkpoint_filename_with_path=checkpoint_path))
+
+    input_alternative_key = (
+        input_alternative_key or
+        saved_model_export_utils.DEFAULT_INPUT_ALTERNATIVE_KEY)
+    input_alternatives, _ = saved_model_export_utils.get_input_alternatives(
+        input_fn_ops)
+    self._feed_tensors = input_alternatives[input_alternative_key]
+
+    (output_alternatives,
+     output_alternative_key) = saved_model_export_utils.get_output_alternatives(
+         model_fn_ops, output_alternative_key)
+    _, fetch_tensors = output_alternatives[output_alternative_key]
+    self._fetch_tensors = fetch_tensors
diff --git a/tensorflow/contrib/predictor/contrib_estimator_predictor_test.py b/tensorflow/contrib/predictor/contrib_estimator_predictor_test.py
new file mode 100644
index 0000000000..4b97a52b1a
--- /dev/null
+++ b/tensorflow/contrib/predictor/contrib_estimator_predictor_test.py
@@ -0,0 +1,70 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for predictor.contrib_estimator_predictor."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tempfile
+import numpy as np
+
+from tensorflow.contrib.predictor import contrib_estimator_predictor
+from tensorflow.contrib.predictor import testing_common
+from tensorflow.python.platform import test
+
+
+KEYS_AND_OPS = (('sum', lambda x, y: x + y),
+                ('product', lambda x, y: x * y,),
+                ('difference', lambda x, y: x - y))
+
+
+class ContribEstimatorPredictorTest(test.TestCase):
+  """Test fixture for `ContribEstimatorPredictor`."""
+
+  def setUp(self):
+    model_dir = tempfile.mkdtemp()
+    self._estimator = testing_common.get_arithmetic_estimator(
+        core=False, model_dir=model_dir)
+    self._prediction_input_fn = testing_common.get_arithmetic_input_fn(
+        core=False, train=False)
+
+  def testSpecifiedSignatureKey(self):
+    """Test prediction with spedicified signatures."""
+    np.random.seed(1234)
+    for key, op in KEYS_AND_OPS:
+      x = np.random.rand()
+      y = np.random.rand()
+      expected_output = op(x, y)
+
+      predictor = contrib_estimator_predictor.ContribEstimatorPredictor(
+          estimator=self._estimator,
+          prediction_input_fn=self._prediction_input_fn,
+          output_alternative_key=key)
+      output_tensor_name = predictor.fetch_tensors[key].name
+      self.assertRegexpMatches(
+          output_tensor_name,
+          key,
+          msg='Unexpected fetch tensor.')
+      output = predictor({'x': x, 'y': y})[key]
+      self.assertAlmostEqual(
+          expected_output, output, places=3,
+          msg='Failed for output key "{}." '
+          'Got output {} for x = {} and y = {}'.format(
+              key, output, x, y))
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/predictor/core_estimator_predictor.py b/tensorflow/contrib/predictor/core_estimator_predictor.py
new file mode 100644
index 0000000000..5557ef5101
--- /dev/null
+++ b/tensorflow/contrib/predictor/core_estimator_predictor.py
@@ -0,0 +1,80 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A `Predictor` constructed from an `learn.python.estimator.Estimator`."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.predictor import predictor
+from tensorflow.python.estimator import model_fn
+from tensorflow.python.framework import ops
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.training import monitored_session
+
+
+def _get_signature_def(
+    serving_input_receiver, estimator, output_key=None):
+  """Construct a `SignatureDef` proto."""
+  if output_key is None:
+    output_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
+  # pylint: disable=protected-access
+  estimator_spec = estimator._call_model_fn(
+      serving_input_receiver.features, None, model_fn.ModeKeys.PREDICT)
+  # pylint: enable=protected-access
+  export_outputs = estimator_spec.export_outputs
+  export_output = export_outputs.get(output_key)
+  if export_output is None:
+    raise KeyError('output_key must be one of {}; got {}'.format(
+        export_outputs.keys(), output_key))
+  return export_output.as_signature_def(serving_input_receiver.receiver_tensors)
+
+
+class CoreEstimatorPredictor(predictor.Predictor):
+  """A `Predictor` constructed from an `learn.python.estimator.Estimator`."""
+
+  def __init__(self,
+               estimator,
+               serving_input_receiver_fn,
+               output_key=None,
+               graph=None):
+    """Initialize a `CoreEstimatorPredictor`.
+
+    Args:
+      estimator: an instance of `learn.python.estimator.Estimator`.
+      serving_input_receiver_fn: a function that takes no arguments and returns
+        an instance of `ServingInputReceiver` compatible with `estimator`.
+      output_key: Optional string specifying the export output to use. If
+        `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used.
+      graph: Optional. The Tensorflow `graph` in which prediction should be
+        done.
+    """
+    self._graph = graph or ops.Graph()
+    with self._graph.as_default():
+      serving_input_receiver = serving_input_receiver_fn()
+      signature_def = _get_signature_def(
+          serving_input_receiver, estimator, output_key)
+      checkpoint_path = estimator.model_dir
+      self._session = monitored_session.MonitoredSession(
+          session_creator=monitored_session.ChiefSessionCreator(
+              checkpoint_filename_with_path=checkpoint_path))
+
+    feed_tensor_info = signature_def.inputs
+    self._feed_tensors = {k: self._graph.get_tensor_by_name(v.name)
+                          for k, v in feed_tensor_info.items()}
+    fetch_tensor_info = signature_def.outputs
+    self._fetch_tensors = {k: self._graph.get_tensor_by_name(v.name)
+                           for k, v in fetch_tensor_info.items()}
diff --git a/tensorflow/contrib/predictor/core_estimator_predictor_test.py b/tensorflow/contrib/predictor/core_estimator_predictor_test.py
new file mode 100644
index 0000000000..4221086794
--- /dev/null
+++ b/tensorflow/contrib/predictor/core_estimator_predictor_test.py
@@ -0,0 +1,81 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for predictor.core_estimator_predictor."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tempfile
+import numpy as np
+
+from tensorflow.contrib.predictor import core_estimator_predictor
+from tensorflow.contrib.predictor import testing_common
+from tensorflow.python.platform import test
+
+
+KEYS_AND_OPS = (('sum', lambda x, y: x + y),
+                ('product', lambda x, y: x * y,),
+                ('difference', lambda x, y: x - y))
+
+
+class CoreEstimatorPredictorTest(test.TestCase):
+  """Test fixture for `CoreEstimatorPredictor`."""
+
+  def setUp(self):
+    model_dir = tempfile.mkdtemp()
+    self._estimator = testing_common.get_arithmetic_estimator(
+        core=True, model_dir=model_dir)
+    self._serving_input_receiver_fn = testing_common.get_arithmetic_input_fn(
+        core=True, train=False)
+
+  def testDefault(self):
+    """Test prediction with default signature."""
+    np.random.seed(1111)
+    x = np.random.rand()
+    y = np.random.rand()
+    predictor = core_estimator_predictor.CoreEstimatorPredictor(
+        estimator=self._estimator,
+        serving_input_receiver_fn=self._serving_input_receiver_fn)
+    output = predictor({'x': x, 'y': y})['sum']
+    self.assertAlmostEqual(output, x + y, places=3)
+
+  def testSpecifiedSignatureKey(self):
+    """Test prediction with spedicified signatures."""
+    np.random.seed(1234)
+    for output_key, op in KEYS_AND_OPS:
+      x = np.random.rand()
+      y = np.random.rand()
+      expected_output = op(x, y)
+
+      predictor = core_estimator_predictor.CoreEstimatorPredictor(
+          estimator=self._estimator,
+          serving_input_receiver_fn=self._serving_input_receiver_fn,
+          output_key=output_key)
+      output_tensor_name = predictor.fetch_tensors[output_key].name
+      self.assertRegexpMatches(
+          output_tensor_name,
+          output_key,
+          msg='Unexpected fetch tensor.')
+      output = predictor({'x': x, 'y': y})[output_key]
+      self.assertAlmostEqual(
+          expected_output, output, places=3,
+          msg='Failed for output key "{}." '
+          'Got output {} for x = {} and y = {}'.format(
+              output_key, output, x, y))
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/predictor/predictor.py b/tensorflow/contrib/predictor/predictor.py
new file mode 100644
index 0000000000..dbc0028259
--- /dev/null
+++ b/tensorflow/contrib/predictor/predictor.py
@@ -0,0 +1,77 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Abstract base class for all predictors."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import abc
+import six
+
+
+@six.add_metaclass(abc.ABCMeta)
+class Predictor(object):
+  """Abstract base class for all predictors."""
+
+  @property
+  def graph(self):
+    return self._graph
+
+  @property
+  def session(self):
+    return self._session
+
+  @property
+  def feed_tensors(self):
+    return self._feed_tensors
+
+  @property
+  def fetch_tensors(self):
+    return self._fetch_tensors
+
+  def __repr__(self):
+    return '{} with feed tensors {} and fetch_tensors {}'.format(
+        type(self).__name__, self._feed_tensors, self._fetch_tensors)
+
+  def __call__(self, input_dict):
+    """Returns predictions based on `input_dict`.
+
+    Args:
+      input_dict: a `dict` mapping strings to numpy arrays. These keys
+        must match `self._feed_tensors.keys()`.
+
+    Returns:
+      A `dict` mapping strings to numpy arrays. The keys match
+      `self.fetch_tensors.keys()`.
+
+    Raises:
+      ValueError: `input_dict` does not match `feed_tensors`.
+    """
+    # TODO(jamieas): make validation optional?
+    input_keys = set(input_dict.keys())
+    expected_keys = set(self.feed_tensors.keys())
+    unexpected_keys = input_keys - expected_keys
+    if unexpected_keys:
+      raise ValueError('Got unexpected keys in input_dict: {}'.format(
+          unexpected_keys))
+
+    feed_dict = {}
+    for key in self.feed_tensors.keys():
+      value = input_dict.get(key)
+      if value is not None:
+        feed_dict[self.feed_tensors[key]] = value
+    return self._session.run(fetches=self.fetch_tensors, feed_dict=feed_dict)
diff --git a/tensorflow/contrib/predictor/predictor_factories.py b/tensorflow/contrib/predictor/predictor_factories.py
new file mode 100644
index 0000000000..e3f30d917d
--- /dev/null
+++ b/tensorflow/contrib/predictor/predictor_factories.py
@@ -0,0 +1,132 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Factory functions for `Predictor`s."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.predictor import contrib_estimator_predictor
+from tensorflow.contrib.predictor import core_estimator_predictor
+from tensorflow.contrib.predictor import saved_model_predictor
+from tensorflow.python.estimator import estimator as core_estimator
+
+
+def from_contrib_estimator(estimator,
+                           prediction_input_fn,
+                           input_alternative_key=None,
+                           output_alternative_key=None,
+                           graph=None):
+  """Constructs a `Predictor` from a `tf.contrib.learn.Estimator`.
+
+  Args:
+    estimator: an instance of `tf.contrib.learn.Estimator`.
+    prediction_input_fn: a function that takes no arguments and returns an
+      instance of `InputFnOps`.
+    input_alternative_key: Optional. Specify the input alternative used for
+      prediction.
+    output_alternative_key: Specify the output alternative used for
+      prediction. Not needed for single-headed models but required for
+      multi-headed models.
+    graph: Optional. The Tensorflow `graph` in which prediction should be
+      done.
+
+  Returns:
+    An initialized `Predictor`.
+
+  Raises:
+    TypeError: if `estimator` is a core `Estimator` instead of a contrib
+      `Estimator`.
+  """
+  if isinstance(estimator, core_estimator.Estimator):
+    raise TypeError('Espected estimator to be of type '
+                    'tf.contrib.learn.Estimator, but got type '
+                    'tf.python.estimator.Estimator. You likely want to call '
+                    'from_estimator.')
+  return contrib_estimator_predictor.ContribEstimatorPredictor(
+      estimator,
+      prediction_input_fn,
+      input_alternative_key,
+      output_alternative_key,
+      graph)
+
+
+def from_estimator(estimator,
+                   serving_input_receiver_fn,
+                   output_key=None,
+                   graph=None):
+  """Constructs a `Predictor` from a `tf.python.estimator.Estimator`.
+
+  Args:
+    estimator: an instance of `learn.python.estimator.Estimator`.
+    serving_input_receiver_fn: a function that takes no arguments and returns
+      an instance of `ServingInputReceiver` compatible with `estimator`.
+    output_key: Optional string specifying the export output to use. If
+      `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used.
+    graph: Optional. The Tensorflow `graph` in which prediction should be
+      done.
+
+  Returns:
+    An initialized `Predictor`.
+
+  Raises:
+    TypeError: if `estimator` is a contrib `Estimator` instead of a core
+      `Estimator`.
+  """
+  if isinstance(estimator, estimator.Estimator):
+    raise TypeError('Espected estimator to be of type '
+                    'tf.python.estimator.Estimator, but got type '
+                    'tf.contrib.learn.Estimator. You likely want to call '
+                    'from_contrib_estimator.')
+  return core_estimator_predictor.CoreEstimatorPredictor(
+      estimator,
+      serving_input_receiver_fn,
+      output_key,
+      graph)
+
+
+def from_saved_model(export_dir,
+                     signature_def_key=None,
+                     signature_def=None,
+                     tags=None,
+                     graph=None):
+  """Constructs a `Predictor` from a `SavedModel` on disk.
+
+  Args:
+    export_dir: a path to a directory containing a `SavedModel`.
+    signature_def_key: Optional string specifying the signature to use. If
+      `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. Only one of
+    `signature_def_key` and `signature_def`
+    signature_def: A `SignatureDef` proto specifying the inputs and outputs
+      for prediction. Only one of `signature_def_key` and `signature_def`
+      should be specified.
+    tags: Optional. Tags that will be used to retrieve the correct
+      `SignatureDef`. Defaults to `DEFAULT_TAGS`.
+    graph: Optional. The Tensorflow `graph` in which prediction should be
+      done.
+
+  Returns:
+    An initialized `Predictor`.
+
+  Raises:
+    ValueError: More than one of `signature_def_key` and `signature_def` is
+      specified.
+  """
+  return saved_model_predictor.SavedModelPredictor(export_dir,
+                                                   signature_def_key,
+                                                   signature_def,
+                                                   tags,
+                                                   graph)
diff --git a/tensorflow/contrib/predictor/saved_model_predictor.py b/tensorflow/contrib/predictor/saved_model_predictor.py
new file mode 100644
index 0000000000..ab2bafa0c8
--- /dev/null
+++ b/tensorflow/contrib/predictor/saved_model_predictor.py
@@ -0,0 +1,143 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A `Predictor` constructed from a `SavedModel`."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import logging
+
+from tensorflow.contrib.predictor import predictor
+from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils
+from tensorflow.python.client import session
+from tensorflow.python.framework import ops
+from tensorflow.python.saved_model import loader
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.tools import saved_model_cli
+
+
+DEFAULT_TAGS = 'serve'
+
+_DEFAULT_INPUT_ALTERNATIVE_FORMAT = 'default_input_alternative:{}'
+
+
+def _get_signature_def(signature_def_key, export_dir, tags):
+  """Construct a `SignatureDef` proto."""
+  signature_def_key = (
+      signature_def_key or
+      signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY)
+
+  metagraph_def = saved_model_cli.get_meta_graph_def(export_dir, tags)
+
+  try:
+    signature_def = signature_def_utils.get_signature_def_by_key(
+        metagraph_def,
+        signature_def_key)
+  except ValueError as e:
+    try:
+      formatted_key = _DEFAULT_INPUT_ALTERNATIVE_FORMAT.format(
+          signature_def_key)
+      signature_def = signature_def_utils.get_signature_def_by_key(
+          metagraph_def, formatted_key)
+
+      logging.warning('Could not find signature def "%s". '
+                      'Using "%s" instead', signature_def_key, formatted_key)
+    except ValueError:
+      raise ValueError(
+          'Got signature_def_key "{}". Available signatures are {}. '
+          'Original error:\n{}'.format(
+              signature_def_key, list(metagraph_def.signature_def), e))
+  return signature_def
+
+
+def _check_signature_arguments(signature_def_key,
+                               signature_def,
+                               input_names,
+                               output_names):
+  """Validates signature arguments for `SavedModelPredictor`."""
+  signature_def_key_specified = signature_def_key is not None
+  signature_def_specified = signature_def is not None
+  input_names_specified = input_names is not None
+  output_names_specified = output_names is not None
+  if input_names_specified != output_names_specified:
+    raise ValueError(
+        'input_names and output_names must both be specified or both be '
+        'unspecified.'
+    )
+
+  if (signature_def_key_specified + signature_def_specified +
+      input_names_specified > 1):
+    raise ValueError(
+        'You must specify at most one of signature_def_key OR signature_def OR'
+        '(input_names AND output_names).'
+    )
+
+
+class SavedModelPredictor(predictor.Predictor):
+  """A `Predictor` constructed from a `SavedModel`."""
+
+  def __init__(self,
+               export_dir,
+               signature_def_key=None,
+               signature_def=None,
+               input_names=None,
+               output_names=None,
+               tags=None,
+               graph=None):
+    """Initialize a `CoreEstimatorPredictor`.
+
+    Args:
+      export_dir: a path to a directory containing a `SavedModel`.
+      signature_def_key: Optional string specifying the signature to use. If
+        `None`, then `DEFAULT_SERVING_SIGNATURE_DEF_KEY` is used. Only one of
+        `signature_def_key` and `signature_def` should be specified.
+      signature_def: A `SignatureDef` proto specifying the inputs and outputs
+        for prediction. Only one of `signature_def_key` and `signature_def`
+        should be specified.
+      input_names: A dictionary mapping strings to `Tensor`s in the `SavedModel`
+        that represent the input. The keys can be any string of the user's
+        choosing.
+      output_names: A dictionary mapping strings to `Tensor`s in the
+        `SavedModel` that represent the output. The keys can be any string of
+        the user's choosing.
+      tags: Optional. Tags that will be used to retrieve the correct
+        `SignatureDef`. Defaults to `DEFAULT_TAGS`.
+      graph: Optional. The Tensorflow `graph` in which prediction should be
+        done.
+    Raises:
+      ValueError: If more than one of signature_def_key OR signature_def OR
+        (input_names AND output_names) is specified.
+    """
+    _check_signature_arguments(
+        signature_def_key, signature_def, input_names, output_names)
+    tags = tags or DEFAULT_TAGS
+    self._graph = graph or ops.Graph()
+
+    with self._graph.as_default():
+      self._session = session.Session()
+      loader.load(self._session, tags.split(','), export_dir)
+
+    if input_names is None:
+      if signature_def is None:
+        signature_def = _get_signature_def(signature_def_key, export_dir, tags)
+      input_names = {k: v.name for k, v in signature_def.inputs.items()}
+      output_names = {k: v.name for k, v in signature_def.outputs.items()}
+
+    self._feed_tensors = {k: self._graph.get_tensor_by_name(v)
+                          for k, v in input_names.items()}
+    self._fetch_tensors = {k: self._graph.get_tensor_by_name(v)
+                           for k, v in output_names.items()}
diff --git a/tensorflow/contrib/predictor/saved_model_predictor_test.py b/tensorflow/contrib/predictor/saved_model_predictor_test.py
new file mode 100644
index 0000000000..f40e2e73d9
--- /dev/null
+++ b/tensorflow/contrib/predictor/saved_model_predictor_test.py
@@ -0,0 +1,170 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for predictor.saved_model_predictor."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.predictor import saved_model_predictor
+from tensorflow.core.framework import tensor_shape_pb2
+from tensorflow.core.framework import types_pb2
+from tensorflow.core.protobuf import meta_graph_pb2
+from tensorflow.python.framework import ops
+from tensorflow.python.platform import test
+from tensorflow.python.saved_model import signature_def_utils
+
+
+KEYS_AND_OPS = (('sum', lambda x, y: x + y),
+                ('product', lambda x, y: x * y,),
+                ('difference', lambda x, y: x - y))
+
+MODEL_DIR_NAME = 'contrib/predictor/test_export_dir'
+
+
+class SavedModelPredictorTest(test.TestCase):
+
+  @classmethod
+  def setUpClass(cls):
+    # Load a saved model exported from the arithmetic `Estimator`.
+    # See `testing_common.py`.
+    cls._export_dir = test.test_src_dir_path(MODEL_DIR_NAME)
+
+  def testDefault(self):
+    """Test prediction with default signature."""
+    np.random.seed(1111)
+    x = np.random.rand()
+    y = np.random.rand()
+    predictor = saved_model_predictor.SavedModelPredictor(
+        export_dir=self._export_dir)
+    output = predictor({'x': x, 'y': y})['outputs']
+    self.assertAlmostEqual(output, x + y, places=3)
+
+  def testSpecifiedSignatureKey(self):
+    """Test prediction with spedicified signature key."""
+    np.random.seed(1234)
+    for signature_def_key, op in KEYS_AND_OPS:
+      x = np.random.rand()
+      y = np.random.rand()
+      expected_output = op(x, y)
+
+      predictor = saved_model_predictor.SavedModelPredictor(
+          export_dir=self._export_dir,
+          signature_def_key=signature_def_key)
+
+      output_tensor_name = predictor.fetch_tensors['outputs'].name
+      self.assertRegexpMatches(
+          output_tensor_name,
+          signature_def_key,
+          msg='Unexpected fetch tensor.')
+
+      output = predictor({'x': x, 'y': y})['outputs']
+      self.assertAlmostEqual(
+          expected_output, output, places=3,
+          msg='Failed for signature "{}." '
+          'Got output {} for x = {} and y = {}'.format(
+              signature_def_key, output, x, y))
+
+  def testSpecifiedSignature(self):
+    """Test prediction with spedicified signature definition."""
+    np.random.seed(4444)
+    for key, op in KEYS_AND_OPS:
+      x = np.random.rand()
+      y = np.random.rand()
+      expected_output = op(x, y)
+
+      inputs = {
+          'x': meta_graph_pb2.TensorInfo(
+              name='inputs/x:0',
+              dtype=types_pb2.DT_FLOAT,
+              tensor_shape=tensor_shape_pb2.TensorShapeProto()),
+          'y': meta_graph_pb2.TensorInfo(
+              name='inputs/y:0',
+              dtype=types_pb2.DT_FLOAT,
+              tensor_shape=tensor_shape_pb2.TensorShapeProto())}
+      outputs = {
+          key: meta_graph_pb2.TensorInfo(
+              name='outputs/{}:0'.format(key),
+              dtype=types_pb2.DT_FLOAT,
+              tensor_shape=tensor_shape_pb2.TensorShapeProto())}
+      signature_def = signature_def_utils.build_signature_def(
+          inputs=inputs,
+          outputs=outputs,
+          method_name='tensorflow/serving/regress')
+      predictor = saved_model_predictor.SavedModelPredictor(
+          export_dir=self._export_dir,
+          signature_def=signature_def)
+
+      output_tensor_name = predictor.fetch_tensors[key].name
+      self.assertRegexpMatches(
+          output_tensor_name,
+          key,
+          msg='Unexpected fetch tensor.')
+
+      output = predictor({'x': x, 'y': y})[key]
+      self.assertAlmostEqual(
+          expected_output, output, places=3,
+          msg='Failed for signature "{}". '
+          'Got output {} for x = {} and y = {}'.format(key, output, x, y))
+
+  def testSpecifiedTensors(self):
+    """Test prediction with spedicified `Tensor`s."""
+    np.random.seed(987)
+    for key, op in KEYS_AND_OPS:
+      x = np.random.rand()
+      y = np.random.rand()
+      expected_output = op(x, y)
+      input_names = {'x': 'inputs/x:0',
+                     'y': 'inputs/y:0'}
+      output_names = {key: 'outputs/{}:0'.format(key)}
+      predictor = saved_model_predictor.SavedModelPredictor(
+          export_dir=self._export_dir,
+          input_names=input_names,
+          output_names=output_names)
+
+      output_tensor_name = predictor.fetch_tensors[key].name
+      self.assertRegexpMatches(
+          output_tensor_name,
+          key,
+          msg='Unexpected fetch tensor.')
+
+      output = predictor({'x': x, 'y': y})[key]
+      self.assertAlmostEqual(
+          expected_output, output, places=3,
+          msg='Failed for signature "{}". '
+          'Got output {} for x = {} and y = {}'.format(key, output, x, y))
+
+  def testBadTagsFail(self):
+    """Test that predictor construction fails for bad tags."""
+    bad_tags_regex = ('.* could not be found in SavedModel')
+    with self.assertRaisesRegexp(RuntimeError, bad_tags_regex):
+      _ = saved_model_predictor.SavedModelPredictor(
+          export_dir=self._export_dir,
+          tags=('zomg, bad, tags'))
+
+  def testSpecifiedGraph(self):
+    """Test that the predictor remembers a specified `Graph`."""
+    g = ops.Graph()
+    predictor = saved_model_predictor.SavedModelPredictor(
+        export_dir=self._export_dir,
+        graph=g)
+    self.assertEqual(predictor.graph, g)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/predictor/test_export_dir/saved_model.pb b/tensorflow/contrib/predictor/test_export_dir/saved_model.pb
new file mode 100644
index 0000000000..9100fefb72
--- /dev/null
+++ b/tensorflow/contrib/predictor/test_export_dir/saved_model.pb
diff --git a/tensorflow/contrib/predictor/test_export_dir/variables/variables.data-00000-of-00001 b/tensorflow/contrib/predictor/test_export_dir/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000000..1b1cb4d44c
--- /dev/null
+++ b/tensorflow/contrib/predictor/test_export_dir/variables/variables.data-00000-of-00001
diff --git a/tensorflow/contrib/predictor/test_export_dir/variables/variables.index b/tensorflow/contrib/predictor/test_export_dir/variables/variables.index
new file mode 100644
index 0000000000..dd32e9b71b
--- /dev/null
+++ b/tensorflow/contrib/predictor/test_export_dir/variables/variables.index
diff --git a/tensorflow/contrib/predictor/testing_common.py b/tensorflow/contrib/predictor/testing_common.py
new file mode 100644
index 0000000000..1767704b99
--- /dev/null
+++ b/tensorflow/contrib/predictor/testing_common.py
@@ -0,0 +1,102 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Common code used for testing `Predictor`s."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.learn.python.learn.estimators import constants
+from tensorflow.contrib.learn.python.learn.estimators import estimator as contrib_estimator
+from tensorflow.contrib.learn.python.learn.estimators import model_fn as contrib_model_fn
+from tensorflow.contrib.learn.python.learn.utils import input_fn_utils
+from tensorflow.python.estimator import estimator as core_estimator
+from tensorflow.python.estimator import model_fn
+from tensorflow.python.estimator.export import export_lib
+from tensorflow.python.estimator.export import export_output
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.saved_model import signature_constants
+
+
+def get_arithmetic_estimator(core=True, model_dir=None):
+  """Returns an `Estimator` that performs basic arithmetic.
+
+  Args:
+    core: if `True`, returns a `tensorflow.python.estimator.Estimator`.
+      Otherwise, returns a `tensorflow.contrib.learn.Estimator`.
+    model_dir: directory in which to export checkpoints and saved models.
+  Returns:
+    An `Estimator` that performs arithmetic operations on its inputs.
+  """
+  def _model_fn(features, labels, mode):
+    _ = labels
+    x = features['x']
+    y = features['y']
+    with ops.name_scope('outputs'):
+      predictions = {'sum': math_ops.add(x, y, name='sum'),
+                     'product': math_ops.multiply(x, y, name='product'),
+                     'difference': math_ops.subtract(x, y, name='difference')}
+    if core:
+      export_outputs = {k: export_output.PredictOutput({k: v})
+                        for k, v in predictions.items()}
+      export_outputs[signature_constants.
+                     DEFAULT_SERVING_SIGNATURE_DEF_KEY] = export_outputs['sum']
+      return model_fn.EstimatorSpec(mode=mode,
+                                    predictions=predictions,
+                                    export_outputs=export_outputs,
+                                    loss=constant_op.constant(0),
+                                    train_op=control_flow_ops.no_op())
+    else:
+      output_alternatives = {k: (constants.ProblemType.UNSPECIFIED, {k: v})
+                             for k, v in predictions.items()}
+      return contrib_model_fn.ModelFnOps(
+          mode=mode,
+          predictions=predictions,
+          output_alternatives=output_alternatives,
+          loss=constant_op.constant(0),
+          train_op=control_flow_ops.no_op())
+  if core:
+    return core_estimator.Estimator(_model_fn)
+  else:
+    return contrib_estimator.Estimator(_model_fn, model_dir=model_dir)
+
+
+def get_arithmetic_input_fn(core=True, train=False):
+  """Returns a input functions or serving input receiver function."""
+  def _input_fn():
+    with ops.name_scope('inputs'):
+      x = array_ops.placeholder_with_default(0.0, shape=[], name='x')
+      y = array_ops.placeholder_with_default(0.0, shape=[], name='y')
+    label = constant_op.constant(0.0)
+    features = {'x': x, 'y': y}
+    if core:
+      if train:
+        return features, label
+      return export_lib.ServingInputReceiver(
+          features=features,
+          receiver_tensors=features)
+    else:
+      if train:
+        return features, label
+      return input_fn_utils.InputFnOps(
+          features=features,
+          labels={},
+          default_inputs=features)
+  return _input_fn