Merging the r1.10 branch back to master.

18 files changed, 124 insertions, 48 deletions

diff --git a/RELEASE.md b/RELEASE.md
index 6b67072f8e..078aafd374 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -1,3 +1,68 @@
+# Release 1.10.0
+
+## Major Features And Improvements
+
+* The `tf.lite` runtime now supports `complex64`.
+* Initial Bigtable integration for `tf.data`.
+* Improved local run behavior in `tf.estimator.train_and_evaluate` which does not reload checkpoints for evaluation.
+* `RunConfig` now sets device_filters to restrict how workers and PS can communicate. This can speed up training and ensure clean shutdowns in some situations. But if you have jobs that require communication between workers, you will have to set custom session_options in your `RunConfig`.
+* Moved Distributions and Bijectors from `tf.contrib.distributions` to [Tensorflow Probability (TFP)](https://github.com/tensorflow/probability). `tf.contrib.distributions` is now deprecated and will be removed by the end of 2018.
+* Adding new endpoints for existing tensorflow symbols. These endpoints are going to be the preferred endpoints going forward and may replace some of the existing endpoints in the future. See below for the complete list. New symbols have been added to the following modules: [`tf.debugging`](https://www.tensorflow.org/versions/master/api_docs/python/tf/debugging), [`tf.dtypes`](https://www.tensorflow.org/versions/master/api_docs/python/tf/dtypes), [`tf.image`](https://www.tensorflow.org/versions/master/api_docs/python/tf/image), [`tf.io`](https://www.tensorflow.org/versions/master/api_docs/python/tf/io), [`tf.linalg`](https://www.tensorflow.org/versions/master/api_docs/python/tf/linalg), [`tf.manip`](https://www.tensorflow.org/versions/master/api_docs/python/tf/manip), [`tf.math`](https://www.tensorflow.org/versions/master/api_docs/python/tf/math), [`tf.quantization`](https://www.tensorflow.org/versions/master/api_docs/python/tf/quantization), [`tf.strings`](https://www.tensorflow.org/versions/master/api_docs/python/tf/strings)
+
+## Breaking Changes
+
+* Prebuilt binaries are now (as of TensorFlow 1.10) built against NCCL 2.2 and no longer include NCCL in the binary install. TensorFlow usage with multiple GPUs and NCCL requires upgrade to [NCCL 2.2](https://developer.nvidia.com/nccl). See updated install guides: [Installing TensorFlow on Ubuntu](https://www.tensorflow.org/install/install_linux#tensorflow_gpu_support) and [Install TensorFlow from Sources](https://www.tensorflow.org/install/install_sources#optional_install_tensorflow_for_gpu_prerequisites).
+* Starting from TensorFlow 1.11, Windows builds will use Bazel. Therefore, we will drop official support for cmake.
+
+## Bug Fixes and Other Changes
+
+* `tf.data`:
+  * `tf.contrib.data.group_by_reducer()` is now available via the public API.
+  * `tf.contrib.data.choose_from_datasets()` is now available via the public API.
+  * Adding `drop_remainder` argument to `tf.data.Dataset.batch()` and `tf.data.Dataset.padded_batch()`, deprecating tf.contrib.data.batch_and_drop_remainder()` and `tf.contrib.data.padded_batch_and_drop_remainder()`.
+* `tf.estimator`:
+  * `Estimator`s now use custom savers included in `EstimatorSpec` scaffolds for saving SavedModels during export.
+  * `EstimatorSpec` will now add a default prediction output for export if no `export_output` is provided, eliminating the need to explicitly include a `PredictOutput` object in the `model_fn` for simple use-cases.
+  * Support sparse_combiner in canned Linear Estimators.
+  * Added batch normalization to `DNNClassifier`, `DNNRegressor`, and `DNNEstimator`.
+  * Adding ranking support for boosted trees.
+  * Adding center bias option for boosted trees.
+* Add `synchronization` and `aggregation` args to get_variable(). These args will be used for distributed variables.
+* Add `synchronization` and `aggregation` args to the layer `add_weight()` API. These args will be used for distributed variables.
+* `tf.losses.*` do not add to the global collection when executing eagerly (to avoid leaking memory).
+* Support different summary and checkpoint directories in `tf.train.MonitoredTrainingSession()`.
+* Added IndRNN, IndyGRU, and IndyLSTM cells to `tf.contrib.rnn`.
+* Add safe static factory functions for SparseTensor and convert all CHECKs to DCHECKs. Using the constructor directly is unsafe and deprecated.
+* Make the Bigtable client connection pool configurable & increase the default # of connections for performance.
+* Added derivative of `tf.random_gamma` with respect to the alpha parameter.
+* Added derivative of `tf.igamma(a, x)` and `tf.igammac(a, x)` with respect to a.
+* Modified Bessel functions of order zero and one.
+* Add FillTriangular Bijector to create triangular matrices.
+* Added support for Type III DCT, and `tf.spectral.idct(type=2|3)`.
+* Correctly handle CuDNN RNN weight loaded when nest in `TimeDistributed`.
+* Adding per-element weight support for `WALSComputePartialLhsAndRhsOp`.
+* ZerosLike and OnesLike ops treated as constants by Graph Transform Tool.
+* Gamma distribution and the derived distributions (Beta, Dirichlet, Student's t, inverse Gamma) now fully reparameterized.
+* Java: Experimental wrapper classes to make graph generation easier. Thanks @karllessard and @kbsriram
+* Build & link in secure gRPC components (switch from the insecure grpc dependency to secure grpc dependency).
+* Adding new endpoints for existing tensorflow symbols. These endpoints are going to be the preferred endpoints going forward and may replace some of the existing endpoints in the future. List of new endpoints:
+  * New endpoints in `tf.image` namespace: `tf.image.extract_image_patches`
+  * New endpoints in `tf.debugging` namespace: `tf.debugging.check_numerics`, `tf.debugging.is_finite`, `tf.debugging.is_inf`, `tf.debugging.is_nan`.
+  * New endpoints in `tf.dtypes` namespace: `tf.dtypes.as_string`.
+  * New endpoints in `tf.io` namespace: `tf.io.decode_base64`, `tf.io.decode_compressed`, `tf.io.decode_json_example`, `tf.io.decode_raw`, `tf.io.encode_base64`, `tf.io.matching_files`, `tf.io.parse_tensor`, `tf.io.read_file, `tf.io.write_file`.
+  * New endpoints in tf.linalg namespace: `tf.linalg.cross`, `tf.linalg.tensor_diag` (corresponds to `tf.diag`), `tf.linalg.tensor_diag_part` (corresponds to `tf.diag_part`).
+  * New endpoints in tf.manip namespace: `tf.manip.batch_to_space_nd`, `tf.manip.gather_nd`, `tf.manip.reshape`, `tf.manip.reverse`, `tf.manip.scatter_nd`, `tf.manip.space_to_batch_nd`, `tf.manip.tile`
+  * New endpoints in tf.math namespace: `tf.math.acos`, `tf.math.acosh`, `tf.math.add`, `tf.math.asin`, `tf.math.asinh`, `tf.math.atan`, `tf.math.atan2`, `tf.math.atanh`, `tf.math.betainc`, `tf.math.ceil`, `tf.math.cos`, `tf.math.cosh`, `tf.math.digamma`, `tf.math.equal`, `tf.math.erfc`, `tf.math.exp`, `tf.math.expm1`, `tf.math.floor`, `tf.math.greater`, `tf.math.greater_equal`, `tf.math.igamma`, `tf.math.igammac`, `tf.math.invert_permutation`, `tf.math.less`, `tf.math.less_equal`, `tf.math.lgamma`, `tf.math.log`, `tf.math.log1p`, `tf.math.logical_and`, `tf.math.logical_not`, `tf.math.logical_or`, `tf.math.maximum`, `tf.math.minimum`, `tf.math.not_equal`, `tf.math.polygamma`, `tf.math.reciprocal`, `tf.math.rint`, `tf.math.rsqrt`, `tf.math.segment_max`, `tf.math.segment_mean`, `tf.math.segment_min`, `tf.math.segment_prod`, `tf.math.segment_sum`, `tf.math.sin`, `tf.math.sinh`, `tf.math.softplus`, `tf.math.softsign`, `tf.math.squared_difference`, `tf.math.tan`, `tf.math.unsorted_segment_max`, `tf.math.unsorted_segment_min`, `tf.math.unsorted_segment_prod`, `tf.math.unsorted_segment_sum`, `tf.math.zeta`.
+  * New endpoints in `tf.quantization` namespace: `tf.quantization.dequantize`, `tf.quantization.fake_quant_with_min_max_args`, `tf.quantization.fake_quant_with_min_max_args_gradient`, `tf.quantization.fake_quant_with_min_max_vars`,  `tf.quantization.fake_quant_with_min_max_vars_gradient`, `tf.quantization.fake_quant_with_min_max_vars_per_channel`,  `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient`.
+  * New endpoints in tf.strings namespace: `tf.strings.join` (corresponds to `tf.string_join`), `tf.strings.regex_replace`, `tf.strings.to_number` (corresponds to `tf.string_to_number`), `tf.strings.strip` (corresponds to `tf.string_strip`), `tf.strings.substr`, `tf.strings.to_hash_bucket` (corresponds to `tf.string_to_hash_bucket`), `tf.strings.to_hash_bucket_fast` (corresponds to `tf.string_to_hash_bucket_fast`), `tf.strings.to_hash_bucket_strong` (corresponds to `tf.string_to_hash_bucket_strong`).
+
+
+## Thanks to our Contributors
+
+This release contains contributions from many people at Google, as well as:
+
+Ag Ramesh, Alex Wiltschko, Alexander Pantyukhin, Amogh Mannekote, An Jiaoyang, Andrei Nigmatulin, Andrew Ginns, BjøRn Moholt, Brett Koonce, Chengzhi Chen, Chinmay Das, Christian Ertler, Christoph Boeddeker, Clayne Robison, Courtial Florian, ctiijima, Dan Douthit, Dan J, Dan Ringwalt, EFanZh, Emanuele Ballarin, eqy, Evgeniy Zheltonozhskiy, Freedom" Koan-Sin Tan, FréDéRic Branchaud-Charron, G K, gracehoney, Guillaume Klein, Guozhong Zhuang, Hsien-Yang Li, hsm207, ImSheridan, Jayaram Bobba, Jiandong Ruan, Jie, Joel Shor, Jonas Rauber, Jongmin Baek, jsawruk, Karan Kaw, Karl Lessard, karl@kubx.ca, Kb Sriram, KinmanLam, leiiwang, Li, Yiqiang, Loo Rong Jie, Mahmoud Abuzaina, Mahmoud Aslan, ManHyuk, Martin Patz, Martin Zeitler, mktozk, Mohammad Ashraf Bhuiyan, mrTsjolder, Naman Bhalla, Nick Felt, Nicolas Lopez, Niranjan Hasabnis, Nishidha Panpaliya, Nitish, nrstott, Nutti, Parag Jain, PeterLee, Philipp Jund, Rach L, Rafal Wojdyla, Roland Zimmermann, Sergei Lebedev, SneakyFish5, Soila Kavulya, Sriram Veturi, Steven Schmatz, Taehoon Lee, Tang, Wenyi, Taras Sereda, Ted Chang, Tim Zaman, Tristan Rice, tucan, vchigrin, Vikram Tiwari, Vincent, WeberXie, William D. Irons, Yan Facai (颜发才), Yong Tang, Yu Yi, Yuxin Wu, Zé ViníCius
+
 # Release 1.9.0
 
 ## Major Features And Improvements
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index cea5e8ffb0..6f564e7e1e 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -19,12 +19,12 @@ limitations under the License.
 // TensorFlow uses semantic versioning, see http://semver.org/.
 
 #define TF_MAJOR_VERSION 1
-#define TF_MINOR_VERSION 9
+#define TF_MINOR_VERSION 10
 #define TF_PATCH_VERSION 0
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
-#define TF_VERSION_SUFFIX ""
+#define TF_VERSION_SUFFIX "-rc1"
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index cf869e8655..5e26facaba 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -38,7 +38,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for macOS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.10.0-rc1.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 4ec7e42773..a59c2741e1 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.10.0-rc1.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index c5f760d254..e9c6650c92 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.9.0</version>
+  <version>1.10.0-rc1</version>
 </dependency>
 ```
 
@@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.9.0</version>
+                 <version>1.10.0-rc1</version>
                </dependency>
              </dependencies>
          </project>
@@ -124,12 +124,12 @@ instead:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>libtensorflow</artifactId>
-  <version>1.9.0</version>
+  <version>1.10.0-rc1</version>
 </dependency>
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>libtensorflow_jni_gpu</artifactId>
-  <version>1.9.0</version>
+  <version>1.10.0-rc1</version>
 </dependency>
 ```
 
@@ -148,7 +148,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or macOS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.10.0-rc1.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.10.0-rc1.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -175,10 +175,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.10.0-rc1.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.10.0-rc1.zip).
   3. Extract this .zip file.
 
 __Note__: The native library (`tensorflow_jni.dll`) requires `msvcp140.dll` at runtime, which is included in the [Visual C++ 2015 Redistributable](https://www.microsoft.com/en-us/download/details.aspx?id=48145) package. 
@@ -227,7 +227,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.9.0.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.10.0-rc1.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -241,11 +241,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and macOS X:
 
-<pre><b>java -cp libtensorflow-1.9.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.10.0-rc1.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.9.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.10.0-rc1.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 3a9a01c57e..005ad437bc 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -436,7 +436,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 <a name="ValidateYourInstallation"></a>
 
@@ -650,13 +650,13 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0rc1-cp27-none-linux_x86_64.whl
 </pre>
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0rc1-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -667,13 +667,13 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0rc1-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0rc1-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -684,13 +684,13 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0rc1-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0rc1-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -701,13 +701,13 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0rc1-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0rc1-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index 1a7b2b815d..30800f3f03 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      TensorFlow in the active Virtualenv is as follows:
 
      <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0rc1-py3-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -242,7 +242,7 @@ take the following steps:
      issue the following command:
 
      <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl</b> </pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0rc1-py3-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -350,7 +350,11 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (<i>targetDirectory</i>)$ <b>pip install --ignore-installed --upgrade \
+<<<<<<< HEAD
      https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py2-none-any.whl</b></pre>
+=======
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0rc1-py2-none-any.whl</b></pre>
+>>>>>>> google/r1.10
 
 
 <a name="ValidateYourInstallation"></a>
@@ -517,7 +521,7 @@ The value you specify depends on your Python version.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0rc1-py2-none-any.whl
 </pre>
 
 
@@ -525,5 +529,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py2-none-any.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0rc1-py3-none-any.whl
 </pre>
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index 31dcad64d4..0df534f289 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -282,6 +282,7 @@ Please specify the NCCL version you want to use. If NCLL 2.2 is not installed, t
 
 Please specify a list of comma-separated Cuda compute capabilities you want to build with.
 You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
+<<<<<<< HEAD
 Please note that each additional compute capability significantly increases your
 build time and binary size. [Default is: 3.5,7.0] <b>6.1</b>
 
@@ -374,10 +375,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl`
 file depends on your platform. For example, the following command will install
 the pip package
 
-for TensorFlow 1.9.0 on Linux:
+for TensorFlow 1.10.0rc1 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.10.0rc1-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
@@ -483,6 +484,8 @@ the error message, ask a new question on Stack Overflow and specify the
 **Linux**
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
+<tr><td>tensorflow-1.10.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.15.0</td><td>N/A</td><td>N/A</td></tr>
+<tr><td>tensorflow_gpu-1.10.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.15.0</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.9.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.11.0</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow_gpu-1.9.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.11.0</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.8.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.10.0</td><td>N/A</td><td>N/A</td></tr>
@@ -508,6 +511,7 @@ the error message, ask a new question on Stack Overflow and specify the
 **Mac**
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
+<tr><td>tensorflow-1.10.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.15.0</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.9.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.11.0</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.8.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.10.1</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.7.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.10.1</td><td>N/A</td><td>N/A</td></tr>
@@ -525,6 +529,8 @@ the error message, ask a new question on Stack Overflow and specify the
 **Windows**
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
+<tr><td>tensorflow-1.10.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
+<tr><td>tensorflow_gpu-1.10.0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.9.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow_gpu-1.9.0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.8.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
diff --git a/tensorflow/tools/ci_build/builds/run_pip_tests.sh b/tensorflow/tools/ci_build/builds/run_pip_tests.sh
index 29680e6882..bbaf59c69a 100755
--- a/tensorflow/tools/ci_build/builds/run_pip_tests.sh
+++ b/tensorflow/tools/ci_build/builds/run_pip_tests.sh
@@ -97,7 +97,8 @@ fi
 #     TF_BUILD_APPEND_ARGUMENTS any user supplied args.
 BAZEL_FLAGS="--define=no_tensorflow_py_deps=true --test_lang_filters=py \
   --build_tests_only -k --test_tag_filters=${PIP_TEST_FILTER_TAG} \
-  --test_timeout 300,450,1200,3600 ${TF_BUILD_APPEND_ARGUMENTS}"
+  --test_timeout 300,450,1200,3600 ${TF_BUILD_APPEND_ARGUMENTS} \
+  --test_output=errors"
 
 BAZEL_TEST_TARGETS="//${PIP_TEST_PREFIX}/tensorflow/contrib/... \
   //${PIP_TEST_PREFIX}/tensorflow/python/... \
diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh
index 221b5b80fb..c3c537328f 100755
--- a/tensorflow/tools/ci_build/install/install_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh
@@ -61,11 +61,11 @@ rm -rf /usr/lib/python3/dist-packages/six*
 # https://github.com/tensorflow/tensorflow/issues/6968
 # This workaround isn't needed for Ubuntu 16.04 or later.
 if $(cat /etc/*-release | grep -q 14.04); then
-  pip2 install --no-binary=:all: --upgrade numpy==1.12.0
-  pip3 install --no-binary=:all: --upgrade numpy==1.12.0
+  pip2 install --no-binary=:all: --upgrade numpy==1.14.5
+  pip3 install --no-binary=:all: --upgrade numpy==1.14.5
 else
-  pip2 install --upgrade numpy==1.12.0
-  pip3 install --upgrade numpy==1.12.0
+  pip2 install --upgrade numpy==1.14.5
+  pip3 install --upgrade numpy==1.14.5
 fi
 
 pip2 install scipy==0.18.1
diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
index 45a30c6e82..b6f5de57c9 100755
--- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
@@ -58,7 +58,7 @@ rm -rf /usr/lib/python3/dist-packages/six*
 # numpy needs to be installed from source to fix segfaults. See:
 # https://github.com/tensorflow/tensorflow/issues/6968
 # This workaround isn't needed for Ubuntu 16.04 or later.
-pip3.5 install --no-binary=:all: --upgrade numpy==1.12.0
+pip3.5 install --no-binary=:all: --upgrade numpy==1.14.5
 
 pip3.5 install scipy==0.18.1
 
diff --git a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh
index d66b2aa18a..8868664132 100755
--- a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh
@@ -70,7 +70,7 @@ rm -rf /usr/lib/python3/dist-packages/six*
 # numpy needs to be installed from source to fix segfaults. See:
 # https://github.com/tensorflow/tensorflow/issues/6968
 # This workaround isn't needed for Ubuntu 16.04 or later.
-pip3 install --no-binary=:all: --upgrade numpy==1.12.0
+pip3 install --no-binary=:all: --upgrade numpy==1.14.5
 
 pip3 install scipy==0.18.1
 
@@ -101,7 +101,7 @@ pip3 install --upgrade termcolor
 pip3 install --upgrade setuptools==39.1.0
 
 # Keras
-pip3.5 install keras_applications==1.0.2
-pip3.5 install keras_preprocessing==1.0.1
+pip3 install keras_applications==1.0.2
+pip3 install keras_preprocessing==1.0.1
 
 # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh)
diff --git a/tensorflow/tools/docker/Dockerfile b/tensorflow/tools/docker/Dockerfile
index a3ff8211e3..bf06214009 100644
--- a/tensorflow/tools/docker/Dockerfile
+++ b/tensorflow/tools/docker/Dockerfile
@@ -30,7 +30,7 @@ RUN pip --no-cache-dir install \
         ipykernel \
         jupyter \
         matplotlib \
-        numpy \
+        numpy==1.14.5 \
         pandas \
         scipy \
         sklearn \
diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel
index f7fe4119da..6552588fac 100644
--- a/tensorflow/tools/docker/Dockerfile.devel
+++ b/tensorflow/tools/docker/Dockerfile.devel
@@ -35,7 +35,7 @@ RUN pip --no-cache-dir install \
         jupyter \
         matplotlib \
         mock \
-        numpy \
+        numpy==1.14.5 \
         scipy \
         sklearn \
         pandas \
@@ -76,7 +76,7 @@ RUN mkdir /bazel && \
 
 # Download and build TensorFlow.
 WORKDIR /tensorflow
-RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git .
+RUN git clone --branch=r1.10 --depth=1 https://github.com/tensorflow/tensorflow.git .
 
 # TODO(craigcitro): Don't install the pip package, since it makes it
 # more difficult to experiment with local changes. Instead, just add
diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu
index 340f96df48..f4c83f85d4 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-gpu
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu
@@ -51,7 +51,7 @@ RUN pip --no-cache-dir install \
         jupyter \
         matplotlib \
         mock \
-        numpy \
+        numpy==1.14.5 \
         scipy \
         sklearn \
         pandas \
@@ -92,7 +92,7 @@ RUN mkdir /bazel && \
 
 # Download and build TensorFlow.
 WORKDIR /tensorflow
-RUN git clone --branch=r1.9 --depth=1 https://github.com/tensorflow/tensorflow.git .
+RUN git clone --branch=r1.10 --depth=1 https://github.com/tensorflow/tensorflow.git .
 
 # Configure the build for our CUDA configuration.
 ENV CI_BUILD_PYTHON python
diff --git a/tensorflow/tools/docker/Dockerfile.devel-mkl b/tensorflow/tools/docker/Dockerfile.devel-mkl
index c85641b383..7afa537c9d 100755
--- a/tensorflow/tools/docker/Dockerfile.devel-mkl
+++ b/tensorflow/tools/docker/Dockerfile.devel-mkl
@@ -3,7 +3,7 @@ FROM ubuntu:16.04
 LABEL maintainer="Clayne Robison <clayne.b.robison@intel.com>"
 
 # These parameters can be overridden by parameterized_docker_build.sh
-ARG TF_BUILD_VERSION=r1.9
+ARG TF_BUILD_VERSION=r1.10
 ARG PYTHON="python"
 ARG PYTHON3_DEV=""
 ARG WHL_DIR="/tmp/pip"
diff --git a/tensorflow/tools/docker/Dockerfile.gpu b/tensorflow/tools/docker/Dockerfile.gpu
index 28d4371da3..5ec1e60f00 100644
--- a/tensorflow/tools/docker/Dockerfile.gpu
+++ b/tensorflow/tools/docker/Dockerfile.gpu
@@ -38,7 +38,7 @@ RUN pip --no-cache-dir install \
         ipykernel \
         jupyter \
         matplotlib \
-        numpy \
+        numpy==1.14.5 \
         pandas \
         scipy \
         sklearn \
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 1f4c3d47bf..07ed7f5195 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -45,13 +45,13 @@ DOCLINES = __doc__.split('\n')
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.9.0'
+_VERSION = '1.10.0-rc1'
 
 REQUIRED_PACKAGES = [
     'absl-py >= 0.1.6',
     'astor >= 0.6.0',
     'gast >= 0.2.0',
-    'numpy >= 1.13.3',
+    'numpy >= 1.13.3, <= 1.14.5',
     'six >= 1.10.0',
     'protobuf >= 3.6.0',
     'setuptools <= 39.1.0',