Merging the 1.6 branch back into master.

RELEASE.md

@@ -1,11 +1,43 @@
-# Release 1.5.0
+# Release 1.6.0
 
 ## Breaking Changes
 * Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
-* Our Linux binaries are built using ubuntu 16 containers, potentially
-  introducing glibc incompatibility issues with ubuntu 14.
-* Starting from 1.6 release, our prebuilt binaries will use AVX instructions.
-  This may break TF on older CPUs.
+* Prebuilt binaries will use AVX instructions. This may break TF on older CPUs.
+
+## Major Features And Improvements
+* New Optimizer internal API for non-slot variables. Descendants of AdamOptimizer that access _beta[12]_power will need to be updated.
+* `tf.estimator.{FinalExporter,LatestExporter}` now export stripped SavedModels. This improves forward compatibility of the SavedModel.
+* FFT support added to XLA CPU/GPU.
+
+## Bug Fixes and Other Changes
+* Documentation updates:
+  * Added a second version of Getting Started, which is aimed at ML
+newcomers.
+  * Clarified documentation on `resize_images.align_corners` parameter.
+  * Additional documentation for TPUs.
+* Google Cloud Storage (GCS):
+  * Add client-side throttle.
+  * Add a `FlushCaches()` method to the FileSystem interface, with an implementation for GcsFileSystem.
+* Other:
+  * Add `tf.contrib.distributions.Kumaraswamy`.
+  * `RetryingFileSystem::FlushCaches()` calls the base FileSystem's `FlushCaches()`.
+  * Add auto_correlation to distributions.
+  * Add `tf.contrib.distributions.Autoregressive`.
+  * Add SeparableConv1D layer.
+  * Add convolutional Flipout layers.
+  * When both inputs of `tf.matmul` are bfloat16, it returns bfloat16, instead of float32.
+  * Added `tf.contrib.image.connected_components`.
+  * Add `tf.contrib.framework.CriticalSection` that allows atomic variable access.
+  * Output variance over trees predictions for classifications tasks.
+  * For `pt` and `eval` commands, allow writing tensor values to filesystem as numpy files.
+  * gRPC: Propagate truncated errors (instead of returning gRPC internal error).
+  * Augment parallel_interleave to support 2 kinds of prefetching.
+  * Improved XLA support for C64-related ops log, pow, atan2, tanh.
+  * Add probabilistic convolutional layers.
+
+## API Changes
+* Introducing prepare_variance boolean with default setting to False for backward compatibility.
+* Move `layers_dense_variational_impl.py` to `layers_dense_variational.py`.
 
 ## Known Bugs
 * Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or
@@ -28,6 +60,42 @@
   TensorFlow will print a warning if you use XLA:GPU with a known-bad version of
   CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122.
 
+## Thanks to our Contributors
+
+This release contains contributions from many people at Google, as well as:
+
+4d55397500, Ag Ramesh, Aiden Scandella, Akimasa Kimura, Alex Rothberg, Allen Goodman,
+amilioto, Andrei Costinescu, Andrei Nigmatulin, Anjum Sayed, Anthony Platanios,
+Anush Elangovan, Armando Fandango, Ashish Kumar Ram, Ashwini Shukla, Ben, Bhavani Subramanian,
+Brett Koonce, Carl Thomé, cclauss, Cesc, Changming Sun, Christoph Boeddeker, Clayne Robison,
+Clemens Schulz, Clint (Woonhyuk Baek), codrut3, Cole Gerdemann, Colin Raffel, Daniel Trebbien,
+Daniel Ylitalo, Daniel Zhang, Daniyar, Darjan Salaj, Dave Maclachlan, David Norman, Dong--Jian,
+dongsamb, dssgsra, Edward H, eladweiss, elilienstein, Eric Lilienstein, error.d, Eunji Jeong, fanlu,
+Florian Courtial, fo40225, Fred, Gregg Helt, Guozhong Zhuang, Hanchen Li, hsm207, hyunyoung2,
+ImSheridan, Ishant Mrinal Haloi, Jacky Ko, Jay Young, Jean Flaherty, Jerome, JerrikEph, Jesse
+Kinkead, jfaath, Jian Lin, jinghuangintel, Jiongyan Zhang, Joel Hestness, Joel Shor, Johnny Chan,
+Julian Niedermeier, Julian Wolff, JxKing, K-W-W, Karl Lessard, Kasper Marstal, Keiji Ariyama,
+Koan-Sin Tan, Loki Der Quaeler, Loo Rong Jie, Luke Schaefer, Lynn Jackson, ManHyuk, Matt Basta,
+Matt Smith, Matthew Schulkind, Michael, michaelkhan3, Miguel Piedrafita, Mikalai Drabovich,
+Mike Knapp, mjwen, mktozk, Mohamed Aly, Mohammad Ashraf Bhuiyan, Myungjoo Ham, Naman Bhalla,
+Namrata-Ibm, Nathan Luehr, nathansilberman, Netzeband, Niranjan Hasabnis, Omar Aflak, Ozge
+Yalcinkaya, Parth P Panchal, patrickzzy, Patryk Chrabaszcz, Paul Van Eck, Paweł Kapica, Peng Yu,
+Philip Yang, Pierre Blondeau, Po-Hsien Chu, powderluv, Puyu Wang, Rajendra Arora, Rasmus, Renat
+Idrisov, resec, Robin Richtsfeld, Ronald Eddy Jr, Sahil Singh, Sam Matzek, Sami Kama, sandipmgiri,
+Santiago Castro, Sayed Hadi Hashemi, Scott Tseng, Sergii Khomenko, Shahid, Shengpeng Liu, Shreyash
+Sharma, Shrinidhi Kl, Simone Cirillo, simsicon, Stanislav Levental, starsblinking, Stephen Lumenta,
+Steven Hickson, Su Tang, Taehoon Lee, Takuya Wakisaka, Ted Chang, Ted Ying, Tijmen Verhulsdonck,
+Timofey Kondrashov, vade, vaibhav, Valentin Khrulkov, vchigrin, Victor Costan, Viraj Navkal,
+Vivek Rane, wagonhelm, Yan Facai (颜发才), Yanbo Liang, Yaroslav Bulatov, yegord, Yong Tang,
+Yoni Tsafir, yordun, Yuan (Terry) Tang, Yuxin Wu, zhengdi, Zhengsheng Wei, 田传武
+
+# Release 1.5.0
+
+## Breaking Changes
+* Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
+* Starting from 1.6 release, our prebuilt binaries will use AVX instructions.
+  This may break TF on older CPUs.
+
 ## Major Features And Improvements
 * [Eager execution](https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager)
   preview version is now available.
@@ -146,6 +214,27 @@
 * Minor refactor: move stats files from `stochastic` to `common` and remove
   `stochastic`.
 
+## Known Bugs
+* Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or
+  `CUDA_ILLEGAL_ADDRESS` failures.
+
+  Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9
+  and CUDA 9.1 sometimes does not properly compute the carry bit when
+  decomposing 64-bit address calculations with large offsets (e.g. `load [x +
+  large_constant]`) into 32-bit arithmetic in SASS.
+
+  As a result, these versions of `ptxas` miscompile most XLA programs which use
+  more than 4GB of temp memory.  This results in garbage results and/or
+  `CUDA_ERROR_ILLEGAL_ADDRESS` failures.
+
+  A fix in CUDA 9.1.121 is expected in late February 2018.  We do not expect a
+  fix for CUDA 9.0.x.  Until the fix is available, the only workaround is to
+  [downgrade](https://developer.nvidia.com/cuda-toolkit-archive) to CUDA 8.0.x
+  or disable XLA:GPU.
+
+  TensorFlow will print a warning if you use XLA:GPU with a known-bad version of
+  CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122.
+
 ## Thanks to our Contributors
 
 This release contains contributions from many people at Google, as well as:

tensorflow/contrib/cmake/tf_tests.cmake

@@ -310,6 +310,8 @@ if (tensorflow_BUILD_PYTHON_TESTS)
       "${tensorflow_source_dir}/tensorflow/python/kernel_tests/control_flow_util_test.py"
       # Flaky replicate_model_fn_test
       "${tensorflow_source_dir}/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py"  # b/71901810
+      # Broken io_utils_test
+      "${tensorflow_source_dir}/tensorflow/python/keras/_impl/keras/utils/io_utils_test.py"  # b/72894325
   )
   endif()
   list(REMOVE_ITEM tf_test_src_py ${tf_test_src_py_exclude})

tensorflow/contrib/eager/python/examples/mnist/mnist.py

@@ -39,7 +39,7 @@ class MNISTModel(tfe.Network):
   """MNIST Network.
 
   Network structure is equivalent to:
-  https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/examples/tutorials/mnist/mnist_deep.py
+  https://github.com/tensorflow/tensorflow/blob/r1.6/tensorflow/examples/tutorials/mnist/mnist_deep.py
   and
   https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py
 

tensorflow/contrib/lite/kernels/internal/BUILD

@@ -351,6 +351,9 @@ cc_library(
         ":x86": [
             ":neon_tensor_utils",
         ],
+        ":k8": [
+            ":neon_tensor_utils",
+        ],
         ":darwin": [
             ":neon_tensor_utils",
         ],

tensorflow/contrib/py2tf/converters/BUILD

@@ -132,7 +132,6 @@ py_test(
     name = "for_canonicalization_test",
     srcs = ["for_canonicalization_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["nomac"],
     deps = [
         ":test_lib",
         "//tensorflow/contrib/py2tf/pyct",

tensorflow/contrib/tpu/profiler/pip_package/setup.py

@@ -20,7 +20,7 @@
 
 from setuptools import setup
 
-_VERSION = '1.5.0-rc1'
+_VERSION = '1.6.0-rc0'
 
 CONSOLE_SCRIPTS = [
     'capture_tpu_profile=cloud_tpu_profiler.main:run_main',

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 5
+#define TF_MINOR_VERSION 6
 #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 "-rc0"
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)

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_64-1.5.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.6.0-rc0.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`

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_64-1.5.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.6.0-rc0.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`

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>1.5.0</version>
+  <version>1.6.0-rc0</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>1.5.0</version>
+                 <version>1.6.0-rc0</version>
                </dependency>
              </dependencies>
          </project>
@@ -123,12 +123,12 @@ instead:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>libtensorflow</artifactId>
-  <version>1.5.0</version>
+  <version>1.6.0-rc0</version>
 </dependency>
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>libtensorflow_jni_gpu</artifactId>
-  <version>1.5.0</version>
+  <version>1.6.0-rc0</version>
 </dependency>
 ```
 
@@ -147,7 +147,7 @@ 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-1.5.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc0.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -166,18 +166,18 @@ 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}-${OS}-x86_64-1.5.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.6.0-rc0.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
 
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc0.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_64-1.5.0.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.6.0-rc0.zip).
   3. Extract this .zip file.
 
 
@@ -225,7 +225,7 @@ 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-1.5.0.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.6.0-rc0.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -239,11 +239,11 @@ 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-1.5.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.6.0-rc0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.5.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.6.0-rc0.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

tensorflow/docs_src/install/install_linux.md

@@ -188,7 +188,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      Virtualenv environment:
 
      <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -293,7 +293,7 @@ take the following steps:
 
      <pre>
      $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl</b>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -480,7 +480,7 @@ 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-1.5.0-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -648,14 +648,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -667,14 +667,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -686,14 +686,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -705,14 +705,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
 </pre>