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Merge remote-tracking branch 'origin/r1.9' into merge_19_into_master

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This commit is contained in:
Michael Case 2018-07-11 13:30:00 -07:00
commit 2af91022e0
9 changed files with 41 additions and 39 deletions

18
RELEASE.md
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@ -34,18 +34,22 @@
* Using `tf.layers` in a subclassed `tf.keras.Model` class. See
[here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details
* `tf.data`:
* The `DatasetBase::DebugString()` method is now `const`.
* Added the `tf.contrib.data.sample_from_datasets()` API for randomly sampling from multiple datasets.
* `Dataset.from_generator()` now accepts an `args` list, in order to create nested generators.
* `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed.
* `tf.contrib.data.sample_from_datasets()` and `tf.contrib.data.choose_from_datasets()` make it easier to sample or deterministically choose elements from multiple datasets.
* `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings, and two infrequently used arguments removed.
* (C++) `DatasetBase::DebugString()` is now `const`.
* (C++) `DatasetBase::MakeIterator()` has been renamed to `DatasetBase::MakeIteratorInternal()`.
* (C++) `IteratorBase::Initialize()` method was added to support raising errors during iterator construction.
* Eager Execution:
* Added the ability to pause recording operations for gradient computation via `tf.GradientTape.stop_recording`.
* Updated documentation, introductory notebooks.
* `tf.keras`:
* Move Keras code out of _impl folder and remove API files.
* `tf.keras.Model.save_weights` now saves in TensorFlow format by default.
* Enable dataset iterators to be passed to `tf.keras.Model` training/eval methods.
* Accelerated Linear Algebra (XLA):
* TensorFlow Debugger (tfdbg): fix an issue in which the TensorBoard Debugger Plugin could not handle total source file size exceeding gRPC message size limit (4 MB).
* TensorFlow Debugger (tfdbg) CLI: fix an issue in which the TensorBoard Debugger Plugin could not handle total source file size exceeding gRPC message size limit (4 MB).
* `tf.contrib`:
* Add `tf.contrib.data.choose_from_datasets()`.
* `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings. Two arguments were removed from `make_csv_dataset`.
* `tf.contrib.framework.zero_initializer` supports ResourceVariable.
* Adding "constrained_optimization" to tensorflow/contrib.
* Other:
@ -55,7 +59,6 @@
* More consistent GcsFileSystem behavior for certain reads past EOF.
* Update benchmark for tf.scan to match ranges across eager and graph modes.
* Fixed bug in `tf.reduce_prod gradient` for complex dtypes.
* Add optional `args` argument to `Dataset.from_generator()`.
* Allow the use of '.' in variables (e.g. "hparams.parse('a.b=1.0')"), which would previously raise an error. This will correspond to an attribute name with an embedded '.' symbol (e.g. 'a.b'), which can only be accessed indirectly (e.g. through getattr and setattr). To set this up the user will first need to explicitly add the variable to the hparam object (e.g. "hparams.add_hparam(name='a.b', value=0.0)").
* Benchmark for tf.scan in graph and eager modes.
* Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D.
@ -65,7 +68,6 @@
* LinearOperator[1D,2D,3D]Circulant added to `tensorflow.linalg`.
* Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter now supports arbitrary.
* Added `tf.train.Checkpoint` for reading/writing object-based checkpoints.
* `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed.
* Added LinearOperatorKronecker, a dense-free implementation of the Kronecker Product.
* Allow LinearOperator to broadcast.
* SavedModelBuilder will now deduplicate asset names that point to files with the same basename and the same contents. Note that this may result in new asset files included in SavedModels in cases where assets with the same name but different contents were previously overwriting each other.

2
tensorflow/core/public/version.h
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@ -24,7 +24,7 @@ limitations under the License.
// TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
// "-beta", "-rc", "-rc.1")
#define TF_VERSION_SUFFIX "-rc0"
#define TF_VERSION_SUFFIX ""
#define TF_STR_HELPER(x) #x
#define TF_STR(x) TF_STR_HELPER(x)

2
tensorflow/docs_src/install/install_c.md
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@ -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-rc0.tar.gz" |
"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0.tar.gz" |
sudo tar -C $TARGET_DIRECTORY -xz
The `tar` command extracts the TensorFlow C library into the `lib`

2
tensorflow/docs_src/install/install_go.md
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@ -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-rc0.tar.gz" |
"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0.tar.gz" |
sudo tar -C $TARGET_DIRECTORY -xz
The `tar` command extracts the TensorFlow C library into the `lib`

22
tensorflow/docs_src/install/install_java.md
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@ -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-rc0</version>
<version>1.9.0</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-rc0</version>
<version>1.9.0</version>
</dependency>
</dependencies>
</project>
@ -124,12 +124,12 @@ instead:
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>libtensorflow</artifactId>
<version>1.9.0-rc0</version>
<version>1.9.0</version>
</dependency>
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>libtensorflow_jni_gpu</artifactId>
<version>1.9.0-rc0</version>
<version>1.9.0</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-rc0.jar),
[libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0.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}-${OS}-x86_64-1.9.0-rc0.tar.gz" |
"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0.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-rc0.jar),
[libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0.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-rc0.zip).
[TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0.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-rc0.jar HelloTF.java</b></pre>
<pre><b>javac -cp libtensorflow-1.9.0.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-rc0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
<pre><b>java -cp libtensorflow-1.9.0.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-rc0.jar;. -Djava.library.path=jni HelloTF</b></pre>
<pre><b>java -cp libtensorflow-1.9.0.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

18
tensorflow/docs_src/install/install_linux.md
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@ -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-1.9.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp34-cp34m-linux_x86_64.whl</b></pre>
<a name="ValidateYourInstallation"></a>
## Validate your installation
@ -676,14 +676,14 @@ This section documents the relevant values for Linux installations.
CPU only:
<pre>
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp27-none-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp27-none-linux_x86_64.whl
</pre>
GPU support:
<pre>
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp27-none-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp27-none-linux_x86_64.whl
</pre>
Note that GPU support requires the NVIDIA hardware and software described in
@ -695,14 +695,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
CPU only:
<pre>
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp34-cp34m-linux_x86_64.whl
</pre>
GPU support:
<pre>
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp34-cp34m-linux_x86_64.whl
</pre>
Note that GPU support requires the NVIDIA hardware and software described in
@ -714,14 +714,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
CPU only:
<pre>
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp35-cp35m-linux_x86_64.whl
</pre>
GPU support:
<pre>
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp35-cp35m-linux_x86_64.whl
</pre>
@ -733,14 +733,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
CPU only:
<pre>
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp36-cp36m-linux_x86_64.whl
</pre>
GPU support:
<pre>
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp36-cp36m-linux_x86_64.whl
</pre>

10
tensorflow/docs_src/install/install_mac.md
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@ -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-1.9.0rc0-py3-none-any.whl</b></pre>
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-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-1.9.0rc0-py3-none-any.whl</b> </pre>
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl</b> </pre>
If the preceding command fails, see
[installation problems](#common-installation-problems).
@ -350,7 +350,7 @@ 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 \
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl</b></pre>
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py2-none-any.whl</b></pre>
<a name="ValidateYourInstallation"></a>
@ -517,7 +517,7 @@ The value you specify depends on your Python version.
<pre>
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py2-none-any.whl
</pre>
@ -525,5 +525,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-a
<pre>
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl
</pre>

4
tensorflow/docs_src/install/install_sources.md
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@ -338,10 +338,10 @@ 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 1.9.0rc0 on Linux:
for TensorFlow 1.9.0 on Linux:
<pre>
$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-py2-none-any.whl</b>
$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0-py2-none-any.whl</b>
</pre>
## Validate your installation

2
tensorflow/tools/pip_package/build_pip_package.sh
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@ -27,7 +27,7 @@ function cp_external() {
pushd .
cd "$src_dir"
for f in `find . ! -type d ! -name '*.py' ! -name '*local_config_cuda*' ! -name '*local_config_tensorrt*' ! -name '*org_tensorflow*'`; do
for f in `find . ! -type d ! -name '*.py' ! -path '*local_config_cuda*' ! -path '*local_config_tensorrt*' ! -path '*org_tensorflow*'`; do
mkdir -p "${dest_dir}/$(dirname ${f})"
cp "${f}" "${dest_dir}/$(dirname ${f})/"
done