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Merge remote-tracking branch 'gtf/r1.6'

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Gunhan Gulsoy 2018-02-15 22:50:04 -08:00
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27
RELEASE.md
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@ -21,7 +21,7 @@ newcomers.
* Other:
* Add `tf.contrib.distributions.Kumaraswamy`.
* `RetryingFileSystem::FlushCaches()` calls the base FileSystem's `FlushCaches()`.
* Add auto_correlation to distributions.
* Add `auto_correlation` to distributions.
* Add `tf.contrib.distributions.Autoregressive`.
* Add SeparableConv1D layer.
* Add convolutional Flipout layers.
@ -31,12 +31,12 @@ newcomers.
* 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.
* 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.
* 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
@ -96,27 +96,6 @@ Yoni Tsafir, yordun, Yuan (Terry) Tang, Yuxin Wu, zhengdi, Zhengsheng Wei, 田
* Starting from 1.6 release, our prebuilt binaries will use AVX instructions.
This may break TF on older CPUs.
## 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.
## Major Features And Improvements
* [Eager execution](https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager)
preview version is now available.

2
tensorflow/contrib/slim/python/slim/evaluation_test.py
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@ -235,7 +235,7 @@ class SingleEvaluationTest(test.TestCase):
def _prepareCheckpoint(self, checkpoint_path):
init_op = control_flow_ops.group(variables.global_variables_initializer(),
variables.local_variables_initializer())
saver = saver_lib.Saver()
saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1)
with self.test_session() as sess:
sess.run(init_op)
saver.save(sess, checkpoint_path)

2
tensorflow/contrib/tpu/profiler/pip_package/setup.py
View File

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

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 "-rc1"
#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_64-1.6.0-rc0.tar.gz" |
"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.6.0-rc1.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_64-1.6.0-rc0.tar.gz" |
"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.6.0-rc1.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
View File

@ -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>1.6.0-rc0</version>
<version>1.6.0-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>1.6.0-rc0</version>
<version>1.6.0-rc1</version>
</dependency>
</dependencies>
</project>
@ -123,12 +123,12 @@ instead:
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>libtensorflow</artifactId>
<version>1.6.0-rc0</version>
<version>1.6.0-rc1</version>
</dependency>
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>libtensorflow_jni_gpu</artifactId>
<version>1.6.0-rc0</version>
<version>1.6.0-rc1</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-1.6.0-rc0.jar),
[libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc1.jar),
which is the TensorFlow Java Archive (JAR).
2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@ -166,7 +166,7 @@ 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}-${OS}-x86_64-1.6.0-rc0.tar.gz" |
"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.6.0-rc1.tar.gz" |
tar -xz -C ./jni
### Install on Windows
@ -174,10 +174,10 @@ 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-1.6.0-rc0.jar),
[libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-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_64-1.6.0-rc0.zip).
[TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.6.0-rc1.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-1.6.0-rc0.jar HelloTF.java</b></pre>
<pre><b>javac -cp libtensorflow-1.6.0-rc1.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-1.6.0-rc0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
<pre><b>java -cp libtensorflow-1.6.0-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-1.6.0-rc0.jar;. -Djava.library.path=jni HelloTF</b></pre>
<pre><b>java -cp libtensorflow-1.6.0-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

22
tensorflow/docs_src/install/install_linux.md
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@ -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-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-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-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b>
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-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-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-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-1.6.0rc0-cp27-none-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp27-none-linux_x86_64.whl
</pre>
GPU support:
<pre>
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp27-none-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-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-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl
</pre>
GPU support:
<pre>
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-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-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp35-cp35m-linux_x86_64.whl
</pre>
GPU support:
<pre>
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-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-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp36-cp36m-linux_x86_64.whl
</pre>
GPU support:
<pre>
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp36-cp36m-linux_x86_64.whl
</pre>

10
tensorflow/docs_src/install/install_mac.md
View File

@ -115,7 +115,7 @@ 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-1.6.0rc0-py3-none-any.whl</b></pre>
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl</b></pre>
If you encounter installation problems, see
[Common Installation Problems](#common-installation-problems).
@ -238,7 +238,7 @@ take the following steps:
issue the following command:
<pre> $ <b>sudo pip3 install --upgrade \
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl</b> </pre>
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl</b> </pre>
If the preceding command fails, see
[installation problems](#common-installation-problems).
@ -347,7 +347,7 @@ 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-1.6.0rc0-py2-none-any.whl</b></pre>
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl</b></pre>
<a name="ValidateYourInstallation"></a>
@ -520,7 +520,7 @@ This section documents the relevant values for Mac OS installations.
<pre>
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-any.whl
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl
</pre>
@ -528,5 +528,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-a
<pre>
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl
</pre>

14
tensorflow/docs_src/install/install_sources.md
View File

@ -359,10 +359,10 @@ 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 1.6.0rc0 on Linux:
for TensorFlow 1.6.0rc1 on Linux:
<pre>
$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0rc0-py2-none-any.whl</b>
$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0rc1-py2-none-any.whl</b>
</pre>
## Validate your installation
@ -460,8 +460,8 @@ Stack Overflow and specify the `tensorflow` tag.
**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-1.6.0rc0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.9.0</td><td>N/A</td><td>N/A</td></tr>
<tr><td>tensorflow_gpu-1.6.0rc0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.9.0</td><td>7</td><td>9</td></tr>
<tr><td>tensorflow-1.6.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.9.0</td><td>N/A</td><td>N/A</td></tr>
<tr><td>tensorflow_gpu-1.6.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.9.0</td><td>7</td><td>9</td></tr>
<tr><td>tensorflow-1.5.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>N/A</td><td>N/A</td></tr>
<tr><td>tensorflow_gpu-1.5.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>7</td><td>9</td></tr>
<tr><td>tensorflow-1.4.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.5.4</td><td>N/A</td><td>N/A</td></tr>
@ -479,7 +479,7 @@ Stack Overflow and specify the `tensorflow` tag.
**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-1.6.0rc0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr>
<tr><td>tensorflow-1.6.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr>
<tr><td>tensorflow-1.5.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr>
<tr><td>tensorflow-1.4.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.5.4</td><td>N/A</td><td>N/A</td></tr>
<tr><td>tensorflow-1.3.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.4.5</td><td>N/A</td><td>N/A</td></tr>
@ -493,8 +493,8 @@ Stack Overflow and specify the `tensorflow` tag.
**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-1.6.0rc0</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-1.6.0rc0</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-1.6.0</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-1.6.0</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-1.5.0</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-1.5.0</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-1.4.0</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>

2
tensorflow/docs_src/install/install_windows.md
View File

@ -47,7 +47,7 @@ installed on your system:
If you have a different version of one of the preceding packages, please
change to the specified versions. In particular, the cuDNN version
must match exactly: TensorFlow will not load if it cannot find `cudnn64_7.dll`.
must match exactly: TensorFlow will not load if it cannot find `cuDNN64_7.dll`.
To use a different version of cuDNN, you must build from source.
## Determine how to install TensorFlow

12
tensorflow/python/training/saver.py
View File

@ -1597,9 +1597,9 @@ class Saver(object):
[Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
Returns:
A string: path prefix used for the checkpoint files. If checkpoint
format is V1 and the saver is sharded, this string ends with:
'-?????-of-nnnnn' where 'nnnnn' is the number of shards created.
A string: path prefix used for the checkpoint files. If the saver is
sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn'
is the number of shards created.
If the saver is empty, returns None.
Raises:
@ -1749,12 +1749,6 @@ class Saver(object):
return
if save_path is None:
raise ValueError("Can't load save_path when it is None.")
if (os.path.isfile(save_path) and
self._write_version not in (
saver_pb2.SaverDef.V1, saver_pb2.SaverDef.LEGACY)):
raise ValueError("The specified path: %s is a file."
" Please specify only the path prefix"
" to the checkpoint files." % save_path)
logging.info("Restoring parameters from %s", save_path)
if context.in_graph_mode():
sess.run(self.saver_def.restore_op_name,

1
tensorflow/python/util/compat_internal.py
View File

@ -20,6 +20,7 @@ from __future__ import print_function
from tensorflow.python.util.compat import as_str_any
def path_to_str(path):
"""Returns the file system path representation of a `PathLike` object,
else as it is.

6
tensorflow/tools/pip_package/setup.py
View File

@ -29,17 +29,17 @@ from setuptools.dist import Distribution
# 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 = '1.6.0-rc0'
_VERSION = '1.6.0-rc1'
REQUIRED_PACKAGES = [
'absl-py >= 0.1.6',
'astor >= 0.6.0',
'gast >= 0.2.0',
'grpcio >= 1.8.6',
'numpy >= 1.12.1',
'numpy >= 1.13.3',
'six >= 1.10.0',
'protobuf >= 3.4.0',
'tensorflow-tensorboard >= 1.5.0, < 1.6.0',
'tensorboard >= 1.6.0, < 1.7.0',
'termcolor >= 1.1.0',
]