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Yun Peng 08ed32dbb9 Windows: Make TensorFlow build without --cpu=x64_windows_msvc ()
* Windows: Make TensorFlow build without --cpu=x64_windows_msvc

Since from Bazel 0.5.0, MSVC toolchain became the default toolchain on
Windows. So --cpu=x64_windows_msvc is not required as long as we adjust
the BUILD files in TensorFlow.

--cpu=x64_windows_msvc is also supported for now, but is depracated.
The configuration for cpu value x64_windows_msvc is a duplicate of
x64_windows, which should be removed in the future.

* Fix breakage on macOS
2017-06-07 23:43:21 -07:00
tensorflow Windows: Make TensorFlow build without --cpu=x64_windows_msvc () 2017-06-07 23:43:21 -07:00
third_party Windows: Make TensorFlow build without --cpu=x64_windows_msvc () 2017-06-07 23:43:21 -07:00
tools Merge commit for internal changes 2017-05-08 19:31:28 -07:00
util/python Remove deleted files. 2017-05-05 16:43:23 -07:00
.gitignore Merge commit for internal changes 2017-05-05 16:33:39 -07:00
ACKNOWLEDGMENTS TensorFlow: Improve performance of Alexnet 2015-11-20 10:30:41 -08:00
ADOPTERS.md Internal file cleanup. 2016-10-18 10:31:29 -07:00
AUTHORS Merge changes from github. 2016-07-11 10:48:23 -07:00
BUILD Depend on protobuf's header only library when building custom ops 2017-03-07 20:46:41 -08:00
configure [Bash] Simplify Conditional () 2017-06-07 23:42:01 -07:00
CONTRIBUTING.md Patch/170506 misspell () 2017-05-06 11:02:54 -07:00
ISSUE_TEMPLATE.md Merge changes from github. 2017-05-05 10:26:00 -07:00
LICENSE Merge changes from github. 2017-02-01 18:33:19 -08:00
models.BUILD Make models.BUILD filegroup include everything but metadata files and archives. 2017-01-10 14:25:53 -08:00
README.md Python 3.6 support on windows. () 2017-06-01 11:10:30 -07:00
RELEASE.md fix merge issues 2017-06-03 00:00:53 -04:00
WORKSPACE Add Clutz to TensorBoard build 2017-06-05 14:25:13 -07:00




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TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

If you'd like to contribute to TensorFlow, be sure to review the contribution guidelines.

We use GitHub issues for tracking requests and bugs, but please see Community for general questions and discussion.

Installation

See Installing TensorFlow for instructions on how to install our release binaries or how to build from source.

People who are a little more adventurous can also try our nightly binaries:

Try your first TensorFlow program

$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> sess.run(hello)
'Hello, TensorFlow!'
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> sess.run(a+b)
42
>>>

For more information

The TensorFlow community has created amazing things with TensorFlow, please see the resources section of tensorflow.org for an incomplete list.