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android / platform / external / tensorflow / bcfd86d52e4
commitbcfd86d52e4d5980b3d53a0021473d15f5709077[log] [tgz]
authorSmit Hinsu <hinsu@google.com>Wed Dec 16 16:37:57 2020 -0800
committerTensorFlower Gardener <gardener@tensorflow.org>Wed Dec 16 16:44:38 2020 -0800
tree21f77c7b9bb600d05fb285bc11d1fe82bb1886e6
parent35f10f3f27d33975a4739cc7ebde0f16982851be [diff]
Always execute TF ops on the local host CPU while constant folding

Currently, the op is executed on local device based on the device attribute but this is not intended and is causing recursive call in case of on-demand compilation of an op.

The op will be executed on host CPU if we don't explicitly set device for the eager op and provide input tensors on the host CPU.

Also, setting placement policy to TFE_DEVICE_PLACEMENT_EXPLICIT so that other devices are not used if no kernel is available on CPU. This will provide consistent behavior in different environments.

I don't see any easy way to have unit test for this commit without having a custom binary with GPU device and using some op that has different behavior on CPU and GPU. We have end to end tests for this in compiler/tests directory.

Removed an existing test that seems to be passing even if we don't force local device which was the original motivation for the change.

PiperOrigin-RevId: 347921660
Change-Id: Ic281cfe0bcf0dada7b1c571430aeaafe96f16d53
15 files changed
tree: 21f77c7b9bb600d05fb285bc11d1fe82bb1886e6
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .pylintrc ⇨ tensorflow/tools/ci_build/pylintrc
  6. .bazelrc
  7. .bazelversion
  8. .gitignore
  9. ACKNOWLEDGMENTS
  10. arm_compiler.BUILD
  11. AUTHORS
  12. BUILD
  13. CODE_OF_CONDUCT.md
  14. CODEOWNERS
  15. configure
  16. configure.cmd
  17. configure.py
  18. CONTRIBUTING.md
  19. ISSUE_TEMPLATE.md
  20. ISSUES.md
  21. LICENSE
  22. models.BUILD
  23. README.md
  24. RELEASE.md
  25. SECURITY.md
  26. WORKSPACE
README.md

Python PyPI

Documentation
Documentation

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

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

TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.

Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.

Install

See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.

To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):

$ pip install tensorflow

A smaller CPU-only package is also available:

$ pip install tensorflow-cpu

To update TensorFlow to the latest version, add --upgrade flag to the above commands.

Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu packages on PyPi.

Try your first TensorFlow program

$ python

>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'

For more examples, see the TensorFlow tutorials.

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If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs, please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.

The TensorFlow project strives to abide by generally accepted best practices in open-source software development:

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Build TypeStatusArtifacts
Linux CPUStatusPyPI
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Linux ppc64le CPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
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Linux aarch64 CPU Nightly (Linaro)Build StatusNightly
Linux aarch64 CPU Stable Release (Linaro)Build StatusRelease 1.x & 2.x
Linux aarch64 CPU Nightly (OpenLab)
Python 3.6		Build StatusNightly
Linux aarch64 CPU Stable Release (OpenLab)Build StatusRelease 1.15 / 2.x
Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) NightlyBuild StatusNightly
Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Stable ReleaseBuild StatusRelease 1.15 / 2.x
Red Hat® Enterprise Linux® 7.6 CPU & GPU
Python 2.7, 3.6		Build Status1.13.1 PyPI

Community Supported Containers

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TensorFlow aarch64 Neoverse-N1 CPU Stable (Linaro)
Debian		StaticRelease 2.3

Resources

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0