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android / platform / external / tensorflow / 49220c36795
commit49220c36795ae978c5def631f7e83237ff12e63d[log] [tgz]
authorAndrew Audibert <aaudibert@google.com>Wed Sep 02 10:12:05 2020 -0700
committerTensorFlower Gardener <gardener@tensorflow.org>Wed Sep 02 10:16:48 2020 -0700
tree83116d324829176565095601900a59f4229a6cef
parent7921c4d517dab0a17a6c24b309a04a12d4f71574 [diff]
Exchange device attributes at group resolution

Previously CollectiveParamResolver queries device attributes when initializing instance params. That has issues when the collective leader fails and restarts quickly between group resolution and instance resolution. In such case, all other workers get the incarnation of the restarted leader, thus they're unable to detect that the leader has failed; the leader will deadlock on the group resolution.

This change doesn't fully fixed the issue because it only exchanges device attributes at group resolution, but doesn't populate the device attributes to DeviceResolver. That will be done in a following change.

This change also changes the behavior when a non-leader fails and restarts. Previously it gets the cached group resolution from the leader, now it will get an error because its incarnation doesn't match with the one in the cached group parameters. This should have no actual effect since that worker will always restart again after the leader has restarted.

This change changes both the client and server without being backward compatible. It assumes that client and server are running the same version of Tensorflow. This should be true since the only way to use CollectiveParamResolverDistributed is through MultiWorkerMirroredStrategy (MWMS). For MWMS, all workers should run the same version of the program.

PiperOrigin-RevId: 329735919
Change-Id: I5c29a3ec8462c7737bcbbbf823a95693b0d27dc3
14 files changed
tree: 83116d324829176565095601900a59f4229a6cef
  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. ADOPTERS.md
  11. arm_compiler.BUILD
  12. AUTHORS
  13. BUILD
  14. CODE_OF_CONDUCT.md
  15. CODEOWNERS
  16. configure
  17. configure.cmd
  18. configure.py
  19. CONTRIBUTING.md
  20. ISSUE_TEMPLATE.md
  21. ISSUES.md
  22. LICENSE
  23. models.BUILD
  24. README.md
  25. RELEASE.md
  26. SECURITY.md
  27. 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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Community Supported Builds

Build TypeStatusArtifacts
Linux AMD ROCm GPU NightlyBuild StatusNightly
Linux AMD ROCm GPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
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Linux ppc64le CPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
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Linux ppc64le GPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
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Python 3.6		Build StatusNightly
Linux aarch64 CPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
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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

Resources

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0