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android / platform / external / tensorflow / 8c81dd1e6d6
commit8c81dd1e6d657d92e98f8c33d7a83ab3d7122a1c[log] [tgz]
authorChenkai Kuang <chenkai@google.com>Mon Aug 24 17:50:21 2020 -0700
committerTensorFlower Gardener <gardener@tensorflow.org>Mon Aug 24 18:08:43 2020 -0700
tree01d2f363c83169684b1d4ca7b3125b775d54e547
parent8779a1bff6cc2d85d99f713aa384f837cb2fa47f [diff]
Add several new features to ShardedVariable in ParameterServer strategy:

1. Dense layer partition. It is learned that in some cases dense layer partition could improve the model training speed.

2. ShardedVariable now supports "assign", "assign_add" and "assign_sub" methods.

3. ParameterServerStrategy now accepts a "variable_partitioner" parameter that controls all variable partitioning under strategy.scope(). It is compatible with tf.compat.v1 partitioner. Default partitioner is same as estimator canned models: each partition would has at least 64MB data.

4. ParameterServerStrategy now is able to do memory-efficient initialization of sharded variables, but it requires a custom initializer that is partition aware.

5. ParameterServerStrategy now is able to partition variables even if their `initial_value` is a Tensor (not a callable).

Meanwhile, removed `strategy.experimental_variable_partitioning_scope` method. Per-layer partitioning using different partitioners is not going to be supported right now.

PiperOrigin-RevId: 328241842
Change-Id: I382743dd8d1a2f6b7ab207576aed2e77d71c5735
9 files changed
tree: 01d2f363c83169684b1d4ca7b3125b775d54e547
  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.

Contribution guidelines

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:

Fuzzing Status CII Best Practices Contributor Covenant

Continuous build status

Official Builds

Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
AndroidStatusDownload
Raspberry Pi 0 and 1StatusPy3
Raspberry Pi 2 and 3StatusPy3
Libtensorflow MacOS CPUStatusGCS
Libtensorflow Linux CPUStatusGCS
Libtensorflow Linux GPUStatusGCS
Libtensorflow Windows CPUStatusGCS
Libtensorflow Windows GPUStatusGCS

Community Supported Builds

Build TypeStatusArtifacts
Linux AMD ROCm GPU NightlyBuild StatusNightly
Linux AMD ROCm GPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
Linux s390x NightlyBuild StatusNightly
Linux s390x CPU Stable ReleaseBuild StatusRelease
Linux ppc64le CPU NightlyBuild StatusNightly
Linux ppc64le CPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
Linux ppc64le GPU NightlyBuild StatusNightly
Linux ppc64le GPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
Linux aarch64 CPU Nightly
Python 3.6		Build StatusNightly
Linux aarch64 CPU Stable ReleaseBuild 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

Resources

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0