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fuchsia / third_party / github.com / tensorflow / tensorflow / 8868823c20727507c161b18299731b7790a1b3ca
commit8868823c20727507c161b18299731b7790a1b3ca[log] [tgz]
authorFiona Lang <flang@google.com>Mon Jun 05 09:57:03 2023 -0700
committerCopybara-Service <copybara-worker@google.com>Mon Jun 05 10:06:30 2023 -0700
tree898722c1028e0131d458c42418f443ca95db2ea5
parent2861f8595abcae680ab6d1db88e41783a4750be7 [diff]
Break the interdependency between tpu/tpu_strategy_util.py,  distribute/cluster_resolver/tpu/tpu_cluster_resolver.py, and eager/remote.py.

Make exported wrappers for initialize_tpu_system and shutdown_tpu_system in tpu_cluster_resolver.py, and make implementation functions for them in tpu_strategy_util.py that they pass a reference of TPUClusterResolver to. Add a test for this.

Replace an isinstance check of TPUClusterResolver in remote.py with an isinstance check of ClusterResolver and a hasattr check.

Make all inline imports regular imports and add all missing BUILD deps. Update references to the new location of initialize_tpu_system.

PiperOrigin-RevId: 537905952
GitOrigin-RevId: 6088a22ddb8fc299d18a34596b91ccf077b86b17
Change-Id: I06951d37f6f70a34df92adf7d80e3765078f2ce0
28 files changed
tree: 898722c1028e0131d458c42418f443ca95db2ea5
  1. .github/
  2. tensorflow/
  3. tools/
  4. .pylintrc ⇨ tensorflow/tools/ci_build/pylintrc
  5. .bazelrc
  6. .bazelversion
  7. .clang-format
  8. .gitignore
  9. .zenodo.json
  10. arm_compiler.BUILD
  11. AUTHORS
  12. BUILD
  13. CITATION.cff
  14. CODE_OF_CONDUCT.md
  15. CODEOWNERS
  16. configure
  17. configure.cmd
  18. configure.py
  19. CONTRIBUTING.md
  20. fuzztest.bazelrc
  21. ISSUE_TEMPLATE.md
  22. ISSUES.md
  23. LICENSE
  24. models.BUILD
  25. README.md
  26. RELEASE.md
  27. SECURITY.md
  28. WORKSPACE
README.md

Python PyPI DOI CII Best Practices OpenSSF Scorecard Fuzzing Status Fuzzing Status OSSRank Contributor Covenant TF Official Continuous TF Official Nightly

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

Other devices (DirectX and MacOS-metal) are supported using Device plugins.

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 Forum 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.

Patching guidelines

Follow these steps to patch a specific version of TensorFlow, for example, to apply fixes to bugs or security vulnerabilities:

  • Clone the TensorFlow repo and switch to the corresponding branch for your desired TensorFlow version, for example, branch r2.8 for version 2.8.
  • Apply (that is, cherry pick) the desired changes and resolve any code conflicts.
  • Run TensorFlow tests and ensure they pass.
  • Build the TensorFlow pip package from source.

Continuous build status

You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.

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 CPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Linux CPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Linux GPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Windows CPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Windows GPUStatus Temporarily UnavailableNightly Binary Official GCS

Resources

Learn more about the TensorFlow community and how to contribute.

Courses

License

Apache License 2.0