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android / platform / external / tensorflow / d0f9944e2fa
commitd0f9944e2fa3b782064143f0b291d94895dc3ff2[log] [tgz]
authorA. Unique TensorFlower <gardener@tensorflow.org>Sat Apr 25 05:15:54 2020 -0700
committerTensorFlower Gardener <gardener@tensorflow.org>Sat Apr 25 05:19:30 2020 -0700
treeac80e99320fc085a81723c06743fc9e963d86e11
parent1ef0ba37ec253dbd4e63c3c43a0b65819624ba54 [diff]
Install multiple python versions simultaneously in our remote build docker.

This allows users of this image to build against different python versions,
which is a precondition to moving release builds to use RBE and significantly
simplifies reproducability of problems due to different python versions.
Furthermore, it gets rid of the requirement that the locally installed python
version on the user's machine must exactly match the python version installed
on the remote image, which makes it very hard to switch presubmits to newer
python versions.

This patch:
- adds a new Dockerfile called ...-multipython; we create a new Dockerfile
  in order to be able to transition with a flag flip in the build instead of
  making it necessary to flip all build configurations simultaneously
- installs all python versions we care about from source, which makes sure all
  our python versions are built the same way - if we get them from third-party
  repositories, they interfere with our system python and are set up slightly
  differently
- adds a script that installs all python dependencies of the build process
  the same way for all python versions; the old script would pin versions in
  order to prefer binary packages over source packages; nowadays pip has the
  option --prefer-binary, which achives the same goal in a much more
  maintainable fashion
- moves the step to link python versions into the sysroot into
  build_devtoolset.sh - this is not yet optimal, as the Dockerfile decides
  which python version to provide; it will be addressed in a subsequent patch

PiperOrigin-RevId: 308407371
Change-Id: I96af4c2c33159757167b642c7b71772c6fae8873
5 files changed
tree: ac80e99320fc085a81723c06743fc9e963d86e11
  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:

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 1Status StatusPy2 Py3
Raspberry Pi 2 and 3Status StatusPy2 Py3

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 CPU with Intel® MKL-DNN NightlyBuild StatusNightly
Linux CPU with Intel® MKL-DNN 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