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android / platform / external / tensorflow / b645ed7fc05
commitb645ed7fc059cef29d0577522d79aa99a85f81d9[log] [tgz]
authorTomer Kaftan <kaftan@google.com>Mon Jul 20 15:33:58 2020 -0700
committerTensorFlower Gardener <gardener@tensorflow.org>Mon Jul 20 15:53:04 2020 -0700
tree1cfabeefb7a0eda93e576dc5396d06093ba86189
parentad8cdb5e558d2274a1c9600c9d8d929744bc4ec3 [diff]
Add a `convert_to_tensor` to the start of Tensor.__getitem__ (_slice_helper) to make sure it dispatches directly, rather than letting the nested tf.strided_slice trigger dispatching.

This is important because `tensor.__getitem__` does some input arg manipulation before getting to the `tf.strided_slice`. So, when we try to run the traced code using the args provided to `strided_slice` (e.g. for KerasTensors), we lose information about constants that TPUs need to compile graphs involving shape manipulation. Tracing `__getitem__` and its input args directly does not seem to run into this problem.

(Note: this TPU situation is separate from the shape value inferring we do in KerasTensors during Functional API construction/tracing time. This happens at model run-time when running the already-traced code)

To get this all to work correctly in practice, this CL also has to:

* Add tf.nest support for flattening & packing python `slice` objects, in case the slice object contains symbolic tensors/values to trace

* Add serialization/deserialization support for `slice` and `ellipsis` objects in Keras

PiperOrigin-RevId: 322239438
Change-Id: If9b72368dff8bd50b61a1adbc6162f0a8da684d3
11 files changed
tree: 1cfabeefb7a0eda93e576dc5396d06093ba86189
  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
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macOSStatusPyPI
Windows CPUStatusPyPI
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Libtensorflow Linux CPUStatusGCS
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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 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