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android / platform / external / tensorflow / f581c55e4d0
commitf581c55e4d01e4ebdeaebf6c095aff547745d893[log] [tgz]
authorJared Duke <jdduke@google.com>Tue May 12 14:16:20 2020 -0700
committerTensorFlower Gardener <gardener@tensorflow.org>Tue May 12 14:19:55 2020 -0700
treea6391dca778630bf153ce66831c4c21594784c51
parent1638fe218d6003345460e33b7a38a8a322887d79 [diff]
Introduce persistent, read-only TFLite tensor type

Several operators (rank, shape) are critical for preserving the ability
to resize graphs correctly at runtime. However, introduction of such ops
in the graph currently makes it impossible to fully propagate shapes when
tensors are allocated. This also prevents delegation of the graph for most
delegates, as it introduces dynamic shapes.

Introduce a new, persistent tensor type that can be treated as "constant"
at the time of TfLiteRegistration::Prepare. This tensor type is allocated
immediately when requested, similar to a dynamic tensor, but promises that
its contents will be populated after the "producing" node is prepared, and
that it won't change across subsequent evals.

Update Rank/Shape operators to use this tensor allocation type.

A follow-up CL will introduce a new pseudo-constant tensor check
that can be used by various kernels to avoid making them dynamic.

PiperOrigin-RevId: 311199934
Change-Id: I050704be7d1ff264fc1a852efade53d4021cb034
12 files changed
tree: a6391dca778630bf153ce66831c4c21594784c51
  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