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android / platform / external / tensorflow / 86d8c80dcb0
commit86d8c80dcb08722ba6e4e0ad74a5b94b5b777029[log] [tgz]
authorChristian Sigg <csigg@google.com>Wed Jan 05 02:47:32 2022 -0800
committerTensorFlower Gardener <gardener@tensorflow.org>Wed Jan 05 02:54:31 2022 -0800
tree4858b1020d5e426d0c8f2ac48923e5fe6acce8b3
parent5ca2294439457d7311c46db6e09f296986ac5037 [diff]
Make `tfrt_gpu.blas/dnn/solver.handle` only refer to context instead of stream.

Change the ops that previously only took a handle to also take a stream. In the kernel implementations, set the handle's stream before executing the actual work. It is the lowering's responsibility to serialize ops that operate on the same handle.

This is a preparation step to wrap the handle creation into a tfrt.once. Those calls should not take a stream, because that stream might have been created internally (instead of passed in) and would therefore not match previous executions.

We want to wrap handle creation into a tfrt.once because handles are quite expensive to create and destroy (~1ms/8.5MB for cuDNN, ~0.4ms/8.5MB for cuBLAS). With this change, we will be able to amortize the cycles and eventually (more work required) allocate the bytes before the main loop.

PiperOrigin-RevId: 419786266
Change-Id: I70a09879f2b228b88fdd64d9bd125082b8a68e51
8 files changed
tree: 4858b1020d5e426d0c8f2ac48923e5fe6acce8b3
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .pylintrc ⇨ tensorflow/tools/ci_build/pylintrc
  6. .bazelrc
  7. .bazelversion
  8. .clang-format
  9. .gitignore
  10. .zenodo.json
  11. arm_compiler.BUILD
  12. AUTHORS
  13. BUILD
  14. CITATION.cff
  15. CODE_OF_CONDUCT.md
  16. CODEOWNERS
  17. configure
  18. configure.cmd
  19. configure.py
  20. CONTRIBUTING.md
  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

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

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.

License

Apache License 2.0