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android / platform / external / tensorflow / d3bbd2ffc93
commitd3bbd2ffc930220455e12a9269057f0e200147f9[log] [tgz]
authorReed Wanderman-Milne <reedwm@google.com>Fri Jun 11 11:12:57 2021 -0700
committerTensorFlower Gardener <gardener@tensorflow.org>Fri Jun 11 11:17:59 2021 -0700
treebffcf8c18febaf7b2acb187ab7363a785a2120d8
parentd20eb1de0902100f218707fd2ea9a5812fc9c005 [diff]
Fix build failure with --config=dbg.

Before, the build would fail with errors such as: "relocation truncated to fit: R_X86_64_32 against .debug_info'". The issue was the debug info was too large. I believe the issue was occurring because offsets into the .debug_info section are stored as 32-bit integers, and so that section cannot exceed 4GiB. To fix, debug info is only included for files under tensorflow/, excluding kernels. This brings the size of the .debug_info section down to about 1.4GiB, well under the 4GiB limit.

Unfortunately, TF kernels and TF dependencies do not have debugging info anymore, but I suspect these are rarely debugged. Debugging info for specific kernels/dependencies can still be explicitly included by the user, e.g. by passing the bazel flags: --config=dbg --per_file_copt=+tensorflow/core/kernels/identity_op.*@-g

See https://github.com/tensorflow/tensorflow/issues/48919 for more context.

PiperOrigin-RevId: 378910826
Change-Id: I4b94e3d53bb3ca00c30d5c83d2a57e4bd390c5a8
1 file changed
tree: bffcf8c18febaf7b2acb187ab7363a785a2120d8
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .pylintrc ⇨ tensorflow/tools/ci_build/pylintrc
  6. .bazelrc
  7. .bazelversion
  8. .gitignore
  9. .zenodo.json
  10. ACKNOWLEDGMENTS
  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 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