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android / platform / external / tensorflow / fd7d975b705
commitfd7d975b7052a5170c44e8bc1a32be4695fa16b4[log] [tgz]
authorA. Unique TensorFlower <gardener@tensorflow.org>Thu Sep 05 11:51:01 2019 -0700
committerTensorFlower Gardener <gardener@tensorflow.org>Thu Sep 05 12:04:58 2019 -0700
tree7ab0b3d1321eb9ff863d64c0df49e0eca6045602
parent90f35c4c87e7d76e7830c23847c55953ff54d33d [diff]
Add SaveOptions.save_debug_info and associated plumbing for SavedModel V2.

The main behavior change is that I chose to mangle the trace key to be:
  op_name "@" func_name

(global ops use func_name = '')
Originally it was just a simple concat: func_name op_name

I don't have a strong opinion on the specific mangling to be used, but I do believe that how it was done is collision prone and best to correct now. Specifically, I chose this form because:
a) func_name does not seem to have strong validation on its naming (and in practice can be quite varied in the presence of lambdas, etc)
b) op_name does have strong validation on its syntax and specifically excludes '@' (matches regex: [A-Za-z0-9.][A-Za-z0-9_.\\-/]*)

Given these points, what I propose should be collision free. I'm not sure I should be making this decision, though. Please advise.

The test coverage was pretty sparse for all of this and I tried to buff it up around my modifications.

PiperOrigin-RevId: 267420099
17 files changed
tree: 7ab0b3d1321eb9ff863d64c0df49e0eca6045602
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .bazelrc
  6. .gitignore
  7. ACKNOWLEDGMENTS
  8. ADOPTERS.md
  9. arm_compiler.BUILD
  10. AUTHORS
  11. BUILD
  12. CODE_OF_CONDUCT.md
  13. CODEOWNERS
  14. configure
  15. configure.cmd
  16. configure.py
  17. CONTRIBUTING.md
  18. ISSUE_TEMPLATE.md
  19. ISSUES.md
  20. LICENSE
  21. models.BUILD
  22. README.md
  23. RELEASE.md
  24. SECURITY.md
  25. WORKSPACE
README.md
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 for the purposes of conducting 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 backwards 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 for CPU-only:

$ pip install tensorflow

Use the GPU package for CUDA-enabled GPU cards:

$ pip install tensorflow-gpu

Nightly binaries are available for testing using the tf-nightly and tf-nightly-gpu packages on PyPi.

Try your first TensorFlow program

$ python

>>> import tensorflow as tf
>>> tf.enable_eager_execution()
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
'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
Linux s390x NightlyBuild StatusNightly
Linux s390x CPU Stable ReleaseBuild StatusRelease
Linux ppc64le CPU NightlyBuild StatusNightly
Linux ppc64le CPU Stable ReleaseBuild StatusRelease
Linux ppc64le GPU NightlyBuild StatusNightly
Linux ppc64le GPU Stable ReleaseBuild StatusRelease
Linux CPU with Intel® MKL-DNN NightlyBuild StatusNightly
Linux CPU with Intel® MKL-DNN
Supports Python 2.7, 3.4, 3.5, and 3.6		Build Status1.13.1 pypi
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