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android / platform / external / tensorflow / 4bd8a427063
commit4bd8a4270638196debca7a139e748bdd1157560f[log] [tgz]
authorA. Unique TensorFlower <gardener@tensorflow.org>Fri Sep 13 19:39:38 2019 -0700
committerTensorFlower Gardener <gardener@tensorflow.org>Fri Sep 13 22:53:10 2019 -0700
treebada99bb4e66cd997810d0573a2db07f21d19f7c
parentdf1b3b396bbf7dbb831f0a9066847eb7c08f3d04 [diff]
Speed up creation of tensors from compressed TensorProtos by 2-3x.

This should speed up some TF models optimized by Grappler in particular, since Grappler tries to compress all constants in a graph.

Run on XXXXX (72 X 2991 MHz CPUs); 2019-09-13T15:55:01.194485871-07:00
CPU: Intel Skylake Xeon with HyperThreading (36 cores) dL1:32KB dL2:1024KB dL3:24MB
Benchmark                          Base (ns)  New (ns) Improvement
------------------------------------------------------------------
BM_FromProto/512                         114       116     -1.8%
BM_FromProto/4k                          692       671     +3.0%
BM_FromProto/32k                        8675      8713     -0.4%
BM_FromProto/256k                     183931    184131     -0.1%
BM_FromProto/1M                       640952    638278     +0.4%
BM_FromProtoCompressed/512               215       118    +45.1%
BM_FromProtoCompressed/4k               1283       490    +61.8%
BM_FromProtoCompressed/32k             14115      8324    +41.0%
BM_FromProtoCompressed/256k            76930     32191    +58.2%
BM_FromProtoCompressed/1M             326284    170167    +47.8%
BM_FromProtoCompressedZero/512           215       119    +44.7%
BM_FromProtoCompressedZero/4k           1302       490    +62.4%
BM_FromProtoCompressedZero/32k         14333      8160    +43.1%
BM_FromProtoCompressedZero/256k        77032     32110    +58.3%
BM_FromProtoCompressedZero/1M         329943    171449    +48.0%
PiperOrigin-RevId: 269027674
5 files changed
tree: bada99bb4e66cd997810d0573a2db07f21d19f7c
  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
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Community Supported Builds

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Linux ppc64le CPU Stable ReleaseBuild StatusRelease
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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