Android Q Preview 6 (QPP6.190730.005)
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Allow NNAPI delegate to report compliation failure

Bug: 123041316
Test: atest VtsHalNeuralnetworksV1_2Benchmark (from change I4a0b83c85775acae2b8e502aae60830ff4f28507)
Change-Id: Ib3229e20348eaee2cb968a213ac1f68bfce7418c
Merged-In: Ib3229e20348eaee2cb968a213ac1f68bfce7418c
(cherry picked from commit d739c1016333e9ed0b282fcc87ae8772ebd77e73)
1 file changed
tree: 8757b4edc7025270c41ee040f60fdc9319b46fee
  1. .bazelrc
  2. .github/
  3. .gitignore
  4. ACKNOWLEDGMENTS
  5. ADOPTERS.md
  6. AUTHORS
  7. Android.bp
  8. BUILD
  9. CODEOWNERS
  10. CODE_OF_CONDUCT.md
  11. CONTRIBUTING.md
  12. ISSUES.md
  13. ISSUE_TEMPLATE.md
  14. LICENSE
  15. METADATA
  16. MODULE_LICENSE_APACHE2
  17. NOTICE
  18. OWNERS
  19. README.md
  20. RELEASE.md
  21. SECURITY.md
  22. WORKSPACE
  23. arm_compiler.BUILD
  24. configure
  25. configure.py
  26. models.BUILD
  27. tensorflow/
  28. tools/
README.md

Documentation
Documentation

TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

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's for C++, Go, Java, JavaScript and Swift.

Keep up to date with release announcements and security updates by subscribing to announce@tensorflow.org.

Installation

To install the current release for CPU-only:

pip install tensorflow

Use the GPU package for CUDA-enabled GPU cards:

pip install tensorflow-gpu

See Installing TensorFlow for detailed instructions, and how to build from source.

People who are a little more adventurous can also try our nightly binaries:

Nightly pip packages

  • We are pleased to announce that TensorFlow now offers nightly pip packages under the tf-nightly and tf-nightly-gpu project on pypi. Simply run pip install tf-nightly or pip install tf-nightly-gpu in a clean environment to install the nightly TensorFlow build. We support CPU and GPU packages on Linux, Mac, and Windows.

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!'

Learn more examples about how to do specific tasks in TensorFlow at the tutorials page of tensorflow.org.

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, so 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

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
IBM s390xBuild StatusTBA
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 Python 2.7
Linux CPU with Intel® MKL-DNN Python 3.4
Linux CPU with Intel® MKL-DNN Python 3.5
Linux CPU with Intel® MKL-DNN Python 3.6
Build Status1.12.0 py2.7
1.12.0 py3.4
1.12.0 py3.5
1.12.0 py3.6

For more information

Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.

License

Apache License 2.0