Add Android specific generated test runner.

  - The test runner is a Android specific trimmed down version of
  - The main difference is the removal of dependencies like absl, re2
  and tensorflow core.
  - The files are forked in to nnapi_tflite_zip_tests/ to avoid problems
  upon rebasing.
  - More models will be added in upcoming CLs when the delegate is updated
  to support these ops

Bug: 130762914
Test: mm
Test: atest TfliteGeneratedNnapiTest
Merged-In: I09b5d5307b4a402c4fa166434744d7fe2f3b90cb
Change-Id: I09b5d5307b4a402c4fa166434744d7fe2f3b90cb
(cherry picked from commit ea303b6c0d289299527d69763ca007cd592130f6)
20 files changed
tree: 3082d37bc4a222e3ee29adaba8bd945531cb9821
  1. .bazelrc
  2. .github/
  3. .gitignore
  7. Android.bp
  8. BUILD
  17. NOTICE
  18. OWNERS
  23. arm_compiler.BUILD
  24. configure
  26. models.BUILD
  27. tensorflow/
  28. tools/


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


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()
>>> 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

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
Windows CPUStatuspypi
Windows GPUStatuspypi
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 for a few ways to participate.


Apache License 2.0