Computation using data flow graphs for scalable machine learning
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Avoid task_TIMESTAMP use on macOS generic-worker
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.bazelrc Use GCC7.2 for ARMv7 and Aarch64 Oct 11, 2018
.gitignore Move bazel.rc to workspace root to support bazel-0.18.0 Sep 28, 2018
.taskcluster.yml Enable TaskCluster branch-filtering Sep 28, 2017
ACKNOWLEDGMENTS TensorFlow: Improve performance of Alexnet Nov 20, 2015 Internal file cleanup. Oct 18, 2016
AUTHORS Merge changes from github. Dec 7, 2017
BUILD Make LICENSE visible to bazel. Jan 2, 2018
CODEOWNERS Remove tensorflow/contrib/linalg library. linalg remains in core. Sep 17, 2018 Merge changes from github. Dec 29, 2017 Merge changes from github. Jul 6, 2018 Add mobile device field to the issue template. Jul 18, 2018
LICENSE Merge changes from github. Jan 4, 2018 Updating program in README to use eager. Sep 25, 2018 Update links to install pages. Sep 21, 2018 fix md link format Jun 13, 2018
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models.BUILD Make models.BUILD filegroup include everything but metadata files and… Jan 10, 2017 Integrate WarpCTC into Tensorflow Aug 21, 2017 Switch to OpenJDK 8 Oct 18, 2017 Avoid task_TIMESTAMP use on macOS generic-worker Oct 16, 2018 Update with new flags and rename GPU to CUDA Oct 4, 2018 Package toco on TaskCluster Sep 21, 2018 Update Nov 30, 2017 Add NCCL 2.2.13 Sep 20, 2018 Removed env variables Oct 4, 2018


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 API and C APIs as well as without API backwards compatibility guarantee like C++, Go, Java, JavaScript and Swift.

Keep up to date with release announcements and security updates by subscribing to


See Installing TensorFlow for instructions on how to install our release binaries or 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)
>>> 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 Type Status Artifacts
Linux CPU Status pypi
Linux GPU Status pypi
Linux XLA Status TBA
MacOS Status pypi
Windows CPU Status pypi
Windows GPU Status pypi
Android Status Download
Raspberry Pi 0 and 1 Status Status Py2 Py3
Raspberry Pi 2 and 3 Status Status Py2 Py3

Community Supported Builds

Build Type Status Artifacts
IBM s390x Build Status TBA
IBM ppc64le CPU Build Status TBA
IBM ppc64le GPU Build Status TBA
Linux CPU with Intel® MKL-DNN Nightly Build Status Nightly
Linux CPU with Intel® MKL-DNN Python 2.7
Linux CPU with Intel® MKL-DNN Python 3.5
Linux CPU with Intel® MKL-DNN Python 3.6
Build Status 1.10.0 py2.7
1.10.0 py3.5
1.10.0 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