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  1. 3cf41e3 Update :python_tracer_impl dependency to tsl. by A. Unique TensorFlower · 17 minutes ago main
  2. 710cd4b compat: Update forward compatibility horizon to 2022-12-04 by A. Unique TensorFlower · 10 hours ago
  3. f3b28c8 Update GraphDef version to 1335. by A. Unique TensorFlower · 10 hours ago
  4. c334a14 Updates .bazelrc to build with clang. by A. Unique TensorFlower · 29 hours ago
  5. bfa9d0f Fix "does not depend on module" error when building `//tensorflow/core/common_runtime/eager:execute` with oneDNN. by Penporn Koanantakool · 32 hours ago

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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 to conduct 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 backward 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, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):

$ pip install tensorflow

Other devices (DirectX and MacOS-metal) are supported using Device plugins.

A smaller CPU-only package is also available:

$ pip install tensorflow-cpu

To update TensorFlow to the latest version, add --upgrade flag to the above commands.

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

Try your first TensorFlow program

$ python
>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'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.

Continuous build status

You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.

Official Builds

Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
AndroidStatusDownload
Raspberry Pi 0 and 1StatusPy3
Raspberry Pi 2 and 3StatusPy3
Libtensorflow MacOS CPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Linux CPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Linux GPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Windows CPUStatus Temporarily UnavailableNightly Binary Official GCS
Libtensorflow Windows GPUStatus Temporarily UnavailableNightly Binary Official GCS

Resources

Learn more about the TensorFlow community and how to contribute.

Courses

License

Apache License 2.0