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TensorFlow is an end-to-end open source platformfor machine learning. It has a comprehensive, flexible ecosystem oftools,libraries, andcommunity resources that letsresearchers push the state-of-the-art in ML and developers easily build anddeploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working withinthe Machine Intelligence team at Google Brain to conduct research in machinelearning and neural networks. However, the framework is versatile enough to beused in other areas as well.

TensorFlow provides stablePythonandC++ APIs, as well as anon-guaranteed backward compatible API forother languages.

Keep up-to-date with release announcements and security updates by subscribingtoannounce@tensorflow.org.See all themailing lists.

See theTensorFlow install guide for thepip package, toenable GPU support, use aDocker container, andbuild from source.

To install the current release, which includes support forCUDA-enabled GPU cards(Ubuntu andWindows):

$ pip install tensorflow

Other devices (DirectX and MacOS-metal) are supported usingDevice 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 abovecommands.

Nightly binaries are available for testing using thetf-nightly andtf-nightly-cpu packages on PyPi.

$ python

>>>importtensorflowastf>>>tf.add(1,2).numpy()3>>>hello=tf.constant('Hello, TensorFlow!')>>>hello.numpy()b'Hello, TensorFlow!'

For more examples, see theTensorFlow tutorials.

If you want to contribute to TensorFlow, be sure to review thecontribution guidelines. This project adheres to TensorFlow'scode of conduct. By participating, you are expected touphold this code.

We useGitHub issues fortracking requests and bugs, please seeTensorFlow Forum for general questions anddiscussion, and please direct specific questions toStack Overflow.

The TensorFlow project strives to abide by generally accepted best practices inopen-source software development.

Follow these steps to patch a specific version of TensorFlow, for example, toapply fixes to bugs or security vulnerabilities:

• Clone the TensorFlow repo and switch to the corresponding branch for yourdesired TensorFlow version, for example, branchr2.8 for version 2.8.
• Apply (that is, cherry-pick) the desired changes and resolve any codeconflicts.
• Run TensorFlow tests and ensure they pass.
• Build the TensorFlow pippackage from source.

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

Build Type	Status	Artifacts
Linux CPU		PyPI
Linux GPU		PyPI
Linux XLA		TBA
macOS		PyPI
Windows CPU		PyPI
Windows GPU		PyPI
Android		Download
Raspberry Pi 0 and 1		Py3
Raspberry Pi 2 and 3		Py3
Libtensorflow MacOS CPU	Status Temporarily Unavailable	Nightly BinaryOfficial GCS
Libtensorflow Linux CPU	Status Temporarily Unavailable	Nightly BinaryOfficial GCS
Libtensorflow Linux GPU	Status Temporarily Unavailable	Nightly BinaryOfficial GCS
Libtensorflow Windows CPU	Status Temporarily Unavailable	Nightly BinaryOfficial GCS
Libtensorflow Windows GPU	Status Temporarily Unavailable	Nightly BinaryOfficial GCS

• TensorFlow.org
• TensorFlow Tutorials
• TensorFlow Official Models
• TensorFlow Examples
• TensorFlow Codelabs
• TensorFlow Blog
• Learn ML with TensorFlow
• TensorFlow Twitter
• TensorFlow YouTube
• TensorFlow model optimization roadmap
• TensorFlow White Papers
• TensorBoard Visualization Toolkit
• TensorFlow Code Search

Learn more about theTensorFlow community and how tocontribute.

• Coursera
• Udacity
• Edx

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