Documentation

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensiveflexible ecosystem of tools, librariesand 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 within the Machine Intelligence team at Google Brain to conduct research in machine learning and neural networks. Howeverthe framework is versatile enough to be used in other areas as well.

TensorFlow provides stable Python and C++ APIsas well as a non-guaranteed backward compatible API for other languages.

Keep up-to-date with release announcements and security updates by subscribing to [email protected]. See all the mailing lists.

See the TensorFlow install guide for the pip packageto enable GPU supportuse a Docker containerand build from source.

To install the current releasewhich 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 TensorFlow package is also available:

 pip install tensorflow-cpu

To update TensorFlow to the latest versionadd the --upgrade flag to the commands above.

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

$ python

>>> import tensorflow as tf
>>> tf.add(12).numpy()
3
>>> hello = tf.constant('HelloTensorFlow!')
>>> hello.numpy()
b'HelloTensorFlow!'

For more examplessee the TensorFlow Tutorials.

If you want to contribute to TensorFlowbe sure to review the Contribution Guidelines. This project adheres to TensorFlow's Code of Conduct. By participatingyou are expected to uphold this code.

We use GitHub Issues for tracking requests and bugsplease see TensorFlow Forum for general questions and discussionand please direct specific questions to Stack Overflow.

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

Follow these steps to patch a specific version of TensorFlowfor exampleto apply fixes to bugs or security vulnerabilities:

• Clone the TensorFlow repository and switch to the appropriate branch for your desired version—for exampler2.8 for version 2.8.
• Apply the desired changes (i.e.cherry-pick them) and resolve any code conflicts.
• Run TensorFlow tests and ensure they pass.
• Build the TensorFlow pip package from source.

You can find more community-supported platforms and configurations in the TensorFlow 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 Binary Official GCS
Libtensorflow Linux CPU	Status Temporarily Unavailable	Nightly Binary Official GCS
Libtensorflow Linux GPU	Status Temporarily Unavailable	Nightly Binary Official GCS
Libtensorflow Windows CPU	Status Temporarily Unavailable	Nightly Binary Official GCS
Libtensorflow Windows GPU	Status Temporarily Unavailable	Nightly Binary Official 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 the TensorFlow Community and how to Contribute.

• Coursera
• Udacity
• Edx

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