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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 on theGoogle Brain team within Google's Machine Intelligence Research organization toconduct machine learning and deep neural networks research. The system isgeneral enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stablePythonandC++ APIs, as well asnon-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

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 Discussfor general questions and discussion, and please direct specific questions toStack Overflow.

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

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

Build Type	Status	Artifacts
Linux AMD ROCm GPU Nightly		Nightly
Linux AMD ROCm GPU Stable Release		Release1.15 /2.x
Linux s390x Nightly		Nightly
Linux s390x CPU Stable Release		Release
Linux ppc64le CPU Nightly		Nightly
Linux ppc64le CPU Stable Release		Release1.15 /2.x
Linux ppc64le GPU Nightly		Nightly
Linux ppc64le GPU Stable Release		Release1.15 /2.x
Linux aarch64 CPU Nightly Python 3.6		Nightly
Linux aarch64 CPU Stable Release		Release1.15 /2.x
Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Nightly		Nightly
Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Stable Release		Release1.15 /2.x
Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6		1.13.1 PyPI

• TensorFlow.org
• TensorFlow Tutorials
• TensorFlow Official Models
• TensorFlow Examples
• DeepLearning.AI TensorFlow Developer Professional Certificate
• TensorFlow: Data and Deployment from Coursera
• Getting Started with TensorFlow 2 from Coursera
• Intro to TensorFlow for Deep Learning from Udacity
• Introduction to TensorFlow Lite from Udacity
• Machine Learning with TensorFlow on GCP
• TensorFlow Chat Room on StackOverflow (not actively monitored by theTensorFlow team)
• TensorFlow Blog
• Learn ML with TensorFlow
• TensorFlow Twitter
• TensorFlow YouTube
• TensorFlow Roadmap
• TensorFlow White Papers
• TensorBoard Visualization Toolkit

Learn more about theTensorFlow community and how tocontribute.

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