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TensorFlow is an open source software library for numerical computationusing data flow graphs. The graph nodes represent mathematical operations, whilethe graph edges represent the multidimensional data arrays (tensors) that flowbetween them. This flexible architecture enables you to deploy computation toone or more CPUs or GPUs in a desktop, server, or mobile device withoutrewriting code. TensorFlow also includesTensorBoard, a data visualizationtoolkit.

TensorFlow was originally developed by researchers and engineersworking on the Google Brain team within Google's Machine Intelligence Researchorganization for the purposes of conducting machine learning and deep neuralnetworks research. The system is general enough to be applicable in a widevariety of other domains, as well.

TensorFlow provides stable Python and C APIs as well as non-guaranteed backwardscompatible API's for C++, Go, Java, JavaScript, and Swift.

Keep up to date with release announcements and security updates bysubscribing toannounce@tensorflow.org.

To install the current release for CPU-only:

pip install tensorflow

Use the GPU package for CUDA-enabled GPU cards:

pip install tensorflow-gpu

SeeInstalling TensorFlow for detailedinstructions, and 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 offersnightly pip packages under thetf-nightly andtf-nightly-gpu project on PyPi.Simply runpip install tf-nightly orpip install tf-nightly-gpu in a cleanenvironment to install the nightly TensorFlow build. We support CPU and GPUpackages on Linux, Mac, and Windows.

$ python

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

Learn more examples about how to do specific tasks in TensorFlow at thetutorials page of tensorflow.org.

If you want to contribute to TensorFlow, be sure to review thecontributionguidelines. 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 in open-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		Py2Py3
Raspberry Pi 2 and 3		Py2Py3

Build Type	Status	Artifacts
Linux s390x Nightly		Nightly
Linux ppc64le CPU Nightly		Nightly
Linux ppc64le CPU Stable Release		Release
Linux ppc64le GPU Nightly		Nightly
Linux ppc64le GPU Stable Release		Release
Linux CPU with Intel® MKL-DNN Nightly		Nightly
Linux CPU with Intel® MKL-DNN Supports Python 2.7, 3.4, 3.5, and 3.6		1.13.1 pypi
Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6		1.13.1 pypi

• TensorFlow Website
• TensorFlow Tutorials
• TensorFlow Model Zoo
• TensorFlow Twitter
• TensorFlow Blog
• TensorFlow Course at Stanford
• TensorFlow Roadmap
• TensorFlow White Papers
• TensorFlow YouTube Channel
• TensorFlow Visualization Toolkit

Learn more about the TensorFlow community at thecommunity page of tensorflow.org for a few ways to participate.

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