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TensorFlow is an open source software library for numerical computation usingdata 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 to oneor more CPUs or GPUs in a desktop, server, or mobile device without rewritingcode. TensorFlow also includesTensorBoard, a data visualization toolkit.

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.

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

SeeInstalling TensorFlow for instructions on how to install our release binaries or 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 offers nightly pip packagesunder 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>>>hello=tf.constant('Hello, TensorFlow!')>>>sess=tf.Session()>>>sess.run(hello)'Hello, TensorFlow!'>>>a=tf.constant(10)>>>b=tf.constant(32)>>>sess.run(a+b)42>>>sess.close()

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. So please seeTensorFlow Discuss for general questionsand 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	TBA
MacOS		pypi
Windows CPU		pypi
Windows GPU		pypi
Android		demo APK,native libsbuild history

Build Type	Status	Artifacts
IBM s390x		TBA
IBM ppc64le CPU		TBA
IBM ppc64le GPU		TBA
Linux CPU with Intel® MKL-DNN®		TBA

• TensorFlow Website
• TensorFlow White Papers
• TensorFlow YouTube Channel
• TensorFlow Model Zoo
• TensorFlow MOOC on Udacity
• TensorFlow Course at Stanford

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

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