GitHub - Intel-tensorflow/tensorflow: Computation using data flow graphs for scalable machine learning (original) (raw)

Documentation

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem oftools,libraries, andcommunity 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. However, the framework is versatile enough to be used in other areas as well.

TensorFlow provides stable Pythonand C++ APIs, as well as a non-guaranteed backward compatible API forother languages.

Keep up-to-date with release announcements and security updates by subscribing toannounce@tensorflow.org. See all the mailing lists.

Install

See the TensorFlow 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 and Windows):

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 above commands.

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

Try your first TensorFlow program

import tensorflow as tf tf.add(1, 2).numpy() 3 hello = tf.constant('Hello, TensorFlow!') hello.numpy() b'Hello, TensorFlow!'

For more examples, see theTensorFlow tutorials.

Contribution guidelines

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 to uphold this code.

We use GitHub issues for tracking requests and bugs, please seeTensorFlow Forum for 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.

Patching guidelines

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

Clone the TensorFlow repo and switch to the corresponding branch for your desired TensorFlow version, for example, branch r2.8 for version 2.8.
Apply (that is, cherry-pick) the desired changes and resolve any code conflicts.
Run TensorFlow tests and ensure they pass.
Build the TensorFlow pip package from source.

Continuous build status

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

Official Builds

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