tensorflow

by tensorflow

GitHub Readme.md

Python PyPI

Documentation Documentation

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and 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 on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

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

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

Install

See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.

To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):

$ 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 above commands.

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

Try your first TensorFlow program

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

For more examples, see the TensorFlow tutorials.

Contribution guidelines

If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs, please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.

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

Fuzzing Status CII Best Practices Contributor Covenant

Continuous build status

Official Builds

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

Community Supported Builds

Build Type Status Artifacts Linux AMD ROCm GPU Nightly Build Status Nightly Linux AMD ROCm GPU Stable Release Build Status Release 1.15 / 2.x Linux s390x Nightly Build Status Nightly Linux s390x CPU Stable Release Build Status Release Linux ppc64le CPU Nightly Build Status Nightly Linux ppc64le CPU Stable Release Build Status Release 1.15 / 2.x Linux ppc64le GPU Nightly Build Status Nightly Linux ppc64le GPU Stable Release Build Status Release 1.15 / 2.x Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Nightly Build Status Nightly Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Stable Release Build Status Release 1.15 / 2.x Red Hat® Enterprise Linux® 7.6 CPU & GPU
Python 2.7, 3.6 Build Status 1.13.1 PyPI

Resources

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0