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.

Tensorflow ROCm port This project is based on TensorFlow 1.14.0. It has been verified to work with the latest ROCm2.6 release. Please follow the instructions here to set up your ROCm stack. A docker container: rocm/tensorflow:latest(https://hub.docker.com/r/rocm/tensorflow/) is readily available to be used:

alias drun='sudo docker run -it --network=host --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v $HOME/dockerx:/dockerx'
drun rocm/tensorflow

We maintain tensorflow-rocm whl packages on PyPI here, to install tensorflow-rocm package using pip:

# Install some ROCm dependencies
sudo apt install rocm-libs hipcub miopen-hip

# Pip3 install the whl package from PyPI
pip3 install --user tensorflow-rocm --upgrade

For details on Tensorflow ROCm port, please take a look at the ROCm-specific README file.

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 for CPU-only:

$ pip install tensorflow

Use the GPU package for CUDA-enabled GPU cards (Ubuntu and Windows):

$ pip install tensorflow-gpu

Nightly binaries are available for testing using the tf-nightly and tf-nightly-gpu 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()
'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:

Continuous build status

Official Builds

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		Py2 Py3
Raspberry Pi 2 and 3		Py2 Py3

Community Supported Builds

Build Type	Status	Artifacts
Linux AMD ROCm GPU Nightly		Nightly
Linux AMD ROCm GPU Stable Release		Release 1.15 / 2.x
Linux s390x Nightly		Nightly
Linux s390x CPU Stable Release		Release
Linux ppc64le CPU Nightly		Nightly
Linux ppc64le CPU Stable Release		Release 1.15 / 2.x
Linux ppc64le GPU Nightly		Nightly
Linux ppc64le GPU Stable Release		Release 1.15 / 2.x
Linux CPU with Intel® MKL-DNN Nightly		Nightly
Linux CPU with Intel® MKL-DNN Stable Release		Release 1.15 / 2.x
Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6		1.13.1 PyPI

Resources

• TensorFlow.org
• TensorFlow tutorials
• TensorFlow official models
• TensorFlow examples
• TensorFlow in Practice from Coursera
• Intro to TensorFlow for Deep Learning from Udacity
• Introduction to TensorFlow Lite from Udacity
• TensorFlow blog
• TensorFlow Twitter
• TensorFlow YouTube
• TensorFlow roadmap
• TensorFlow white papers
• TensorBoard visualization toolkit

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0

Feature Prioritization Survey

The TensorFlow team is working on building/improving features, and understands that it is very important to prioritize these efforts based on what TF users need.

The goal of this short, < 5min survey, is to help the TensorFlow team better understand what features to prioritize based on your feedback. Participation is of course optional.

Take the survey HERE.