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**`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 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 Python
and C++ APIs, as well as a
non-guaranteed backward compatible API for
other languages.
Keep up-to-date with release announcements and security updates by subscribing
to
[email protected].
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)*:
Other devices (DirectX and MacOS-metal) are supported using
Device Plugins.
A smaller CPU-only TensorFlow package is also available:
To update TensorFlow to the latest version, add the `--upgrade` flag to the
commands above.
*Nightly binaries are available for testing using the
tf-nightly and
tf-nightly-cpu packages on PyPI.*
*Try your first TensorFlow program*
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 Forum 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.
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 repository and switch to the appropriate branch for
your desired version—for example, `r2.8` for version 2.8.
* Apply the desired changes (i.e., cherry-pick them) 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 the
TensorFlow 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
Resources
* TensorFlow.org
* TensorFlow Tutorials
* TensorFlow Official Models
* TensorFlow Examples
* TensorFlow Codelabs
* TensorFlow Blog
* Learn ML with TensorFlow
* TensorFlow Twitter
* TensorFlow YouTube
* TensorFlow model optimization roadmap
* TensorFlow White Papers
* TensorBoard Visualization Toolkit
* TensorFlow Code Search
Learn more about the
TensorFlow Community and how to
Contribute.
Courses
* Coursera
* Udacity
* Edx
License
Apache License 2.0