Documentation

Triton

This is the development repository of Triton, a language and compiler for writing highly efficient custom Deep-Learning primitives. The aim of Triton is to provide an open-source environment for expressing tensor math workloads that offers high flexibility, developer productivity and end to end performance.

The foundations of this project are described in the following MAPL2019 publication: Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations. Please consider citing this work if you use Triton!

The official documentation contains installation instructions and tutorials.

Quick Installation

You can install the latest stable release of Triton from pip:

pip install triton

Binary wheels are available for CPython 3.6-3.9 and PyPy 3.6-3.7.

And the latest nightly release:

pip install -U --pre triton

Install from source

git clone https://github.com/openai/triton.git;
cd triton/python;
pip install cmake; # build time dependency
pip install -e .

Changelog

Version 1.1 is out! New features include:

• Many, many bugfixes
• More documentation
• Automatic on-disk caching of compiled binary objects
• Random Number Generation
• Faster (up to 2x on A100), cleaner blocksparse ops

Contributing

Community contributions are more than welcome, whether it be to fix bugs or to add new features. Feel free to open GitHub issues about your contribution ideas, and we will review them. A contributor's guide containing general guidelines is coming soon!

If you’re interested in joining our team and working on Triton & GPU kernels, we’re hiring!

Compatibility

Supported Platforms:

• Linux

Supported Hardware:

• NVIDIA GPUs (Compute Capability 7.0+)
• Under development: AMD GPUs, CPUs

Disclaimer

Triton is a fairly recent project, and it is under active development. We expect it to be pretty useful in a wide variety of cases, but don't be surprised if it's a bit rough around the edges :)