The best way to contribute to the MLCommons is to get involved with one of our many project communities. You find more information about getting involved with MLCommons here.
Generally we encourage people to become a MLCommons member if they wish to contribute to MLCommons projects, but outside pull requests are very welcome too.
Regardless of if you are a member, your organization needs to sign the MLCommons CLA. Please fill out this CLA sign up form form to get started.
MLCommons project work is tracked with issue trackers and pull requests. Modify the project in your own fork and issue a pull request once you want other developers to take a look at what you have done and discuss the proposed changes. Ensure that cla-bot and other checks pass for your Pull requests.
We suggest you to join the open MLCommons Collective Knowledge taskforce to learn how to use the CM framework, enhance existing CM components for MLOps and DevOps, run MLPerf benchmarks and contribute your own artifacts, scripts and workflows in the CM format.
Please check this guide to plug your own artifacts, scripts and workflows into CM.
You can then share CM components in your own Git repository or in the MLCommons CM repository to help the community reuse them and build upon them.
These components must automatically download, install, build and preprocess different applications, models, data sets, frameworks, compilers, SDKs and tools across different platforms and operating systems to help the community modularize, simplify, co-design, benchmark, optimize and deploy efficient AI/ML systems.
You are very welcome to provide feedback and report bugs here.
Thank you for your support and looking forward to collaborating with you!
- Sam Ainsworth (University of Cambridge, UK)
- Saheli Bhattacharjee (@sahelib25)
- Ethan Cheng (Nvidia)
- Jiahao Chen (MIT)
- Gianfranco Costamagna
- Chris Cummins (Facebook)
- Valentin Dalibard <valentin.dalibard@cl.cam.ac.uk>
- Alastair Donaldson <alastair.donaldson@imperial.ac.uk>
- Thibaut Dumontet
- Daniil Efremov (Xored)
- Leonid Fursin
- Todd Gamblin (LLNL)
- Chandan Reddy Gopal (ENS Paris)
- Leo Gordon (dividiti)
- Dave Greasley (University of Bristol)
- Herve Guillou
- Vincent Grevendonk (Arm)
- Michael Goin (Nerual Magic)
- Christophe Guillon (STMicroelectronics)
- Sven van Haastregt (Arm)
- Michael Haidl
- Stephen Herbein (LLNL)
- Mehrdad Hessar (OctoML): support for TinyML automation
- Patrick Hesse (College of Saint Benedict and Saint John's University)
- Nikolay Istomin (Xored)
- Kenan Kalajdzic
- Yuriy Kashnikov
- Jason Knight (OctoML)
- Alexey Kravets (Arm)
- Michael Kruse <MichaelKruse@meinersbur.de>
- Andrei Lascu <andrei.lascu10@imperial.ac.uk>
- Anton Lokhmotov <anton@dividiti.com>
- Graham Markall <graham.markall@continuum.io>
- Michael Mcgeagh (Arm)
- Thierry Moreau (OctoML)
- Luigi Nardi
- Ivan Ospiov (Xored)
- Lakshman Patel @Patel230
- Egor Pasko (Google)
- Ed Plowman (Arm)
- Lahiru Rasnayake (NTNU)
- Alex Redshaw (Arm)
- Vincent Rehm
- Toomas Remmelg (University of Edinburgh)
- Andrew Reusch (OctoML): support for TinyML automation
- Jarrett Revels (MIT)
- Jared Roesch (OctoML)
- Dmitry Savenko (Xored)
- Thomas Schmid (OctoML)
- Gavin Simpson (Arm)
- Aaron Smith (Microsoft)
- Michel Steuwer (University of Edinburgh)
- Abdul Wahid Memon <engrwahidmemon@gmail.com>
- Cedric Nugteren <web@cedricnugteren.nl>
- Lucas Nussbaum (Universite de Lorraine)
- Flavio Vella (Free University of Bozen-Bolzano)
- Gaurav Verma (Stony Brook University)
- Emanuele Vitali
- Dave Wilkinson (University of Pittsburgh)
- Sergey Yakushkin (Synopsys)
- Eiko Yoneki <eiko.yoneki@cl.cam.ac.uk>
- Thomas Zhu (Oxford University) <thomas.zhu.sh@gmail.com>
- @filven
- @ValouBambou
See more acknowledgments at the end of this journal article.
- Grigori Fursin (OctoML, MLCommons, cTuning foundation)
- Arjun Suresh (OctoML, MLCommons)