Collective Knowledge (CK, CM, CM4MLOps, CM4MLPerf and CMX) is an educational community project to learn how to run AI, ML and other emerging workloads in the most efficient and cost-effective way across diverse models, data sets, software and hardware.
- Copyright (c) 2021-2024 MLCommons
- Copyright (c) 2014-2021 cTuning foundation
- CM/CM4Research/CM4MLPerf-results: Grigori Fursin
- CM4MLOps: Arjun Suresh and Anandhu Sooraj
- CMX (the next generation of CM) Grigori Fursin
If you found the CM automation framework helpful, kindly reference this article: [ ArXiv ], [ BibTex ].
To learn more about the motivation behind CK and CM technology, please explore the following presentations:
- "Enabling more efficient and cost-effective AI/ML systems with Collective Mind, virtualized MLOps, MLPerf, Collective Knowledge Playground and reproducible optimization tournaments": [ ArXiv ]
- ACM REP'23 keynote about the MLCommons CM automation framework: [ slides ]
- ACM TechTalk'21 about Collective Knowledge project: [ YouTube ] [ slides ]
- CM installation GUI
- CM Getting Started Guide and FAQ
- Full documentation
- CM development tasks
- CM and CK history
The open-source Collective Knowledge project (CK, CM, CM4MLOps/CM4MLPerf, CM4Research and CMX) was created by Grigori Fursin and sponsored by cTuning.org, OctoAI and HiPEAC. Grigori donated CK to MLCommons to benefit the community and to advance its development as a collaborative, community-driven effort. We thank MLCommons and FlexAI for supporting this project, as well as our dedicated volunteers and collaborators for their feedback and contributions!