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Collective Mind workflow automation framework (MLCommons CM)

This Python package contains 2 front-ends:

License

Apache 2.0

Copyright

Copyright (c) 2021-2025 MLCommons

Grigori Fursin, the cTuning foundation and OctoML donated this project to MLCommons to benefit everyone.

Copyright (c) 2014-2021 cTuning foundation

Author

Maintainers

Concepts

To learn more about the concepts and motivation behind this project, please explore the following articles and presentations:

  • HPCA'25 article "MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI": [ Arxiv ], [ tutorial to reproduce results using CM/CMX ]
  • NeuralMagic's vLLM MLPerf inference 4.1 submission automated by CM: [README]
  • SDXL MLPerf inference 4.1 submission automated by CM: [README]
  • "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 ]
  • Journal of Royal Society'20: [ paper ]

Citation

If you found the CM, CMX and MLPerf automations helpful, kindly reference this article: [ ArXiv ], [ BibTex ].

You are welcome to contact the author to discuss long-term plans and potential collaboration.

Metadata

Release files for cmind 4.2.0

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