cmind 4.2.0
Common Metadata eXchange framework (CMX) and Collective Mind automation framework (CM)
License: Apache 2.0
Requires Python: >=3.7
Keywords: cmind, cm, cmx, cmx-mlcflow, cmx-mlcr, common metadata exchange, collective mind, automation, portability, reusability, mlops, devops, vmlops, api, cli

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[![License](https://img.shields.io/badge/License-Apache%202.0-green)](LICENSE.md)
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# Collective Mind workflow automation framework (MLCommons CM)

This Python package contains 2 front-ends:
* [Collective Mind eXtension or Common Metadata eXchange (CMX, 2024+)](https://github.com/mlcommons/ck/blob/master/cm/README.CMX.md)
* [Legacy Collective Mind (CM, 2021-2024)](https://github.com/mlcommons/ck/blob/master/cm/README.CM.md)

## License

[Apache 2.0](LICENSE.md)

## 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

* [Grigori Fursin](https://cKnowledge.org/gfursin)

## Maintainers

* CM, CM4MLOps and MLPerf automations: [MLCommons infra WG](https://mlcommons.org)
* CMX (the next generation of CM): [Grigori Fursin](https://cKnowledge.org/gfursin)

## 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](https://arxiv.org/abs/2410.12032) ], [ [tutorial to reproduce results using CM/CMX](https://github.com/aryatschand/MLPerf-Power-HPCA-2025/blob/main/measurement_tutorial.md) ]
* NeuralMagic's vLLM MLPerf inference 4.1 submission automated by CM: [ [README] ](https://github.com/mlcommons/inference_results_v4.1/blob/main/open/NeuralMagic/measurements/4xH100-SXM-80GB_vLLM_FP8-reference-cpu-pytorch-v2.3.1-default_config/llama2-70b-99/server/README.md)
* SDXL MLPerf inference 4.1 submission automated by CM: [ [README] ](https://github.com/mlcommons/inference_results_v4.1/tree/main/open/CTuning/code/stable-diffusion-xl)
* "Enabling more efficient and cost-effective AI/ML systems with Collective Mind, virtualized MLOps, MLPerf, Collective Knowledge Playground and reproducible optimization tournaments": [ [ArXiv](https://arxiv.org/abs/2406.16791) ]
* ACM REP'23 keynote about the MLCommons CM automation framework: [ [slides](https://doi.org/10.5281/zenodo.8105339) ] 
* ACM TechTalk'21 about Collective Knowledge project: [ [YouTube](https://www.youtube.com/watch?v=7zpeIVwICa4) ] [ [slides](https://learning.acm.org/binaries/content/assets/leaning-center/webinar-slides/2021/grigorifursin_techtalk_slides.pdf) ]
* Journal of Royal Society'20: [ [paper](https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0211) ]

## Citation

If you found the CM, CMX and MLPerf automations helpful, kindly reference this article:
[ [ArXiv](https://arxiv.org/abs/2406.16791) ], [ [BibTex](https://github.com/mlcommons/ck/blob/master/citation.bib) ].

You are welcome to contact the [author](https://cKnowledge.org/gfursin) to discuss long-term plans and potential collaboration.