[](https://pepy.tech/project/cmind)
[](https://github.com/mlcommons/ck/tree/master/cm/cmind)
[](LICENSE.md)
[](https://pepy.tech/project/cmind)
[](https://arxiv.org/abs/2406.16791)
# 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) ]
* "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.
Raw data
{
"_id": null,
"home_page": null,
"name": "cmind",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "Grigori Fursin <grigori.fursin@ctuning.org>",
"keywords": "cmind, cm, cmx, cmx-mlcflow, cmx-mlcr, common metadata exchange, collective mind, automation, portability, reusability, mlops, devops, vmlops, api, cli",
"author": null,
"author_email": "Grigori Fursin <grigori.fursin@ctuning.org>",
"download_url": "https://files.pythonhosted.org/packages/a8/42/02f183818a4d3731d1a447a66fcf0777be994a041eacba8958a80b5639f9/cmind-4.1.1.tar.gz",
"platform": null,
"description": "[](https://pepy.tech/project/cmind)\r\n[](https://github.com/mlcommons/ck/tree/master/cm/cmind)\r\n[](LICENSE.md)\r\n[](https://pepy.tech/project/cmind)\r\n[](https://arxiv.org/abs/2406.16791)\r\n\r\n# Collective Mind workflow automation framework (MLCommons CM)\r\n\r\nThis Python package contains 2 front-ends:\r\n* [Collective Mind eXtension or Common Metadata eXchange (CMX, 2024+)](https://github.com/mlcommons/ck/blob/master/cm/README.CMX.md)\r\n* [Legacy Collective Mind (CM, 2021-2024)](https://github.com/mlcommons/ck/blob/master/cm/README.CM.md)\r\n\r\n## License\r\n\r\n[Apache 2.0](LICENSE.md)\r\n\r\n## Copyright\r\n\r\nCopyright (c) 2021-2025 MLCommons\r\n\r\nGrigori Fursin, the cTuning foundation and OctoML donated this project to MLCommons to benefit everyone.\r\n\r\nCopyright (c) 2014-2021 cTuning foundation\r\n\r\n## Author\r\n\r\n* [Grigori Fursin](https://cKnowledge.org/gfursin)\r\n\r\n## Maintainers\r\n\r\n* CM, CM4MLOps and MLPerf automations: [MLCommons infra WG](https://mlcommons.org)\r\n* CMX (the next generation of CM): [Grigori Fursin](https://cKnowledge.org/gfursin)\r\n\r\n## Concepts\r\n\r\nTo learn more about the concepts and motivation behind this project, please explore the following articles and presentations:\r\n\r\n* 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) ]\r\n* \"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) ]\r\n* ACM REP'23 keynote about the MLCommons CM automation framework: [ [slides](https://doi.org/10.5281/zenodo.8105339) ] \r\n* 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) ]\r\n* Journal of Royal Society'20: [ [paper](https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0211) ]\r\n\r\n## Citation\r\n\r\nIf you found the CM, CMX and MLPerf automations helpful, kindly reference this article:\r\n[ [ArXiv](https://arxiv.org/abs/2406.16791) ], [ [BibTex](https://github.com/mlcommons/ck/blob/master/citation.bib) ].\r\n\r\nYou are welcome to contact the [author](https://cKnowledge.org/gfursin) to discuss long-term plans and potential collaboration.\r\n",
"bugtrack_url": null,
"license": "Apache 2.0",
"summary": "Common Metadata eXchange framework (CMX) and Collective Mind automation framework (CM)",
"version": "4.1.1",
"project_urls": {
"Changelog": "https://github.com/mlcommons/ck/blob/master/cm/CHANGES.md",
"Documentation": "https://docs.mlcommons.org/ck",
"Homepage": "https://cKnowledge.org",
"Issues": "https://github.com/mlcommons/ck/issues",
"Repository": "https://github.com/mlcommons/ck/tree/master/cm"
},
"split_keywords": [
"cmind",
" cm",
" cmx",
" cmx-mlcflow",
" cmx-mlcr",
" common metadata exchange",
" collective mind",
" automation",
" portability",
" reusability",
" mlops",
" devops",
" vmlops",
" api",
" cli"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "14f68635a56db05b95d711c6eadaecd8ab5f5d07598c090bf8ffea09898e97d5",
"md5": "635b9a9964f77d680acb7c484e04e81b",
"sha256": "5f545b8a4a7b00d9b528249e9453cc84970c514d6edfafbdb30190cfa0ed8a96"
},
"downloads": -1,
"filename": "cmind-4.1.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "635b9a9964f77d680acb7c484e04e81b",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 84488,
"upload_time": "2025-02-17T10:40:35",
"upload_time_iso_8601": "2025-02-17T10:40:35.041821Z",
"url": "https://files.pythonhosted.org/packages/14/f6/8635a56db05b95d711c6eadaecd8ab5f5d07598c090bf8ffea09898e97d5/cmind-4.1.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a84202f183818a4d3731d1a447a66fcf0777be994a041eacba8958a80b5639f9",
"md5": "c668a4c270e0c58c0da45695f56d2a17",
"sha256": "30746985dc8e75042d425f0aa35379256bb050b98ce4e6653762a76527a60aa6"
},
"downloads": -1,
"filename": "cmind-4.1.1.tar.gz",
"has_sig": false,
"md5_digest": "c668a4c270e0c58c0da45695f56d2a17",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 77019,
"upload_time": "2025-02-17T10:40:36",
"upload_time_iso_8601": "2025-02-17T10:40:36.897207Z",
"url": "https://files.pythonhosted.org/packages/a8/42/02f183818a4d3731d1a447a66fcf0777be994a041eacba8958a80b5639f9/cmind-4.1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-02-17 10:40:36",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "mlcommons",
"github_project": "ck",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "cmind",
"specs": [
[
">=",
"3.5.2"
]
]
},
{
"name": "pyyaml",
"specs": []
},
{
"name": "requests",
"specs": []
},
{
"name": "setuptools",
"specs": []
},
{
"name": "giturlparse",
"specs": []
}
],
"lcname": "cmind"
}
JSON
