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PyPI version Python Version License Downloads

CM test CM script automation features test

About

Collective Knowledge (CK) in a community project to develop open-source tools, platforms and automation recipes that can help researchers and engineers automate their repetitive, tedious and time-consuming tasks to build, run, benchmark and optimize AI, ML and other applications and systems across diverse and continuously changing models, data, software and hardware.

CK consists of several ongoing sub-projects:

  • Collective Mind framework (CM) (~1MB) - a very light-weight Python-based framework with minimal dependencies to help users implement, share and reuse cross-platform automation recipes to build, benchmark and optimize applications on any platform with any software and hardware. CM attempts to extends the cmake concept with reusable automation recipes and workflows written in plain Python or native OS scripts, accessible via a human readable interface with simple tags, and shareable in public and private repositories in a decentralized way. Furthermore, in comparison with cmake, these automation recipes can not only detect missing code but also download artifacts (models, data sets), preprocess them, build missing dependencies, install them and run the final code on diverse platforms in a unified and automated way. You can read more about the CM concept in this presentation.

  • Collective Knowledge Playground - an external platform being developed by cKnowledge to list CM scripts similar to PYPI, aggregate AI/ML Systems benchmarking results in a reproducible format with CM workflows, and organize public optimization challenges and reproducibility initiatives to find the most performance and cost-effective AI/ML Systems.

License

Apache 2.0

Documentation

MLCommons is updating the CM documentation based on user feedback - please stay tuned for more details.

Citing this project

Please use this BibTex file.

Acknowledgments

The open-source Collective Knowledge (CK v1,v2) and Collective Mind automation frameworks (CM) were developed by Grigori Fursin and donated to MLCommons to benefit everyone. You can learn more about the motivation behind this project from the following presentations:

  • ACM REP'23 keynote about the MLCommons CM automation framework: [ slides ]
  • ACM TechTalk'21 about automating research projects: [ YouTube ] [ slides ]

We would like to thank all volunteers, collaborators and contributors for their support, fruitful discussions, and useful feedback! We thank the cTuning foundation, cKnowledge.org and MLCommons for sponsoring this project!