Awesome

About

Collective Knowledge (CK) in an educational project to 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 sub-projects:

• Collective Mind framework (CM) - a very lightweight 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 interface to run MLPerf inference benchmarks

  • CM4MLOPS - a collection of portable, extensible and technology-agnostic automation recipes with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on diverse platforms with any software and hardware: see online catalog at CK playground, online MLCommons catalog

  • CM4ABTF - a unified CM interface and automation recipes to run automotive benchmark across different models, data sets, software and hardware from different vendors.

• CMX (the next generation of CM) - we are developing the next generation of CM to make it simpler and more flexible based on user feedback. Please follow this project here.

• Collective Knowledge Playground - a unified platform 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 co-design more efficient and cost-effiective software and hardware for emerging workloads.

• Artifact Evaluation - automating artifact evaluation and reproducibility initiatives at ML and systems conferences.

License

Apache 2.0

Copyright

• Copyright (c) 2021-2024 MLCommons
• Copyright (c) 2014-2021 cTuning foundation

Motivation and long-term vision

You can learn more about the motivation behind these projects from the following articles and 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 automating research projects: [ YouTube ] [ slides ]

CM Documentation

• CM installation GUI
• CM Getting Started Guide and FAQ
  • Common CM interface to run MLPerf inference benchmarks
  • Common CM interface to re-run experiments from ML and Systems papers including MICRO'23 and the Student Cluster Competition @ SuperComputing'23
  • CM automation recipes for MLOps and DevOps
  • Other CM tutorials
• Full documentation
• CM development tasks
• CM and CK history

Acknowledgments

Collective Knowledge (CK) and Collective Mind (CM) were created by Grigori Fursin, sponsored by cKnowledge.org and cTuning.org, and donated to MLCommons to benefit everyone. Since then, this open-source technology (CM, CM4MLOps, CM4MLPerf, CM4ABTF, CM4Research, etc) is being developed as a community effort thanks to all our volunteers, collaborators and contributors!