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https://github.com/mlcommons/ck
Collective Knowledge (CK) is an educational community project to learn how to run AI, ML and other emerging workloads in the most efficient and cost-effective way across diverse models, data sets, software and hardware using MLCommons CM (Collective Mind workflow automation framework)
https://github.com/mlcommons/ck
automation best-practices cknowledge collaboration ctuning education mlops mlperf modularity optimization portability reusability tournaments workflow-automation
Last synced: 17 days ago
JSON representation
Collective Knowledge (CK) is an educational community project to learn how to run AI, ML and other emerging workloads in the most efficient and cost-effective way across diverse models, data sets, software and hardware using MLCommons CM (Collective Mind workflow automation framework)
- Host: GitHub
- URL: https://github.com/mlcommons/ck
- Owner: mlcommons
- License: apache-2.0
- Created: 2014-11-05T17:14:43.000Z (about 10 years ago)
- Default Branch: master
- Last Pushed: 2024-10-29T10:26:59.000Z (about 1 month ago)
- Last Synced: 2024-10-29T10:57:37.623Z (about 1 month ago)
- Topics: automation, best-practices, cknowledge, collaboration, ctuning, education, mlops, mlperf, modularity, optimization, portability, reusability, tournaments, workflow-automation
- Language: Python
- Homepage: https://cKnowledge.org
- Size: 32.8 MB
- Stars: 606
- Watchers: 52
- Forks: 114
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Citation: citation.bib
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- project-awesome - mlcommons/ck - Collective Knowledge (CK) is an educational community project to learn how to run AI, ML and other emerging workloads in the most efficient and cost-effective way across diverse models, data sets, sof (Python)
- jimsghstars - mlcommons/ck - Collective Knowledge (CK, CM, CM4MLOps, CM4MLPerf and CMX) is an educational community project to learn how to run AI, ML and other emerging workloads in the most efficient and cost-effective way acro (Python)
README
[](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)
[](https://github.com/mlcommons/ck/actions/workflows/test-cm.yml)
[](https://github.com/mlcommons/ck/actions/workflows/test-cm-script-features.yml)
[](https://github.com/mlcommons/ck/actions/workflows/test-cm-mlperf-inference-resnet50.yml)
[](https://github.com/mlcommons/ck/actions/workflows/test-cmx-image-classification-onnx.yml)
### About
[Collective Knowledge (CK, CM, CM4MLOps, CM4MLPerf and CMX)](https://cKnowledge.org)
is an educational community project to learn how to run AI, ML and other emerging workloads
in the most efficient and cost-effective way across diverse models, data sets, software and hardware.
CK consists of several sub-projects:
* [Collective Mind framework (CM)](cm) - a very lightweight Python-based framework with minimal dependencies
intended 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.
* [CM4MLOPS](https://github.com/mlcommons/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](https://access.cknowledge.org/playground/?action=scripts),
[online MLCommons catalog](https://docs.mlcommons.org/cm4mlops/scripts)
* [CM interface to run MLPerf inference benchmarks](https://docs.mlcommons.org/inference)
* [CM4ABTF](https://github.com/mlcommons/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 and CM4MLOps)](cm/docs/cmx) - we are developing the next generation of CM
to make it simpler and more flexible based on user feedback. Please follow
this project [here]( https://github.com/orgs/mlcommons/projects/46 ).
* [Collective Knowledge Playground](https://access.cKnowledge.org) - 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](https://access.cknowledge.org/playground/?action=challenges)
to co-design more efficient and cost-effiective software and hardware for emerging workloads.
* [Artifact Evaluation](https://cTuning.org/ae) - automating artifact evaluation and reproducibility initiatives at ML and systems conferences.
### License
[Apache 2.0](LICENSE.md)
### Copyright
* Copyright (c) 2021-2024 MLCommons
* Copyright (c) 2014-2021 cTuning foundation
### Maintainers
* CM/CM4Research/CM4MLPerf-results: [Grigori Fursin](https://cKnowledge.org/gfursin)
* CM4MLOps: [Arjun Suresh](https://github.com/arjunsuresh) and [Anandhu Sooraj](https://github.com/anandhu-eng)
* CMX (the next generation of CM) [Grigori Fursin](https://cKnowledge.org/gfursin)
### Citing our project
If you found the CM automation framework helpful, kindly reference this article:
[ [ArXiv](https://arxiv.org/abs/2406.16791) ], [ [BibTex](https://github.com/mlcommons/ck/blob/master/citation.bib) ].
To learn more about the motivation behind CK and CM technology, please explore the following presentations:
* "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) ]
### CM Documentation
* [CM installation GUI](https://access.cknowledge.org/playground/?action=install)
* [CM Getting Started Guide and FAQ](docs/getting-started.md)
* [Common CM interface to run MLPerf inference benchmarks](docs/mlperf/inference)
* [Common CM interface to re-run experiments from ML and Systems papers including MICRO'23 and the Student Cluster Competition @ SuperComputing'23](docs/tutorials/common-interface-to-reproduce-research-projects.md)
* [CM automation recipes for MLOps and DevOps](https://access.cknowledge.org/playground/?action=scripts)
* [Other CM tutorials](docs/tutorials)
* [Full documentation](docs/README.md)
* [CM development tasks](docs/taskforce.md#current-tasks)
* [CM and CK history](docs/history.md)
### Acknowledgments
The open-source Collective Knowledge project (CK, CM, CM4MLOps/CM4MLPerf,
CM4Research and CMX) was created by [Grigori Fursin](https://cKnowledge.org/gfursin)
and sponsored by cTuning.org, OctoAI and HiPEAC.
Grigori donated CK to MLCommons to benefit the community
and to advance its development as a collaborative, community-driven effort.
We thank MLCommons and FlexAI for supporting this project,
as well as our dedicated [volunteers and collaborators](https://github.com/mlcommons/ck/blob/master/CONTRIBUTING.md)
for their feedback and contributions!