Aggregated CM and CMX automations for MLOps and MLPerf

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This repository is powered by the CM and/or CMX framework.

Two key automations developed using CM are script and cache, which streamline machine learning (ML) workflows, including managing Docker runs. Both Script and Cache automations are part of the cm4mlops repository.

The CM scripts, also housed in this repository, consist of hundreds of modular Python-wrapped scripts accompanied by yaml metadata, enabling the creation of robust and flexible ML workflows.

License

Apache 2.0

Copyright

© 2022-2025 MLCommons. All Rights Reserved.

Grigori Fursin, the cTuning foundation and OctoML donated the CK and CM projects to MLCommons to benefit everyone and encourage collaborative development.

Maintainers

• CM, CM4MLOps and MLPerf automations: MLCommons
• CMX (the next generation of CM): Grigori Fursin

Author

Grigori Fursin

We sincerely appreciate all contributors for their invaluable feedback and support!

Concepts

Check our ACM REP'23 keynote and the white paper.

Test image classification and MLPerf R-GAT inference benchmark via CMX GitHub repo

pip install cmind
cmx pull repo mlcommons@ck --dir=cm4mlops/cm4mlops
cmx run script "python app image-classification onnx" --quiet
cmx run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --submitter="MLCommons" --adr.inference-src.tags=_branch.dev --pull_changes=yes --pull_inference_changes=yes  --submitter="MLCommons" --hw_name=ubuntu-latest_x86 --model=rgat --implementation=python --backend=pytorch --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --category=datacenter --quiet  --v --target_qps=1

Parent project

Visit the parent Collective Knowledge project for further details.

Citing this project

If you found the CM, CMX and MLPerf automations helpful, kindly reference this article: [ ArXiv ]