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A collection of reusable and cross-platform automation recipes (CM scripts) with a human-friendly interface and minimal dependencies to make it easier to build, run, benchmark and optimize AI, ML and other applications and systems across diverse and continuously changing models, data sets, software and hardware (cloud/edge)

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Unified and cross-platform CM interface for DevOps, MLOps and MLPerf

License Python Version Powered by CM. Downloads

This repository contains reusable and cross-platform automation recipes to run DevOps, MLOps, AIOps and MLPerf via a simple and human-readable Collective Mind interface (CM) while adapting to different opearting systems, software and hardware.

All СM scripts have a simple Python API, extensible JSON/YAML meta description and unifed input/output to make them reusable in different projects either individually or by chaining them together into portable automation workflows, applications and web services adaptable to continuously changing models, data sets, software and hardware.

These automation recipes are being developed and maintained by the MLCommons Task Force on Automation and Reproducibility with great contributions from the community.

Tests

CM script automation test CM script automation features test MLPerf loadgen with HuggingFace bert onnx fp32 squad model MLPerf inference MLCommons C++ ResNet50 image classification with ONNX

Catalog

See the automatically generated catalog online.

License

Apache 2.0

Copyright

2022-2024 MLCommons

Acknowledgments

This open-source technology is being developed by the MLCommons Task Force on Automation and Reproducibility as a community effort based on user feedback.

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!

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A collection of reusable and cross-platform automation recipes (CM scripts) with a human-friendly interface and minimal dependencies to make it easier to build, run, benchmark and optimize AI, ML and other applications and systems across diverse and continuously changing models, data sets, software and hardware (cloud/edge)

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  • Python 72.2%
  • Shell 9.5%
  • C++ 8.1%
  • Dockerfile 4.5%
  • C 3.3%
  • Batchfile 2.1%
  • Other 0.3%