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finished aggregating legacy CM automations (#1383)
All changes should be backward compatible with CM, CMX and MLPerf.
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| Collective Knowledge (CK), Collective Mind (CM) and Common Metadata eXchange (CMX) | ||
| were created by [Grigori Fursin](https://arxiv.org/abs/2406.16791), | ||
| sponsored by cKnowledge.org and cTuning.org, and donated to MLCommons | ||
| to benefit everyone. Since then, this open-source automation technology | ||
| (CM, CMX, MLPerf automations, etc) is being extended | ||
| as a community effort thanks to all our volunteers, collaborators | ||
| and contributors listed here in alphabetical order: | ||
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| # MLCommons Collective Mind (CM) | ||
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| * @Henryfzh | ||
| * @Leonard226 | ||
| * @Oseltamivir | ||
| * @Submandarine | ||
| * Resmi Arjun | ||
| * Omar Benjelloun (Google) | ||
| * Alice Cheng (Nvidia) | ||
| * Jiahao Chen (MIT) | ||
| * Ramesh N Chukka (Intel) | ||
| * Ray DeMoss (One Stop Systems) | ||
| * Ryan T DeRue (Purdue University) | ||
| * Himanshu Dutta (Indian Institute of Technology) | ||
| * Nicolas Essayan | ||
| * Justin Faust (One Stop Systems) | ||
| * Diane Feddema (Red Hat) | ||
| * Leonid Fursin (United Silicon Carbide) | ||
| * Anirban Ghosh (Nvidia) | ||
| * James Goel (Qualcomm) | ||
| * Michael Goin (Neural Magic) | ||
| * Jose Armando Hernandez (Paris Saclay University) | ||
| * Mehrdad Hessar (OctoML) | ||
| * Miro Hodak (AMD) | ||
| * Sachin Idgunji (Nvidia) | ||
| * Tom Jablin (Google) | ||
| * Nino Jacob | ||
| * David Kanter (MLCommons) | ||
| * Alex Karargyris | ||
| * Jason Knight (OctoML) | ||
| * Ilya Kozulin (Deelvin) | ||
| * @makaveli10 (Collabora) | ||
| * Steve Leak(NERSC) | ||
| * Amija Maji (Purdue University) | ||
| * Peter Mattson (Google, MLCommons) | ||
| * Kasper Mecklenburg (Arm) | ||
| * Pablo Gonzalez Mesa | ||
| * Thierry Moreau (OctoML) | ||
| * Sachin Mudaliyar | ||
| * Stanley Mwangi (Microsoft) | ||
| * Ashwin Nanjappa (Nvidia) | ||
| * Hai Ah Nam (NERSC) | ||
| * Nandeeka Nayak (UIUC) | ||
| * Datta Nimmaturi (Nutanix) | ||
| * Lakshman Patel | ||
| * Arun Tejusve Raghunath Rajan (Cruise) | ||
| * Vijay Janapa Reddi (Harvard University) | ||
| * Andrew Reusch (OctoML) | ||
| * Anandhu Sooraj (Kerala Technical University) | ||
| * Sergey Serebryakov (HPE) | ||
| * Warren Schultz (Principled Technologies) | ||
| * Amrutha Sheleenderan (Kerala Technical University) | ||
| * Micah J Sheller (Intel) | ||
| * Byoungjun Seo (TTA) | ||
| * Aditya Kumar Shaw (Indian Institute of Science) | ||
| * Ilya Slavutin (Deelvin) | ||
| * Jinho Suh (Nvidia) | ||
| * Arjun Suresh | ||
| * Badhri Narayanan Suresh (Intel) | ||
| * David Tafur (MLCommons) | ||
| * Chloe Tessier | ||
| * Gaurav Verma (Stony Brook University) | ||
| * Zixian Wang | ||
| * Nathan Wasson | ||
| * Scott Wasson (MLCommons) | ||
| * Haoyang Zhang (UIUC) | ||
| * Bojian Zheng (University of Toronto) | ||
| * Thomas Zhu (Oxford University) | ||
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| See more acknowledgments at the end of this [article](https://arxiv.org/abs/2406.16791), | ||
| which describes the Collective Mind workflow automation framework. | ||
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| # Legacy Collective Knowledge framework (CK) | ||
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| * Sam Ainsworth (University of Cambridge, UK) | ||
| * Saheli Bhattacharjee (@sahelib25) | ||
| * Gianfranco Costamagna | ||
| * Chris Cummins (Facebook) | ||
| * Valentin Dalibard <[email protected]> | ||
| * Alastair Donaldson <[email protected]> | ||
| * Thibaut Dumontet | ||
| * Daniil Efremov (Xored) | ||
| * Todd Gamblin (LLNL) | ||
| * Chandan Reddy Gopal (ENS Paris) | ||
| * Leo Gordon (dividiti) | ||
| * Dave Greasley (University of Bristol) | ||
| * Herve Guillou | ||
| * Vincent Grevendonk (Arm) | ||
| * Christophe Guillon (STMicroelectronics) | ||
| * Sven van Haastregt (Arm) | ||
| * Michael Haidl | ||
| * Stephen Herbein (LLNL) | ||
| * Patrick Hesse (College of Saint Benedict and Saint John's University) | ||
| * Nikolay Istomin (Xored) | ||
| * Kenan Kalajdzic | ||
| * Yuriy Kashnikov | ||
| * Alexey Kravets (Arm) | ||
| * Michael Kruse <[email protected]> | ||
| * Andrei Lascu <[email protected]> | ||
| * Anton Lokhmotov (Krai) | ||
| * Graham Markall <[email protected]> | ||
| * Michael Mcgeagh (Arm) | ||
| * Abdul Wahid Memon <[email protected]> | ||
| * Sachin Mudaliyar | ||
| * Luigi Nardi | ||
| * Cedric Nugteren <[email protected]> | ||
| * Lucas Nussbaum (Universite de Lorraine) | ||
| * Ivan Ospiov (Xored) | ||
| * Lakshman Patel @Patel230 | ||
| * Egor Pasko (Google) | ||
| * Ed Plowman (Arm) | ||
| * Lahiru Rasnayake (NTNU) | ||
| * Vijay Janapa Reddi (Harvard University) | ||
| * Alex Redshaw (Arm) | ||
| * Vincent Rehm | ||
| * Toomas Remmelg (University of Edinburgh) | ||
| * Jarrett Revels (MIT) | ||
| * Dmitry Savenko (Xored) | ||
| * Gavin Simpson (Arm) | ||
| * Aaron Smith (Microsoft) | ||
| * Michel Steuwer (University of Edinburgh) | ||
| * Flavio Vella (Free University of Bozen-Bolzano) | ||
| * Gaurav Verma (Stony Brook University) | ||
| * Emanuele Vitali | ||
| * Dave Wilkinson (University of Pittsburgh) | ||
| * Sergey Yakushkin (Synopsys) | ||
| * Eiko Yoneki <[email protected]> | ||
| * Thomas Zhu (Oxford University) <[email protected]> | ||
| * @filven | ||
| * @ValouBambou | ||
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| See more acknowledgments at the end of this [article](https://doi.org/10.1098/rsta.2020.0211), | ||
| which describes the original Collective Knowledge workflow automation framework. |
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| TBD | ||
| # Aggregated CM and CMX automations for MLOps and MLPerf | ||
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| [](LICENSE.md) | ||
| [](https://pypi.org/project/cmind). | ||
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| This repository is powered by the [Collective Mind workflow automation framework](https://github.com/mlcommons/ck/tree/master/cm). | ||
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| 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 **cmx4mlops** repository. | ||
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| The [CM scripts](https://access.cknowledge.org/playground/?action=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. | ||
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| ## License | ||
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| [Apache 2.0](LICENSE.md) | ||
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| ## Copyright | ||
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| © 2022-2025 MLCommons. All Rights Reserved. | ||
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| Grigori Fursin, the cTuning foundation and OctoML donated the CK and CM projects to MLCommons to benefit everyone and encourage collaborative development. | ||
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| ## Maintainers | ||
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| * CM, CM4MLOps and MLPerf automations: MLCommons | ||
| * CMX (the next generation of CM): Grigori Fursin | ||
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| ## Author | ||
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| [Grigori Fursin](https://cKnowledge.org/gfursin) | ||
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| We sincerely appreciate all [contributors](https://github.com/mlcommons/ck/blob/master/CONTRIBUTORS.md) | ||
| for their invaluable feedback and support! | ||
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| ## Concepts | ||
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| Check our [ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) and the [white paper](https://arxiv.org/abs/2406.16791). | ||
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| ## Test image classification and MLPerf R-GAT inference benchmark via CMX PYPI package | ||
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| ```bash | ||
| pip install cmind | ||
| pip install cmx4mlops | ||
| 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 | ||
| ``` | ||
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| ## Test image classification and MLPerf R-GAT inference benchmark via CMX GitHub repo | ||
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| ```bash | ||
| pip uninstall cmx4mlops | ||
| pip install cmind | ||
| cmx pull repo mlcommons@ck --dir=cmx4mlops/cmx4mlops | ||
| 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 | ||
| ``` | ||
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| ## Parent project | ||
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| Visit the [parent Collective Knowledge project](https://github.com/mlcommons/ck) for further details. | ||
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| ## Citing this project | ||
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| If you found the CM automations helpful, kindly reference this article: | ||
| [ [ArXiv](https://arxiv.org/abs/2406.16791) ] |
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| ### Contributing to the MLCommons | ||
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| The best way to contribute to the MLCommons is to get involved with one of our many project communities. | ||
| You find more information about getting involved with MLCommons [here](https://mlcommons.org/en/get-involved/#getting-started). | ||
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| Generally we encourage people to become a MLCommons member if they wish to contribute to MLCommons projects, | ||
| but outside pull requests are very welcome too. | ||
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| Regardless of if you are a member, your organization needs to sign the MLCommons CLA. | ||
| Please fill out this [CLA sign up form](https://forms.gle/Ew1KkBVpyeJDuRw67) form to get started. | ||
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| MLCommons project work is tracked with issue trackers and pull requests. | ||
| Modify the project in your own fork and issue a pull request once you want other developers | ||
| to take a look at what you have done and discuss the proposed changes. | ||
| Ensure that cla-bot and other checks pass for your Pull requests. | ||
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