From dfc74a6e42b489d700e3af2b0c00aa8602abca04 Mon Sep 17 00:00:00 2001 From: Grigori Fursin Date: Tue, 3 Dec 2024 19:34:06 +0100 Subject: [PATCH] ## V3.5.1 - added Collective Mind architecture diagram: https://github.com/mlcommons/ck/tree/master/docs/specs/cm-diagram-v3.5.1.png - added tabulate as requirement based on user feedback - updated white paper: https://arxiv.org/abs/2406.16791 - merged Anandu's docs update - added links to archived repositories to keep development history (ck, cm-mlops, etc) --- README.md | 139 +++++++++++++------ cm/CHANGES.md | 5 +- cm/README.md | 120 +++++++++++++--- cmr.yaml | 2 + docs/_generator/api/index.rst | 2 +- docs/specs/cm-diagram-v3.5.1.png | Bin 0 -> 331642 bytes {cmx4mlops => incubator/cmx4mlops}/README.md | 0 {cmx4mlops => incubator/cmx4mlops}/cmr.yaml | 0 incubator/cmx4mlperf-results/README.md | 1 + incubator/cmx4mlperf-results/cmr.yaml | 12 ++ 10 files changed, 217 insertions(+), 64 deletions(-) create mode 100644 docs/specs/cm-diagram-v3.5.1.png rename {cmx4mlops => incubator/cmx4mlops}/README.md (100%) rename {cmx4mlops => incubator/cmx4mlops}/cmr.yaml (100%) create mode 100644 incubator/cmx4mlperf-results/README.md create mode 100644 incubator/cmx4mlperf-results/cmr.yaml diff --git a/README.md b/README.md index ec97b6802c..e5b595b3ad 100755 --- a/README.md +++ b/README.md @@ -9,77 +9,126 @@ [![MLPerf inference resnet50](https://github.com/mlcommons/ck/actions/workflows/test-cm-mlperf-inference-resnet50.yml/badge.svg)](https://github.com/mlcommons/ck/actions/workflows/test-cm-mlperf-inference-resnet50.yml) [![CMX: image classification with ONNX](https://github.com/mlcommons/ck/actions/workflows/test-cmx-image-classification-onnx.yml/badge.svg)](https://github.com/mlcommons/ck/actions/workflows/test-cmx-image-classification-onnx.yml) -### About +## Collective Knowledge [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. +It includes the following sub-projects. -CK consists of several sub-projects: +### Collective Mind (CM) -* [Collective Mind framework (CM)](https://github.com/mlcommons/ck/tree/master/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. +[Collective Mind (CM)](https://github.com/mlcommons/ck/tree/master/cm) - +a very lightweight Python-based framework with a unified CLI, Python API and 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 / CM4MLPerf](https://github.com/mlcommons/cm4mlops) - - a collection of portable, extensible and technology-agnostic automation recipes - with a common CLI and Python API (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) +Collective Mind is continuously enhanced through public and private CM4* Git repositories, +which serve as the unified interface for various collections of reusable automations and artifacts. - * [CM interface to run MLPerf inference benchmarks](https://docs.mlcommons.org/inference) +The CM architecture diagram is available for viewing +[here](https://github.com/mlcommons/ck/tree/master/docs/specs/cm-diagram-v3.5.1.png). - * [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. +### Collective Mind repositories -* [CMX (the next generation of CM, CM4MLOps and CM4MLPerf)](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 ). +#### CM4MLOps +[CM4MLOPS repository powered by CM](https://github.com/mlcommons/cm4mlops) - +a collection of portable, extensible and technology-agnostic automation recipes +with a common CLI and Python API (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. -* [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. +The two key automations are *script" and *cache*: +see [online catalog at CK playground](https://access.cknowledge.org/playground/?action=scripts), +[online MLCommons catalog](https://docs.mlcommons.org/cm4mlops/scripts). - * [CM4MLPerf-results](https://github.com/mlcommons/cm4mlperf-results) - - a simplified and unified representation of the past MLPerf results - for further visualization and analysis using [CK graphs](https://access.cknowledge.org/playground/?action=experiments) - (*the new version is coming soon*). +CM scripts extend the concept of `cmake` with simple Python automations, native scripts +and JSON/YAML meta descriptions. They require Python 3.7+ with minimal dependencies and are +[continuously extended by the community and MLCommons members](https://github.com/mlcommons/ck/blob/master/CONTRIBUTING.md) +to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux +and any other operating system, in a cloud or inside automatically generated containers +while keeping backward compatibility. +See the [online documentation](https://docs.mlcommons.org/inference) +at MLCommons to run MLPerf inference benchmarks across diverse systems using CM. -* [Artifact Evaluation](https://cTuning.org/ae) - automating artifact evaluation and reproducibility initiatives at ML and systems conferences. +#### CM4ABTF -### Deprecated and archived projects +[CM4ABTF repository powered by CM](https://github.com/mlcommons/cm4abtf) - +a collection of portable automations and CM scripts to run the upcoming +automotive MLPerf benchmark across different models, data sets, software +and hardware from different vendors. -* [CM-MLOps](cm-mlops) - now [CM4MLOps](cm4mlops) -* [CK automation framework v1 and v2](ck) - now [CM](cm) +#### CM4MLPerf-results +[CM4MLPerf-results powered by CM](https://github.com/mlcommons/cm4mlperf-results) - +a simplified and unified representation of the past MLPerf results +in the CM format for further visualization and analysis using [CK graphs](https://access.cknowledge.org/playground/?action=experiments). -### License +#### CM4Research + +[CM4Research repository powered by CM](https://github.com/ctuning/cm4research) - +a unified interface designed to streamline the preparation, execution, and reproduction of experiments in research projects. + + +### Projects powered by Collective Mind + +#### Collective Knowledge Playground + +[Collective Knowledge Playground](https://access.cKnowledge.org) - +a unified and open-source platform designed to [index all CM scripts](https://access.cknowledge.org/playground/?action=scripts) similar to PYPI, +assist users in preparing CM commands to: + +* [run MLPerf benchmarks](https://access.cknowledge.org/playground/?action=howtorun) +* aggregate, process, visualize, and compare [benchmarking results](https://access.cknowledge.org/playground/?action=experiments) for AI and ML systems +* organize [open, reproducible optimization challenges and tournaments](https://access.cknowledge.org/playground/?action=challenges). + +These initiatives aim to help academia and industry +collaboratively enhance the efficiency and cost-effectiveness of AI systems. + +#### Artifact Evaluation + +[Artifact Evaluation automation](https://cTuning.org/ae) - a community-driven initiative +leveraging the Collective Mind framework to automate artifact evaluation +and support reproducibility efforts at ML and systems conferences. + + +## Incubator + +[CMX](https://github.com/mlcommons/ck/tree/master/cmx) - the next evolution of the Collective Mind framework, +designed to enhance simplicity, flexibility, and extensibility of automations +based on user feedback. Follow the project's progress [here]( https://github.com/orgs/mlcommons/projects/46 ). + + +## Archived projects + +* [CM-MLOps](https://github.com/mlcommons/ck/tree/master/cm-mlops) - now [CM4MLOps](https://github.com/mlcommons/ck/tree/master/cm4mlops) +* [CK automation framework v1 and v2](https://github.com/mlcommons/ck/tree/master/ck) - now [CM](https://github.com/mlcommons/ck/tree/master/cm) + + +## License [Apache 2.0](LICENSE.md) -### Copyright +## Copyright * Copyright (c) 2021-2024 MLCommons * Copyright (c) 2014-2021 cTuning foundation -### Author +## Author * [Grigori Fursin](https://cKnowledge.org/gfursin) (FlexAI, cTuning) -### Maintainers +## Maintainers + +* Collective Mind (CM): [Grigori Fursin](https://cKnowledge.org/gfursin) +* CM4MLOps repository: [Arjun Suresh](https://github.com/arjunsuresh) and [Anandhu Sooraj](https://github.com/anandhu-eng) +* CMX (the next generation of CM and CM4MLOps): [Grigori Fursin](https://cKnowledge.org/gfursin) -* [Collective Mind (CM)](cm): [Grigori Fursin](https://cKnowledge.org/gfursin) -* CM4MLOps (CM automation recipes): [Arjun Suresh](https://github.com/arjunsuresh) and [Anandhu Sooraj](https://github.com/anandhu-eng) -* CMX (the next generation of CM, CM4MLOps and CM4MLPerf): [Grigori Fursin](https://cKnowledge.org/gfursin) -### Citing our project +## Citing Collective Mind and Collective Knowledge 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) ]. @@ -92,17 +141,19 @@ To learn more about the motivation behind CK and CM technology, please explore t * Journal of Royal Society'20: [ [paper](https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0211) ] -### CM Documentation +## CM Documentation -* [CM installation GUI](https://access.cknowledge.org/playground/?action=install) +* [Collective Mind white paper](https://arxiv.org/abs/2406.16791) +* [CM/CMX architecture](https://github.com/mlcommons/ck/tree/master/docs/specs/cm-diagram-v3.5.1.png) +* [CM/CMX installation GUI](https://access.cknowledge.org/playground/?action=install) * [CM Getting Started Guide and FAQ](https://github.com/mlcommons/ck/tree/master/docs/getting-started.md) * [Common CM interface to run MLPerf inference benchmarks](https://github.com/mlcommons/ck/tree/master/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](https://github.com/mlcommons/ck/tree/master/docs/tutorials/common-interface-to-reproduce-research-projects.md) - * [CM automation recipes for MLOps and DevOps](https://access.cknowledge.org/playground/?action=scripts) + * [CM4MLOps automation recipes for MLOps and DevOps](https://access.cknowledge.org/playground/?action=scripts) * [Other CM tutorials](https://github.com/mlcommons/ck/tree/master/docs/tutorials) * [Full documentation](https://github.com/mlcommons/ck/tree/master/docs/README.md) * [CM taskforce](https://github.com/mlcommons/ck/tree/master/docs/taskforce.md) -* [CMX, CM and CK history](https://github.com/mlcommons/ck/tree/master/docs/history.md) +* [CK, CM and CMX history](https://github.com/mlcommons/ck/tree/master/docs/history.md) ### Acknowledgments diff --git a/cm/CHANGES.md b/cm/CHANGES.md index 723b334f85..ba2f4fd4ff 100644 --- a/cm/CHANGES.md +++ b/cm/CHANGES.md @@ -1,5 +1,8 @@ ## V3.5.1 - - added tabulate as requirement + - added Collective Mind architecture diagram: + https://github.com/mlcommons/ck/tree/master/docs/specs/cm-diagram-v3.5.1.png + - added tabulate as requirement based on user feedback + - updated white paper: https://arxiv.org/abs/2406.16791 - merged Anandu's docs update - added links to archived repositories to keep development history (ck, cm-mlops, etc) diff --git a/cm/README.md b/cm/README.md index 2985200d34..569127634f 100644 --- a/cm/README.md +++ b/cm/README.md @@ -9,24 +9,109 @@ ## Collective Mind (CM) -Collective Mind (CM) is a small [Python package](https://pypi.org/project/cmind) -with a unified CLI and API designed for creating and managing -portable and technology-agnostic automations for MLOps, DevOps and ResearchOps. - -It is intended to make it easier to build, run, benchmark and optimize applications -across diverse models, data sets, software and hardware. - -CM is a part of [Collective Knowledge (CK)](https://github.com/mlcommons/ck) - +Collective Mind (CM) is a very lightweight [Python-based framework](https://github.com/mlcommons/ck/tree/master/cm) +featuring a unified CLI, Python API, and minimal dependencies. It is available through [PYPI](https://pypi.org/project/cmind). + +CM is designed for creating and managing portable and technology-agnostic automations for MLOps, DevOps and ResearchOps. +It aims to assist researchers and engineers in automating their repetitive, tedious and time-consuming tasks +to build, run, benchmark and optimize various applications +across diverse and continuously changing models, data, software and hardware. + +Collective Mind is a part of [Collective Knowledge (CK)](https://github.com/mlcommons/ck) - 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 -and continuously changing systems using the MLPerf benchmarking methodology. +and ever-evolving systems using the MLPerf benchmarking methodology. + +## Collective Mind architecture + +The diagram below illustrates the primary classes, functions, and internal automations within the Collective Mind framework: + +![](https://github.com/mlcommons/ck/tree/master/docs/specs/cm-diagram-v3.5.1.png) + +The CM API documentation is available [here](https://cknowledge.org/docs/cm/api/cmind.html). + +## Collective Mind repositories + +Collective Mind is continuously enhanced through public and private CM4* Git repositories, +which serve as the unified interface for various collections of reusable automations and artifacts. + +The most notable projects and repositories powered by CM are: + +#### CM4MLOps + +[CM4MLOPS repository powered by CM](https://github.com/mlcommons/cm4mlops) - +a collection of portable, extensible and technology-agnostic automation recipes +with a common CLI and Python API (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. + +The two key automations are *script" and *cache*: +see [online catalog at CK playground](https://access.cknowledge.org/playground/?action=scripts), +[online MLCommons catalog](https://docs.mlcommons.org/cm4mlops/scripts). + +CM scripts extend the concept of `cmake` with simple Python automations, native scripts +and JSON/YAML meta descriptions. They require Python 3.7+ with minimal dependencies and are +[continuously extended by the community and MLCommons members](https://github.com/mlcommons/ck/blob/master/CONTRIBUTING.md) +to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux +and any other operating system, in a cloud or inside automatically generated containers +while keeping backward compatibility. + +CM scripts were originally developed based on the following requirements from the +[MLCommons members](https://mlcommons.org) +to help them automatically compose and optimize complex MLPerf benchmarks, applications and systems +across diverse and continuously changing models, data sets, software and hardware +from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: +* must work out of the box with the default options and without the need to edit some paths, environment variables and configuration files; +* must be non-intrusive, easy to debug and must reuse existing + user scripts and automation tools (such as cmake, make, ML workflows, + python poetry and containers) rather than substituting them; +* must have a very simple and human-friendly command line with a Python API and minimal dependencies; +* must require minimal or zero learning curve by using plain Python, native scripts, environment variables + and simple JSON/YAML descriptions instead of inventing new workflow languages; +* must have the same interface to run all automations natively, in a cloud or inside containers. + +See the [online documentation](https://docs.mlcommons.org/inference) +at MLCommons to run MLPerf inference benchmarks across diverse systems using CM. + +#### CM4ABTF + +[CM4ABTF repository powered by CM](https://github.com/mlcommons/cm4abtf) - +a collection of portable automations and CM scripts to run the upcoming +automotive MLPerf benchmark across different models, data sets, software +and hardware from different vendors. + +#### CM4MLPerf-results + +[CM4MLPerf-results powered by CM](https://github.com/mlcommons/cm4mlperf-results) - +a simplified and unified representation of the past MLPerf results +in the CM format for further visualization and analysis using [CK graphs](https://access.cknowledge.org/playground/?action=experiments). + +#### CM4Research + +[CM4Research repository powered by CM](https://github.com/ctuning/cm4research) - +a unified interface designed to streamline the preparation, execution, and reproduction of experiments in research projects. + + +### Projects powered by Collective Mind + +#### Collective Knowledge Playground + +[Collective Knowledge Playground](https://access.cKnowledge.org) - +a unified and open-source platform designed to [index all CM scripts](https://access.cknowledge.org/playground/?action=scripts) similar to PYPI, +assist users in preparing CM commands to: + +* [run MLPerf benchmarks](https://access.cknowledge.org/playground/?action=howtorun) +* aggregate, process, visualize, and compare [benchmarking results](https://access.cknowledge.org/playground/?action=experiments) for AI and ML systems +* organize [open, reproducible optimization challenges and tournaments](https://access.cknowledge.org/playground/?action=challenges). + +These initiatives aim to help academia and industry +collaboratively enhance the efficiency and cost-effectiveness of AI systems. -CM supportes CM4MLOps, CM4MLPerf, CM4ABTF, CM4Research and other projects. +#### Artifact Evaluation -For example, CM4MLOps repository includes -a [collection of portable, extensible and technology-agnostic automation recipes](https://access.cknowledge.org/playground/?action=scripts) -(aka CM scripts) to unify and automate different steps required to compose, run, benchmark and optimize complex ML/AI applications -on any platform with any software and hardware. +[Artifact Evaluation automation](https://cTuning.org/ae) - a community-driven initiative +leveraging the Collective Mind framework to automate artifact evaluation +and support reproducibility efforts at ML and systems conferences. CM scripts extend the concept of `cmake` with simple Python automations, native scripts and JSON/YAML meta descriptions. They require Python 3.7+ with minimal dependencies and are @@ -88,9 +173,9 @@ from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: [Apache 2.0](LICENSE.md) -### Citing CM project +### Citing Collective Mind -If you found CM automations useful, please cite this article: +If you found CM automations, please cite this article: [ [ArXiv](https://arxiv.org/abs/2406.16791) ], [ [BibTex](https://github.com/mlcommons/ck/blob/master/citation.bib) ]. You can learn more about the motivation behind these projects from the following presentations: @@ -101,8 +186,7 @@ You can learn more about the motivation behind these projects from the following ### Acknowledgments -The Collective Mind (CM) automation framework was originally -developed by [Grigori Fursin](https://cKnowledge.org/gfursin), +Collective Mind (CM) was originally developed by [Grigori Fursin](https://cKnowledge.org/gfursin), as a part of the [Collective Knowledge educational initiative](https://cKnowledge.org), sponsored by [cTuning.org](https://cTuning.org) and [cKnowledge.org](https://cKnowledge.org), and contributed to MLCommons for the benefit of all. diff --git a/cmr.yaml b/cmr.yaml index cc317fb295..e63088c04a 100644 --- a/cmr.yaml +++ b/cmr.yaml @@ -7,6 +7,8 @@ prefix: cm-mlops version: 2.3.2 +author: "Grigori Fursin" + deps: - alias: mlcommons@cm4mlops uid: 9e97bb72b0474657 diff --git a/docs/_generator/api/index.rst b/docs/_generator/api/index.rst index d621a5d48f..303f30a84f 100644 --- a/docs/_generator/api/index.rst +++ b/docs/_generator/api/index.rst @@ -1,4 +1,4 @@ -.. Copyright 2021-2024 MLCommons +.. Copyright MLCommons .. 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