diff --git a/.github/workflows/publish-docs.yml b/.github/workflows/publish-docs.yml index 7ea7d218c..368007a7d 100644 --- a/.github/workflows/publish-docs.yml +++ b/.github/workflows/publish-docs.yml @@ -10,6 +10,7 @@ on: paths: - docs/** - mkdocs.yml + - README.md jobs: diff --git a/README.md b/README.md index 9668c1e08..e383508f9 100755 --- a/README.md +++ b/README.md @@ -24,17 +24,18 @@ CK consists of several sub-projects: * [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 + 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) * [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 +* [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 ). @@ -44,6 +45,12 @@ CK consists of several sub-projects: 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. + * [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*). + + * [Artifact Evaluation](https://cTuning.org/ae) - automating artifact evaluation and reproducibility initiatives at ML and systems conferences. @@ -63,8 +70,8 @@ CK consists of several sub-projects: ### 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) +* [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 diff --git a/cm/README.md b/cm/README.md index 953584a86..ac726975a 100644 --- a/cm/README.md +++ b/cm/README.md @@ -9,18 +9,20 @@ ### About -Collective Mind (CM) is a small, modular, cross-platform and decentralized workflow automation framework -with a human-friendly interface to make it easier to build, run, benchmark and optimize applications -across diverse models, data sets, software and hardware. +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) - -an educational community project to learn how to run emerging workloads +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. +and continuously changing systems using the MLPerf benchmarking methodology. -CM includes a collection of portable, extensible and technology-agnostic automation recipes -with a common API and CLI (aka CM scripts) to unify and automate different steps -required to compose, run, benchmark and optimize complex ML/AI applications +CM 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. CM scripts extend the concept of `cmake` with simple Python automations, native scripts @@ -44,9 +46,9 @@ from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: 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. -### Maintainers +### Author and maintainer -* [Grigori Fursin](https://cKnowledge.org/gfursin) +* [Grigori Fursin](https://cKnowledge.org/gfursin) (FlexAI, cTuning) ### Resources diff --git a/cmx4mlops/cmr.yaml b/cmx4mlops/cmr.yaml index 54158c39c..13493b9e9 100644 --- a/cmx4mlops/cmr.yaml +++ b/cmx4mlops/cmr.yaml @@ -6,3 +6,7 @@ git: true version: "0.5.1" author: "Grigori Fursin" + +install_python_requirements: false + +min_cm_version: "3.4.4"