fixing requirements (#1365)

.github/workflows/publish-docs.yml

@@ -10,6 +10,7 @@ on:
     paths:
       - docs/**
       - mkdocs.yml
+      - README.md
 
 jobs:
 

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

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
 

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"