releasing CM v1.5.3 and Collective Knowledge v3 supporting MLPerf inf…

…erence v3.1+ (#956)

README.md

@@ -5,30 +5,43 @@
 [![CM test](https://github.com/mlcommons/ck/actions/workflows/test-cm.yml/badge.svg)](https://github.com/mlcommons/ck/actions/workflows/test-cm.yml)
 [![CM script automation features test](https://github.com/mlcommons/ck/actions/workflows/test-cm-script-features.yml/badge.svg)](https://github.com/mlcommons/ck/actions/workflows/test-cm-script-features.yml)
 
+### Breaking news
 
-### About
-
-The [MLCommons Task Force on Automation and Reproducibility](https://github.com/mlcommons/ck/blob/master/docs/taskforce.md), 
-[cTuning foundation](https://cTuning.org) and [cKnowledge.org](https://cKnowledge.org) 
-are developing Collective Knowledge v3 - an open-source technology 
-enabling collaborative, reproducible, automated and unified benchmarking, optimization and comparison of AI, ML and other emerging workloads
-across diverse and rapidly evolving models, data sets, software and hardware from different vendors.
+Breaking news: our open-source CM automation language and CK playground 
+enabled the 1st mass-scale community submission of 10000+ MLPerf
+inference benchmarking results (representing more than 90% of all v3.1 submissions)
+across diverse models, data sets, software and hardware 
+from different vendors via the [cTuning foundation](https://cTuning.org) - 
+see the [HPC Wire article](https://www.hpcwire.com/2023/09/13/mlperf-releases-latest-inference-results-and-new-storage-benchmark)
+for more details and get in touch via our [public Discord server](https://discord.gg/JjWNWXKxwT) 
+if you want to automate and optimize your future MLPerf submissions
+using our open-source technology! 
 
-Collective Knowledge v3 includes:
-* [Non-intrusive, technology-agnostic and plugin-based Collective Mind automation language](cm)
-* [Collective Knowledge Playground](https://access.cKnowledge.org)
-* [Modular Inference Library](https://cknowledge.org/mil)
 
-[The community](https://access.cknowledge.org/playground/?action=challenges) successfully validated CM automation language and CK playground to automate > 90% of all [MLPerf inference v3.1 results](https://mlcommons.org/en/news/mlperf-inference-storage-q323/) 
-and cross 10000 submissions in one round for the first time (submitted via [cTuning foundation](https://cTuning.org))!
-Here is the [list of the new CM/CK capabilities](docs/news-mlperf-v3.1.md) available to everyone 
-to prepare and automate their future MLPerf submissions.
+### About
 
-See related [HPC Wire'23 article](https://www.hpcwire.com/2023/09/13/mlperf-releases-latest-inference-results-and-new-storage-benchmark),
-[ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339), 
+Collective Knowledge (CK) is an open-source, non-intrusive and technology-agnostic toolset
+to facilitate reproducible research and automate development, benchmarking, optimization, comparison and 
+deployment of Pareto-efficient AI/ML Systems across diverse and rapidly evolving models, data sets, 
+software and hardware from different vendors and users.
+
+Collective Knowledge v3 has been developed from scratch by the [MLCommons Task Force on Automation and Reproducibility](https://github.com/mlcommons/ck/blob/master/docs/taskforce.md), 
+[cTuning foundation](https://cTuning.org), [cKnowledge.org](https://cKnowledge.org) and [the community](CONTRIBUTING.md) 
+and includes the following sub-projects:
+
+* [Collective Mind automation Language (CM)](cm) - a simple automation language
+  that helps to implement modular, portable and technology-agnostic 
+  benchmarks and applications with a common API that can automatically
+  plug in diverse and rapidly evolving models, data sets, software and hardware
+  from different vendors and users.
+* [CM scripts](cm-mlops/scripts) - a database of portable, reusable and technology-agnostic automations to modularize benchmarks, software projects and AI/ML Systems.
+* [Modular Inference Library (MIL)](https://cknowledge.org/mil) - a universal and modular C++ implementation of MLPerf inference benchmarks.
+* [Collective Knowledge Playground](https://access.cKnowledge.org) - an open platform to benchmark and optimize AI and ML Systems via community challenges.
+
+See related [ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339), 
 [ACM Tech Talk](https://learning.acm.org/techtalks/reproducibility) 
 and [MLPerf submitters orientation](https://doi.org/10.5281/zenodo.8144274) 
-to learn more about our open-source developments and long-term vision.
+to learn more about our open-source technology and long-term vision.
 
 Join our [public Discord server](https://discord.gg/JjWNWXKxwT) to learn how to run and extend MLPerf benchmarks, participate in future MLPerf submissions, 
 automate reproducibility initiatives at ACM/IEEE/NeurIPS conferences and co-design efficient AI Systems.
@@ -51,7 +64,7 @@ automate reproducibility initiatives at ACM/IEEE/NeurIPS conferences and co-desi
 * [CM installation](https://github.com/mlcommons/ck/blob/master/docs/installation.md)
 * [All CM tutorials](https://github.com/mlcommons/ck/blob/master/docs/tutorials)
 
-#### Run Python Hello World app
+#### Run Python Hello World app on Linux, Windows and MacOS
 
 ```bash
 python3 -m pip install cmind
@@ -118,7 +131,7 @@ We invite the community to participate in collaborative benchmarking and optimiz
 
 ### Acknowledgments
 
-Collective Knowledge Technology v3 (including Collective Mind automation language and Collective Knowledge playground)
+Collective Knowledge Technology v3 (including Collective Mind automation language and Collective Knowledge Playground)
 was developed from scratch by [Grigori Fursin](https://cKnowledge.org/gfursin) 
 and [Arjun Suresh](https://www.linkedin.com/in/arjunsuresh) in 2022-2023
 within the [MLCommons Task Force on Automation and Reproducibility](docs/taskforce.md)

cm-mlops/script/import-mlperf-tiny-to-experiment/README-extra.md

@@ -28,7 +28,8 @@ Install repositories with raw MLPerf inference benchmark results:
 ```bash
 cm run script "get git repo _repo.https://github.com/mlcommons/tiny_results_v0.7" --extra_cache_tags=mlperf-tiny-results,version-0.7
 cm run script "get git repo _repo.https://github.com/mlcommons/tiny_results_v1.0" --extra_cache_tags=mlperf-tiny-results,version-1.0
-```
+cm run script "get git repo _repo.https://github.com/mlcommons/tiny_results_v1.1" --extra_cache_tags=mlperf-tiny-results,version-1.1
+````
 
 You can also add private results to compare submissions locally before they become public:
 ```bash

cm-mlops/script/import-mlperf-training-to-experiment/README-extra.md

@@ -28,6 +28,7 @@ cmr "get git repo _repo.https://github.com/mlcommons/training_results_v1.0" --ex
 cmr "get git repo _repo.https://github.com/mlcommons/training_results_v1.1" --extra_cache_tags=mlperf-training-results,version-1.1 --branch=main --depth=""
 cmr "get git repo _repo.https://github.com/mlcommons/training_results_v2.0" --extra_cache_tags=mlperf-training-results,version-2.0 --branch=main --depth=""
 cmr "get git repo _repo.https://github.com/mlcommons/training_results_v2.1" --extra_cache_tags=mlperf-training-results,version-2.1 --branch=main
+cmr "get git repo _repo.https://github.com/mlcommons/training_results_v3.0" --extra_cache_tags=mlperf-training-results,version-3.0
 ```
 
 You can install private submission repository as follows:

cm/README.md

@@ -6,27 +6,35 @@
 [![CM test](https://github.com/mlcommons/ck/actions/workflows/test-cm.yml/badge.svg)](https://github.com/mlcommons/ck/actions/workflows/test-cm.yml)
 [![CM script automation features test](https://github.com/mlcommons/ck/actions/workflows/test-cm-script-features.yml/badge.svg)](https://github.com/mlcommons/ck/actions/workflows/test-cm-script-features.yml)
 
+### Breaking news
+
+Our open-source CM automation language and CK playground 
+enabled the 1st mass-scale community submission of 10000+ MLPerf
+inference benchmarking results (representing > 90% of all v3.1 submissions)
+across diverse models, data sets, software and hardware 
+from different vendors via the [cTuning foundation](https://cTuning.org) - 
+see the [HPC Wire article](https://www.hpcwire.com/2023/09/13/mlperf-releases-latest-inference-results-and-new-storage-benchmark)
+for more details and get in touch via our [public Discord server](https://discord.gg/JjWNWXKxwT) 
+if you want to automate and optimize your future MLPerf submissions
+using our open-source technology! 
+
+
 ### About
 
-The [MLCommons Task Force on Automation and Reproducibility](https://github.com/mlcommons/ck/blob/master/docs/taskforce.md), 
-[cTuning foundation](https://cTuning.org) and [cKnowledge.org](https://cKnowledge.org) have developed
-a non-intrusive, technology-agnostic and plugin-based [Collective Mind automation language (CM)](https://doi.org/10.5281/zenodo.8105339)
-to enable collaborative, reproducible, automated and unified benchmarking, optimization and comparison of AI, ML and other emerging workloads
-across diverse and rapidly evolving models, data sets, software and hardware from different vendors.
-
-The CM automation language was successfully validated by the community and [cTuning foundation](https://cTuning.org)
-via [public optimization and reproducibility challenges at the CK playground](https://access.cknowledge.org/playground/?action=challenges)
-to automate > 90% of all [MLPerf inference v3.1 results](https://mlcommons.org/en/news/mlperf-inference-storage-q323/) 
-and cross 10000 submissions in one round for the first time - please read this [HPC Wire article](https://www.hpcwire.com/2023/09/13/mlperf-releases-latest-inference-results-and-new-storage-benchmark)
-about the cTuning's community submission and check the [list of the new CM/CK capabilities](https://github.com/mlcommons/ck/blob/master/docs/news-mlperf-v3.1.md) 
-available to everyone to prepare and automate their future MLPerf submissions.
-
-See related [ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339), 
-[ACM Tech Talk](https://learning.acm.org/techtalks/reproducibility) 
+Collective Mind automation Language (CM) is a simple automation language that helps to implement modular, portable and technology-agnostic 
+benchmarks and applications with a common API that can automatically plug in diverse and rapidly evolving models, data sets, software and hardware
+from different vendors and users. It is extended via [CM scripts](cm-mlops/scripts) - a database of portable, reusable and technology-agnostic automations 
+to modularize benchmarks, software projects and AI/ML Systems.
+
+CM is used by the community to provide a common interface to all shared knowledge, facilitate reproducible research 
+and automate development, benchmarking, optimization, comparison and deployment of AI/ML Systems.
+See related [ACM REP'23 keynote](https://doi.org/10.5281/zenodo.8105339) 
 and [MLPerf submitters orientation](https://doi.org/10.5281/zenodo.8144274) 
-to learn more about our open-source developments and long-term vision.
+to learn more about our open-source technology and long-term vision.
 
-Join our [Discord server](https://discord.gg/JjWNWXKxwT) to ask questions, provide feedback and participate in collaborative developments.
+Join our [public Discord server](https://discord.gg/JjWNWXKxwT) to learn how to use CM, 
+modularize your projects and benchmarks, run and extend MLPerf benchmarks, participate in future MLPerf submissions, 
+automate reproducibility initiatives at ACM/IEEE/NeurIPS conferences and co-design efficient AI/ML Systems.
 
 ### Documentation
 
@@ -37,7 +45,7 @@ Join our [Discord server](https://discord.gg/JjWNWXKxwT) to ask questions, provi
 * [CM installation](https://github.com/mlcommons/ck/blob/master/docs/installation.md)
 * [All CM tutorials](https://github.com/mlcommons/ck/blob/master/docs/tutorials)
 
-#### Run Python Hello World app
+#### Run Python Hello World app on Linux, Windows and MacOS
 
 ```bash
 python3 -m pip install cmind

docs/tutorials/sc22-scc-mlperf.md

@@ -133,15 +133,8 @@ You can find the location of a pulled repository as follows:
 cm find repo mlcommons@ck
 ```
 
-You can also pull the stable version of this CM repository that we have used for this tutorial 
-using checkout *[f9abc76](https://github.com/mlcommons/ck/releases/tag/cm-v1.1.1)*:
-
-```bash
-cm pull repo mlcommons@ck --checkout=ac3fda5
-```
-
 You can now use the unified CM CLI/API of [reusable and cross-platform CM scripts](https://github.com/mlcommons/ck/blob/master/docs/list_of_scripts.md))
-to detect or install all artifacts (tools, models, datasets, libraries, etc) 
+to detect or install all the latest artifacts (tools, models, datasets, libraries, etc) 
 required for a given software project (MLPerf inference benchmark with RetinaNet, Open Images and ONNX in our case).
 
 Conceptually, these scripts take some environment variables and files as an input, perform a cross-platform action (detect artifact, download files, install tools),