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CM test CM script automation features test Dockerfile update for CM scripts


Collective Mind (CM) is a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes with a human-friendly interface (aka CM scripts) to automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on any platform with any software and hardware. They require Python 3.7+ with minimal dependencies and can run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux and any other operating system, in a cloud or inside automatically generated containers. Furthermore, CM scripts are continuously extended by the community to encode new knowledge and best practices about AI systems while keeping backward compatibility!

CM scripts were originally developed based on the following requirements from the MLCommons engineers and researchers to help them automatically build, benchmark and optimize complex MLPerf benchmarks 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.

Below you can find and try a few examples of the most-commonly used automation recipes that can be chained into more complex automation workflows using simple JSON or YAML.

Note that MLCommons CM is a collaborative engineering effort to gradually improve portability and functionality across continuously changing models, data sets, software and hardware based on your feedback - please check this installation guide, report encountered issues here and contact us via public Discord Server to help this community effort!

CM human-friendly command line

pip install cmind

cm pull repo mlcommons@ck

cm run script "python app image-classification onnx"
cmr "python app image-classification onnx"

cmr "download file _wget" --url= --verify=no --env.CM_DOWNLOAD_CHECKSUM=45ae5c940233892c2f860efdf0b66e7e
cmr "python app image-classification onnx" --input=computer_mouse.jpg
cmr "python app image-classification onnx" --input=computer_mouse.jpg --debug

cm find script "python app image-classification onnx"
cm load script "python app image-classification onnx" --yaml

cmr "get python" --version_min=3.8.0 --name=mlperf-experiments
cmr "install python-venv" --version_max=3.10.11 --name=mlperf

cmr "get ml-model stable-diffusion"
cmr "get ml-model huggingface zoo _model-stub.alpindale/Llama-2-13b-ONNX" --model_filename=FP32/LlamaV2_13B_float32.onnx --skip_cache
cmr "get dataset coco _val _2014"
cmr "get dataset openimages" -j

cm show cache
cm show cache "get ml-model stable-diffusion"

cmr "get generic-python-lib _package.onnxruntime" --version_min=1.16.0
cmr "python app image-classification onnx" --input=computer_mouse.jpg

cmr "python app image-classification onnx" --input=computer_mouse.jpg --debug

cm rm cache -f
cmr "python app image-classification onnx" --input=computer_mouse.jpg --adr.onnxruntime.version_max=1.16.0

cmr "get cuda" --version_min=12.0.0 --version_max=12.3.1
cmr "python app image-classification onnx _cuda" --input=computer_mouse.jpg

cm gui script "python app image-classification onnx"

cm docker script "python app image-classification onnx" --input=computer_mouse.jpg
cm docker script "python app image-classification onnx" --input=computer_mouse.jpg -j -docker_it

cm docker script "get coco dataset _val _2017" --to=d:\Downloads\COCO-2017-val --store=d:\Downloads --docker_cm_repo=ctuning@mlcommons-ck

cmr "run common mlperf inference" --implementation=nvidia --model=bert-99 --category=datacenter --division=closed
cm find script "run common mlperf inference"

cmr "get generic-python-lib _package.torch" --version=2.1.2
cmr "get generic-python-lib _package.torchvision" --version=0.16.2
cmr "python app image-classification torch" --input=computer_mouse.jpg

cm rm repo mlcommons@ck
cm pull repo --url=

cmr "install llvm prebuilt" --version=17.0.6
cmr "app image corner-detection"

cm run experiment --tags=tuning,experiment,batch_size -- echo --batch_size={{VAR1{range(1,8)}}}
cm replay experiment --tags=tuning,experiment,batch_size

cmr "get conda"

cm pull repo ctuning@cm-reproduce-research-projects
cmr "reproduce paper micro-2023 victima _install_deps"
cmr "reproduce paper micro-2023 victima _run" 

CM unified Python API

import cmind
output=cmind.access({'action':'run', 'automation':'script',
if output['return']==0: print (output)

Examples of modular containers and GitHub actions with CM commands

CM scripts were successfully used to modularize MLPerf inference benchmarks and help the community automate more than 95% of all performance and power submissions in the v3.1 round across more than 120 system configurations (models, frameworks, hardware) while reducing development and maintenance costs.

Besides automating MLCommons projects, the community also started started using and extending CM scripts to modularize, run and benchmark other software projects and make it easier to rerun, reproduce and reuse research projects from published papers at Systems and ML conferences.

Please check the Getting Started Guide to understand how CM automation recipes work, how to use them to automate your own projects, and how to implement and share new automations in your public or private projects.


Apache 2.0


2022-2024 MLCommons

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