Collective Mind Getting Started Guide and FAQ

Why CM?

Collective Mind (CM) is a community project to develop a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes for MLOps and DevOps with a human-friendly interface (aka CM scripts) that can help to automate all the manual steps required to prepare, build, 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.

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.

Let's use a relatively simple image classification example to explain how CM achieves that and how it helps to automate much more complex projects including MLPerf benchmarks and reproducibility initatives at ML and Systems conferences.

Expand to see the feedback and requirements from MLCommons researchers and engineers

While image classification sounds like a trivial example nowadays, it may still require many manual steps to download some validation data sets and models, install frameworks and low-level dependencies and update various environment variables and paths depending on your platform and target hardware (for example CPU vs CUDA). You may also need to make sure that all dependencies are compatible (for example that ONNX run-time or PyTorch framework is compatible with your CUDA version, etc). Of course, you can also develop a container and fix all the versions but what if you or someone else want to try a different CUDA version or newer ONNX/TF/PyTorch framework or different operating system or different model or different data set or different framework or different hardware? While helping MLCommons automate [MLPerf inference benchmarks](https://github.com/mlcommons/inference) and run them across diverse models, data sets, software and hardware, we've realized that there is no portable and technology-agnostic automation tool that can handle such cases. The feedback from [MLCommons engineers and researchers](taskforce.md) motivated us to develop a simple automation framework that can help them assemble, run, benchmark and optimize complex AI/ML applications across diverse and continuously changing models, data sets, software and hardware from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors.

CM automation recipe for image classification

We designed CM as a small Python library with a human-friendly command line, simple Python API and minimal dependencies needed to implement automation recipes (Python 3.7+, PIP, pyyaml, git, wget) and chain them into portable workflows. CM scripts can run natively (development mode) or inside containers that CM generates on the fly (stable mode).

Most of the time, these dependencies are already installed on your platform. In such case, you should be able to prepare and run image classification with ONNX, ImageNet validation data set and ResNet-50 on Linux, MacOS, Windows and any other operating system using a few CM commands:

pip install cmind
cm pull repo mlcommons@cm4mlops --checkout=dev
cm run script "python app image-classification onnx _cpu"

Note that you may need to re-login when you install cmind for the first time to let your platform pick up path to the cm command line front-end.

You can also run and customize above automation recipe in alternative ways as follows:

cm run script "python app image-classification onnx _cpu" --help

cm run script "download file _wget" --url=https://cKnowledge.org/ai/data/computer_mouse.jpg --verify=no --env.CM_DOWNLOAD_CHECKSUM=45ae5c940233892c2f860efdf0b66e7e
cm run script "python app image-classification onnx _cpu" --input=computer_mouse.jpg

cmr "python app image-classification onnx _cpu" --input=computer_mouse.jpg
cmr --tags=python,app,image-classification,onnx,_cpu --input=computer_mouse.jpg
cmr 3d5e908e472b417e --input=computer_mouse.jpg

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

cm gui script "python app image-classification onnx _cpu"

If you encounter some issues, please check CM installation guide - if it doesn't help you, please report your issues here and/or contact us via our public Discord server - CM is a community project being developed and improved across diverse software and hardware based on your feedback!

How CM scripts works?

Next, we briefly explain how CM commands work - it will help you understand what happens when you see similar commands in MLPerf results, README files, technical reports, research papers, Jupyter notebooks, Google colab, containers, scripts and artifact appendices.

Whenever you run cm run script "python app image-classification onnx _cpu" or cmr "python app image-classification onnx _cpu", the CM script automation will simply search for _cm.yaml and _cm.json files (CM meta-description dictionary) in all script directories in all software projects registered in CM via cm pull repo.

In our case, we've pulled github.com/mlcommons/ck project that has most MLCommons' CM automation recipes embedded in a cm-mlops/script directory.

Note that you can pull any public or private Git repository, download any software project or register any local directory in the CM to search for embedded automation recipes.

CM will then try to match all your tags without _ prefix (_ in tags mark the so-called CM script variations that customize a give script behavior and will be described later) with a tags list in the CM meta-description dictionary. In our case, it will match the corresponding _cm.yaml in $HOME/CM/repos/mlcommons@cm4mlops/script/app-image-classification-onnx-py/_cm.yaml - a wrapper for a given CM automation recipe.

Note that if you use unique ID instead of tags to identify automation (such as 3d5e908e472b417e), CM will try to match uid string in the CM meta descriptions instead of tags.

How CM runs automation recipes?

Whenever CM finds a directory with a requested automation recipe, it performs the following steps: * run preprocess function in customize.py if exists * run run.sh (Linux) or run.bat (Windows) if exists * run postprocess function in customize.py if exists

Such organization makes it possible to use either Python or native OS scripts or both to implement CM automation recipes while minimizing the learning curve for CM understanding, development and debugging as requested by CM users.

Furthermore, CM scripts can keep the source code of image classification (as shown here) that we can easily move around between projects without hardwiring paths and names.

How CM unifies inputs, outputs and environment variables?

CM allows you to pass environment variables to customize.py and native scripts using --env.ENV=VALUE.

When you use some flags such as --input in our image classification example, it will be also converted into an environment variable using input_mapping dictionary in the CM meta description of this script.

All environment variables are aggregated in env dictionary inside CM and then passed to preprocess function in customize.py where you can modify it programmatically.

They are then passed to the run script. Since new environment variables are not preserved after run script, one can pass new environment variables back to CM using tmp-run-env.out with ENV=KEY strings as shown here or using tmp-run-state.json as shown here.

How CM chains automation recipes into portable workflows?

CM scripts provide a technology-agnostic wrapper with simple tags, CLI and Python API to prepare and run user code snippets and native scripts/tools while unifying their inputs and outputs, paths and environment variables.

Such architecture makes it possible to easily chain existing user scripts and tools into portable, technology-agnostic and powerful workflows instead of substituting or rewriting them.

It is possible to chain CM scripts using simple deps list in a meta description of a given script:

deps:
- tags: detect,os
- tags: get,sys-utils-cm
- names:
  - python
  - python3
  tags: get,python3

- tags: get,cuda
  names:
  - cuda
  enable_if_env:
    USE_CUDA:
    - yes
- tags: get,cudnn
  names:
  - cudnn
  enable_if_env:
    USE_CUDA:
    - yes

- tags: get,dataset,imagenet,image-classification,original
- tags: get,dataset-aux,imagenet-aux,image-classification
- tags: get,ml-model,resnet50,_onnx,image-classification
  names:
  - ml-model

- tags: get,generic-python-lib,_package.Pillow
- tags: get,generic-python-lib,_package.numpy
- tags: get,generic-python-lib,_package.opencv-python


- tags: get,generic-python-lib,_onnxruntime
  names:
  - onnxruntime
  skip_if_env:
    USE_CUDA:
    - yes
- tags: get,generic-python-lib,_onnxruntime_gpu
  names:
  - onnxruntime
  enable_if_env:
    USE_CUDA:
    - yes

Each entry in this list is a dictionary that specifies which CM script to run using tags. Internally, CM will be updating env dictionary (flat environment) and state dictionary (to let scripts exchange complex data structures besides environment variables).

If you run CM via command line, you can see internal env and state dictionaries by adding -j flag:

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

Note that we use similar approach for updating environment variables similar to calling native scripts - by default, they do not alter environment variables at the host. However, CM allows you to do that by explicitly specifying which environment variables and state keys will be updated at the host using new_env_keys and new_state_keys in the meta of a given script as shown here. This helped us make behavior of complex CM workflows more deterministic and reproducible.

Each sub-dependency can be turned on or off using environment variables using enable_if_env dictionary or disable_if_env dictionary.

You can also specify version_min, version_max and version in these dependencies. You can also give them some specific names such as python and pass versions and environment variables only to a specific script in a pipeline as follows:

cmr "python app image-classification onnx _cpu" --input=computer_mouse.jpg --adr.python.version_min=3.9

This functionality is usually implemented inside ad-hoc bash or shell scripts with many hardwired paths and names - CM simply makes such scripts and tools portable and reusable while enabling technology-agnostic automation workflows with a unified interface that can adapt to any operating system and are easy to understand.

We can now assemble complex automation workflows by reusing all portable scripts from the community.

In our example, we reused CM scripts to detect OS features, install system dependencies on any supported OS (Ubuntu, MacOS, RHEL, Arch, Debian, SLES, Windows, etc), detect or install Python and PIP packages, download and preprocess data sets and models, etc.

How to add new CM scripts?

One the main requirement for CM was to provide a very light-weight connectors between existing automation scripts and tools rather than substituting them.

You can add your own scripts and tools to CM using the following command that will create a ready-to-use dummy CM script:

cm add script my-script --tags=my,script

You can already run this dummy script and plug it into other CM workflows:

cmr "my script"

You can also run it from python as follows:

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

How to customize CM scripts using variations?

Sometimes we need to set multiple environment variables or run a set of extra CM scripts for a specific purpose (different hardware target or model or dataset).

We introduced special tags with _, called variations or variation tags, that allow you to update a set of environment variables and add extra scripts to the chain of dependencies.

Such variations are defined using variations dictionary in the meta description of a given CM script.

For example, our script has 2 variations _cuda and _cpu.

If you want to use CUDA implementation of the image classification example, you can add this variation to the tags that will set USE_CUDA environment to yes and will turn on a specific CM script in deps to install ONNX for CUDA:

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

How to cache and reuse CM scripts' output?

By default, CM scripts run in the current directory and record all new files there.

For example, the following universal download script will download computer mouse image to the current directory:

cm run script "download file _wget" --url=https://cKnowledge.org/ai/data/computer_mouse.jpg --verify=no --env.CM_DOWNLOAD_CHECKSUM=45ae5c940233892c2f860efdf0b66e7e

In some cases, we want to cache and reuse the output of automation recipes (such as downloading models, preprocessing data sets or building some applications) rather than just downloading it to the current directory.

Following the feedback from our users, we implemented a cache automation in CM similar to script. Whenever CM encounters "cache":true in a meta description of a given script, it will create a cache directory in $HOME/CM/repos/local with some unique ID and the same tags as script, and will execute that script there to record all the data in cache.

Whenever the same CM script is executed and CM finds an associated cache entry, it will skip execution and will reuse files from that entry.

Furthermore, it is possible to reuse large cached files in other projects that call the same CM scripts!

You can see cache entries and find a specific one as follows:

cmr "get ml-model resnet50 _onnx" -j

cm show cache
cm show cache "get ml-model resnet50 _onnx" 
cm find cache "download file ml-model resnet50 _onnx" 
cm info cache "download file ml-model resnet50 _onnx"

You can clean some cache entries as follows:

cm rm cache --tags=ml-model,resnet50

You can also clean all CM cache entries and start from scratch as follows:

cm rm cache -f

In fact, you can remove $HOME/CM to reset CM framework completely and remove all downloaded repositories and cached entries.

How to use CM with Python virtual environments?

Using CM cache makes it possible to run CM automations for multiple virtual environments installed inside CM cache entries. It is possible to run CM automations with different Python virtual environments transparently to users while avoiding messing up native user environment.

We created the following CM automation recipe to create virtual environments:

cmr "install python-venv" --name=mlperf
cm show cache "python-venv name-mlperf"
export CM_SCRIPT_EXTRA_CMD="--adr.python.name=mlperf"

If you now run our image classification automation recipe, it will reuse model and dataset from the cache, but will use

How to debug CM scripts?

One of the requirements from CM users was to avoid new and/or complex ways to debug CM automations. Using native scripts and Python code makes it possible to apply standard techniques and tools to debug CM automations.

We were also asked to add --debug flag to open a shell after the last native script is executed - this allows users to rerun the last command line with all environment variables and paths assembled by CM while having a full and native access to change environment and run the final command (such as pinning threads, changing batch sizes, modifying files, etc).

You can try it as follows on Linux, MacOS, Windows or other operating system as follows:

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

You can also use GDB via environment variable --env.CM_RUN_PREFIX="gdb --args " to run the final command via GDB.

How to extend/improve CM scripts?

CM is a community project where CM scripts are continuously improved to run on different hardware with different software while keeping backward compatibility through the unified CM interface, tags and variations.

Whenever you encounter an issue or want to have support for your own project and environment, please update these scripts and send a PR to the CM GitHub.

You can also reach us via public Discord server if you questions or suggestions.

How to use CM with containers?

One of the key requirements for CM was to run automation natively or inside containers in the same way.

We want CM scripts to adapt to the current/latest environment natively or run in the container automatically generated on the fly when requested by user for more stability and determinism.

In such case, we can get rid of separate development of native scripts/workflows and Dockerfile and use the same CM commands instead.

To run a given script in an automatically-generated container, you can simply substitute cm run script with cm docker script or cmr with cmrd:

cm docker script "python app image-classification onnx _cpu"

CM will automatically generate a Dockerfile with Ubuntu 22.04 in the dockerfiles directory of a given script, will build container with the same CM command and will run it inside container.

If you want to stay in the container, you can add flag --docker_it.
You can change OS inside container using --docker_base_image, --docker_os and --docker_os_version.

The tricky part is when we want to use host files and directories with a given CM script inside container. To make it easier for users, we have implemented automatic detection and mounting of files and directories in CM script.

Developers of a CM script just need to specify which flags and environment variables are local files or directories using input_paths in docker dictionary of the meta-description of this script:

docker:
  skip_run_cmd: 'no'
  all_gpus: 'yes'
  input_paths:
    - input
    - env.CM_IMAGE
    - output
  skip_input_for_fake_run:
    - input
    - env.CM_IMAGE
    - output
    - j
  pre_run_cmds:
    - echo \"CM pre run commands\"

When you run the same script via container with the local computer_mouse.jpg file as an input, CM will automatically mount current directory and will update the input to the CM script inside container with the internal path:

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

...

docker build  -f D:\Work1\CM\ck\cm-mlops\script\app-image-classification-onnx-py\dockerfiles\ubuntu_22.04.Dockerfile \
              -t cknowledge/cm-script-app-image-classification-onnx-py:ubuntu-22.04-latest .

...

Container launch command:
docker run  --entrypoint ""  --gpus=all -v D:\Work1\CM\ck\docs\computer_mouse.jpg:/cm-mount/Work1/CM/ck/docs/computer_mouse.jpg 
                            cknowledge/cm-script-app-image-classification-onnx-py:ubuntu-22.04-latest 
                            bash -c "echo \"CM pre run commands\" && 
                            cm run script --tags=python,app,image-classification,onnx,_cpu 
                            --input=/cm-mount/Work1/CM/ck/docs/computer_mouse.jpg "

CM pre run commands

It is now possible to download large data sets and models to the host from CM containers or pass host scratch pads and data to CM containers transparently to a user!

How to use CM GUI to run automation recipes?

Another request from CM/MLCommons users was to have a simple GUI that can generate CM commands with user-friendly selector.

We've implemented a CM script called gui that provides a universal Streamlit GUI for any CM script.

You just need to describe the inputs for a given script via meta-description as shown for our image classification example:

input_description:
  input: 
    desc: "Path to JPEG image to classify"
  output: 
    desc: "Output directory (optional)"
  j:
    desc: "Print JSON output"
    boolean: true

You can run this GUI for your CM script as follows:

cm gui script "python app image-classification onnx _cpu"

This GUI will allow you to customize your script and run it on your host.

How to run MLPerf benchmarks via CM?

CM was originally designed to make it easier to run MLPerf inference benchmarks.

While MLPerf inference has a common benchmarking engine called loadgen, setting up a given platform, installing all tools, downloading and preprocessing all models and data sets, updating paths and environment variables, figuring out default parameters for various scenarios, preparing a loadgen command line, keeping track of continuous updates in MLPerf rules, running multiple experiments and submitting results is a major challenge for old and new submitters (see MLPerf inference v4.0 submitter orientation for automation.

We created several CM scripts to prepare and run different implementations of MLPerf inference (reference, Nvidia, Intel, Qualcomm, Deep Sparse, etc) with a master CM script to run them all out-of-the-box natively or inside automatically-generated containers run-mlperf-inference-app. CM helped us to implement it as a simple pipeline with a common and human-friendly interface while reusing all existing automation recipes.

This script was successfully validated 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.

Please check this documentation for more details.

How to use CM to reproduce research papers?

Following the successful validation of CM concept to modularize and run MLPerf inference benchmarks across diverse software and hardware, the community test it to make it easier to reproduce results from research papers during artifact evaluation and other reproducibility initiatives at systems conferences.

The idea is to provide a common interface to prepare and run experiments from research papers. See the latest CM scripts to rerun some experiments from the ACM/IEEE MICRO'23 conference and from the Student Cluster Competition at Supercomputing'23.

How to use CM as a common interface to other projects?

While CM was successfully validated to unify, modularize and automate MLPerf benchmarks, it turned out to be applicable to any software project.

The community started using CM as a common and human-friendly interface to run other software projects and manage experiments across diverse models, data sets, software and hardware while making them more modular, portable and reusable.

Please check other CM tutorials, CM documentation and our ACM REP'23 keynote for more details.

Where to read about the CM vision and history?

ACM REP'23 keynote about MLCommons CM: slides YouTube
ACM TechTalk'21 about automating research projects: YouTube slides
Project history

How to get in touch with the CM community?

This is a community project being developed by the MLCommons Task Force on Automation and Reproducibility based on your feedback and contributions - please join our public Discord server if you would like to help with developments or have questions, suggestions and feature requests.