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Collective Knowledge - a lightweight knowledge manager to organize, cross-link, share and reuse artifacts and workflows based on FAIR principles

Project description

Note that the 1st generation of the CK framework was discontinued in summer 2022 after the 2nd generation of this framework (CM) was released by the open taskforce on education and reproducibility at MLCommons.

Collective Knowledge framework (CK)

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While Machine Learning is becoming more and more important in everyday life, designing efficient ML Systems and deploying them in the real world is becoming increasingly challenging, time consuming and costly. Researchers and engineers must keep pace with rapidly evolving software stacks and a Cambrian explosion of hardware platforms from the cloud to the edge. Such platforms have their own specific libraries, frameworks, APIs and specifications and often require repetitive, tedious and ad-hoc optimization of the whole model/software/hardware stack to trade off accuracy, latency, throughout, power consumption, size and costs depending on user requirements and constraints.

The CK framework

The Collective Knowledge framework (CK) is our attempt to develop a common plug&play infrastructure that can be used by the community similar to Wikipedia to learn how to solve above challenges and make it easier to co-design, benchmark, optimize and deploy Machine Learning Systems in the real world across continuously evolving software, hardware and data sets (see our ACM TechTalk for more details):

Community developments

We collaborated with the community to reproduce 150+ ML and Systems papers and implement the following reusable automation recipes in the CK format:

Please contact Grigori Fursin if you are interested to join this community effort!



Development version

We are developing the 2nd generation of the CK framework (aka CM) based on your feedback:

Stable versions

The latest version of the CK automation suite supported by MLCommons™:

Current projects



Follow this guide to install CK framework on your platform.

CK supports the following platforms:

As a host platform As a target platform
Generic Linux
Linux (Arm)
Raspberry Pi
MacOS ±
Android ±
Bare-metal (edge devices) - ±


Portable CK workflow (native environment without Docker)

Here we show how to pull a GitHub repo in the CK format and use a unified CK interface to compile and run any program (image corner detection in our case) with any compatible data set on any compatible platform:

python3 -m pip install ck

ck pull repo:mlcommons@ck-mlops

ck ls program:*susan*

ck search dataset --tags=jpeg

ck pull repo:ctuning-datasets-min

ck search dataset --tags=jpeg

ck detect soft:compiler.gcc
ck detect soft:compiler.llvm

ck show env --tags=compiler

ck compile program:image-corner-detection --speed

ck run program:image-corner-detection --repeat=1 --env.MY_ENV=123 --env.TEST=xyz

You can check output of this program in the following directory:

cd `ck find program:image-corner-detection`/tmp


You can now view this image with detected corners.

Check CK docs for further details.

MLPerf™ benchmark workflows

Portable CK workflows inside containers

We have prepared adaptive CK containers to demonstrate MLOps capabilities:

You can run them as follows:

ck pull repo:mlcommons@ck-mlops
ck build docker:ck-template-mlperf --tag=ubuntu-20.04
ck run docker:ck-template-mlperf --tag=ubuntu-20.04

Portable workflow example with virtual CK environments

You can create multiple virtual CK environments with templates to automatically install different CK packages and workflows, for example for MLPerf™ inference:

ck pull repo:mlcommons@ck-venv
ck create venv:test --template=mlperf-inference-main
ck ls venv
ck activate venv:test

ck pull repo:mlcommons@ck-mlops
ck install package --ask --tags=dataset,coco,val,2017,full
ck show env

Integration with web services and CI platforms

All CK modules, automation actions and workflows are accessible as a micro-service with a unified JSON I/O API to make it easier to integrate them with web services and CI platforms as described here.

Other use cases

CK portal

We have developed the portal to help the community organize and find all the CK workflows and components similar to PyPI:

Containers to test CK automation recipes and workflows

The community provides Docker containers to test CK and components using different ML/SW/HW stacks (DSE).


Users can extend the CK functionality via CK modules or external GitHub reposities in the CK format as described here.

Please check this documentation if you want to extend the CK core functionality and modules.

Note, that we plan to redesign the CK framework to be more pythonic (we wrote the first prototype without OO to be able to port it to bare-metal devices in C but eventually we decided to drop this idea).

Please contact Grigori Fursin to join this community effort.



We would like to thank all contributors and collaborators for their support, fruitful discussions, and useful feedback! See more acknowledgments in the CK journal article and ACM TechTalk.

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