The latest version of the CK automation suite supported by MLCommons™: v2.5.8 (Apache 2.0 license):
We plan to develop a new version of the CK framework (v3) as a collaborative effort within different MLCommons workgroups - please feel free to join this community effort!
Versions 1.x including v1.17.0 and 1.55.5 (BSD license) are stable but not officially supported anymore. Please get in touch and we will help you to upgrade your infrastructure to use the latest MLCommons technology!
Collective Knowledge framework (CK) helps to organize software projects as a database of reusable components with common automation actions and extensible meta descriptions based on FAIR principles (findability, accessibility, interoperability and reusability) as described in our journal article (shorter pre-print).
Our goal is to help researchers and practitioners share, reuse and extend their knowledge in the form of portable workflows, automation actions and reusable artifacts with a common API, CLI, and meta description. See how CK helps to automate benchmarking, optimization and design space exploration of AI/ML/software/hardware stacks, simplifies MLPerf™ inference benchmark submissions and supports collaborative, reproducible and reusable ML Systems research:
Follow this guide to install CK framework on your platform.
CK supports the following platforms:
|As a host platform||As a target platform|
|Bare-metal (edge devices)||-||±|
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 detect soft --tags=compiler,gcc ck detect soft --tags=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 ls processed-image.pgm
You can now view this image with detected corners.
Check CK docs for further details.
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
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
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.
We have developed the cKnowledge.io portal to help the community organize and find all the CK workflows and components similar to PyPI:
The community provides Docker containers to test CK and components using different ML/SW/HW stacks (DSE).
Note, that we plan to redesign the CK core 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).