Collective Knowledge framework (CK)
- V2+ : Apache 2.0
- V1.x : BSD 3-clause
- Project website
- CK-powered MLPerf™ benchmark automation
- Community projects to improve and redesign CK
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™ submissions and supports collaborative, reproducible and reusable ML Systems research:
- ACM TechTalk
- AI/ML/MLPerf™ automation workflows and components from the community;
- Reddit discussion about reproducing 150 papers;
- Our reproducibility initiatives: methodology, checklist, events.
- Developing a platform to automate SW/HW co-design for ML Systems across diverse models, data sets, frameworks and platforms based on user constraints in terms of speed, accuracy, energy and costs: OctoML.ai & cKnowledge.io
- Automating MLPerf(tm) inference benchmark and packing ML models, data sets and frameworks as CK components with a unified API and meta description
- Providing a common format to share artifacts at ML, systems and other conferences: video, Artifact Evaluation
- Redesigning CK together with the community based on user feedback
- Other real-world use cases from MLPerf™, Arm, General Motors, IBM, the Raspberry Pi foundation, ACM and other great partners;
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)||-||±|
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:octoml@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.
MLPerf™ benchmark workflows
Portable CK workflow (with Docker)
We have prepared m CK containers with ML Systems components:
You can run them as follows:
ck pull repo:octoml@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:octoml@venv ck create venv:test --template=mlperf-inference-main ck ls venv ck activate venv:test ck pull repo:octoml@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.
- See docs
We have developed the cKnowledge.io portal to help the community organize and find all the CK workflows and components similar to PyPI:
- Search CK components
- Browse CK components
- Find reproduced results from papers
- Test CK workflows to benchmark and optimize ML Systems
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).