ck

The Collective Knowledge framework (CK) aims at making it easier to design, benchmark, optimize and deploy Machine Learning Systems in the real world across continuously evolving software, hardware and data sets with the help of the community. See MLPerf inference benchmark automation use case powered by CK:

Showing:

Popularity

Downloads/wk

0

GitHub Stars

432

Maintenance

Last Commit

1d ago

Contributors

21

Package

Dependencies

0

License

Apache 2.0

Categories

Readme

Collective Knowledge framework (CK)

PyPI version Downloads Python Version

Build Status Windows Build status Coverage Status

Documentation Status Colab Colab

Motivation

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:

Further community developments are supported by MLCommons™, OctoML and the cTuning foundation within the MLCommons' Design Space Exploration workgroup. Please contact Grigori Fursin if you are interested to join this community effort!

Releases

Stable versions

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

Development versions

We plan to develop a new version of the CK framework (v3) within the MLCommons' Design Space Exploration workgroup - please contact Grigori Fursin to join this community effort!

Deprecated versions

Versions 1.x including v1.17.0 and 1.55.5 (BSD license) are not supported anymore. Please get in touch and we will help you to upgrade your infrastructure to use the latest MLCommons technology!

Current projects

Documentation

Installation

Follow this guide to install CK framework on your platform.

CK supports the following platforms:

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

Examples

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
ls

processed-image.pgm

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 cKnowledge.io 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).

Contributions

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 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).

Please consider joining the MLCommons' Design Space Exploration workgroup to join this community effort - contact Grigori Fursin for more details.

Author and coordinator

Acknowledgments

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.

Rate & Review

Great Documentation0
Easy to Use0
Performant0
Highly Customizable0
Bleeding Edge0
Responsive Maintainers0
Poor Documentation0
Hard to Use0
Slow0
Buggy0
Abandoned0
Unwelcoming Community0
100