Collective Knowledge (software)

The Collective Knowledge (CK) project is an open-source framework and repository to enable collaborative, reproducible and sustainable research and development of complex computational systems.[1] CK is a small, portable, customizable and decentralized infrastructure helping researchers and practitioners:

  • share their code, data and models as reusable Python components and automation actions[2] with unified JSON API, JSON meta information, and a UID
  • assemble portable workflows from shared components (such as multi-objective autotuning[3])
  • automate, crowdsource and reproduce benchmarking of complex computational systems[4]
  • unify predictive analytics (scikit-learn, R, DNN)
  • enable reproducible and interactive papers[5]
Collective Knowledge (CK)
Developer(s)Grigori Fursin and the cTuning foundation
Initial release2014 (2014)
Stable release
1.12.1 / January 27, 2020 (2020-01-27)
Written inPython
Operating systemLinux, Mac OS X, Microsoft Windows, Android
TypeKnowledge management, Artifact Evaluation, Package management system, Scientific workflow system, DevOps, Continuous integration, Reproducibility
LicenseBSD License 3-clause
Websitecknowledge.io, github.com/ctuning/ck

Notable usages

Portable package manager for portable workflows

CK has an integrated cross-platform package manager with Python scripts, JSON API and JSON meta-description to automatically rebuild software environment on a user machine required to run a given research workflow.[16]

Reproducibility of experiments

CK enables reproducibility of experimental results via community involvement similar to Wikipedia and physics. Whenever a new workflow with all components is shared via GitHub, anyone can try it on a different machine, with different environment and using slightly different choices (compilers, libraries, data sets). Whenever an unexpected or wrong behavior is encountered, the community explains it, fixes components and shares them back as described in.[3]

References
  1. Fursin, Grigori; Anton Lokhmotov; Ed Plowman (January 2016). Collective Knowledge: Towards R&D Sustainability. Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE). Retrieved 14 September 2016.
  2. reusable CK components and actions to automate common research tasks
  3. Grigori Fursin, Anton Lokhmotov, Dmitry Savenko, Eben Upton. A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques, arXiv:1801.08024, January 2018 (arXiv link, interactive report with reproducible experiments)
  4. Online repository with reproduced results
  5. Index of reproduced papers
  6. HiPEAC info (page 17) (PDF), January 2016
  7. Ed Plowman; Grigori Fursin, ARM TechCon'16 presentation "Know Your Workloads: Design more efficient systems!"
  8. Reproducibility of Results in the ACM Digital Library
  9. Artifact Evaluation for systems and machine learning conferences
  10. EU TETRACOM project to combine CK and CLSmith (PDF)
  11. Artifact Evaluation Reproduction for "Software Prefetching for Indirect Memory Accesses", CGO 2017, using CK
  12. GitHub development website for CK-powered Caffe
  13. Open-source Android application to let the community participate in collaborative benchmarking and optimization of various DNN libraries and models
  14. Reproducing Quantum results from Nature – how hard could it be?
  15. MLPerf crowd-benchmarking
  16. List of shared CK packages
  • Development site:
  • Documentation:
  • Public repository with crowdsourced experiments:
  • International Workshop on Adaptive Self-tuning Computing System (ADAPT) uses CK to enable public reviewing of publications and artifacts via Reddit:
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