The cTuning foundation is a non-profit research and development organization established by Grigori Fursin
to develop the methodology and open-source CK framework
for collaborative and reproducible ML systems research as explained
in this ACM TechTalk and several articles:
2009,
2014,
2017,
2019,
2020a,
2020b.
The ultimate goal is to automate the development of efficient computing systems (speed, accuracy, energy, costs)
and accelerate knowledge discovery to solve real-world problems (see how CK helps to automate
MLPerf™ benchmark).
We pursue these goals by organizing all research knowledge
in this public repository in the form of portable workflows,
automation actions, reusable artifacts and reproducible papers.
Feel free to get in touch to discuss practical use-cases, collaborations, and new projects!
News
- 2021 September: We are excited to announce that we've donated our CK framework
and the MLPerf inference automation suite v2.5.8
to MLCommons
(github.com/mlcommons/ck and
github.com/mlcommons/ck-mlops) to benefit everyone! .
- 2021 March: Our ACM TechTalk about "reproducing 150 Research Papers and Testing Them in the Real World"
is available on the ACM YouTube channel.
- 2021 March: The report from the "Workflows Community Summit: Bringing the Scientific Workflows Community Together"
is available in ArXiv.
- 2021 March: Our paper about the CK techology has appeared in the Philosophical Transactions A, the world's longest-running journal where Newton published: DOI, ArXiv.
- 2020.December: We are honored to join MLCommons
as a founding member to accelerate machine learning innovation.
- 2020.November: We are very excited to announce that we have completed
the prototyping phase of our Collective Knowledge framework (CK)
and successfully validated it in multiple industrial and academic projects
as described in this white paper
and the FASTPath'20 presentation.
We have helped our partners and the community to use CK as an extensible playground to implement reusable
components
with automation actions for AI, ML, and systems R&D.;
We used such components to assemble portable workflows
from reproduced research papers
during our reproducibility initiatives at ML and systems conferences.
We then demonstrated that it was possible to use such portable workflows
to automate the co-design process of efficient software, hardware and models,
simplify MLPerf inference benchmark submissions,
and quickly deploy emerging AI, ML, and IoT technology in production
in the most efficient way (speed, accuracy, energy, costs)
across diverse platforms from data centers to edge devices.
We are honored to collaborate with great academic and industrial partners
on the following open-source projects and reproducibility initiatives:
Do not hesitate to contact us if you are interested to support our community activities.
Sponsors (2021)
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