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Collective knowledge: organizing research projects as a database of reusable components and portable workflows with common interfaces.

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Titel: Collective knowledge: organizing research projects as a database of reusable components and portable workflows with common interfaces.
Autoren: Fursin G; cTuning foundation and cKnowledge SAS.
Quelle: Philosophical transactions. Series A, Mathematical, physical, and engineering sciences [Philos Trans A Math Phys Eng Sci] 2021 May 17; Vol. 379 (2197), pp. 20200211. Date of Electronic Publication: 2021 Mar 29.
Publikationsart: Journal Article
Sprache: English
Info zur Zeitschrift: Publisher: The Royal Society Country of Publication: England NLM ID: 101133385 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1471-2962 (Electronic) Linking ISSN: 1364503X NLM ISO Abbreviation: Philos Trans A Math Phys Eng Sci Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s): Original Publication: London : The Royal Society, c1996-
Abstract: This article provides the motivation and overview of the Collective Knowledge Framework (CK or cKnowledge). The CK concept is to decompose research projects into reusable components that encapsulate research artifacts and provide unified application programming interfaces (APIs), command-line interfaces (CLIs), meta descriptions and common automation actions for related artifacts. The CK framework is used to organize and manage research projects as a database of such components. Inspired by the USB 'plug and play' approach for hardware, CK also helps to assemble portable workflows that can automatically plug in compatible components from different users and vendors (models, datasets, frameworks, compilers, tools). Such workflows can build and run algorithms on different platforms and environments in a unified way using the customizable CK program pipeline with software detection plugins and the automatic installation of missing packages. This article presents a number of industrial projects in which the modular CK approach was successfully validated in order to automate benchmarking, auto-tuning and co-design of efficient software and hardware for machine learning and artificial intelligence in terms of speed, accuracy, energy, size and various costs. The CK framework also helped to automate the artifact evaluation process at several computer science conferences as well as to make it easier to reproduce, compare and reuse research techniques from published papers, deploy them in production, and automatically adapt them to continuously changing datasets, models and systems. The long-term goal is to accelerate innovation by connecting researchers and practitioners to share and reuse all their knowledge, best practices, artifacts, workflows and experimental results in a common, portable and reproducible format at https://cKnowledge.io/. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico '.
Contributed Indexing: Keywords: DevOps; FAIR principles; portability; reproducibility; research automation; reusability
Entry Date(s): Date Created: 20210329 Date Completed: 20210330 Latest Revision: 20210330
Update Code: 20250114
DOI: 10.1098/rsta.2020.0211
PMID: 33775147
Datenbank: MEDLINE
Beschreibung
Abstract:This article provides the motivation and overview of the Collective Knowledge Framework (CK or cKnowledge). The CK concept is to decompose research projects into reusable components that encapsulate research artifacts and provide unified application programming interfaces (APIs), command-line interfaces (CLIs), meta descriptions and common automation actions for related artifacts. The CK framework is used to organize and manage research projects as a database of such components. Inspired by the USB 'plug and play' approach for hardware, CK also helps to assemble portable workflows that can automatically plug in compatible components from different users and vendors (models, datasets, frameworks, compilers, tools). Such workflows can build and run algorithms on different platforms and environments in a unified way using the customizable CK program pipeline with software detection plugins and the automatic installation of missing packages. This article presents a number of industrial projects in which the modular CK approach was successfully validated in order to automate benchmarking, auto-tuning and co-design of efficient software and hardware for machine learning and artificial intelligence in terms of speed, accuracy, energy, size and various costs. The CK framework also helped to automate the artifact evaluation process at several computer science conferences as well as to make it easier to reproduce, compare and reuse research techniques from published papers, deploy them in production, and automatically adapt them to continuously changing datasets, models and systems. The long-term goal is to accelerate innovation by connecting researchers and practitioners to share and reuse all their knowledge, best practices, artifacts, workflows and experimental results in a common, portable and reproducible format at https://cKnowledge.io/. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico '.
ISSN:1471-2962
DOI:10.1098/rsta.2020.0211