Collective Knowledge: organizing research projects as a database of reusable components and portable workflows with common APIs
This addresses the challenge of reproducibility and reuse in research for researchers and practitioners, though it is incremental as it builds on modular and automation concepts.
The paper tackles the problem of organizing research projects by introducing the Collective Knowledge framework, which decomposes projects into reusable components with common APIs and portable workflows, and successfully validated this approach in industrial projects for automating benchmarking, auto-tuning, and co-design in ML/AI.
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 universal 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 (ML) and artificial intelligence (AI) 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 .