AuthorsJ. Forde, T. Head, C. Holdgraf, Y. Panda, G. Nalvarte, M. Pacer, F. Perez, B. Ragan-Kelley and E. Sundell
TitleReproducible Research Environments with Repo2Docker
AfilliationScientific Computing, Machine Learning
Project(s)Department of Numerical Analysis and Scientific Computing
Publication TypeProceedings, refereed
Year of Publication2018
Conference NameICML 2018 Reproducible Machine Learning
Date Published07/2018
Keywordsdocker, jupyter, python, reproducibility

Reproducibility challenges in machine learning often center on questions of software engineering practices. Researchers struggle to reproduce another scientist's work because they cannot translate a paper into code with similar results or run an author's code. repo2docker provides a simple tool for checking the minimum requirements to reproduce a paper by building a Docker image based on a repository path or URL. Its goal is to minimize the effort needed to convert a static repository into a working software environment. By inspecting a repository for standard configuration files used in contemporary software engineering and leveraging containerization methods, repo2docker deterministically reproduces the environment of the author so the researcher can reproduce the author's experiments.

Citation Key26122

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