An introduction to Docker for reproducible research

Abstract

As computational work becomes more and more integral to many aspects of scientific research, computational reproducibility has become an issue of increasing importance to computer systems researchers and domain scientists alike. Though computational reproducibility seems more straight forward than replicating physical experiments, the complex and rapidly changing nature of computer environments makes being able to reproduce and extend such work a serious challenge. In this paper, I explore common reasons that code developed for one research project cannot be successfully executed or extended by subsequent researchers. I review current approaches to these issues, including virtual machines and workflow systems, and their limitations. I then examine how the popular emerging technology Docker combines several areas from systems research -such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a 'DevOps' philosophy, to address these challenges. I illustrate this with several examples of Docker use with a focus on the R statistical environment.

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“…Clear arrangements for the storage and preservation of the code should be made, instructions need to be provided that will allow the code to be compiled and run without issue, and the code should be accompanied by a description of the core functionalities and hard-and software requirements for its use. This in turn means that source code alone is not sufficient: the software environment needs to be described too, including for instance, any linked libraries, any runtime environments or virtual machines, The open source container engine Docker is intended to provide an efficient solution for computational reproducibility (see www.docker.com) [25,26]. 7 Researchers sometimes prefer not to share code because of a lack of complete and clear documentation.…”

An empirical analysis of the Docker container ecosystem on GitHub

“…However, despite this shortage of empirical work, the importance of Docker for academia and industry is rarely doubted in literature. For instance, Boettiger and Cito et al have concurrently proposed that containerization technology may be an important game changer in making systems and software engineering research more reproducible [4], [25]. In industry, Docker is increasingly being used to build next generation Platform-as-a-Service clouds [26].…”

“…Given the fast rise in popularity, its ubiquitous nature in industry, and its surrounding claim of enabling reproducibility [4], we study the Docker ecosystem with respect to quality of Dockerfiles and their change and evolution behavior within software repositories. We developed a tool chain that transforms Dockerfiles and their evolution in Git repositories into a relational database model.…”

Enabling reproducible obia with open-source software in docker containers

“…A container can be paused, stopped, and restarted, or be removed from the host. While not being intentioned for it, Docker is a means to ensure long term reproducibility of computational research, as demonstrated for example for R (Boettiger, 2015). A Docker image suffices to capture the data, software, and runtime environment in a well-defined manner and facilitates reproducibility.…”