Using containers to improve reproducibility

Overview

Teaching: 20 min
Exercises: 0 min

Questions

• How can I use container images to make my research more reproducible?

Objectives

• Understand how container images can help make research more reproducible.

• Understand what practical steps I can take to improve the reproducibility of my research.

Although this workshop is titled “Reproducible computational environments using containers”, so far we have mostly covered the mechanics of using Docker with little reference the the reproducibility aspects. In this section, we discuss these aspects in more detail.

Work in progress…

Note that reproducibility aspects of software and containers are an active area of research, discussion and development so are subject to many changes. We will present some ideas and approaches here but best practices will likely evolve in the near future.

Reproducibility

By reproducibility here we mean the ability of someone else (or your future self) being able to reproduce what you did computationally at a particular time (be this in research, analysis or something else) as closely as possible even if you do not have access to exactly the same hardware resources that you had when you did the original work.

Hopefully it is reasonably self-evident why containers are an attractive technology to help with reproducibility:

• The same computational work can be run across multiple different technologies seamlessly (e.g. Windows, macOS, Linux).
• You can save the exact process that you used for your computational work (rather than relying on potentially incomplete notes).
• You can save the exact versions of software and their dependencies in the image.
• You can access legacy versions of software and underlying dependencies which may not be generally available any more.
• You can archive and share the image as well as associating a persistent identifier with an image to allow other researchers to reproduce and build on your work.

Sharing images

As we have already seen, the Docker Hub provides a platform for sharing images publicly. Once you have uploaded an image, you can point people to its public location and they can download and build upon it.

This is fine for working collaboratively with images on a day-to-day basis but the Docker Hub is not a good option for long time archive of images in support of research and publications as it does not support adding persistent identifiers to images and it is easy to overwrite tagged images with newer versions by mistake.

Archiving and persistently identifying images using Zenodo

When you publish your work or make it publicly available in some way it is good practice to make images that you used for computational work available in an immutable, persistent way and to have an identifier that allows people to cite and give you credit for the work you have done. Zenodo provides this functionality.

Zenodo supports the archiving of tar archives and we saw earlier how to capture our Docker images as tar archives using the docker save command:

docker save image/name:tag -o image-archive.tar

These tar images can become quite large and Zenodo supports uploads up to 50GB so you may need to compress your archive to make it fit on Zenodo using a tool such as gzip (or zip):

gzip image-archive.tar

Once you have your archive, you can deposit it on Zenodo and this will:

• Create a long-term archive snapshot of your Docker image which people (including your future self) can download and reuse or reproduce your work.
• Create a persistent DOI (Digital Object Identifier) that you can cite in any publications or outputs to enable reproducibility and recognition of your work.

In addition to the archive file itself, the deposit process will ask you to provide some basic metadata to classify the image and the associated work.

Note that Zenodo is not the only option for archiving and generating persistent DOIs for images. There are other services out there - some institutions may provide their own, equivalent, service.

Reproducibility good practice

• Make use of images to capture the computational environment required for your work.
• Decide on the appropriate granularity for the images you will use for your computational work - this will be different for each project/area. Take note of accepted practice from contemporary work in the same area. What are the right building blocks for individual images in your work?
• Document what you have done and why - this can be put in comments in the Dockerfile and the use of the image desccribed in associated documentation and/or publications. Make sure that references are made in both directions so that the image and the documentation are appropriately linked.
• When you publish work (in whatever way) use an archiving and DOI service such as Zenodo to make sure your image is captured as it was used for the work and that is obtains a persistent DOI to allow it to be cited and referenced properly.

Key Points

• Container images allow us to encapsulate the computation (and data) we have used in our research.

• Using a service such as Docker Hub allows us to easily share computational work we have done.

• Using container images along with a DOI service such as Zenodo allows us to capture our work and enables reproducibility.