Virtual environments, containers and scientific reproducibility

How to use Docker or Singularity to build containers in order to ease workflows dissemination.

Containers provide an easy-to-use, secure, and reproducible environment for scientists to transport their studies between computational resources. As more communities are using Docker or Singularity, we enter into the age of high reproducibility or at least replicability so that the use of containers is mandatory for any study.

The need to produce reproducible experiments in computer science

What is reproducibility ?

Reproducibility is the closeness of the agreement between the results of measurements of the same measurand carried out with the same methodology described in the corresponding scientific evidence (e.g. a publication in a peer-reviewed journal and associated source code)

Why work in a reproducible way ?

In practice, this serves to validate scientific experience without guaranteeing its accuracy, correctness or relevance. It is also relevant to test assumptions and facilitate the publication of results. About computer sciences, numerical calculation and modelisation must be replicable and reproducible but also reusable.

How to work in a reproducible way ?

Principle of the R5 (ReSciences)






Virtual environments are reproducible environments

Today, industries and research centers make massive use of virtual machines. These machines run apps under a guest operating system, which uses virtual hardware emulated by the host operating system of the real machine. In this article, we focus on virtualisation. The great interest being to be able to ignore all the attributes of the host system and to emulate another system in order to have a portable and therefore reproducible environment from one machine to another. For instance, Java works on this principle of virtualisation (yes. JVM stands for Java Virtual Machine). Virtual Box is one of the best known tools for making Virtual Machines.

What is a container ?

The isolation between guest and host is excellent, but it is expensive to emulate the virtual hardware and run a complete guest operating system. Containers can be seen as a lighter variant: by exploiting more directly the lower layers of the host system (kernel), the containers provide almost as much isolation as virtual machines, at a much lower performance cost.

So, a container is a lightweight, memory-loaded virtual machine that is used to launch an application within an isolated and reproducible environment. A container is instantiated from an image. An image is a static description of a container which you can exchange with your employees and from which containers can be instantiated and executed.


Singularity enables users to have full control of their environment. Singularity containers can be used to package entire scientific workflows, software and libraries, and even data. This means that you don’t have to ask your cluster admin to install anything for you - you can put it in a Singularity container and run.

Build an image

In the file below, which we will call Singularity.myRecipe, we start from an ubuntu docker image, on which we add the wget and build-essential packages. We also create a directory that will contain our data, in fact we can use any bash command in this bootstrap file.

dockerFrom: ubuntu:latest%post
apt-get update && apt-get -y install
wget build-essential
mkdir /data

To build the container for this recipe file, use the command build:

sudo singularity build example.simg Singularity.myRecipe

The file containing the container is example.simg. We created a portable virtual environment for our scientific analysis. Note that you need to have sudo privilege to build a container with singularity.


To get into the container, you launch a shell inside:

singularity shell example.simg

To execute a command (for instance ls /data) from inside the container:

singularity exec example.simg ls /data

A tricky case as you are working on a mounted disk at /media/disk you need to change the path of the container too:

singularity --bind /media/disk:/media/disk shell example.simg

Download ready-to-use container

Already built images are available on (check my personnal collection of containers I made here).

For instance, you can download a container with the required software to run my metabarcoding analysis:

singularity pull --name ednatools.simg shub://Grelot/bioinfo_singularity_recipes:ednatools


Docker can package an application and its dependencies in a virtual container that can run on any Linux server. This enables the application to run in a variety of locations, such as on-premises, in a public cloud, and/or in a private cloud.

Build an image

Docker creates a container from a configuration file named Dockerfile or recipe in the workdir directory. Let's build an image for apache.

FROM debian:stable
RUN apt-get update && apt-get upgrade -y && apt-get install -y apache2 telnet elinks openssh-server
ENV VARIABLE ma-variable
CMD ["/usr/sbin/apache2ctl","-D","FOREGROUND"]

To build the container for this recipe file, use the command build:

docker build -t mycontainer workdir

Now you can run apache using the container:

docker run -d -P 9999:80 --name=Debianapache mycontainer:latest


Execute a command from whithin the container:

docker exec -d mycontainer echo "hello"

Interactive shell from the container:

docker exec -it mycontainer bash

Download ready-to-use docker container

To download a particular image, or set of images (i.e., a repository), use docker pull. If no tag is provided, Docker Engine uses the :latest tag as a default. This command pulls the debian:jessie image:

docker pull debian:jessie

Check your images on docker

docker image ls

Docker vs. Singularity for data processing

Singularity is a container runtime, like Docker, but it starts from a very different place. It favors integration rather than isolation, while still preserving security restrictions on the container, and providing reproducible images.