Docker packages software into self-contained environments, called containers, that include necessary dependencies to run. Containers can run on any operating system including Windows and Mac (using modern Linux kernels) via the Docker engine.
Containers can also be deployed in the cloud using Amazon Elastic Container Service or Google Kubernetes Engine.
- Quick start
- Why Use Containers
- Current Containers
- Deprecation Notice
- Using Containers
- Modifying Image Container
- Singularity
- Acknowledgements
-
Install Docker
-
Run container with Bioconductor and RStudio
docker run \ -e PASSWORD=bioc \ -p 8787:8787 \ bioconductor/bioconductor_docker:devel
This command will run the docker container
bioconductor/bioconductor_docker:devel
on your local machine.RStudio will be available on your web browser at
https://localhost:8787
. The USER is fixed to always beingrstudio
. The password in the above command is given asbioc
but it can be set to anything.8787
is the port being mapped between the docker container and your host machine. NOTE: password cannot berstudio
.The user is logged into the
rstudio
user by default.
With Bioconductor containers, we hope to enhance
-
Reproducibility: If you run some code in a container today, you can run it again in the same container (with the same tag) years later and know that nothing in the container has changed. You should always take note of the tag you used if you think you might want to reproduce some work later.
-
Ease of use: With one command, you can be running the latest release or devel Bioconductor. No need to worry about whether packages and system dependencies are installed.
-
Convenience: Easily start a fresh R session with no packages installed for testing. Quickly run an analysis with package dependencies not typical of your workflow. Containers make this easy.
Our aim is to provide up-to-date containers for the current release and devel versions of Bioconductor, and some older versions. Bioconductor’s Docker images are stored in Docker Hub; the source Dockerfile(s) are on Github.
Our release images and devel images are based on the Rocker Project - rocker/rstudio image and built when a Bioconductor release occurs.
A few of our key goals to migrate to a new set of Docker containers are,
-
to keep the image size being shipped by the Bioconductor team at a manageable size.
-
easy to extend, so developers can just use a single image to inherit and build their docker image.
-
easy to maintain, by streamlining the docker inheritance chain.
-
Adopt a "best practices" outline so that new community contributed docker images get reviewed and follow standards.
-
Adopt a deprecation policy and life cycle for images similar to Bioconductor packages.
-
Replicate the Linux build machines (malbec2) on the
bioconductor/bioconductor_docker:devel
image as closely as possible. While this is not fully possible just yet, this image can be used by maintainers who wish to reproduce errors seen on the Bioconductor Linux build machine and as a helpful debugging tool.
For each supported version of Bioconductor, we provide
-
bioconductor/bioconductor_docker:RELEASE_X_Y
-
bioconductor/bioconductor_docker:devel
Bioconductor's Docker images are stored in Docker Hub; the source Dockerfile(s) are in Github.
For previous users of docker containers for Bioconductor, please note that we are deprecating the following images. These images were maintained by Bioconductor Core, and also the community.
These images are NO LONGER MAINTAINED and updated. They will however be available to use should a user choose. They are not supported anymore by the Bioconductor Core team.
Bioconductor Core Team: bioc-issue-bot@bioconductor.org
- bioconductor/devel_base2
- bioconductor/devel_core2
- bioconductor/release_base2
- bioconductor/release_core2
Steffen Neumann: sneumann@ipb-halle.de, Maintained as part of the "PhenoMeNal, funded by Horizon2020 grant 654241"
- bioconductor/devel_protmetcore2
- bioconductor/devel_metabolomics2
- bioconductor/release_protmetcore2
- bioconductor/release_metabolomics2
Laurent Gatto: lg390@cam.ac.uk
- bioconductor/devel_mscore2
- bioconductor/devel_protcore2
- bioconductor/devel_proteomics2
- bioconductor/release_mscore2
- bioconductor/release_protcore2
- bioconductor/release_proteomics2
RGLab: wjiang2@fredhutch.org
First iteration containers
- bioconductor/devel_base
- bioconductor/devel_core
- bioconductor/devel_flow
- bioconductor/devel_microarray
- bioconductor/devel_proteomics
- bioconductor/devel_sequencing
- bioconductor/devel_metabolomics
- bioconductor/release_base
- bioconductor/release_core
- bioconductor/release_flow
- bioconductor/release_microarray
- bioconductor/release_proteomics
- bioconductor/release_sequencing
- bioconductor/release_metabolomics
The new Bioconductor Docker image bioconductor/bioconductor_docker
makes it possible to easily install any package the user chooses since
all the system dependencies are built in to this new image. The
previous images did not have all the system dependencies built in to
the image. The new installation of packages can be done with,
BiocManager::install(c("package_name", "package_name"))
Other reasons for deprecation:
-
the chain of inheritance of Docker images was too complex and hard to maintain.
-
Hard to extend because there were multiple flavors of images.
-
Naming convention was making things harder to use.
-
Images which were not maintained were not deprecated.
Please report issues with the new set of images on GitHub Issues or the Bioc-devel mailing list.
These issues can be questions about anything related to this piece of software such as, usage, extending Docker images, enhancements, and bug reports.
A well organized guide to popular docker commands can be found
here. For
convenience, below are some commands to get you started. The following
examples use the bioconductor/bioconductor_docker:devel
image.
Note: that you may need to prepend sudo
to all docker
commands. But try them without first.
Prerequisites: On Linux, you need Docker installed and on Mac or Windows you need Docker Toolbox installed and running.
docker images
docker ps
docker ps -a
docker start <CONTAINER ID>
docker exec -it <CONTAINER ID> /bin/bash
docker stop <CONTAINER ID>
docker rm <CONTAINER ID>
docker rmi bioconductor/bioconductor_docker:devel
The above commands can be helpful but the real basics of running a Bioconductor Docker involves pulling the public image and running the container.
docker pull bioconductor/bioconductor_docker:devel
docker run -e PASSWORD=<password> \
-p 8787:8787 \
bioconductor/bioconductor_docker:devel
You can then open a web browser pointing to your docker host on
port 8787. If you're on Linux and using default settings, the docker
host is 127.0.0.1
(or localhost
, so the full URL to RStudio would
be http://localhost:8787)
. If you are on Mac or Windows and running
Docker Toolbox
, you can determine the docker host with the
docker-machine ip default
command.
In the above command, -e PASSWORD=
is setting the RStudio password
and is required by the RStudio Docker image. It can be whatever you
like except it cannot be rstudio
. Log in to RStudio with the
username rstudio
and whatever password was specified.
If you want to run RStudio as a user on your host machine, in order to read/write files in a host directory, please read this.
NOTE: If you forget to add the tag devel
or RELEASE_X_Y
while
using the bioconductor/bioconductor_docker
image, it will
automatically use the latest
tag which points to the latest RELEASE
version of Bioconductor.
docker run -it --user rstudio bioconductor/bioconductor_docker:devel R
docker run -it --user rstudio bioconductor/bioconductor_docker:devel bash
Note: The docker run
command is very powerful and versatile.
For full documentation, type docker run --help
or visit
the help page.
[ Back to top ]
One such option for docker run
is -v
to mount an additional volume
to the docker image. This might be useful for say mounting a local R
install directory for use on the docker. The path on the docker image
that should be mapped to a local R library directory is
/usr/local/lib/R/host-site-library
.
The follow example would mount my locally installed packages to this
docker directory. In turn, that path is automatically loaded in the R
.libPaths
on the docker image and all of my locally installed
package would be available for use.
-
Running it interactively,
docker run \ -v /home/my-devel-library:/usr/local/lib/R/host-site-library \ -it \ --user rstudio \ bioconductor/bioconductor_docker:devel
without the
--user rstudio
option, the container is started and logged in as theroot
user.The
-it
flag gives you an interactive tty (shell/terminal) to the docker container. -
Running it with RStudio interface
docker run \ -v /home/my-devel-library:/usr/local/lib/R/host-site-library \ -e PASSWORD=password \ -p 8787:8787 \ bioconductor/bioconductor_docker:devel
[ Back to top ]
There are two ways to modify these images:
-
Making changes in a running container and then committing them using the
docker commit
command.docker commit
-
Using a Dockerfile to declare the changes you want to make.
The second way is the recommended way. Both ways are documented here.
Example 1:
My goal is to add a python package 'tensorflow' and to install a Bioconductor package called 'scAlign' on top of the base docker image i.e bioconductor/bioconductor_docker:devel.
As a first step, my Dockerfile should inherit from the
bioconductor/bioconductor_docker:devel
image, and build from
there. Since all docker images are Linux environments, and this
container is specifically 'Debian', I need some knowledge on how to
install libraries on Linux machines.
In your new Dockerfile
, you can have the following commands
# Docker inheritance
FROM bioconductor/bioconductor_docker:devel
# Update apt-get
RUN apt-get update \
## Install the python package tensorflow
&& pip install tensorflow \
## Remove packages in '/var/cache/' and 'var/lib'
## to remove side-effects of apt-get update
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
# Install required Bioconductor package
RUN R -e 'BiocManager::install("scAlign")'
This Dockerfile
can be built with the command, (note: you can name
it however you want)
docker build -t bioconductor_docker_tensorflow:devel .
This will let you use the docker image with 'tensorflow' installed and
also scAlign
package.
docker run -p 8787:8787 -e PASSWORD=bioc bioconductor_docker_tensorflow:devel
Example 2:
My goal is to add all the required infrastructure to be able to
compile vignettes and knit documents into pdf files. My Dockerfile
will look like the following for this requirement,
# This docker image has LaTeX to build the vignettes
FROM bioconductor/bioconductor_docker:devel
# Update apt-get
RUN apt-get update \
&& apt-get install -y --no-install-recommends apt-utils \
&& apt-get install -y --no-install-recommends \
texlive \
texlive-latex-extra \
texlive-fonts-extra \
texlive-bibtex-extra \
texlive-science \
texi2html \
texinfo \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
## Install BiocStyle
RUN R -e 'BiocManager::install("BiocStyle")'
This Dockerfile
can be built with the command,
docker build -t bioconductor_docker_latex:devel .
This will let you use the docker image as needed to build and compile vignettes for packages.
docker run -p 8787:8787 -e PASSWORD=bioc bioconductor_docker_latex:devel
[ Back to top ]
The latest bioconductor/bioconductor_docker
images are available on
Singularity Hub as well. Singularity is a container runtime just like
Docker, and Singularity Hub is the host registry for Singularity
containers.
You can find the Singularity containers collection on this link https://singularity-hub.org/collections/3955.
These images are particularly useful on compute clusters where you
don't need admin access. You need to have the module singularity
installed. See https://singularity.lbl.gov/docs-installation (contact your
IT department when in doubt).
If you have Singularity installed on your machine or cluster are:
Inspect available modules
module available
If Singularity is available,
module load singularity
Please check this link for specific usage instructions relevant to Singularity containers: https://singularity-hub.org/collections/3955/usage
Thanks to the rocker project for providing the R/RStudio Server containers upon which ours are based.