DEPRECATED. This repository is no longer actively maintained. It has been replaced with the following:
- Databio/dockerfiles is the new source for lab dockerfiles
- Bulker is a polished, updated version of my 'containerized executables' that is now much more approachable than this initial version.
I'll leave this repository here just for reference.
This repository contains a complete solution to dockerizing common commands. It includes both Dockerfiles for building various docker containers, as well as a
bin directory with executables that are run in their respective container.
Getting started: An example
Just clone this repository:
git clone https://github.com/nsheff/docker.git
Then, build any containers you want. For example, to build the pandoc container:
cd docker make pandoc
Now, you can run this containerized
pandoc command like this:
In this repo is a Makefile with recipes for building each image. Type
make IMAGE, where
IMAGE is one of the values in the
Dockerfile_IMAGE. You can also tab-complete to see which images can be built. You can also re-build any container from scratch (don't use caches) by adding '-nocache' to the target name, e.g.
In the /bin folder are little shell wrappers that execute commands in containers. These wrappers will do all the magic so that the commands will: 1) run in your current working directory; 2) mount your filesystem appropriately, and 3) run as the current user/group to preserve permissions. Thus, each of the command in the
bin is a drop-in replacement for the corresponding utility, so that you don't have to install any of this stuff natively. I add this
bin folder to my
PATH and then immediately have access to each of these tools using the respective containers:
shny - running a shiny server
I use the
bin/shny executable. Run an app in the shiny server container like so:
Since this is going to
docker exec the
shiny::runApp() function in
R, the container needs to be already running at the moment (start it with
rxgn - Roxygenize in container
jekyll - build and serve a Jekyll website from a container
jekyll container has Ruby, Jekyll, and friends, so I can serve up local jekyll sites for testing without installing Ruby and so forth.
Serve up a jekyll site with:
cd path/to/blog jekyll serve
Then visit the site at
This container combines Liquid templates and YAML data to build a simple, command-line templating system. The container just has
liquid and a simply ruby script. The executable,
bin/liquify, uses the container to populate a template with data from a yaml file, and spit the output into an outfile. That's it! You can see the command-line options with
-t, --template TEMPLATE Liquid template file -d, --data DATA YAML data file -o, --outfile OUTFILE Output file
pandoc in docker
pandocker is a containerized drop-in replacement for the
pandoc executable. I build a container with
pandoc and a bunch of
latex prereqs, and then you can just use the
pandocker executable and it will run your stuff through
pandoc in the container. I softlinked
pandocker and now I don't have to install pandoc or any latex stuff on any of my computers.
bin/jabref as an executable to run containerized Jabref. This is more convenient than using the Java CLI (
java -jar blah blah), and also lets me run multiple
jabrefs at the same time, which is nice, because I can run exports even when
jabref is already running.
Run containerized libreoffice with:
Non-executable container descriptions
My R production environment, based on the bioconductor Docker containers, then installs a bunch of packages I use regularly (the packages lists are, for example, in Rsetup/rpack_basic.txt).
Currently this is the same as rdev.
My R development environment, for building packages. Comes with everything you need to run
R CMD check and
R CMD BiocCheck -- and a couple of helper scripts in
This will build, RCheck, and BiocCheck the package, all in a docker container with all dependencies already required.
This will run your unit tests in a docker container:
My docker container to convert Inkscape SVG figures into PDF, and other graphics processing tasks. Under construction.
- put svg2pdfpng in here
- write a wrapper script (a la djserve) to run the conversion in a container
dpipe script in
/bin lets you create slick containerized version of software that will behave in the host OS as if the software were installed locally (it will just handle permissions, setting userID, etc), so that you don't have to install them. As an example, look at
pandocker, which can replace the
pandoc executable if you stick
/bin in your
I should produce other things using the same system.
I'm working on a script to be able to run any of these
script/drun followed by the name of the image.
Installing an R environment
Originally, I was install packages inside my R containers. This seems to be the preferred way to do this from the rocker and bioconductor containers. After a few years of doing it this way, I've decided I changed my mind: I think it makes more sense to just containerize the basic requirements and then install and host the packages locally. The reason is that otherwise the containers become huge, and it gives more control over which packages I want to hold on which systems, while still providing all the system prerequisites and R versions.
You can also use these to install all these nice R packages in one shot.
gclo nsheff/docker cd code/docker Rscript Rsetup/install_bioconductor.R Rscript Rsetup/install_fonts.R Rscript Rsetup/Rsetup.R Rscript Rsetup/Rsetup.R --packages=Rsetup/rpack_basic.txt Rscript Rsetup/Rsetup.R --packages=Rsetup/rpack_bio.txt
Some useful commands:
docker rm $(docker ps -a -q): cleans all stopped containers
docker image prune: removes all dangling images