Containerized data analysis pipelines
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walrus is a small tool for executing data analysis pipelines using Docker containers. It is very simple: walrus reads a pipeline description from either a JSON or YAML file and starts Docker containers as described in this file. We have used walrus to develop analysis pipelines for analyzing whome-exome as well as RNA sequencing datasets.


A pipeline has a name, a list of pipeline stages, optional comments and variables. See below for an example pipeline.

Pipeline stage

A pipeline stage has a name, a Docker image it is based on, a list of pipeline stages that it depends on (e.g. if it relies on output from these), and a command it runs on start up.


Each pipeline stage should write any output data to the directory /walrus/STAGENAME that is automatically mounted onside the docker container on start-up. walrus automatically mounts input directories from its dependencies on start-up at /walrus/INPUT_STAGENAME. The user specifies where this /walrus directory is on the host OS by using the -output command line flag (see Usage for more information). On default it writes everything to a walrus directory in the current working directory of where the user executes the walrus command.


Pipeline stages that could be run in parallel are run in parallel by default.


You can declare variables in the pipeline description as well. You declare these as {"Name": "variableName", "Value": "variableValue"} and use them in the pipeline description by wrapping them like this {{variableName}}. See pipeline.json for an example.

Reproducible pipelines


Since walrus requires that tools are packaged within Docker containers, it provides a simple mechanism to ensure a reproducible execution envirionment.


We reccommend that you use git to version control your pipeline descriptions. This will ensure that you can keep track of the different parameters to the different tools as you develop your analysis pipeline.


walrus automatically tracks data in the pipeline with git-lfs. When users start a pipeline walrus will track any output data from any of the pipeline stages and commit them to the repository versioning the pipeline description, If you do not have a repository walrus will set one up for you.

Using git to version control your pipeline data is completely optional, and users can of course opt out of versioning data with git-lfs by using the walrus -version-control=false parameter.

git-lfs requires a server for hosting the large files, and while Github, BitBucket provide hosting opportunities, we have added a -lfs-server flag that starts a local git-lfs-server for use with git-lfs. Users can use this server to store files with git-lfs or push them to some other remote.


You may experience that git-lfs uses some time to start keeping track of your data. Adding the NA12878 WGS (270GB) bam file takes roughly 1 hour on our fat server (80 Intel xenon CPUs, 10 cores/CPU, ~1TB memory). Bear in mind that git-lfs runs on a single CPU. Most of the time spent is simply copying the data into the .git/lfs folder. Hopefully this will improve in later versions of git-lfs.

Installation and usage

There are two options for installing and using walrus: install walrus and its dependencies natively on your system, or use our walrus Docker image. It may sound a bit silly to have a Docker container orchestrate other containers, but by sharing the Docker socket (/var/run/docker.sock) with the walrus container it works! There are drawbacks to sharing the Docker socket and we only encourage this approach if you want to try out walrus without thinking about setting up your own environment.


There's only a single command needed to start analyzing data using the walrus Docker container. Let's assume you have a pipeline.json pipeline description in your working directory. You can analyze it by running

    docker run -v /var/run/docker.sock:/var/run/docker.sock -v $(pwd):$(pwd) -t fjukstad/walrus -i $(pwd)/pipeline.json -o $(pwd)/output

and it will write the output to a directory output/ in your current working directory.

While there's a single command you also have to take special care when specifying the volumes in your pipeline description. You must use the full path, not just relative path, where your data is on your host.

Below is a short example to analyze the fruit_stand example, that assumes that you have downloaded walrus to your GOPATH. Before you can run the pipeline you have to modify one line of the first stage in pipeline.json from

    "Volumes": ["data:/data"],


    "Volumes": ["GOPATH/src/"],

where you have to substitute GOPATH with your actual GOPATH. If your data is elsewhere you'll have to substitute the path with the full path on your system. Once you have updated the path you can then run the pipeline using

    docker run -v /var/run/docker.sock:/var/run/docker.sock -v $(pwd):$(pwd) -t fjukstad/walrus -i $(pwd)/pipeline.json -o $(pwd)/output


Prerequisites and dependencies

We are working on simplifying the installation process. In short you need to install go, git-lfs, libgit2, git2go, and the Docker Go packages before you can install walrus. You also need cmake to compile libgit2 (install it via your preferred package manager. In addition to the instructions below you can also have a look at the Dockerfile which lists all the necessary commands.


Follow the instructions on to install Go. You also need to set up your GOPATH.

Libgit2 and git2go

First install libgit, specifically version 26.

    cd libgit2-0.26.0/

    mkdir build && cd build
    cmake ..
    cmake --build . --target install

Make sure that you have added the install directory to your LD_LIBRARY_PATH before continuing. For example, like this:

    echo "export LD_LIBRARY_PATH=/usr/local/lib" >> ~/.bash_profile

After libgit2 is installed you can install version 26 of git2go

    go get


Install git-lfs following the instructions on the git-lfs homepage.

Docker Go packages

We need to do some wrangling of the Docker Go packages before we can install walrus. First download the packages, then remove the vendor directories before continuing.

    go get -u
    rm -rf $GOPATH/src/ $GOPATH/src/

You also need to set your environment variable DOCKER_API_VERSION=1.35.


    go get


Once you have installed walrus you can start analyzing data with


where $PIPELINE_DESCRIPTION is the filename of a pipeline description you've created. For more details run $ walrus --help.

Example pipeline

Here's a small example pipeline. It consists of two stages: the first writes all filenames in the / directory to a file /walrus/stage1/file, the second writes all filenames with bin in the name to a new file /walrus/stage2/file2.

name: example
- name: stage1
  image: ubuntu:latest
  - sh
  - -c
  - ls / > /walrus/stage1/file
- name: stage2
  image: ubuntu:14.04
  - sh
  - -c
  - grep bin /walrus/stage1/file > /walrus/stage2/file2
  - stage1
comment: This is the first example pipeline!


Because every data analysis framework has to be named after a big animal. Right?

There is something remarkably fantastic and prehistoric about these monsters. I could not help thinking of a merman, or something of the kind, as it lay there just under the surface of the water, blowing and snorting for quite a long while at a time, and glaring at us with its round glassy eyes.

  • Fridtjof Nansen on walruses