Using GMSO with Docker

As much of scientific software development happens in unix platforms, to avoid the quirks of development dependent on system you use, a recommended way is to use docker or other containerization technologies. This section is a how to guide on using GMSO with docker.


A docker installation in your machine. Follow this link to get a docker installation working on your machine. If you are not familiar with docker and want to get started with docker, the Internet is full of good tutorials like the ones here and here.

Quick Start

After you have a working docker installation, please use the following command to use run a jupyter-notebook with all the dependencies for GMSO installed:

$ docker pull mosdef/gmso:latest
$ docker run -it --name gmso -p 8888:8888 mosdef/gmso:latest

If no command is provided to the container (as above), the container starts a jupyter-notebook at the (container) location /home/anaconda/data. To access the notebook, paste the notebook URL into a web browser on your computer. When you are finished, you can control-C to exit the notebook as usual. The docker container will exit upon notebook shutdown.

Alternatively, you can also start a Bourne shell to use python from the container’s terminal:

$ docker run -it --mount type=bind,source=$(pwd),target="/home/anaconda/data" mosdef/gmso:latest sh
~ $ source .profile
(gmso-dev) ~ $


Containers by nature are ephemeral, so filesystem changes (e.g., adding a new notebook) only persist until the end of the container’s lifecycle. If the container is removed, any changes or code additions will not persist. See the section below for persistent data.


The -it flags connect your keyboard to the terminal running in the container. You may run the prior command without those flags, but be aware that the container will not respond to any keyboard input. In that case, you would need to use the docker ps and docker kill commands to shut down the container.

Persisting User Volumes

If you will be using GMSO from a docker container, a recommended way is to mount what are called user volumes in the container. User volumes will provide a way to persist all filesystem/code additions made to a container regardless of the container lifecycle. For example, you might want to create a directory called gmso-notebooks in your local system, which will store all your GMSO notebooks/code. In order to make that accessible to the container(where the notebooks will be created/edited), use the following steps:

$ mkdir -p /path/to/gmso-notebooks
$ cd /path/to/gmso-notebooks
$ docker run -it --name gmso --mount type=bind,source=$(pwd),target=/home/anaconda/data -p 8888:8888 mosdef/gmso:latest

You can easily mount a different directory from your local machine by changing source=$(pwd) to source=/path/to/my/favorite/directory.


The --mount flag mounts a volume into the docker container. Here we use a bind mount to bind the current directory on our local filesystem to the /home/anaconda/data location in the container. The files you see in the jupyter-notebook browser window are those that exist on your local machine.


If you are using the container with jupyter notebooks you should use the /home/anaconda/data location as the mount point inside the container; this is the default notebook directory.

Running Python scripts in the container

Jupyter notebooks are a great way to explore new software and prototype code. However, when it comes time for production science, it is often better to work with python scripts. In order to execute a python script ( that exists in the current working directory of your local machine, run:

$ docker run --mount type=bind,source=$(pwd),target=/home/anaconda/data mosdef/gmso:latest "python data/"

Note that once again we are bind mounting the current working directory to /home/anaconda/data. The command we pass to the container is python data/ Note the prefix data/ to the script; this is because we enter the container in the home folder (/home/anaconda), but our script is located under /home/anaconda/data.


Do not bind mount to target=/home/anaconda. This will cause errors.

If you don’t require a Jupyter notebook, but just want a Python interpreter, you can run:

$ docker run --mount type=bind,source=$(pwd),target=/home/anaconda/data mosdef/gmso:latest python

If you don’t need access to any local data, you can of course drop the --mount command:

$ docker run mosdef/gmso:latest python

Cleaning Up

You can remove the created container by using the following command:

$ docker container rm gmso


Instead of using latest, you can use the image mosdef/gmso:stable for most recent stable release of GMSO and run the tutorials.