Docker Stacks

Here, you find the necessary files for building various pyiron docker images. All of these images can be pulled from docker-hub. We provide following flavors based on the main pyiron modules:

Image Name	Derived From	Additional Dependencies	Pull Command
pyiron/base	jupyter/base-notebook	pyiron_base	docker pull pyiron/base
pyiron/md	pyiron/base	LAMMPS, pyiron, NGLview	docker pull pyiron/md
pyiron/pyiron	pyiron/md	SPHInX, GPAW	docker pull pyiron/pyiron
pyiron/experimental	pyiron/base	TEMMETA, pyprismatic, match-series, pyxem, pystem	docker pull pyiron/experimental
pyiron/continuum	pyiron/md	damask, sqsgenerator, fenics	docker pull pyiron/continuum
pyiron/potentialworkshop	pyiron/pyiron	atomicrex, calphy, pyiron_contrib, pyiron_gpl, python-ace, runner	docker pull pyiron/potentialworkshop
pyiron/mpie_cmti	pyiron/pyiron	atomicrex, calphy, fitsnap, pyiron_contrib, pyiron_gpl, pyiron_gui, pyiron_workflow, python-ace, pytorch, runner, tensorflow	docker pull pyiron/mpie_cmti

By deriving the images from each other the size of Docker layers is reduced to a minimum. The images also include some example notebooks to get you started.

Execute Docker Container

Running one of these container and spawning a Jupyter server from within will provide you with a ready-to-start environment for using pyiron. If you like a simple Jupyter notebook, run

docker run -i -t -p 8888:8888 <image name> /bin/bash -c "source /opt/conda/bin/activate; jupyter notebook --notebook-dir=/home/jovyan/ --ip='*' --port=8888"

replace <image_name> with respective image you want to use, e.g. pyiron/md. If you prefer to use Jupyter lab, run

docker run -i -t -p 8888:8888 <image_name> /bin/bash -c "source /opt/conda/bin/activate; jupyter lab --notebook-dir=/home/jovyan/ --ip='*' --port=8888"

These commands do a number of things:

• docker run <image_name> spawns a container based on image <image_name>. In case the image isn't already on your system, it will be downloaded. Also, if not further specified, the latest tag will be assumed and outdated local versions may be updated.
• -i -t: the container is spanwed in "interactive mode" by allocating a pseudo-tty (-t).
• -p 8888:8888: port 8888 of the container instance is forwarded to port 8888 of the host.
• <image name>: the image's name.
• /bin/bash: inside the container, a bash shell is started.
• -c "source /opt/conda/bin/activate; jupyter notebook --notebook-dir=/home/jovyan/ --ip='*' --port=8888": the shell executes the command inside the quotation marks:
  • source /opt/conda/bin/activate: activate the conda environment
  • jupyter notebook or jupyter lab: start a Jupyter server running a notebbok/lab. Do this in the user's (jovyan) home-directory (--notebook-dir=/home/jovyan/) and allow connections from any IP address (--ip='*') on port 8888 (--port=8888) which is connected to the outside.

Data Persistence

In case you want to keep data you worked on/created while using the container, it may be convenient to mount a local directory into the home directory of the docker container by adding -v <local_path>:/home/jovyan/ to the docker run command:

docker run -i -t -v <local_path>:/home/jovyan/ -p 8888:8888 <image_name> /bin/bash -c "source /opt/conda/bin/activate; jupyter notebook --notebook-dir=/home/jovyan/ --ip='*' --port=8888"