Docker Stacks are a set of ready-to-run Docker images containing Jupyter applications, IceCube tools, and interactive computing tools.
Several images are provided that layer to a fully enabled image that contains python data science tools, icecube software (combo, skylab, etc).
- base-notebook - Base notebook based on nvidia CUDA images, currentl built on Ubuntu 18.04. Adds conda setup for dedicated python toolset based on Jupyter.
- minimal-notebook - Adds some additional OS tools (tex, etc)
- scipy-notebook - Adds several conda packages to support Scientific computing (scipy, numpy, astropy, pandas, etc)
- datascience-notebook - Adds additional data science packages (Julia, R,...)
- tensorflow-notebook - Adds tensorflow and Keras
- icecube-notebook - Adds many icetray dependencies packages, test-data, photon tables, etc. Many dependencies are added to the conda install so that compiled icetray works well with "conda python".
- icetray-notebook - Builds and adds latest combo release to runtime enviroment.
To start the icetray-notebook locally in your docker enviroment, such as Docker Desktop, use:
docker run -ti --rm -v ~/jupyter-notebooks:/home/jovyan -p 8888:8888 blaufuss/icetray-notebook:latest start.sh jupyter lab
This command pulls the latesticetraty-notebookfrom Docker Hub if it is not already present on the local host. It then starts an ephemeral container running a Jupyter Notebook server and exposes the server on host port 8888. The command mounts the ~/jupyter-notebooks directory on the host as /home/jovyan/work in the container. Visiting http://<hostname>:8888/?token=<token> in a browser loads JupyterLab, where hostname is the name of the computer running docker and token is the secret token printed in the console. Docker destroys the container after notebook server exit, but any files written to ~/jupyter-notebooks in the container remain intact on the host.
Talk to Erik. @blaufuss on slack.