Setting up a Jupyterhub Dockercontainer to spawn Jupyter Notebooks with GPU support (containing Tensorflow, Pytorch and Keras)
Clone or download
Pull request Compare This branch is 21 commits ahead, 14 commits behind FAU-DLM:master.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Jupyter_Image
Jupyterhub_image
.env
.gitignore
Dockerfile.jupyterhub
Readme.md
docker-compose.yml
jupyterhub_config.py

Readme.md

This repo contains the docker-compose.yml for defining the dependencies to run the Jupyter Notebook Image described in the Dockerfile with the Jupyterhub container described in the Dockerfile.jupyterhub. For more details visit our website https://www.dlm.med.fau.de/setting-jupyterhub-deep-learning/

ac edits

in this version I've:

  • pruned dependencies I personally don't need (like medical imaging libraries)
  • moved to the nvidia cuda 10.0/ubuntu 18.04 container, and dealt with dependency changes stemming from that
  • tried to clarify the use/install process a little by adding missing .env variables, cleaning up config files, adding comments, etc.
  • added a shared data directory for common data so users aren't storing copies of the same data
  • pinned the miniconda version
  • specified use of locally built deep learning notebook container

the deep learning notebook container is still super, super huge, and could stand to be paired down even more. also, the python environment is still pretty messy, with copies of the same libraries being installed by both conda and pip alongside each other. but of course cleaning those things up is hugely time intensive because you've got to rebuild the container every time you make a change.