Setting up a Jupyterhub Dockercontainer to spawn Jupyter Notebooks with GPU support (containing Tensorflow, Pytorch and Keras)
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Jupyter_Image
Jupyterhub_image
.env
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 Images described in the Dockerfiles under Jupyter_Images. There are two sets of images. CUDA10 and Ubuntu18.04 or CUDA9.2 and Ubuntu16.04 based ones. They will be spwaned from a Jupyterhub which is defined in the Dockerfile.jupyterhub under the folder Jupyterhub_image. For more details visit our website https://www.dlm.med.fau.de/setting-jupyterhub-deep-learning/

Just in short some features: -Jupyterhub Image definition with DockerSpawner for spawning Jupyternotebooks.

  • Data is persisted: --> -locally (Docker-Volume --> cookie secrets)

-Spawned Images run in single Docker-Containers
- Data is persisted: --> user based: -locally (Docker-Volume--> :/home/Deep_Learner/work/local) and per network associated folder
(:/home/Deep_Learner/work/network). --> commonly shared: - per network associated folder (:/home/Deep_Learner/shared). - They contain either CUDA10 and Ubuntu18.04 (needs Nvidia Driver v. >410) or CUDA9.2 and Ubuntu16.04 (needs Nvidia Driver v. >390) (choose what suits you best) Installed: Virtual Environments (Conda env.) for (DeepLearning)Python3, (DeepLearning)Python2, (local Python3), OmeroR, SOS Notebook: The workhorses are (DeepLearning)Python3, (DeepLearning)Python2: They include (and many others--> see Dockerfiles)

       Tensorflow-GPU 1.12 and
       Pytorch-GPU 1.0.0