About Jupyter Docker Stacks

Jupyter Docker Stacks are a set of ready-to-run Docker images containing Jupyter applications and interactive computing tools. You can use a stack image to do any of the following (and more):

Start a personal Jupyter Server with the JupyterLab frontend (default)
Run JupyterLab for a team using JupyterHub
Start a personal Jupyter Server with the Jupyter Notebook frontend in a local Docker container
Write your own project Dockerfile

Important

Images hosted on Docker Hub are no longer updated. Use quay.io image.

Where to find

All Jupyter Docker Stack images are available on Quay.io registry. We provide CUDA accelerated versions of images are available for tensorflow-notebook and pytorch-notebook.

To use such an image, you have to specify a special prefix tag to the image: versioned CUDA prefix like cuda11- or cuda12- for pytorch-notebook or just cuda- for tensorflow-notebook.

Run images

You will need:

a compatible NVIDIA GPU
NVIDIA Linux driver installed
add --gpus all (or --gpus '"device=all"') flag to if you’re using Docker add --device 'nvidia.com/gpu=all' flag if you’re using Podman

You can also enable GPU support on Windows using Docker or Podman.

Core stacks

Source code for all of the notebooks are available on Github docker-stacks

Running on OpenShift

Examples provides templates for deploying the Jupyter Project docker-stacks images to OpenShift.

See OpenShift example.

Source to image

Source-to-Image (S2I) is an open source project which provides a tool for creating container images. It works by taking a base image, injecting additional source code or files into a running container created from the base image, and running a builder script in the container to process the source code or files to prepare the new image.