Selecting an Image#

Using one of the Jupyter Docker Stacks requires two choices:

  1. Which Docker image you wish to use

  2. How you wish to start Docker containers from that image

This section provides details about the first.

Core Stacks#

The Jupyter team maintains a set of Docker image definitions in the GitHub repository. The following sections describe these images, including their contents, relationships, and versioning strategy.


Source on GitHub | Dockerfile commit history | Docker Hub image tags

jupyter/base-notebook is a small image supporting the options common across all core stacks. It is the basis for all other stacks and contains:

  • Minimally-functional Jupyter Notebook server (e.g., no LaTeX support for saving notebooks as PDFs)

  • Miniforge Python 3.x in /opt/conda with two package managers

    • conda: “cross-platform, language-agnostic binary package manager”.

    • mamba: “reimplementation of the conda package manager in C++”. We use this package manager by default when installing packages.

  • notebook, jupyterhub and jupyterlab packages

  • No preinstalled scientific computing packages

  • Unprivileged user jovyan (uid=1000, configurable, see options in the common features section of this documentation) in group users (gid=100) with ownership over the /home/jovyan and /opt/conda paths

  • tini as the container entrypoint and a script as the default command

  • A script useful for launching containers in JupyterHub

  • A script useful for running alternative commands in the container (e.g. ipython, jupyter kernelgateway, jupyter lab)

  • Options for a self-signed HTTPS certificate and passwordless sudo


Source on GitHub | Dockerfile commit history | Docker Hub image tags

jupyter/minimal-notebook adds command-line tools useful when working in Jupyter applications.

It contains:

  • Everything in jupyter/base-notebook

  • TeX Live for notebook document conversion

  • git, vi (actually vim-tiny), nano (actually nano-tiny), tzdata, and unzip


Source on GitHub | Dockerfile commit history | Docker Hub image tags

jupyter/r-notebook includes popular packages from the R ecosystem listed below:


Source on GitHub | Dockerfile commit history | Docker Hub image tags

jupyter/scipy-notebook includes popular packages from the scientific Python ecosystem.


Source on GitHub | Dockerfile commit history | Docker Hub image tags

jupyter/tensorflow-notebook includes popular Python deep learning libraries.

  • Everything in jupyter/scipy-notebook and its ancestor images

  • tensorflow machine learning library


Source on GitHub | Dockerfile commit history | Docker Hub image tags

jupyter/datascience-notebook includes libraries for data analysis from the Julia, Python, and R communities.

  • Everything in the jupyter/scipy-notebook and jupyter/r-notebook images, and their ancestor images

  • rpy2 package

  • The Julia compiler and base environment

  • IJulia to support Julia code in Jupyter notebooks

  • HDF5, Gadfly, RDatasets packages


Source on GitHub | Dockerfile commit history | Docker Hub image tags

jupyter/pyspark-notebook includes Python support for Apache Spark.

  • Everything in jupyter/scipy-notebook and its ancestor images

  • Apache Spark with Hadoop binaries

  • pyarrow library


Source on GitHub | Dockerfile commit history | Docker Hub image tags

jupyter/all-spark-notebook includes Python, R, and Scala support for Apache Spark.

Image Relationships#

The following diagram depicts the build dependency tree of the core images. (i.e., the FROM statements in their Dockerfiles). Any given image inherits the complete content of all ancestor images pointing to it.

Image inheritancediagram


Every Monday and whenever a pull request is merged, images are rebuilt and pushed to the public container registry.

Versioning via image tags#

Whenever a docker image is pushed to the container registry, it is tagged with:

  • a latest tag

  • a 12-character git commit SHA like b9f6ce795cfc

  • a date formatted like 2021-08-29

  • a set of software version tags like python-3.9.6 and lab-3.0.16

For stability and reproducibility, you should either reference a date formatted tag from a date before the current date (in UTC time) or a git commit SHA older than the latest git commit SHA in the default branch of the jupyter/docker-stacks GitHub repository.

Community Stacks#

The core stacks are but a tiny sample of what’s possible when combining Jupyter with other technologies. We encourage members of the Jupyter community to create their own stacks based on the core images and link them below. See the contributing guide for information about how to create your own Jupyter Docker Stack.






More than 200 Jupyter Notebooks with example C# code



nbgrader and RISE on top of the datascience-notebook image



Based on IHaskell. Includes popular packages and example notebooks



IJava kernel on top of the minimal-notebook image



sagemath kernel on top of the minimal-notebook image



Major geospatial Python & R libraries on top of the datascience-notebook image



Kotlin kernel for Jupyter/IPython on top of the base-notebook image



Transformers and NLP libraries such as Tensorflow, Keras, Jax and PyTorch



Scraper tools (selenium, chromedriver, beatifulsoup4, requests) on minimal-notebook image

GPU enabled notebooks#




Power of your NVIDIA GPU and GPU calculations using Tensorflow and Pytorch in collaborative notebooks. This is done by generating a Dockerfile that consists of the nvidia/cuda base image, the well-maintained docker-stacks that is integrated as submodule and GPU-able libraries like Tensorflow, Keras and PyTorch on top of it


PRP (Pacific Research Platform) maintained registry for jupyter stack based on NVIDIA CUDA-enabled image. Added the PRP image with Pytorch and some other python packages and GUI Desktop notebook based on