docker-stacks/docs/using/selecting.md

Selecting an Image

• Core Stacks
• Image Relationships
• Community Stacks

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 https://github.com/jupyter/docker-stacks GitHub repository. The following sections describe these images including their contents, relationships, and versioning strategy.

jupyter/base-notebook

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.

• 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++".
• No preinstalled scientific computing packages
• Unprivileged user jovyan (uid=1000, configurable, see options) in group users (gid=100) with ownership over the /home/jovyan and /opt/conda paths
• tini as the container entrypoint and a start-notebook.sh script as the default command
• A start-singleuser.sh script useful for launching containers in JupyterHub
• A start.sh 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

jupyter/minimal-notebook

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

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

• Everything in jupyter/base-notebook
• TeX Live for notebook document conversion
• git, vi (actually vim-tiny), nano (actually nano-tiny), tzdata, and unzip

jupyter/r-notebook

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

jupyter/r-notebook includes popular packages from the R ecosystem.

• Everything in jupyter/minimal-notebook and its ancestor images
• The R interpreter and base environment
• IRKernel to support R code in Jupyter notebooks
• tidyverse packages from conda-forge
• devtools, shiny, rmarkdown, forecast, rsqlite, nycflights13, caret, tidymodels, rcurl, and randomforest packages from conda-forge

jupyter/scipy-notebook

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

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

• Everything in jupyter/minimal-notebook and its ancestor images
• dask, pandas, numexpr, matplotlib, scipy, seaborn, scikit-learn, scikit-image, sympy, cython, patsy, statsmodel, cloudpickle, dill, numba, bokeh, sqlalchemy, hdf5, vincent, beautifulsoup, protobuf, xlrd, bottleneck, and pytables packages
• ipywidgets and ipympl for interactive visualizations and plots in Python notebooks
• Facets for visualizing machine learning datasets

jupyter/tensorflow-notebook

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 and keras machine learning libraries

jupyter/datascience-notebook

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
• The Julia compiler and base environment
• IJulia to support Julia code in Jupyter notebooks
• HDF5, Gadfly, and RDatasets packages

jupyter/pyspark-notebook

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

jupyter/all-spark-notebook

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

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

• Everything in jupyter/pyspark-notebook and its ancestor images
• IRKernel to support R code in Jupyter notebooks
• Apache Toree and spylon-kernel to support Scala code in Jupyter notebooks
• ggplot2, sparklyr, and rcurl packages

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.

Builds

Pull requests to the jupyter/docker-stacks repository trigger builds of all images on GitHub Actions. These images are for testing purposes only and are not saved for use. When pull requests merge to master, all images rebuild on Docker Cloud and become available to docker pull from Docker Hub.

Versioning

The latest tag in each Docker Hub repository tracks the master branch HEAD reference on GitHub. latest is a moving target, by definition, and will have backward-incompatible changes regularly.

Every image on Docker Hub also receives a 12-character tag which corresponds with the git commit SHA that triggered the image build. You can inspect the state of the jupyter/docker-stacks repository for that commit to review the definition of the image (e.g., images with tag 7c45ec67c8 were built from https://github.com/jupyter/docker-stacks/tree/7c45ec67c8e7).

You must refer to git-SHA image tags when stability and reproducibility are important in your work. (e.g. FROM jupyter/scipy-notebook:7c45ec67c8e7, docker run -it --rm jupyter/scipy-notebook:7c45ec67c8e7). You should only use latest when a one-off container instance is acceptable (e.g., you want to briefly try a new library in a notebook).

Community Stacks

The core stacks are just 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.

• csharp-notebook is a community Jupyter Docker Stack image. Try C# in Jupyter Notebooks. The image includes more than 200 Jupyter Notebooks with example C# code and can readily be tried online via mybinder.org. Click here to launch .

• education-notebook is a community Jupyter Docker Stack image. The image includes nbgrader and RISE on top of the datascience-notebook image. Click here to launch it on .

• crosscompass/ihaskell-notebook

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

  crosscompass/ihaskell-notebook is based on IHaskell. Includes popular packages and example notebooks.

  Try it on

• java-notebook is a community Jupyter Docker Stack image. The image includes IJava kernel on top of the minimal-notebook image. Click here to launch it on .

• sage-notebook is a community Jupyter Docker Stack image with the sagemath kernel on top of the minimal-notebook image. Click here to launch it on .

• GPU-Jupyter: Leverage Jupyter Notebooks with the power of your NVIDIA GPU and perform 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 GPU Jupyter repo and Registry: 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 https://github.com/jupyterhub/jupyter-remote-desktop-proxy.

• cgspatial-notebook is a community Jupyter Docker Stack image. The image includes major geospatial Python & R libraries on top of the datascience-notebook image. Try it on binder:

• kotlin-notebook is a community Jupyter Docker Stack image. The image includes Kotlin kernel for Jupyter/IPython on top of the base-notebook image. Try it on Binder:

See the contributing guide for information about how to create your own Jupyter Docker Stack.