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 Notebook server in a local Docker container * Run JupyterLab servers for a team using JupyterHub * Write your own project Dockerfile Quick Start ----------- You can try a `relatively recent build of the jupyter/base-notebook image on mybinder.org `_ by simply clicking the preceding link. Otherwise, three examples below may help you get started if you `have Docker installed `_, know :doc:`which Docker image ` you want to use and want to launch a single Jupyter Notebook server in a container. The other pages in this documentation describe additional uses and features in detail. **Example 1:** This command pulls the ``jupyter/scipy-notebook`` image tagged ``33add21fab64`` from Docker Hub if it is not already present on the local host. It then starts a container running a Jupyter Notebook server and exposes the server on host port 8888. The server logs appear in the terminal. Visiting ``http://:8888/?token=`` in a browser loads the Jupyter Notebook dashboard page, where ``hostname`` is the name of the computer running docker and ``token`` is the secret token printed in the console. The container remains intact for restart after the notebook server exits.:: docker run -p 8888:8888 jupyter/scipy-notebook:33add21fab64 **Example 2:** This command performs the same operations as **Example 1**, but it exposes the server on host port 10000 instead of port 8888. Visiting ``http://:10000/?token=`` in a browser loads Jupyter Notebook server, where ``hostname`` is the name of the computer running docker and ``token`` is the secret token printed in the console.:: docker run -p 10000:8888 jupyter/scipy-notebook:33add21fab64 **Example 3:** This command pulls the ``jupyter/datascience-notebook`` image tagged ``33add21fab64`` from Docker Hub if it is not already present on the local host. It then starts an *ephemeral* container running a Jupyter Notebook server and exposes the server on host port 10000. The command mounts the current working directory on the host as ``/home/jovyan/work`` in the container. The server logs appear in the terminal. Visiting ``http://:10000/lab?token=`` in a browser loads JupyterLab, where ``hostname`` is the name of the computer running docker and ``token`` is the secret token printed in the console. Docker destroys the container after notebook server exit, but any files written to ``~/work`` in the container remain intact on the host.:: docker run --rm -p 10000:8888 -e JUPYTER_ENABLE_LAB=yes -v "${PWD}":/home/jovyan/work jupyter/datascience-notebook:33add21fab64 CPU Architectures ----------------- All published containers support amd64 (x86_64) and aarch64, except for datascience and tensorflow, which only support amd64 for now. Caveats for arm64 images ^^^^^^^^^^^^^^^^^^^^^^^^ - The manifests we publish in this projects wiki as well as the image tags for the multi platform images that also support arm, are all based on the amd64 version even though details about the installed packages versions could differ between architectures. For the status about this, see `#1401 `_. - Only the amd64 images are actively tested currently. For the status about this, see `#1402 `_. Table of Contents ----------------- .. toctree:: :maxdepth: 2 :caption: User Guide using/selecting using/running using/common using/specifics using/recipes .. toctree:: :maxdepth: 2 :caption: Contributor Guide contributing/issues contributing/packages contributing/recipes contributing/translations contributing/lint contributing/tests contributing/features contributing/stacks .. toctree:: :maxdepth: 2 :caption: Maintainer Guide maintaining/tasks .. toctree:: :maxdepth: 2 :caption: Getting Help Issue Tracker on Github Jupyter Discourse Forum Jupyter Website