Python jupiter notebook docker

How to Run Jupyter Notebook in a Docker Container

Running a Jupyter Notebook in Docker is simpler than expected. No need to install anything locally (other than Docker, of course).

Assuming we have Docker installed, let’s decide which Jupyter Notebook template we want to use.

1. Choose a Jupyter Notebook template

These are the available notebook templates in the Jupyter Docker Stack.

These are ready-to-run Docker images containing Jupyter applications.

• base-notebook: small baseline environment
• datascience-notebook: environment with common data science packages
• minimal-notebook: minimalistic environment
• pyspark-notebook: environment with PySpark
• all-spark-notebook: environment with Scala, R, etc.
• r-notebook: environment with R
• scipy-notebook: environment with SciPy
• tensorflow-notebook: environment with TensorFlow

Let’s say we want to use the scipy-notebook .

If you’re running Docker without root , prepend all the commands with sudo .

2. Start the container

The following command is all we need to get a container up and running.

docker run -p 8888:8888 jupyter/scipy-notebook 

However, ideally, we’ll want to edit a Jupyter Notebook that already exists, or at least save a notebook to our local machine.

This requires us to mount a directory on the host inside the container. This will expose our host directory inside the container, which can then read from and write to this directory.

We can achieve this with the -v argument.

docker run -v $(pwd):/home/jovyan/work -p 8888:8888 jupyter/scipy-notebook 

The $(pwd) will refer to the directory in which this command is run, so anything in the current directory will be accessible inside the container. We can also specify an absolute or relative path. Either way, we’ll want our existing notebooks to reside in that location.

The /home/jovyan/work is the starting workspace inside the Docker image.

jovyan is a play on the word jovian , which means “like Jupiter”, and refers to members of the Jupyter community (source).

3. Open localhost

After running docker run , we should see logs that look something like this.

[I 01:50:11.178 NotebookApp] Serving notebooks from local directory: /home/jovyan [I 01:50:11.178 NotebookApp] Jupyter Notebook 6.4.5 is running at: [I 01:50:11.178 NotebookApp] http://64fdfb2c87f1:8888/?token=d4b65f7b870928aca803efc025b914725adc4160b70b8c44 [I 01:50:11.178 NotebookApp] or http://127.0.0.1:8888/?token=d4b65f7b870928aca803efc025b914725adc4160b70b8c44 [I 01:50:11.178 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). [C 01:50:11.191 NotebookApp] To access the notebook, open this file in a browser: file:///home/jovyan/.local/share/jupyter/runtime/nbserver-8-open.html Or copy and paste one of these URLs: http://64fdfb2c87f1:8888/?token=d4b65f7b870928aca803efc025b914725adc4160b70b8c44 or http://127.0.0.1:8888/?token=d4b65f7b870928aca803efc025b914725adc4160b70b8c44 

This log contains the generated localhost URL and a token. We can easily open up the 127.0.0.1:8888 URL with the token parameter in our browser to start using the notebook.

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Ready-to-run Docker images containing Jupyter applications

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jupyter/docker-stacks

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* Some fixes for Jupyter Notebook 7 * Fix jupyter nbextension * Remove explicit port setting

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README.md

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 Notebook server in a local Docker container
• Write your own project Dockerfile

You can try a relatively recent build of the jupyter/base-notebook image on mybinder.org by simply clicking the preceding link. Otherwise, the examples below may help you get started if you have Docker installed, know which Docker image you want to use and want to launch a single Jupyter Server in a container.

The User Guide on ReadTheDocs describes additional uses and features in detail.

This command pulls the jupyter/scipy-notebook image tagged 2023-06-01 from Docker Hub if it is not already present on the local host. It then starts a container running a Jupyter Server and exposes the container’s internal port 8888 to port 10000 of the host machine:

docker run -p 10000:8888 jupyter/scipy-notebook:2023-06-01

You can modify the port on which the container’s port is exposed by changing the value of the -p option to -p 8888:8888 .

Visiting http://:10000/?token= in a browser loads JupyterLab, where:

• hostname is the name of the computer running Docker
• token is the secret token printed in the console.

The container remains intact for restart after the Jupyter Server exits.

This command pulls the jupyter/datascience-notebook image tagged 2023-06-01 from Docker Hub if it is not already present on the local host. It then starts an ephemeral container running a Jupyter Server and exposes the server on host port 10000.

docker run -it --rm -p 10000:8888 -v "$ ":/home/jovyan/work jupyter/datascience-notebook:2023-06-01

The use of the -v flag in the command mounts the current working directory on the host ( $ in the example command) as /home/jovyan/work in the container. The server logs appear in the terminal.

Visiting http://:10000/?token= in a browser loads JupyterLab.

Due to the usage of the flag —rm Docker automatically cleans up the container and removes the file system when the container exits, but any changes made to the ~/work directory and its files in the container will remain intact on the host. The -it flag allocates pseudo-TTY.

Please see the Contributor Guide on ReadTheDocs for information about how to contribute recipes, features, tests, and community maintained stacks.

We value all positive contributions to the Docker stacks project, from bug reports to pull requests to help with answering questions. We’d also like to invite members of the community to help with two maintainer activities:

• Issue triaging: Reading and providing a first response to issues, labeling issues appropriately, redirecting cross-project questions to Jupyter Discourse
• Pull request reviews: Reading proposed documentation and code changes, working with the submitter to improve the contribution, deciding if the contribution should take another form (e.g., a recipe instead of a permanent change to the images)

Anyone in the community can jump in and help with these activities anytime. We will happily grant additional permissions (e.g., the ability to merge PRs) to anyone who shows an ongoing interest in working on the project.

Jupyter Notebook Deprecation Notice

Following Jupyter Notebook notice, JupyterLab is now the default for all the Jupyter Docker stack images. It is still possible to switch back to Jupyter Notebook (or to launch a different startup command). You can achieve this by passing the environment variable DOCKER_STACKS_JUPYTER_CMD=notebook (or any other valid jupyter subcommand) at container startup; more information is available in the documentation.

According to the Jupyter Notebook project status and its compatibility with JupyterLab, these Docker images may remove the classic Jupyter Notebook interface altogether in favor of another classic-like UI built atop JupyterLab.

This change is tracked in the issue #1217; please check its content for more information.

• jupyter/repo2docker — Turn git repositories into Jupyter-enabled Docker Images
• openshift/source-to-image — A tool for building artifacts from source and injecting them into docker images
• jupyter-on-openshift/jupyter-notebooks — OpenShift compatible S2I builder for basic notebook images

• We publish containers for both x86_64 and aarch64 platforms
• Single-platform images have either aarch64- or x86_64- tag prefixes, for example, jupyter/base-notebook:aarch64-python-3.10.5
• Starting from 2022-09-21 , we create multi-platform images (except tensorflow-notebook )
• Starting from 2023-06-01 , we create multi-platform tensorflow-notebook image as well

This project only builds one set of images at a time. If you want to use older Ubuntu and/or python version, you can use following images:

Build Date	Ubuntu	Python	Tag
2022-10-09	20.04	3.7	1aac87eb7fa5
2022-10-09	20.04	3.8	a374cab4fcb6
2022-10-09	20.04	3.9	5ae537728c69
2022-10-09	20.04	3.10	f3079808ca8c
2022-10-09	22.04	3.7	b86753318aa1
2022-10-09	22.04	3.8	7285848c0a11
2022-10-09	22.04	3.9	ed2908bbb62e
2023-05-30	22.04	3.10	4d70cf8da953
weekly build	22.04	3.11	latest

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Ready-to-run Docker images containing Jupyter applications

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A docker-compose file for launching Jupyter Notebooks in a container.

stefanproell/jupyter-notebook-docker-compose

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README.md

Jupyter with Docker Compose

This repository contains a simple docker-compose definition for launching the popular Jupyter Data Science Notebook. You can define a password with the script generate_token.py -p S-E-C-R-E-T and generate SSL certificates as described below.

• docker-compose up mounts the directory and starts the container
• docker-compose down destroys the container

The compose file: docker-compose.yml

version: '3' services: datascience-notebook: image: jupyter/datascience-notebook volumes: - $ :/home/jovyan/work - $ :/home/jovyan/work/datasets - $ :/home/jovyan/work/modules - $ :/etc/ssl/notebook ports: - $ :8888 container_name: jupyter_notebook command: "start-notebook.sh \ --NotebookApp.password=$ \ --NotebookApp.certfile=/etc/ssl/notebook/jupyter.pem"

Example with a custom user

version: '2' services: datascience-notebook: image: jupyter/base-notebook:latest volumes: - /tmp/jupyter_test_dir:/home/docker_worker/work ports: - 8891:8888 command: "start-notebook.sh" user: root environment: NB_USER: docker_worker NB_UID: 1008 NB_GID: 1011 CHOWN_HOME: 'yes' CHOWN_HOME_OPTS: -R 

# Define a local data directory # Set permissions for the container: # sudo chown -R 1000 $ LOCAL_WORKING_DIR=/data/jupyter/notebooks # Generate an access token like this # import IPython as IPython # hash = IPython.lib.passwd("S-E-C-R-E-T") # print(hash) # You can use the script generate_token.py ACCESS_TOKEN=sha1:d4c78fe19cb5:0c8f830971d52da9d74b9985a8b87a2b80fc6e6a # Host port PORT=8888 # Provide data sets LOCAL_DATASETS=/data/jupyter/datasets # Provide local modules LOCAL_MODULES=/home/git/python_modules # SSL # Generate cert like this: # openssl req -x509 -nodes -newkey rsa:2048 -keyout jupyter.pem -out jupyter.pem # Copy the jupyter.pem file into the location below. LOCAL_SSL_CERTS=/opt/ssl-certs/jupyter

Make sure to have the latest versions installed. You can use the Notebook Browser interface.

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