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Use Docker to run JupyterLab in a reproducible Python environment, work with notebooks in your project folder, and keep files when a container is removed. Start with docker run; then put the image and run settings in Compose so the setup is easier to repeat.
How Docker fits a data-science workflow
A Dockerfile describes how to build an image, which contains the base software and dependencies for an environment. A container is a running instance of that image. Docker’s JupyterLab guide walks through running a notebook server, customizing its image, and recording its configuration.
For notebook work, keep the distinction between the environment and your work clear: dependencies belong in the image, while notebooks and datasets should be stored in a mounted directory or volume rather than only in a container’s writable layer.
Run JupyterLab in a container
Docker’s local tutorial uses the quay.io/jupyter/base-notebook image, maps host port 8889 to container port 8888, and opens JupyterLab at localhost:8889/lab. A basic launch looks like this:
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docker run -p 8889:8888 quay.io/jupyter/base-notebookOpen http://localhost:8889/lab in your browser. The Jupyter image may print an access URL with a token in the container logs; use the URL provided for your own running instance. Docker’s guide includes a sample token in its tutorial command, but that value is not a credential to reuse. This is a local learning example, not a complete security setup for a server exposed to other people or the internet.
Use existing notebooks and keep them on your computer
Without a mount, files saved only inside a container can be lost when that container is removed. A bind mount connects a directory on your computer to a directory in the container. It is a practical default when you want notebooks to remain in your project folder and be accessible to your normal editor and version-control tools.
From the project directory, mount the current folder at the Jupyter user’s work directory:
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docker run -p 8889:8888 -v "$PWD":/home/jovyan/work quay.io/jupyter/base-notebookThis shell form uses $PWD; the exact host-path syntax varies by operating system and shell. Docker’s JupyterLab guide provides platform-specific command variants. After opening JupyterLab, use the work directory to access the mounted project files. Changes saved there are made to the host folder.
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| Storage choice | Host visibility | After container removal | Host-path dependence |
|---|---|---|---|
| Bind mount | Files are directly in the host directory you mount. | Files remain in that host directory. | Uses a host path; Docker notes bind mounts depend on the host’s directory structure and operating system. |
| Named volume | Managed by Docker rather than presented as an ordinary project directory by default. | Persists independently of the container unless explicitly removed. | Docker manages the storage location. |
Docker’s storage documentation explains the distinction between bind mounts and volumes. For an active project that you edit on the host, prefer a bind mount. Choose a named volume when you want Docker-managed persistence without tying storage to a particular host directory.
To use a named volume for Jupyter’s work directory, Docker’s guide demonstrates:
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docker run -p 8889:8888 -v jupyter-data:/home/jovyan/work quay.io/jupyter/base-notebookDocker creates and manages the jupyter-data volume. Removing the container does not by itself remove this volume, so it can be reused by a later container.
Put Python dependencies in a custom image
Installing packages interactively in a running notebook can make an environment difficult to reproduce. Instead, add required packages to a Dockerfile so each container made from the image starts with those dependencies already installed.
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FROM quay.io/jupyter/base-notebook
RUN pip install --no-cache-dir matplotlib scikit-learnBuild the image from that directory:
docker build -t my-jupyter-image .Then launch it with the same port mapping and, if you want the notebooks in your project folder, the bind mount:
docker run -p 8889:8888 -v "$PWD":/home/jovyan/work my-jupyter-imageThe packages in this example are installed while building the image, rather than being reinstalled in every new container. Add your project’s other required packages to the build instructions as needed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make the setup repeatable with Compose
A docker run command is convenient for a first launch, but its ports, mounts, image build, and startup command are easy to forget or mistype. Compose keeps those settings together in a YAML file. Docker Docs puts the distinction this way: “A Dockerfile provides instructions to build a container image while a Compose file defines your running containers.” The current recommended Compose format is the Compose Specification; a version declaration from older Compose file formats is not required.
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Save the following as compose.yaml beside the Dockerfile:
services:
notebook:
build: .
ports:
- "8889:8888"
volumes:
- .:/home/jovyan/workStart the service from that directory:
docker compose up --buildCompose builds the local image and starts JupyterLab with the declared port and project-directory mount. Visit http://localhost:8889/lab. To stop the foreground service, press Ctrl+C; you can also stop the Compose project with docker compose down.
Compose is useful for one service because it records how to run it. It becomes more useful when an analysis also needs supporting services: Docker’s Python guide extends a Compose setup with PostgreSQL and a persistent named volume.
Clean up without deleting notebook data
Stopping or removing the Compose container is different from deleting a named volume. The command docker compose down -v removes named volumes declared for the project as well as the containers and networks. Use the -v flag only when you intend to delete persisted volume data. A project bind mount points to files in your host directory, so removing the container does not remove those host files.
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