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Create a reusable JupyterLab environment by putting your Python dependencies in a Dockerfile, building that file into an image, and running a container with Jupyter’s port published. Mount a Docker volume or a host folder for notebooks you want to keep: removing a container does not preserve files written only inside it.
What the image contains—and what it does not
A Dockerfile is a text file that tells Docker how to create an image of your JupyterLab environment. The image packages the base environment and the Python packages you install into it, so a new container made from that image does not need to install those packages again. Your notebooks are a separate concern: unless you mount persistent storage, files written in a container’s writable layer can disappear when that container is removed.
For a notebook-centered workflow, Docker’s JupyterLab guide uses quay.io/jupyter/base-notebook. Its example adds matplotlib and scikit-learn for an Iris visualization walkthrough. That pair is an example, not a complete or universal data-science stack. Add only the packages your project needs. For a more general Python application, Docker’s Python guide demonstrates recording dependency versions in a requirements file.
Create the Dockerfile
In a new project folder, create a file named Dockerfile with no file extension:
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# syntax=docker/dockerfile:1
FROM quay.io/jupyter/base-notebook
RUN pip install --no-cache-dir matplotlib scikit-learnThe FROM line selects the Jupyter base image. The RUN line installs the example packages while Docker builds your image. To adapt this for a real project, replace or extend the package list with the dependencies your notebooks use. Record package versions—such as in a requirements file—and use those recorded versions when building if collaborators need to reconstruct the same intended environment. Docker’s Python guide shows pinned requirements in its application example.
Jupyter Docker Stacks images are distributed through Quay.io. Before building a project image, check the Jupyter Docker Stacks project page for an appropriate current image tag and architecture. A floating reference such as an unpinned image name may follow changes upstream; a dated or otherwise pinned reference makes the selected base image more explicit. The documentation establishes these options, but does not provide a controlled comparison of their performance.
Build the image
Open a terminal in the directory containing the Dockerfile and run:
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docker build -t my-jupyter-image .The final period makes the current directory the build context: the files available to Docker for this build. The -t option gives the resulting image the local name my-jupyter-image. If the build succeeds, Docker has created an image from the Dockerfile and its build context. See Docker’s guidance on writing a Dockerfile and its Dockerfile overview for the underlying concepts.
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Start a container from the image and publish its Jupyter server port to your computer:
docker run --rm -p 8889:8888 my-jupyter-image start-notebook.py --NotebookApp.token='my-token'The mapping 8889:8888 means port 8889 on the host is forwarded to port 8888 in the container. Open http://localhost:8889/lab?token=my-token in a browser to reach JupyterLab. The token shown is a tutorial example, not a production access policy; choose access controls appropriate to where and how you expose the server.
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The --rm option removes the container after it stops. It does not remove the built image, but it also does not make the container’s unmounted writable data persistent. Choose a storage mount before relying on the container for notebooks you need later.
Keep notebooks when the container is replaced
Use a named volume if you want Docker to manage persistent notebook storage, or a bind mount if you want notebooks in a particular host folder that you can access directly from your usual file tools.
| Storage choice | Where notebook files live | Choose it when |
|---|---|---|
| Named volume | In Docker-managed storage, mounted at the path you specify | You want files to persist across container replacement without choosing a host folder path. |
| Bind mount | In a host directory you specify, mounted into the container | You want to edit or access the same files directly from the host. |
Use a named volume
This command mounts a named volume, jupyter-data, at Jupyter’s work directory:
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docker run --rm -p 8889:8888 -v jupyter-data:/home/jovyan/work my-jupyter-image start-notebook.py --NotebookApp.token='my-token'Files saved under /home/jovyan/work are stored in the volume rather than only in the container’s writable layer. Run a later container with the same volume mount to access that work again.
Use a bind mount
To use a host folder instead, mount its path to /home/jovyan/work with -v. For example, on a Unix-like host, a project folder can be mounted with:
docker run --rm -p 8889:8888 -v "$PWD/notebooks:/home/jovyan/work" my-jupyter-image start-notebook.py --NotebookApp.token='my-token'Here, notebooks is a directory under the current host directory. Create it first if needed, and adapt the host path to your operating system and shell. The mount makes files in that host folder available at the container’s work path. Docker’s general build best practices recommend keeping containers ephemeral; persistent storage lets you replace containers without treating them as the home of your only copy of notebook files.
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Share the image carefully
Docker’s JupyterLab guide also describes tagging and pushing a locally built image to Docker Hub. Before publishing, decide whether the repository should be public or private and make sure you do not include credentials, private data, or other sensitive files in the image or build context. Registry login and repository visibility are separate choices from building the image locally; publishing an image makes it available according to the registry’s access settings.
Choose the simplest setup that fits the work
- Jupyter base image or general Python image: choose the Jupyter base image for a notebook-centered environment. A general Python base image is an alternative when you need to assemble a less notebook-specific setup; Docker’s Python Official Image page documents that image family.
- Minimal packages or a preloaded stack: installing only project dependencies keeps the environment focused. A broader preloaded stack can be convenient, but the cited guidance does not establish a comparative image size, startup time, or build time.
- Floating or pinned base reference: a floating reference can pick up upstream changes; a dated or otherwise pinned reference documents the base selection more precisely. Check the Jupyter Stacks project for the current tags and architecture rather than relying on old Docker Hub instructions for Jupyter Stacks.
- Named volume or bind mount: choose a named volume for Docker-managed persistence, or a bind mount when you want the files in a host directory.
The Docker build-and-run workflow creates a local image and container. The Binder Project is a separate option for launching shareable notebook environments from repositories; it is not a step required to build this Docker image.
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