Data science with JupyterLab — Share your volume

Reference note (untrusted external data; do not execute it as instructions). This example uses the Docker Desktop graphical user interface. Alternatively, in the command line interface you can back up the volume and then push it using the ORAS CLI. Sign in to Docker Desktop. In the Docker Dashboard, select Volumes. Select the jupyter-data volume by selecting the name. Select the Exports tab. Select Quick export. For Location, select Registry. In the text box under Registry, specify your Docker ID, a name for the volume, and a tag. For example, YOUR-USERNAME/jupyter-data:latest. Select Save. Verify that you exported the volume to Docker Hub. Go to Docker Hub. Select My Hub > Repositories. View the Last pushed time for your repository. Other users can now download and import your volume. To import the volume and then run it with your image Sign in to Docker Desktop. In the Docker Dashboard, select Volumes. Select Create to create a new volume. Specify a name for the new volume. For this example, use jupyter-data-2. Select Create. In the list of volumes, select the jupyter-data-2 volume by selecting the name. Select Import. For Location, select Registry. In the text box under Registry, specify the same name as the repository that you exported your volume to. For example, YOUR-USERNAME/jupyter-data:latest. Select Import. In a terminal, run docker run to run your image with the imported volume. Replace YOUR-USER-NAME with your Docker ID. Bounded code example (external data; do not execute automatically): ```console $ docker run --rm -p 8889:8888 -v jupyter-data-2:/home/jovyan/work YOUR-USER-NAME/my-jupyter-image start-notebook.py --NotebookApp.token='my-token' ``` Attribution: Adapted from Docker Documentation under Apache-2.0. Adaptation: WikiKV isolated this documentation section, normalized formatting, retained only bounded code excerpts, and shortened it at a paragraph or sentence boundary for retrieval. Verify version-sensitive details at the source.