Sharing Docker Images and Volumes on Docker Hub

### Share your image 1. [Sign up](https://www.docker.com/pricing?utm_source=docker&utm_medium=webreferral&utm_campaign=docs_driven_upgrade) or sign in to [Docker Hub](https://hub.docker.com). 2. Rename your image so that Docker knows which repository to push it to. Open a terminal and run the following `docker tag` command. Replace `YOUR-USER-NAME` with your Docker ID. ```console $ docker tag my-jupyter-image YOUR-USER-NAME/my-jupyter-image ``` 3. Run the following `docker push` command to push the image to Docker Hub. Replace `YOUR-USER-NAME` with your Docker ID. ```console $ docker push YOUR-USER-NAME/my-jupyter-image ``` 4. Verify that you pushed the image to Docker Hub. 1. Go to [Docker Hub](https://hub.docker.com). 2. Select **My Hub** > **Repositories**. 3. View the **Last pushed** time for your repository. Other users can now download and run your image using the `docker run` command. They need to replace `YOUR-USER-NAME` with your Docker ID. ```console $ docker run --rm -p 8889:8888 YOUR-USER-NAME/my-jupyter-image start-notebook.py --NotebookApp.token='my-token' ``` ### Share your volume This example uses the Docker Desktop graphical user interface. Alternatively, in the command line interface you can [back up the volume](/engine/storage/volumes/#back-up-a-volume) and then [push it using the ORAS CLI](/manuals/docker-hub/repos/manage/hub-images/oci-artifacts.md#push-a-volume). 1. Sign in to Docker Desktop. 2. In the Docker Dashboard, select **Volumes**. 3. Select the **jupyter-data** volume by selecting the name. 4. Select the **Exports** tab. 5. Select **Quick export**. 6. For **Location**, select **Registry**. 7. 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`. 8. Select **Save**. 9. Verify that you exported the volume to Docker Hub. 1. Go to [Docker Hub](https://hub.docker.com). 2. Select **My Hub** > **Repositories**. 3. 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: 1. Sign in to Docker Desktop. 2. In the Docker Dashboard, select **Volumes**. 3. Select **Create** to create a new volume. 4. Specify a name for the new volume. For this example, use `jupyter-data-2`. 5. Select **Create**. 6. In the list of volumes, select the **jupyter-data-2** volume by selecting the name. 7. Select **Import**. 8. For **Location**, select **Registry**. 9. 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`. 10. Select **Import**. 11. In a terminal, run `docker run` to run your image with the imported volume. Replace `YOUR-USER-NAME` with your Docker ID. ```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' ``` ## Summary In this guide, you learned how to leverage Docker and JupyterLab to create reproducible data science environments, facilitating the development and sharing of data science projects. This included, running a personal JupyterLab server, customizing the environment with necessary tools and packages, and sharing notebooks and environments with other data scientists. Related information: - [Dockerfile reference](/reference/dockerfile/) - [Compose file reference](/reference/compose-file/) - [Docker CLI reference](reference/cli/docker/) - [Jupyter Docker Stacks docs](https://jupyter-docker-stacks.readthedocs.io/en/latest/)

This section details how to share both Docker images and volumes on Docker Hub for collaboration and reproducibility. It explains how to tag and push images, as well as how to export and import volumes using the Docker Desktop GUI. It also provides the necessary `docker run` commands for users to download and use shared images and volumes, emphasizing the importance of replacing placeholders with the correct Docker ID.