edrixs and docker

Run edrixs in a docker container

To make life easier, we have built a Docker image based on Ubuntu Linux 24.04 for edrixs, so you do not need to install its compiler toolchain yourself. The image is published for both AMD64 and ARM64 systems. The latest tag tracks the latest stable release. For reproducible work, pin a release tag such as edrixs/edrixs:v0.1.3. The docker image can be used on any OS as long as the docker application is available. Follow these steps to use the docker image:

• Install the docker application on your system.

• Once Docker is running, create a directory to store data and create a file called docker-compose.yml with contents

  services:
    edrixs-jupyter:
        image: edrixs/edrixs
        volumes:
          - ./:/home/rixs
        working_dir: /home/rixs
        ports:
          - 8888:8888
        command: "jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root"
    edrixs-ipython:
        image: edrixs/edrixs
        volumes:
          - ./:/home/rixs
        working_dir: /home/rixs
  

  and execute

  docker compose up
  

  This will return a URL, which you can open to connect to the Jupyter session.

• If you would like to access a terminal rather than jupyter run

  docker compose run --rm edrixs-ipython
  

Use the docker image as a Dev Container

You can use the edrixs docker image as a Dev Container in VS Code or GitHub Codespaces without cloning the full repository. Create a file .devcontainer/devcontainer.json in your project with the following contents

{
    "name": "edrixs",
    "image": "edrixs/edrixs"
}

Open the project in VS Code with the Dev Containers extension installed and choose Reopen in Container when prompted.

See Dev Container for more details on using Dev Containers with edrixs.

Sharing your code

Using Docker is a nice way to straightforwardly share your code with others. The standard way to specify which docker image is needed to run your code is to include a file named Dockerfile with the following contents

FROM edrixs/edrixs

You might like to checkout the jupyter-repo2docker project, which helps automate the process of building and connecting to docker images. The mybinder project might also be helpful as this will open a github respository of notebooks in an executable environment, making your code immediately reproducible by anyone, anywhere.