ml-tooling/ml-workspace

Hey @sim-san , if you use the Docker-local version you can either create an nfs-volume before (see https://unix.stackexchange.com/a/320717/239011) and add the volume mount to the JupyterHub config (e.g. c.DockerSpawner.volumes={'my-nfs-share': '/workspace/nfs'} or it should also be possible to add something like the following configuration to the JupyterHub config (I did not test it, so it's only meant to be a hint):

c.DockerSpawner.extra_host_kwargs={
  mounts=[{
    "target": "<container_path>",
    "source": "nfsvolume",
    "type": "volume",
    "driver_config": {"name": "local", options: {"type": "nfs", "device": ":<nfs export path>", "o": "addr=<nfs-server-addr>"}}
  }]
}

In the first solution, to avoid having the same nfs folder mounted in all directories, you could create a separate nfs volume (my-nfs-share-{username}) for each workspace and add the directive c.DockerSpawner.volumes={'my-nfs-share-{username}': '/workspace/nfs'}. However, this requires manual effort (as also the host directory has to be created). An automated way of handling nfs shares does not seem to be supported right now by DockerSpawner and also not by our MLHubDockerSpawner.

In a Kubernetes-setup this might be a little bit easier as you can work with dynamic PVC provisioning etc.