I’d like give the user access to data that is saved in an S3 compliant server.
Is it possible that they can mount the bucket to the JupyterHub notebook via running Python code? what is the best practice for doing so?
It’s not possible for a user to mount S3 because it requires changing the K8S pod configuration, as well as additional privileges on the K8S cluster. An admin could configure it, for example see
Alternatively the user should be able to access S3 using the Python API:
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I’ve created a JupyterHub Spawner for this.
It is called “JupyterHub DataMount Spawner” - Installing the Extension - JupyterLab DataMount
Check out the documentation to learn more about the architecture, installation and configuration options. You can configure mounts (S3, NextCloud, everything that’s supported by rlcone) before starting a Jupyter Server or users can mount stuff at runtime.