When would someone use jupyterhub? I've been running my own notebook server for years, but it's single-user, single machine. Is hub for like providing separate jupyterlab instances for a bunch of different users/different machines?
User goes with the web browser to our jupyterhub URL, logs in with our usual credentials, selects a job type (amount of memory and max duration), and jupyterhub takes care of launching a jupyter kernel as a slurm batch job on a compute node in the cluster, and proxies http I/O via the jupyterhub node to the user web browser. In the jupyter notebook, users have access to the same cluster filesystems as if she would login traditionally via ssh.
Then there's the PITA of integration with the site auth system, but that tends to be site specific..
I'd wager that almost no data scientists write object oriented code.. it's probably mostly done one calculation at a time. executed in the notebooks repl. So the value you get from ide debuggers is tiny, as you're already doing everything one step at a time.
I ended up getting really frustrated with setting it up. Followed several different tutorials, had it blow up in a different way each time.
Going to have to revisit this to see if the documentation has gotten better.