Willi Rath

And nb_conda_kernels depends on conda.

But you can install the ipykernel package in the new env and then do sth along the lines of python -m ipykernel install --name ... --display-name ... from the new...

I have a few TLJH instances that did exactly this successfully. I'm not sure if it's also necessary to restart the user server, to see the new kernel, though. A...

Yes, I'm still using this in production a lot.

Never did myself, but this issue could have hints: https://github.com/Anaconda-Platform/nb_conda_kernels/issues/162

If I understand that correctly, you have a total of 4GB of RAM on the whole machine? That's probably too short for 20 concurrent users. (It's 200MB each. That's likely...

That's great news! I'll have a look.

Looks a lot more stable. Scaling the cluster up and down doesn't seem to break the Slurm scheduler anymore.

Briefly chiming in here: I just had a chat with an HPC sysadmin who said that job arrays would not help solving the problem that forces him to limit the...

In addition to starting a scheduler on a different node, using an existing scheduler could be a way to create heterogeneous clusters by using the same scheduler for more than...