Dharhas Pothina

conda-forge will install the CPU version of PyTorch unless you use that env flag listed above. This happens because conda-store builds the env on a non-gpu worker and conda-forge detects...

Those both need updating, I spent a while getting working environments recently on another project. Let me do a few tests and then update here.

Troubleshooting. First, make sure you actually have a GPU instance running with nvidia drivers. You can do this by running `nvidia-smi` from a terminal  Once you have done this...

Actually. @rsignell-usgs environment worked as well. Rich can you run `nvidia-smi` and post the output. ```yaml channels: - pytorch - nvidia - conda-forge dependencies: - python=3.11 - pytorch::pytorch - pytorch::pytorch-cuda=11.8...

cc: @pavithraes re: conflicting GPU best practices pages in docs.

This needs `xelatex` in the jupyterlab environment. This isn't something we probably want to put in the base Nebari image. In the past, latex related files could balloon the docker...

> NVME storage as swap to accomodate conda store solves with high memory usage in a cost effective way I think these volumes might be more expensive than what we...

@marcelovilla please move this to the main nebari tracker so we can try it out at some point.

I think the new jhub-apps ui, lets you control this more. i.e many of these are optional services and we need a way for the menu and the jhub-app ui...

@aktech @jbouder could this be related to the UI changes y'all have been working on.