Jupyter Lab and package/libraries installation

[Yann.Sagon]

@Sajad.Hamzenejadi

I can use Baobab and Bamboo through OOD, is that correct?

You can use the three clusters: hpc:how_to_use_openondemand [eResearch Doc]

@Sajad.Hamzenejadi

do you have any recommendations on how I choose between them

Check here:hpc:hpc_clusters [eResearch Doc] but the doc is not very helpful I must admit! My advice is to use Bamboo as this is the more recent cluster with faster storage.

I’ve posted a tutorial that should do what you want:

[Tutorial] Create a Custom Kernel for JupyterLab Using cotainr

This tutorial explains how to build a GPU‑ready container image using cotainr, install PyTorch and other Python packages inside it, and expose it as a custom Jupyter kernel in an HPC environment using Open OnDemand. 1. Create the Conda Environment File (env.yml) Create a file named env.yml with the following content: name: torch-gpu channels: - pytorch - nvidia - conda-forge - defaults dependencies: - python=3.10 - pip - pytorch - torchvision - torchaudio - pytorch-cuda=1…