User documentation on how to setup jobs or run large analysis

[berombau]

Is your feature request related to a problem? Please describe.
Current documentation shows small examples. Working on real large datasets varies in some ways and has specific needs:

• limiting Dask workers to limit memory usage
• job-based instead of interactive
• sometimes needs to use a specific HPC setup
• requesting resources via SLURM or using workflow managers like Nextflow, Snakemake...
• working with a distributed Dask cluster

Describe the solution you'd like
A documentation page should explain this and link to existing resources. It would also be interesting to gather existing documentation of executing large jobs with SpatialData.

Some resources:

• Dask cluster:
  • https://docs.dask.org/en/stable/deploying-python.html?#localcluster
  • https://docs.dask.org/en/latest/scheduling.html
  • https://docs.dask.org/en/latest/deploying-hpc.html
  • https://docs.dask.org/en/latest/deploying.html#advanced-understanding
  • https://jobqueue.dask.org/en/latest/
• Developing with Python environments on HPC: https://docs.hpc.ugent.be/Linux/setting_up_python_virtual_environments/?h=venv
• SpatialData workflows on HPC:
  • Hydra: https://harpy.readthedocs.io/en/latest/tutorials/hpc/index.html
  • Nextflow:
    • https://github.com/LucaMarconato/spatialdata-mcmicro
    • https://nf-co.re/configs/vsc_ugent
  • Snakemake: https://gustaveroussy.github.io/sopa/tutorials/snakemake/

[berombau]

Some new notebook ideas:

• intermediate notebook on limiting workers when segmenting with map apply https://docs.dask.org/en/stable/deploying-python.html?#localcluster
• advanced notebook on setting up distributed Dask cluster https://jobqueue.dask.org/en/latest/runners-overview.html
• working with Dask dashboard
  • working with Dask span for fine performance metrics