Update content of dask_introduction.ipynb

[guillaumeeb]

Here are some propositions as discussed in https://github.com/pangeo-data/foss4g-2022/pull/45.

Please indicate whether it's OK for you (especially @tinaok):

• [x] Add a little part on Dask Clusters, talk about Client, Scheduler, Worker, dask-gateway, Kubernetes, HPC. Only quick words on the last terms. Talk about Dask Dashboard (which needs to be accessible in the platforms we propose).
  • Maybe we wan to do a short demonstration of Dask and its Dashboard on some code independent of our dataset?
• [x] Is the call to optimize for the Dask graphs really important? I find it weird, you don't need this with Dataframes.
• [x] Remove the part about installing packages on worker pods. I think this is an advanced topic and should not be usefull if we fix https://github.com/pangeo-data/pangeo-eosc/issues/3.
• [x] Add some code at the end with the global scaling of some process as announced in the beginning of the notebook.

[annefou]

@annefou will add explicitely dask local (with sub-title) instead of having embedded.

Will also check once we have the infra ready.

[tinaok]

Is the call to optimize for the Dask graphs really important? I find it weird, you don't need this with Dataframes.

Yes with Xarray it dramatically change.

Remove the part about installing packages

same as Anne's comment we need to wait that infra is ready.

Add some code at the end with the global scaling

We might limit the contents due to our 4 hour time limit.

[guillaumeeb]

I addressed some point in #83. But there are still discussion to have:

• Warning on spilling, I think this is too complex to introduce yet.
• dask.optimize(): should we really introduce this? This makes a different in number of tasks displayed, but upon execution, Dask is clever enough to not execute the useless tasks, I tested with and without optimization on graph, and the result is almost the same. I'm under the impression that Dask does optimize things anyway upon calling compute.

You can have a look to the review I made in #83 for detailed pointers of what I'm talking about.