Do you have any experience in dealing with ragged data return in dask.delayed?
I utilize dask.distributed and dask.delayed to distribute the workload (see the script below). The return from the function (func) is a ragged data with multiple arrays having different dimensions. Right now I get all the arrays in a single tuple (results). Is there a nicer way where I get the corresponding arrays be stacked nicely as one would get in Dask.dataframe.
with Client(cluster) as client:
delayed_results = [ delayed(func)(x_train, y_train, idx) for idx in contexts]