Parallel fitting #7
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returning xr Datasets. Common use case is to pass chunks, to load as lazy/dask Dataset
Now uses linalg.solve instead of matrix inversion See https://stackoverflow.com/questions/76826928/unexpected-results-with-parallel-numba-jit/76827464#76827464
linalg.solve approach Doctrings updated accordingly with a note about over-subsription
(parallelization attempt reverted)
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I initially wanted to improve compatibility with dask (hence the branch name), and permit things like lazy/distributed fitting, but there were too many obstacles, starting with the fancy indexing used for masking and not supported by dask.
This instead adds parallel capabilities to most fitting methods thanks to numba's parallel accelerator.
For a couple of fitting functions I had to use
np.linalg.solvein place ofnp.linalg.invfor the parallel version to work. More details here.Using
pinvinstead ofinvappears to work as well. I attempted to do that for_recresid, for which the inversion isn't easy to bypass, but parallel code ends up running much slower than the sequential, despite high cpu resources use (I can't explain why...). I have therefore reverted parallelism for_recresidandroc_stable_fitOne new gallery example on parallel fitting
Can you have a quick look @jonasViehweger ?