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  • Parallel map/apply powered by dask.array · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
249059201 https://github.com/pydata/xarray/issues/585#issuecomment-249059201 https://api.github.com/repos/pydata/xarray/issues/585 MDEyOklzc3VlQ29tbWVudDI0OTA1OTIwMQ== monocongo 1328158 2016-09-22T23:39:41Z 2017-03-07T05:32:04Z NONE

This is good news for me as the functions I will apply take a ndarray as input and return a corresponding ndarray as output. Once this is available in xarray I'll be eager to give it a whirl...

 
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   Parallel map/apply powered by dask.array 107424151
248969870 https://github.com/pydata/xarray/issues/585#issuecomment-248969870 https://api.github.com/repos/pydata/xarray/issues/585 MDEyOklzc3VlQ29tbWVudDI0ODk2OTg3MA== monocongo 1328158 2016-09-22T17:23:22Z 2016-09-22T17:23:22Z NONE

I'm adding this note to express an interest in the functionality described in Stephan's original description, i.e. a parallel_apply method/function which would apply a function in parallel utilizing multiple CPUs. I have (finally) worked out how to use groupby and apply for my application but it would be much more useful if I could apply functions in parallel to take advantage of multiple CPUs. What's the expected effort to make something like this available in xarray? Several months ago I worked on doing this sort of thing without xarray using the multiprocessing module and a shared memory object and I may revisit that soon, but I expect that a solution using xarray will be more elegant so if such a thing is coming in the foreseeable future then I may wait on that and focus on other tasks. Can anyone advise?

 
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   Parallel map/apply powered by dask.array 107424151

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