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  • shoyer · 1 ✖

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  • Document using a spawning multiprocessing pool for multiprocessing with dask · 1 ✖

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  • MEMBER · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
269573022 https://github.com/pydata/xarray/issues/1189#issuecomment-269573022 https://api.github.com/repos/pydata/xarray/issues/1189 MDEyOklzc3VlQ29tbWVudDI2OTU3MzAyMg== shoyer 1217238 2016-12-29T02:30:16Z 2016-12-29T02:30:16Z MEMBER

Actually, I just tested it and it appears that forking also works, as long as you create the pool before opening any files. Otherwise, the netCDF library crashes (https://github.com/pydata/xarray/pull/1128#issuecomment-261841025).

A local "distributed" scheduler might indeed also work, but at least when operating on a single machine it makes sense to bring all data into a single process once it's been loaded for multi-threaded data analysis.

 
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   Document using a spawning multiprocessing pool for multiprocessing with dask 197939448

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