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  • mrocklin · 2 ✖

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  • Document using a spawning multiprocessing pool for multiprocessing with dask · 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
269573421 https://github.com/pydata/xarray/issues/1189#issuecomment-269573421 https://api.github.com/repos/pydata/xarray/issues/1189 MDEyOklzc3VlQ29tbWVudDI2OTU3MzQyMQ== mrocklin 306380 2016-12-29T02:36:08Z 2016-12-29T02:36:08Z MEMBER

Dask.distributed now creates a forkserver at startup. This seems to be working well so far. It nicely balances having a well defined environment and fast startup time.

How much inter-worker data transfer would you expect? It might be worth running through a few classic algorithms with it instead of the threaded scheduler and looking at performance changes. The diagnostic pages would be a nice bonus here and might help to highlight some performance issues.

If anyone is interested in this the thing to do is

$ conda install -c conda-forge dask distributed

>>> from dask.distributed import Client
>>> c = Client()  # sets global scheduler by default

And then operate as normal.

 
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   Document using a spawning multiprocessing pool for multiprocessing with dask 197939448
269572088 https://github.com/pydata/xarray/issues/1189#issuecomment-269572088 https://api.github.com/repos/pydata/xarray/issues/1189 MDEyOklzc3VlQ29tbWVudDI2OTU3MjA4OA== mrocklin 306380 2016-12-29T02:17:40Z 2016-12-29T02:17:40Z MEMBER

Can you remind me the motivation to use a spawning multiprocessing pool instead of a fork or forkserver solution?

For mixed multi-threading/multi-processing would a local "distributed" scheduler suffice? This would be several single-threaded processes on a single machine. The scheduler would be aware of data locality and avoid inter-node communication when possible.

 
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   Document using a spawning multiprocessing pool for multiprocessing with dask 197939448

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