issue_comments: 269573421

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https://github.com/pydata/xarray/issues/1189#issuecomment-269573421 https://api.github.com/repos/pydata/xarray/issues/1189 269573421 MDEyOklzc3VlQ29tbWVudDI2OTU3MzQyMQ== 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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