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  • Remove caching logic from xarray.Variable · 2 ✖

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  • CONTRIBUTOR · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
265966887 https://github.com/pydata/xarray/pull/1128#issuecomment-265966887 https://api.github.com/repos/pydata/xarray/issues/1128 MDEyOklzc3VlQ29tbWVudDI2NTk2Njg4Nw== mangecoeur 743508 2016-12-09T09:08:48Z 2016-12-09T09:08:48Z CONTRIBUTOR

@shoyer thanks, with a little testing it seems lock=False is fine (so don't automatically need dask dev for lock=dask.utils.SerializableLock()). Using spawning pool is necessary, just doesn't work without. Also looks like using dask distributed ipython backend works fine (works similar to spawn pool in that the worker engines aren't forked but kinda live in their own little world) - this is really nice because ipython in turn has good support for HPC systems (SGE batch scheduling + MPI for process handling).

 
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   Remove caching logic from xarray.Variable 189817033
265875012 https://github.com/pydata/xarray/pull/1128#issuecomment-265875012 https://api.github.com/repos/pydata/xarray/issues/1128 MDEyOklzc3VlQ29tbWVudDI2NTg3NTAxMg== mangecoeur 743508 2016-12-08T22:28:25Z 2016-12-08T22:28:25Z CONTRIBUTOR

I'm trying out the latest code to subset a set of netcdf4 files with dask.multiprocessing using set_options(get=dask.multiprocessing.get) but I'm still getting TypeError: can't pickle _thread.lock objects - this expect or there something specific I need to do to make it work?

 
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   Remove caching logic from xarray.Variable 189817033

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