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  • Limiting threads/cores used by xarray(/dask?) · 2 ✖

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  • NONE · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
460325261 https://github.com/pydata/xarray/issues/2417#issuecomment-460325261 https://api.github.com/repos/pydata/xarray/issues/2417 MDEyOklzc3VlQ29tbWVudDQ2MDMyNTI2MQ== andytraumueller 10809480 2019-02-04T16:57:27Z 2019-02-04T20:07:09Z NONE

hi, my testcode is running properly on 5 threads thanks for the help

```python import xarray as xr import os import numpy import sys import dask from multiprocessing.pool import ThreadPool

dask-worker = --nthreads 1

with dask.config.set(schedular='threads', pool=ThreadPool(5)): dset = xr.open_mfdataset("/data/Environmental_Data/Sea_Surface_Height//.nc", engine='netcdf4', concat_dim='time', chunks={"latitude":180,"longitude":360}) dset1 = dset["adt"]-dset["sla"] dset1.to_dataset(name = 'ssh_mean') dset["ssh_mean"] = dset1 dset = dset.drop("crs") dset = dset.drop("lat_bnds") dset = dset.drop("lon_bnds") dset = dset.drop("xarray_dataarray_variable") dset = dset.drop("nv") dset_all_over_monthly_mean = dset.groupby("time.month").mean(dim="time", skipna=True) dset_all_over_season1_mean = dset_all_over_monthly_mean.sel(month=[1,2,3]) dset_all_over_season1_mean.mean(dim="month",skipna=True) dset_all_over_season1_mean.to_netcdf("/data/Environmental_Data/dump/mean/all_over_season1_mean_ssh_copernicus_0.25deg_season1_data_mean.nc") ```

 
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   Limiting threads/cores used by xarray(/dask?) 361016974
460292772 https://github.com/pydata/xarray/issues/2417#issuecomment-460292772 https://api.github.com/repos/pydata/xarray/issues/2417 MDEyOklzc3VlQ29tbWVudDQ2MDI5Mjc3Mg== andytraumueller 10809480 2019-02-04T15:34:04Z 2019-02-04T15:34:04Z NONE

i am also interest, I am running a lot of critical processes and I want to at least have 5 cores idleing.

 
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   Limiting threads/cores used by xarray(/dask?) 361016974

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