issue_comments: 376689828
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html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
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https://github.com/pydata/xarray/pull/1983#issuecomment-376689828 | https://api.github.com/repos/pydata/xarray/issues/1983 | 376689828 | MDEyOklzc3VlQ29tbWVudDM3NjY4OTgyOA== | 2443309 | 2018-03-27T21:59:35Z | 2018-03-27T21:59:35Z | MEMBER |
I have. See below for a simple example using this feature on Cheyenne. ```python In [1]: import xarray as xr ...: ...: import glob ...: In [2]: pattern = '/glade/u/home/jhamman/workdir/LOCA_daily/met_data/CESM1-BGC/16th/rcp45/r1i1p1//nc' In [3]: len(glob.glob(pattern)) Out[3]: 285 In [4]: %time ds = xr.open_mfdataset(pattern) CPU times: user 15.5 s, sys: 2.62 s, total: 18.1 s Wall time: 42.4 s In [5]: ds.close() In [6]: %time ds = xr.open_mfdataset(pattern, parallel=True) CPU times: user 18.4 s, sys: 5.28 s, total: 23.6 s Wall time: 30.7 s In [7]: ds.close() In [8]: from dask.distributed import Client In [9]: client = Client() clien In [10]: client Out[10]: <Client: scheduler='tcp://127.0.0.1:39853' processes=72 cores=72> In [11]: %time ds = xr.open_mfdataset(pattern, parallel=True, autoclose=True) CPU times: user 10.8 s, sys: 808 ms, total: 11.6 s Wall time: 12.4 s ``` |
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