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- Should we be testing against multiple dask schedulers? · 4 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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453865008 | https://github.com/pydata/xarray/issues/1971#issuecomment-453865008 | https://api.github.com/repos/pydata/xarray/issues/1971 | MDEyOklzc3VlQ29tbWVudDQ1Mzg2NTAwOA== | jhamman 2443309 | 2019-01-13T20:58:20Z | 2019-01-13T20:58:20Z | MEMBER | Closing this now. The distributed integration test module seems to be covering our IO use cases well enough. I don't think we need to do anything here at this time. |
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Should we be testing against multiple dask schedulers? 302930480 | |
371462262 | https://github.com/pydata/xarray/issues/1971#issuecomment-371462262 | https://api.github.com/repos/pydata/xarray/issues/1971 | MDEyOklzc3VlQ29tbWVudDM3MTQ2MjI2Mg== | mrocklin 306380 | 2018-03-08T11:35:25Z | 2018-03-08T11:35:25Z | MEMBER | FWIW most of the logic within the dask collections (array, dataframe, delayed) is only tested with Obviously though for things like writing to disk it's useful to check different schedulers. |
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Should we be testing against multiple dask schedulers? 302930480 | |
371334589 | https://github.com/pydata/xarray/issues/1971#issuecomment-371334589 | https://api.github.com/repos/pydata/xarray/issues/1971 | MDEyOklzc3VlQ29tbWVudDM3MTMzNDU4OQ== | jhamman 2443309 | 2018-03-08T00:27:52Z | 2018-03-08T00:27:52Z | MEMBER | I managed to dig up some more information here. I was having a test failure in
../../../anaconda/envs/xarray36/lib/python3.6/site-packages/tornado/ioloop.py:457: TimeoutError ``` From then on we were using the distributed scheduler and any tests that used dask resulted in a additional timeout (or similar error). Unfortunately, my attempts to provide a mcve have come up short. If I can come up with one, I'll report upstream but as it is, I can't really transfer this behavior outside of my example. cc @mrocklin |
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Should we be testing against multiple dask schedulers? 302930480 | |
371004338 | https://github.com/pydata/xarray/issues/1971#issuecomment-371004338 | https://api.github.com/repos/pydata/xarray/issues/1971 | MDEyOklzc3VlQ29tbWVudDM3MTAwNDMzOA== | shoyer 1217238 | 2018-03-07T02:48:16Z | 2018-03-07T02:48:16Z | MEMBER | Huh, that's interesting. Yes, I suppose should at least consider parametric tests using both dask's multithreaded and distributed schedulers. Though I'll note that for test we actually set the default scheduler to dask's basic non-parallelized get, for easier debugging: https://github.com/pydata/xarray/blob/54468e1924174a03e7ead3be8545f687f084f4dd/xarray/tests/init.py#L87 For #1793, the key thing would be to ensure that we run the tests in the isolated context without changing the default scheduler. |
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Should we be testing against multiple dask schedulers? 302930480 |
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