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  • Comprehensive benchmarking suite · 6 ✖

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964099251 https://github.com/pydata/xarray/issues/4648#issuecomment-964099251 https://api.github.com/repos/pydata/xarray/issues/4648 IC_kwDOAMm_X845dvyz TomAugspurger 1312546 2021-11-09T12:17:32Z 2021-11-09T12:17:32Z MEMBER

"In charge of" is overstating it a bit. It's been segfaulting when building pandas and I haven't had a chance to debug it.

If / when I get around to fixing it I'll try adding xarray, but it might be a bit.

 
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   Comprehensive benchmarking suite 756425955
963563959 https://github.com/pydata/xarray/issues/4648#issuecomment-963563959 https://api.github.com/repos/pydata/xarray/issues/4648 IC_kwDOAMm_X845btG3 dcherian 2448579 2021-11-08T20:54:19Z 2021-11-08T20:54:19Z MEMBER

@TomAugspurger are you still in charge of the pydata benchmarking machine? If so, could you add xarray to the list please (https://pandas.pydata.org/speed/)? @Illviljan has made major improvements so it should be a lot faster now

 
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   Comprehensive benchmarking suite 756425955
901370189 https://github.com/pydata/xarray/issues/4648#issuecomment-901370189 https://api.github.com/repos/pydata/xarray/issues/4648 IC_kwDOAMm_X841udFN dcherian 2448579 2021-08-18T19:24:15Z 2021-08-18T19:26:31Z MEMBER

Looks like Quansight thinks that GH actions is a good place to benchmark scikit-learn: https://labs.quansight.org/blog/2021/08/github-actions-benchmarks/ so may be we can set that up for our existing benchmarks.

Here's the workflow: https://github.com/jaimergp/scikit-image/blob/main/.github/workflows/benchmarks-cron.yml

 
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   Comprehensive benchmarking suite 756425955
752731737 https://github.com/pydata/xarray/issues/4648#issuecomment-752731737 https://api.github.com/repos/pydata/xarray/issues/4648 MDEyOklzc3VlQ29tbWVudDc1MjczMTczNw== max-sixty 5635139 2020-12-30T19:25:44Z 2020-12-30T19:25:44Z MEMBER

This would be great.

Down a couple of levels — I think potentially we could run this as a cron job on GitHub Actions. NCAR would also be a good plan. I'm also happy to supply a VM if that's helpful.

 
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   Comprehensive benchmarking suite 756425955
738969196 https://github.com/pydata/xarray/issues/4648#issuecomment-738969196 https://api.github.com/repos/pydata/xarray/issues/4648 MDEyOklzc3VlQ29tbWVudDczODk2OTE5Ng== dcherian 2448579 2020-12-04T19:19:55Z 2020-12-04T19:22:04Z MEMBER

Thanks @scottyhq

One other thing that often gets neglected in test suites is operating on remote data.

This is lining up with the "pangeo integration tests" that came up in a Pangeo meeting (cc @rabernat).

Regardless whether it fits, I think adding benchmarks+tests for the xarray+zarr+fsspec (or xarray+mfdataset+netCDF) is an important and unmet need of the Pangeo community in general that we could address.

 
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   Comprehensive benchmarking suite 756425955
738190759 https://github.com/pydata/xarray/issues/4648#issuecomment-738190759 https://api.github.com/repos/pydata/xarray/issues/4648 MDEyOklzc3VlQ29tbWVudDczODE5MDc1OQ== scottyhq 3924836 2020-12-03T18:17:13Z 2020-12-03T18:17:13Z MEMBER

thanks for the ping @dcherian, i really like the idea! One other thing that often gets neglected in test suites is operating on remote data. I understand the need to avoid long-running tests and tests prone to network failures for PRs, but running these sorts of examples as a cron job could be very helpful for benchmarking and detecting issues.

In intake-xarray we recently added tests against a local HTTP server and "S3" server: https://github.com/intake/intake-xarray/blob/master/intake_xarray/tests/test_remote.py

Also added several simple tests requiring a network connection to public data (no auth required) that we run locally but not in CI currently: https://github.com/intake/intake-xarray/blob/master/intake_xarray/tests/test_network.py 

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