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  • shoyer · 3 ✖

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  • Feature/benchmark · 3 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
318415555 https://github.com/pydata/xarray/pull/1457#issuecomment-318415555 https://api.github.com/repos/pydata/xarray/issues/1457 MDEyOklzc3VlQ29tbWVudDMxODQxNTU1NQ== shoyer 1217238 2017-07-27T16:31:14Z 2017-07-27T16:31:14Z MEMBER

Awesome, thanks @TomAugspurger !

 
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   Feature/benchmark 236347050
315273074 https://github.com/pydata/xarray/pull/1457#issuecomment-315273074 https://api.github.com/repos/pydata/xarray/issues/1457 MDEyOklzc3VlQ29tbWVudDMxNTI3MzA3NA== shoyer 1217238 2017-07-14T05:24:04Z 2017-07-14T05:24:04Z MEMBER

We should do this to the extent that it is helpful in driving development. Even just a few realistic use cases can be helpful, especially for guarding against performance regressions. On Thu, Jul 13, 2017 at 3:37 PM Joe Hamman notifications@github.com wrote:

@rabernat https://github.com/rabernat - do you have any thoughts on this?

@pydata/xarray https://github.com/orgs/pydata/teams/xarray - I'm trying to decide if this is worth spending any more time on. What sort of coverage would we want before we merge this first PR?

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/pydata/xarray/pull/1457#issuecomment-315220704, or mute the thread https://github.com/notifications/unsubscribe-auth/ABKS1rVPL6fCRqwi1Szmtq09chkah9odks5sNpwQgaJpZM4N74gy . 

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   Feature/benchmark 236347050
308925978 https://github.com/pydata/xarray/pull/1457#issuecomment-308925978 https://api.github.com/repos/pydata/xarray/issues/1457 MDEyOklzc3VlQ29tbWVudDMwODkyNTk3OA== shoyer 1217238 2017-06-16T03:50:33Z 2017-06-16T03:50:33Z MEMBER

@wesm just setup a machine for dedicated benchmarking of pandas and possibly other pydata/scipy project (if there's extra capacity as expected). @TomAugspurger has been working on getting it setup. So that's potentially an option, at least for single machine benchmarks.

The lore I've heard is that benchmarking on shared cloud resources (e.g., Travis-CI) can have reproducibility issues due to resource contention and/or jobs getting scheduled on slightly different machine types. I don't know how true this still is, or if there are good work arounds for particular cloud platforms. I suspect this should be solvable, though. I can certainly make an internal inquiry about benchmarking on GCP if we can't find answers on our own.

 
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   Feature/benchmark 236347050

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