issue_comments: 308925978
This data as json
| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/pull/1457#issuecomment-308925978 | https://api.github.com/repos/pydata/xarray/issues/1457 | 308925978 | MDEyOklzc3VlQ29tbWVudDMwODkyNTk3OA== | 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. |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
236347050 |