issue_comments: 524348393
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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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/3232#issuecomment-524348393 | https://api.github.com/repos/pydata/xarray/issues/3232 | 524348393 | MDEyOklzc3VlQ29tbWVudDUyNDM0ODM5Mw== | 923438 | 2019-08-23T15:00:02Z | 2019-08-23T15:00:02Z | NONE | I haven't used JAX - but was just browsing through its documentation and it looks super cool. Any ideas on how it compares with Pytorch in terms of: a) Cxecution speed, esp. on GPU b) Memory management on GPUs. Pytorch has the 'Dataloader/Dataset' paradigm which uses background multithreading to shuttle batches of data back and forth - along with a lot of tips and tricks on efficient memory usage. c) support for deep-learning optimization algorithms ? |
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