issue_comments: 765710268
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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-765710268 | https://api.github.com/repos/pydata/xarray/issues/3232 | 765710268 | MDEyOklzc3VlQ29tbWVudDc2NTcxMDI2OA== | 19956442 | 2021-01-22T22:04:20Z | 2021-01-22T22:14:50Z | NONE | I'd like to cast my vote in favor of getting this functionality in. It would be nice to autodiff through xarray operations. From reading this and related threads, I'm trying to determine a gameplan to make this happen. I'm not familiar with xarray code, so any guidance would be much appreciated. This is what I'm thinking : 1) Create a custom subclass of PyTorch's Tensors which meets the duck array required methods and attributes. Since this isn't officially supported, looks like I could run into issues getting this subclass to persist through tensor operations.
2) Implement the __array_function__ protocol for PyTorch similar to how is demo-ed here.
3) Pass this custom class into data array constructors and hope the My first attempts at this haven't been successful. Whatever custom class I make and past to the Any suggestions would be appreciated. I'm hoping to figure out the shortest path to a working prototype. |
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