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  • dcherian · 2 ✖

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  • Use pytorch as backend for xarrays · 2 ✖

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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
1183301651 https://github.com/pydata/xarray/issues/3232#issuecomment-1183301651 https://api.github.com/repos/pydata/xarray/issues/3232 IC_kwDOAMm_X85Gh8AT dcherian 2448579 2022-07-13T14:31:55Z 2022-07-13T14:32:01Z MEMBER

I'd be happy to turn this into a PR with some tests.

Absolutely!

 
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   Use pytorch as backend for xarrays 482543307
606228143 https://github.com/pydata/xarray/issues/3232#issuecomment-606228143 https://api.github.com/repos/pydata/xarray/issues/3232 MDEyOklzc3VlQ29tbWVudDYwNjIyODE0Mw== dcherian 2448579 2020-03-30T20:24:08Z 2020-03-30T20:24:08Z MEMBER

Just chiming in quickly. I think there's definitely interest in doing this through NEP-18.

It looks like CUDA has implemented __array_function__ (https://docs-cupy.chainer.org/en/stable/reference/interoperability.html) so many things may "just work". There was some work earlier on plugging in pydata/sparse, and there is some ongoing work to plug in pint. With both these efforts, a lot of xarray's code should be "backend-agnostic" but its not perfect.

Have you tried creating DataArrays with cupy arrays yet? I would just try things and see what works vs what doesn't.

Practically, our approach so far has been to add a number of xfailed tests (test_sparse.py and test_units.py) and slowly start fixing them. So that's one way to proceed if you're up for it.

 
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   Use pytorch as backend for xarrays 482543307

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