issue_comments: 522820303

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https://github.com/pydata/xarray/issues/3232#issuecomment-522820303 https://api.github.com/repos/pydata/xarray/issues/3232 522820303 MDEyOklzc3VlQ29tbWVudDUyMjgyMDMwMw== 1217238 2019-08-20T01:55:46Z 2019-08-20T01:55:46Z MEMBER

If pytorch implements overrides of NumPy's API via the __array_function__ protocol, then this could work with minimal effort. We are already using this to support sparse arrays (this isn't an official release yet, but functionality is working in the development version).

I think there has been some discussion about this, but I don't know the current status (CC @rgommers). The biggest challenge for pytorch would be defining the translation layer that implements NumPy's API.

Personally, I think the most viable way to achieve seamless integration with deep learning libraries would be to support integration with JAX, which already implements NumPy's API almost exactly. I have an experimental pull request adding __array_function__ to JAX, but it still needs a bit of work to finish it up, e.g., we probably want to hide this behind a flag at first.

 
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