issue_comments: 660192634

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https://github.com/pydata/xarray/issues/4234#issuecomment-660192634 https://api.github.com/repos/pydata/xarray/issues/4234 660192634 MDEyOklzc3VlQ29tbWVudDY2MDE5MjYzNA== 1610850 2020-07-17T16:07:31Z 2020-07-17T16:08:34Z CONTRIBUTOR

Those as_ methods sounds good. Would as_dense be the same as as_numpy?

Yeah it is possible to read direct to GPU from storage with GDS. We've experimented a little with zarr, I expect if something like zarr got GDS support and a zarr dataset was configured to use GDS then xr.open_zarr('zarr/path') would be already backed by cupy because the zarr internal array would be cupy.

Re: index variables.Can we avoid this for now?

I think that would be best.

However I did run into issues when trying to run the Compare weighted and unweighted mean temperature example with cupy. In that example the weights data array is generated from the latitude index. So the resulting DataArray is backed by numpy. I would expect that if it were a cuDF Index it would end up as a cupy data array.

In my testing I just cast the weights data array to cupy and carried on. So perhaps with this change users will just need to sprinkle some weights = weights.as_cupy() or weights = weights.asarray(cp.ndarray) type calls throughout their code when they need to.

 
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