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/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](https://developer.nvidia.com/blog/gpudirect-storage/). 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](http://xarray.pydata.org/en/stable/examples/area_weighted_temperature.html) 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.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,659129613
https://github.com/pydata/xarray/issues/4234#issuecomment-660154476,https://api.github.com/repos/pydata/xarray/issues/4234,660154476,MDEyOklzc3VlQ29tbWVudDY2MDE1NDQ3Ng==,1610850,2020-07-17T14:58:53Z,2020-07-17T14:58:53Z,CONTRIBUTOR,"The only things I can think of that would make sense initially in an accessor would be `to_cupy`/`from_cupy` to move back and forth from the GPU, plus maybe an alias for `get` to `from_cupy` to match the cupy API.

An accessor does seem like a reasonable place to put that logic, but it also seems like a tiny amount of code to make, ship and maintain a separate package for. Plus those methods will either need to be used or duplicated in the core codebase to support things like plotting.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,659129613
https://github.com/pydata/xarray/issues/4234#issuecomment-660148433,https://api.github.com/repos/pydata/xarray/issues/4234,660148433,MDEyOklzc3VlQ29tbWVudDY2MDE0ODQzMw==,1610850,2020-07-17T14:48:01Z,2020-07-17T14:54:41Z,CONTRIBUTOR,"This does sound like an option. However there are many situations within xarray where we need explicit cupy logic. Converting back to numpy before plotting is one example. I don't think that kind of logic can live in an accessor.

Unless you expect users to do something like this.

```python
import xarray as xr
import cupy as cp

ds = xr.tutorial.load_dataset(""air_temperature"")
gds = ds.cupy.to_cupy()

# Do some manipulation on the GPU

# Grab a time slice
time_slice = gds.air.isel(time=0)

time_slice.cupy.to_numpy().plot()  # I would hope that time_slice.plot() would work
```

I would be tempted to say that cupy is more like dask in that it is trying to implement the numpy array interface exactly but in a different paradigm (distributed, GPU, etc). And of course there are limitations and divergences because of the different paradigm. However it's not the same as pint which is trying to extend numpy and add more functionality.

So it makes sense to me that accessors for pint exist to add this extra functionality to xarray. But at least in theory cupy should be a drop-in replacement for numpy. So I don't expect a huge amount of logic will live in an accessor, compared to the amount of compatibility code that will need to go into xarray itself.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,659129613
https://github.com/pydata/xarray/issues/4234#issuecomment-660136749,https://api.github.com/repos/pydata/xarray/issues/4234,660136749,MDEyOklzc3VlQ29tbWVudDY2MDEzNjc0OQ==,1610850,2020-07-17T14:26:35Z,2020-07-17T14:32:03Z,CONTRIBUTOR,"@jthielen Something like this?

```python
import xarray as xr
import cupy as cp


@xr.register_dataset_accessor(""cupy"")
class CupyAccessor:
    def to_cupy(self):
        """"""Convert all data arrays to cupy.""""""

        for var in self.data_vars: 
            self.data_vars[var].data = cp.asarray(self.data_vars[var].data)

        return self

```

Which would then be used like this.

```python
import xarray as xr
import cupy as cp

ds = xr.open_mfdataset(""/path/to/files/*.nc"")
gds = ds.cupy.to_cupy()
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,659129613
https://github.com/pydata/xarray/issues/4234#issuecomment-660030480,https://api.github.com/repos/pydata/xarray/issues/4234,660030480,MDEyOklzc3VlQ29tbWVudDY2MDAzMDQ4MA==,1610850,2020-07-17T10:37:46Z,2020-07-17T10:37:46Z,CONTRIBUTOR,cc @quasiben,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,659129613