issue_comments

@nvictus, thank you for your work!

I just tried this on CuPy arrays, and it seems to be working during array creation:

```python In [1]: import cupy as cp

In [2]: import xarray as xr

In [3]: x = cp.arange(6).reshape(2, 3).astype('f')

In [4]: y = cp.ones((2, 3), dtype='int')

In [5]: x
Out[5]: array([[0., 1., 2.], [3., 4., 5.]], dtype=float32)

In [6]: y
Out[6]: array([[1, 1, 1], [1, 1, 1]])

In [7]: y.device
Out[7]: <CUDA Device 0>

In [8]: x.device
Out[8]: <CUDA Device 0>

In [9]: ds = xr.Dataset()

In [10]: ds['x'] = xr.DataArray(x, dims=['lat', 'lon'])

In [11]: ds['y'] = xr.DataArray(y, dims=['lat', 'lon'])

In [12]: ds
Out[12]: <xarray.Dataset> Dimensions: (lat: 2, lon: 3) Dimensions without coordinates: lat, lon Data variables: x (lat, lon) float32 ... y (lat, lon) int64 ...

In [13]: ds.x.data.device
Out[13]: <CUDA Device 0>

In [14]: ds.y.data.device
Out[14]: <CUDA Device 0> ```

Even though it failed when I tried applying an operation on the dataset, this is still awesome! I am very excited and looking forward to seeing this feature in xarray:

```python In [15]: m = ds.mean(dim='lat')

TypeError Traceback (most recent call last) <ipython-input-15-8e4d5e7d5ee3> in <module> ----> 1 m = ds.mean(dim='lat')

/glade/work/abanihi/devel/pangeo/xarray/xarray/core/common.py in wrapped_func(self, dim, skipna, kwargs) 65 return self.reduce(func, dim, skipna=skipna, 66 numeric_only=numeric_only, allow_lazy=True, ---> 67 kwargs) 68 else: 69 def wrapped_func(self, dim=None, **kwargs): # type: ignore

/glade/work/abanihi/devel/pangeo/xarray/xarray/core/dataset.py in reduce(self, func, dim, keep_attrs, keepdims, numeric_only, allow_lazy, kwargs) 3532 keepdims=keepdims, 3533 allow_lazy=allow_lazy, -> 3534 kwargs) 3535 3536 coord_names = set(k for k in self.coords if k in variables)

/glade/work/abanihi/devel/pangeo/xarray/xarray/core/variable.py in reduce(self, func, dim, axis, keep_attrs, keepdims, allow_lazy, kwargs) 1392 input_data = self.data if allow_lazy else self.values 1393 if axis is not None: -> 1394 data = func(input_data, axis=axis, kwargs) 1395 else: 1396 data = func(input_data, **kwargs)

/glade/work/abanihi/devel/pangeo/xarray/xarray/core/duck_array_ops.py in mean(array, axis, skipna, kwargs) 370 return _to_pytimedelta(mean_timedeltas, unit='us') + offset 371 else: --> 372 return _mean(array, axis=axis, skipna=skipna, kwargs) 373 374

/glade/work/abanihi/devel/pangeo/xarray/xarray/core/duck_array_ops.py in f(values, axis, skipna, kwargs) 257 258 try: --> 259 return func(values, axis=axis, kwargs) 260 except AttributeError: 261 if isinstance(values, dask_array_type):

/glade/work/abanihi/devel/pangeo/xarray/xarray/core/nanops.py in nanmean(a, axis, dtype, out) 158 return dask_array.nanmean(a, axis=axis, dtype=dtype) 159 --> 160 return np.nanmean(a, axis=axis, dtype=dtype) 161 162

/glade/work/abanihi/softwares/miniconda3/envs/xarray-tests/lib/python3.7/site-packages/numpy/core/overrides.py in public_api(args, kwargs) 163 relevant_args = dispatcher(args, **kwargs) 164 return implement_array_function( --> 165 implementation, public_api, relevant_args, args, kwargs) 166 167 if module is not None:

TypeError: no implementation found for 'numpy.nanmean' on types that implement array_function: [<class 'cupy.core.core.ndarray'>] ```