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id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
1322112135 I_kwDOAMm_X85OzdSH 6847 Please expose __cuda_array_interface__ via the xarray.__array__() function if present MurrayData 8914493 open 0     5 2022-07-29T11:08:51Z 2022-08-01T12:38:13Z   NONE      

Is your feature request related to a problem?

When using an array type with GPU support, such as CuPy arrays, Numba device arrays or Numba mapped arrays (shared), __cuda_array_interface__ is not exposed by the xarray.__array() function.

I'm using large NetCDF files which I wish to process against reference dataframes and use GPU acceleration to do this.

For example, Numba mapped array:

```

points = np.random.randn(2, 3) map_points = nb.cuda.mapped_array_like(points) map_points.array_interface {'data': (140399865758208, False), 'strides': None, 'descr': [('', '<f8')], 'typestr': '<f8', 'shape': (2, 3), 'version': 3}

map_points.cuda_array_interface {'shape': (2, 3), 'strides': None, 'data': (140399865758208, False), 'typestr': '<f8', 'stream': None, 'version': 3} ```

When copied to xarray:

```

data = xr.DataArray(map_points, dims=("x", "y"), coords={"x": [10, 20]}) data xarray.DataArray x: 2y: 3 array([[0., 0., 0.], [0., 0., 0.]]) Coordinates: x (x) int64 10 20 Attributes: (0) ```

Array interface confirms same address for the base (CPU) array as above, i.e. Zero Copy

```

data.array().array_interface {'data': (140399865758208, False), 'strides': None, 'descr': [('', '<f8')], 'typestr': '<f8', 'shape': (2, 3), 'version': 3} ```

However the __cuda_array_interface__ is not exposed

```

data.array().cuda_array_interface

AttributeError Traceback (most recent call last) Input In [23], in <cell line: 1>() ----> 1 data.array().cuda_array_interface

AttributeError: 'numpy.ndarray' object has no attribute 'cuda_array_interface' ```

Describe the solution you'd like

Expose __cuda_array_interface__ via the xarray.__array() function so it is available to CuPy and Numba CUDA functions.

Describe alternatives you've considered

As a workaround, I'm not using xarray for NetCDF files. Instead I'm converting them into an dictionary of arrays which provides me with the GPU interfaces.

Additional context

No response

 
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     xarray 13221727 issue

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