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  • shoyer · 1 ✖

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  • apply_ufunc with dask='parallelized' and vectorize=True fails on compute_meta · 1 ✖

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  • MEMBER · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
565107345 https://github.com/pydata/xarray/issues/3574#issuecomment-565107345 https://api.github.com/repos/pydata/xarray/issues/3574 MDEyOklzc3VlQ29tbWVudDU2NTEwNzM0NQ== shoyer 1217238 2019-12-12T17:33:43Z 2019-12-12T17:33:43Z MEMBER

The problem is that Dask, as of version 2.0, calls functions applied to dask arrays with size zero inputs, to figure out the output array type, e.g., is the output a dense numpy.ndarray or a sparse array?

Unfortunately, numpy.vectorize doesn't know how to large of a size 0 array to make, because it doesn't have anything like the output_sizes argument.

For xarray, we have a couple of options: 1. we can safely assume that if the applied function is a np.vectorize, then it should pass meta=np.ndarray into the relevant dask functions (e.g., dask.array.blockwise). This should avoid the need to evaluate with size 0 arrays. 1. we could add an output_sizes argument to np.vectorize either upstream in NumPy or into a wrapper in Xarray.

(1) is probably easiest here.

 
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   apply_ufunc with dask='parallelized' and vectorize=True fails on compute_meta 528701910

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