On 11/10/20 8:19 PM, Ralf Gommers wrote:
> Hi all,
>
> I'd like to share an update on this topic. The draft array API
> standard is now ready for wider review:
>
> - Blog post: https://data-apis.org/blog/array_api_standard_release
> <https://data-apis.org/blog/array_api_standard_release/>
> - Array API standard document:
> https://data-apis.github.io/array-api/latest/
> - Repo: https://github.com/data-apis/array-api/
>
> It would be great if people - and in particular, NumPy maintainers -
> could have a look at it and see if that looks sensible from a NumPy
> perspective and whether the goals and benefits of adopting it are
> described clearly enough and are compelling.
>
I think it is compelling for a first version. The test suite and
benchmark suite will be valuable tools. I hope future versions
standardize complex numbers as a dtype.
Yes, that's definitely a desire - when implementations are there/ready. At the moment most libraries have very incomplete support for complex dtypes, largely because they're not very important for deep learning. Also NumPy's implementations/choices are shaky in places, and that's being turned up by the PyTorch effort that's ongoing now to implement complex dtype support in a NumPy-compatible way.
I realize there is a limit to
the breadth of the scope of functions to be covered. Is there a page
that lists them in one place? For instance I tried to look up what the
standard has to say on issue https://github.com/numpy/numpy/issues/17760
about using bincount on unt64 arrays. It took me a while to figure out
that bincount was not in the API (although unique(..., return_counts) is).
That's a good idea and still missing, thanks for asking. The test suite that's in development has a complete list [1]. In the document itself Sphinx search works, but it should be easier to get a complete overview perhaps (although it requires some thought - the NumPy docs don't have everything on one page either).
Cheers,
Ralf
Matti
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