Supported versions of array libraries

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
numpy, python
Domain
data

Research direction

The issue names no files or tests. Start by reviewing how CI currently installs and tests NumPy, torch, CuPy, and Dask across Python versions, then determine which versions downstream consumers support; done means agreeing on lower bounds and updating the testing policy accordingly.

Written by the indexing model from the issue text.

Description

question

For numpy, we test against numpy 1.22, 1.26, "latest" and "dev".
For other array libraries (torch, cupy, dask) do not specify the version, and effectively test with whatever pip installs for a given python version.
Are we happy with this, or do we want to specify a lower limit for torch and cupy, too?

Case in point: CuPy merges an array-api compatible fix for 14.0. When 14.0 is out, do we

  • keep testing with 13.x and 14.x (hence carry a workaround for 13.x in array-api-compat/cupy), or
  • just test against 14.x (hence no need for a workaround in array-api-compat/cupy)

I suppose the deciding factor here is what downstream consumers support.

Dominant language
Python
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131
Forks
49
Avg merge
2d 8h
Merged PRs (30d)
6

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