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[REVIEW] Update minimum pandas and numpy pinnings - #12887
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Can we also bump numpy (currently it's unpinned) to align with dask and resolve this issue? #12785 |
pandas version from 1.0 to 1.3pandas and numpy pinnings
Done 👍 |
| - cupy >=9.5.0,<12.0.0a0 | ||
| - numba >=0.56.2 | ||
| - numpy | ||
| - numpy >=1.21 |
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Current versions of numba require numpy<1.24 (until numba 0.57 is released with this fix). We're getting this pinning from the upstream feedstock but this pinning doesn't work if the user installs numba another way.
@jayrodge ran into this locally when installing RAPIDS 23.02 on WSL, perhaps with a numba from some source other than conda-forge.
Should we actively prevent that problem by pinning like this until numba 0.57 is released?
| - numpy >=1.21 | |
| - numpy >=1.21,<1.24 |
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Even if we pin numpy in our feedstock, if the user install numba or as a matter of fact numpy in another we wouldn't be able to prevent these kinds of breakages no? I'm only hesitant on having an upper bound on numpy because we don't use it's internal APIs and this will have a maintenance overhead of tracking numpy releases and unpinning them manually. Whereas leaving it unpinned on the upper bound could help us detect breakages in our CI and have it fixed ASAP.
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I understand the hesitation. I think this is a numba problem, and not something we should change in our dependencies. I just wanted to pose the idea. Let’s leave it alone.
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I think this is just waiting to resolve the numpy upper bound pin for numba < 0.57, but is otherwise good to go? |
Let’s not block on that. I think we may take no action on that upstream problem. |
This PR updates `pandas` & `numpy` pinnings to be in-sync with `cudf`: NVIDIA/cudf#12887 Authors: - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - Peter Andreas Entschev (https://github.com/pentschev) - Ray Douglass (https://github.com/raydouglass) URL: #1139
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/merge |
Description
This PR:
pandasversion from1.0to1.3.numpyas>=1.21pandocversion constraint.Resolves #12785.
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