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Test on numpy 2.0 #688
Test on numpy 2.0 #688
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Ok, I can put together a repodata patch to make it retroactive on conda-forge. The first step is to add it to our pyproject.toml, and ideally make a release containing the pin. (Conda-forge core prefers to see such retroactive changes being made for something that's already in the feedstock, so that's why a release would be good.) Any idea on the timeline before the non-rc release drops? |
Couldn't find anything conclusive, but sounds like soon: numpy/numpy#24300 |
This will help with publishing a repodata patch for conda-forge as per <pymc-devs#688 (comment)>. This will no longer be necessary when we are finished with <pymc-devs#689>.
This will help with publishing a repodata patch for conda-forge as per <#688 (comment)>. This will no longer be necessary when we are finished with <#689>.
On the conda-forge side I wrote a repodata patch, and then when I tested it didn't actually do anything... Happily, in terms of Numpy, the conda-forge infrastructure currently automatically pins There's the question of how to move forward once 2.0 becomes available, and this I'm less sure about. I'm testing adding a pin in conda-forge/pytensor-suite-feedstock#88, but there's the possibility that this interferes with the conda-forge magic (see conda-forge/pytensor-suite-feedstock#35). My recommendation on how to proceed is that we keep an eye on the pins by checking the artifacts in conda-forge/pytensor-suite-feedstock before merging any of the version update PRs. I'm very relieved that there is no threat of PyTensor breaking as soon as Numpy 2 is released. 😅 |
Here us the issue to track ecosystem compatibility with numpy 2.0 numpy/numpy#26191 (comment) |
Hmmm, when looking around, I had stumbled on Aesara fork of Theano, but somehow not Please let me know if this is a big issue, or if I can give some pointers in how to just replace the whole thing. |
Is there a NumPy 2 tracking issue for PyMC as well? Searched for one, but didn't find it (though could very easily have missed it) |
No, the only blocker for PyMC is PyTensor compatibility with numpy 2.0 (I think) |
Description
We may need to pin <2.0 until any break changes are fixed (or we could be very lucky and nothing would happen). First step is to test, which we can do with RC versions
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