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Add another patch for numpy 2.0 (v1.84.x branch) #211
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Hi! This is the friendly automated conda-forge-linting service. I just wanted to let you know that I linted all conda-recipes in your PR ( |
@conda-forge-admin please rerender |
…nda-forge-pinning 2024.07.16.17.39.45
I tested the patched Boost libraries with the code that I maintain with numpy versions 1.19 to 2.0, and the tests pass with the patched libraries. We have numerical arrays exposed to Python using Boost and we get segmentation faults without the fix (numpy 2 migrator for cctbx-base package) I ran the tests with GitHub Actions and tested linux, Windows, and macOS (Intel and Apple Silicon). The patched packages for Boost and cctbx-base are on my testing channel (https://anaconda.org/cctbx-dev).
There is a My concern with I can add the |
What are the next steps here? |
Rebase, or at least fix the conflict.
Thanks for the extensive testing. IIUC, things work with numpy 1.19 (which is the absolute lowest bound that any package produced here would require anyway), so then we don't need to add anything else here. |
@conda-forge-admin , please re-render |
Hi! This is the friendly automated conda-forge-webservice. I tried to rerender for you, but it looks like there was nothing to do. This message was generated by GitHub actions workflow run https://github.com/conda-forge/boost-feedstock/actions/runs/10068564811. |
Hi! This is the friendly automated conda-forge-linting service. I just wanted to let you know that I linted all conda-recipes in your PR ( |
That should fix the conflict |
Co-authored-by: h-vetinari <[email protected]>
Anything else? |
Checking in to see if anything else needs to be done. Thanks! |
Thank you! |
Thanks! Feel free to ping me if there are any issues. |
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(if the version changed)conda-smithy
(Use the phrase@conda-forge-admin, please rerender
in a comment in this PR for automated rerendering)This fixes an issue that came up starting with numpy 1.21 (segmentation faults in certain cases where a C++ array was exposed to Python), but was patched in numpy. numpy 2 removed the fix so the solution is to fix Boost. More details are described in this issue (boostorg/python#376). The upstream PR in Boost is here (boostorg/python#443).