Increment version to 1.10

[mnorris11]

Experimenting with fork -> make updates -> run CI (Thanks @h-vetinari for the tips over email)

Eventual goals:

• Release 1.10 for both CPU and GPU on conda-forge channel (currently it is on Pytorch channel)
• Get AVX512 working
• Either integrate all patches to Faiss or remove them if no longer needed. Only leave them if really necessary.

[conda-forge-admin]

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 (recipe/meta.yaml) and found it was in an excellent condition.

I do have some suggestions for making it better though...

For recipe/meta.yaml:

• ℹ️ The recipe is not parsable by parser conda-souschef (grayskull). This parser is not currently used by conda-forge, but may be in the future. We are collecting information to see which recipes are compatible with grayskull.
• ℹ️ The recipe is not parsable by parser conda-recipe-manager. The recipe can only be automatically migrated to the new v1 format if it is parseable by conda-recipe-manager.

_{This message was generated by GitHub Actions workflow run https://github.com/conda-forge/conda-forge-webservices/actions/runs/13188284398. Examine the logs at this URL for more detail.}

[h-vetinari]

You might have an easier start if you start off from the branch in #83, which I've already cleaned up. There's something not working out with linking CUDA bits on windows, but the rest looks fine.

[mnorris11]

You might have an easier start if you start off from the branch in #83, which I've already cleaned up. There's something not working out with linking CUDA bits on windows, but the rest looks fine.

@h-vetinari it looks like there are some changes required to the yaml. I am trying it on our CI on windows to see if I can repro. I'll take a look!

[h-vetinari]

it looks like there are some changes required to the yaml

You mean for fixing the issues on windows+CUDA in #83? From what I can tell, the linker cannot find the CUDA symbols anymore. Normally I'd start digging on the CUDA side, but given that this recipe has worked with the same CUDA setup & version before, it's probable that something in the faiss-handling changed somehow.

[h-vetinari]

It's worth noting though that this only happens in the python bindings (i.e. the much bigger libtorch builds fine), so probably we can narrow it down to the setup.py and/or swig compilation.