Any wheel for CUDA 12 coming?

compile
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I’ve been struggling with this for ages. NVIDIA only supports CUDA 12 for Fedora 36 and beyond. Fedora 35 is deprecated. CUDA 12 has been out since September. PyTorch supposedly has supported CUDA 12 since late December, but it’s mid-March and there’s still no wheel for it - half a year after it came out.

https://download.pytorch.org/whl/torch_stable.html

I’m struggling to compile it (gcc 12.2.1), but that’s just been a fail thusfar. Appeared to be failing due to a ton of warnings and PyTorch’s use of -Werror (I’m a big fan of the use of Werror in development… but in production?). I guessed that my gcc is too new and so downgraded it, but then I got even more errors. Now I’m trying again by trying to suppress Werror and seeing how that goes…

Any chance we’ll get a wheel at any point? Thanks! :slight_smile:

(ED: Nope, compile failed… Fbgemm is still using Werror for some reason and complaining about possible uninitialized variables… sigh…)

(ED2: Well, I managed to shoehorn it into compiling by literally replacing my systemwide gcc/g++/cpp/c++ binaries with wrapper scripts that strip out all -Werror arguments. But now when I actually include torch/python.h, type_caster is broken:

/usr/local/lib64/python3.10/site-packages/torch/include/pybind11/detail/…/cast.h:42:120: error: expected template-name before ‘<’ token
42 | return caster.operator typename make_caster::template cast_op_type();
| ^
/usr/local/lib64/python3.10/site-packages/torch/include/pybind11/detail/…/cast.h:42:120: error: expected identifier before ‘<’ token

… I give up :frowning: )

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We are still in the process of discussing and merging the last few needed changes into the code base before starting with the CUDA 12.x bringup for the binaries.
If you need to use this CUDA version, you would indeed need to build PyTorch from source or just use the latest NGC containers.
As a workaround for now you could use the current binaries (stable or nightly) with CUDA 11.7 or 11.8 which already support all released GPU architectures.
Also note that your locally installed CUDA toolkit won’t be used when executing the binaries as they ship with their own dependencies unless you build a custom CUDA extension.

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Okay, thanks for the update (I know that maintaining packages is a rather thankless job!)

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