Furthermore, you are referring to CUDA versions which PyTorch provides prebuilt binaries for—you are also free to build PyTorch from source (and PyTorch’s CUDA components using your local CUDA toolkit) if you wish to use a newer CUDA toolkit.
I tried to download a NVIDIA driver from their advanced driver search page.
If you look at the screenshot below, it does not include 11.8 or 12.1.
I chatted with a rep at NVIDIA and they confirmed that this driver search page is accurate.
I acknowledge that I might be missing something (such as building PyTorch from source).
Since I can do that, I believe that I can install CUDA 12.2 on our GPU’s and just build PyTorch from source.
That’s unfortunate that the download page is missing those entries. However minor version compatibility should be a thing and you should be able to use e.g., the driver for 12.0 under 12.1, and the driver for 11.7 under 11.8, etc.
Hi, I faced a similar issue (with A100) and I tried different versions of Cuda and Pytorch. Even with PyTorch 2.0.0 and Cuda version 11.7 I still get the following error while running torch.cuda.is_available():
python3.10/site-packages/torch/cuda/__init__.py:107: UserWarning: CUDA initialization: CUDA driver initialization failed, you might not have a CUDA gpu. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)
return torch._C._cuda_getDeviceCount() > 0
False