NVIDIA A100 GPU - RuntimeError: cuDNN error: CUDNN_STATUS_MAPPING_ERROR

Hi all - I started experiencing this issue recently on a new set of A100 GPUs. I am using the following configuration:

NVIDIA A100 GPU
torch == 1.4.0
Python == 3.7.4-GCCcore-8.3.0
CUDA == 10.1.243-GCC-8.3.0
cudnn == 7.6.5.32-10.1

The error is occurring when I am calling forward on a sequence of convolutional operations in my code.

I have used the same configuration on other GPUs, such as K80, P100, V100 and RTX8000, and never had the error. Anybody found a solution to this issue?

Just updated to the latest torch 1.8.1 and now the error has changed to:

....python3.7/site-packages/torch/cuda/__init__.py:104: UserWarning:
A100-PCIE-40GB with CUDA capability sm_80 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.
If you want to use the A100-PCIE-40GB GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

I had a look at the link provided, but I am not very sure how I can update or change my pytorch installation to allow sm_80.

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I think I fixed my own problem, but posting the answer here in case anybody ever comes across this later down the line.

The first issue comes from the fact that (at least in my case) the A100 GPUs comes with sm_80, but also with pre-installed CUDA 11+. In this case, I was sabotaging myself by module loading CUDA 10.1 and cudnn 7.6.5.32 on the cluster.

The solution came by using the CUDA 11.1 version of pytorch from here: Start Locally | PyTorch

pip3 install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

Then, I just stopped loading CUDA 10.1 and cudnn 7.6.5 as separate modules, which made all errors go away. Running a few tests jobs now, and will post any updates in case other things get broken.

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Thanks, Andrei–just wanted to let you know that this saved me today!

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