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Runtime error
Doesnt work
Runtime error
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Traceback (most recent call last):
File "/home/user/app/app.py", line 34, in
model = WhisperModel(model_size, device="cuda", compute_type="float16")
File "/home/user/.local/lib/python3.10/site-packages/faster_whisper/transcribe.py", line 130, in init
self.model = ctranslate2.models.Whisper(
RuntimeError: CUDA failed with error CUDA driver version is insufficient for CUDA runtime version
I am having the same problem, using Linux Mint 22 (based on Ubuntu 24). CPU operation works, but not GPU. I don't know if it makes a difference, but I am using podman rather than docker.
Doing an nvidia check in a container gives me the same CUDA version as my host PC -- 12.4. I upgraded drivers to V550 to make sure the CUDA was not too old. This is how I checked the container GPU: podman run --rm --device nvidia.com/gpu=all --security-opt=label=disable ubuntu nvidia-smi
It looks like the container is looking for version 11.8 of CUDA, so I don't know why 12.4 doesn't work.
root@d5ffe373a0a9:/app# ll /usr/local/
total 12
drwxr-xr-x 1 root root 6 Nov 10 2023 ./
drwxr-xr-x 1 root root 10 Oct 4 2023 ../
drwxr-xr-x 1 root root 654 Jan 30 2024 bin/
lrwxrwxrwx 1 root root 22 Nov 10 2023 cuda -> /etc/alternatives/cuda/
lrwxrwxrwx 1 root root 25 Nov 10 2023 cuda-11 -> /etc/alternatives/cuda-11/
drwxr-xr-x 1 root root 34 Nov 10 2023 cuda-11.8/
I tried a test script I found, and it says CUDA is not available to PyTorch. That would be a problem! Is there any other way of checking if GPU/CUDA is being passed through properly into the container?
root@e27de5c88794:/app/uploads# cat test_cuda.py
import torch
def check_cuda():
print("Is CUDA available in PyTorch:", torch.cuda.is_available())
if torch.cuda.is_available():
print("Number of CUDA devices:", torch.cuda.device_count())
for i in range(torch.cuda.device_count()):
print("CUDA Device #{}: {}".format(i, torch.cuda.get_device_name(i)))
if __name__ == "__main__":
check_cuda()
gives
root@e27de5c88794:/app/uploads# python3 test_cuda.py
Is CUDA available in PyTorch: False