Cuda too old
2023-12-19T14:21:37.212836490Z The NVIDIA driver on your system is too old (found version 11070). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver.: str
2023-12-19T14:21:37.214841659Z Traceback (most recent call last):
2023-12-19T14:21:37.214853339Z File "/stable-diffusion-webui/modules/errors.py", line 84, in run
2023-12-19T14:21:37.214858479Z code()
2023-12-19T14:21:37.214861749Z File "/stable-diffusion-webui/modules/devices.py", line 63, in enable_tf32
2023-12-19T14:21:37.214865109Z if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
2023-12-19T14:21:37.214868209Z File "/stable-diffusion-webui/modules/devices.py", line 63, in <genexpr>
2023-12-19T14:21:37.214871259Z if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
2023-12-19T14:21:37.214874339Z File "/opt/conda/lib/python3.10/site-packages/torch/cuda/init.py", line 435, in get_device_capability
2023-12-19T14:21:37.214877390Z prop = get_device_properties(device)
2023-12-19T14:21:37.214880450Z File "/opt/conda/lib/python3.10/site-packages/torch/cuda/init.py", line 449, in get_device_properties
2023-12-19T14:21:37.214883470Z _lazy_init() # will define _get_device_properties
2023-12-19T14:21:37.214886600Z File "/opt/conda/lib/python3.10/site-packages/torch/cuda/init.py", line 298, in _lazy_init
2023-12-19T14:21:37.214891770Z torch._C._cuda_init()...
Download the latest official NVIDIA drivers
Download the latest official NVIDIA drivers
PyTorch
PyTorch
8 Replies
is it my dependency problem?
Is this an error that you saw, which template are you using?
yeah
im using my custom template
FROM pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime
RUN pip install torchvision torchaudio --prefer-binary
RUN pip install --no-cache-dir xformers pyngrok --no-dependencies --prefer-binary
i guess these are the lines that you need, on some machines my template works
like 90%ish of the time it works
sometimes it gets this error
When you launch the pod are you using the CUDA filter to select the minimal version?
im using serverless
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Got it, let me check what the status is with adding that feature
oh ya there is something related with the machine's nvidia driver
i only get the problem in A4500 A4000 i think