For example, in the pytorch project, it is encountered in autoinit.py
pycuda._ driver.Error:cuInit failed:unknown error
Solution: install NVIDIA modprobe package:
sudo apt-get install nvidia-modprobe
Read More:
- How to Fix NVIDIA-SMI has failed because it couldn‘t communicate with the NVIDIA driver.
- [Solved] NVIDIA-SMI has failed because it couldn‘t communicate with the NVIDIA driver.
- The nvidia-smi has failed because it could’t communicate with the NVIDIA driver
- Nvidia-smi has failed because it could’t communicate with the NVIDIA driver
- Error reporting using NVIDIA SMI
- Resolved failed call to cuinit: CUDA_ ERROR_ NO_ DEVICE
- NVIDIA NVML Driver/library version mismatch
- nvidia-settings: ERROR: nvidia-settings could not find the registry key file
- NVIDIA-SMI has failed because it couldn’t communicate with the NVIDIA driver
- Failed to initialize nvml driver / library version mismatch due to automatic update of NVIDIA driver
- torch.cuda.is_ Available() returns false
- A TPM error (7) occurred attempting to read a pcr value
- Use NVIDIA to solve NVIDIA’s
- failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error
- Solution to x service error when installing NVIDIA graphics driver under Ubuntu
- NVIDIA docker failed to start normally
- Some problems in installing wsl2 and NVIDIA docker in win10
- RuntimeError: CUDA out of memory. Tried to allocate 600.00 MiB (GPU 0; 23.69 GiB total capacity)
- The difference and usage of insmod and modprobe
- Pytorch RuntimeError CuDNN error CUDNN_STATUS_SUCCESS (How to Fix)