It was good before. After restarting the computer running program, this error is reported:
failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error retrieving CUDA diagnositic information for host: ...
Then it runs with the old slow CPU.
Ubuntu 20.04TensorFlow 2.5cudatoolkit 11.2cudnn 8.1
The probability is that the graphics card driver is stained with something.
Because I happened to update the system (Ubuntu) automatically before, I probably moved some NVIDIA files or something. Then I can’t restart.
Then I opened the Software Updater, completely updated it, restarted it, and finished it.
- Failed to initialize nvml driver / library version mismatch due to automatic update of NVIDIA driver
- NVIDIA NVML Driver/library version mismatch
- The nvidia-smi has failed because it could’t communicate with the NVIDIA driver
- Problems in the construction of CUDA environment (GPU parallel programming)
- Error: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version
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- Error reporting using NVIDIA SMI
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- torch.cuda.is_ Available() returns false
- How to Fix NVIDIA-SMI has failed because it couldn‘t communicate with the NVIDIA driver.
- Resolved failed call to cuinit: CUDA_ ERROR_ NO_ DEVICE
- After the new video card rtx3060 arrives, configure tensorflow and run “TF. Test. Is”_ gpu_ The solution of “available ()” output false
- CUDA Visual Studio Integration Installation failed
- Configure Nvidia graphics card on ubuntu18.04 xrandr Failed to get size of gamma for output default
- Nvidia-smi has failed because it could’t communicate with the NVIDIA driver
- Some problems in installing wsl2 and NVIDIA docker in win10
- Problem solving: importerror: libcublas.so .9.0: cannot open shared object file: No such file
- FCOS No CUDA runtime is found, using CUDA_HOME=’/usr/local/cuda-10.0′
- How to install cuda10.01in Ubuntu18.04