- 1 year ago
How to solve "Torch is not able to use GPU"error?
The "Torch is not able to use GPU" error can have various causes. Here are some possible solutions you can try:
Check GPU availability: Make sure that your system has a compatible GPU installed and that it's properly configured. You can check the GPU availability by running the following code in a Python environment:
goimport torch
print(torch.cuda.is_available())
This should print
True
if a compatible GPU is available and properly configured.Install compatible CUDA toolkit: Make sure that you have installed a compatible version of the CUDA toolkit for your GPU and that it's properly configured. You can check the CUDA toolkit version by running the following code in a Python environment:
goimport torch
print(torch.version.cuda)
Make sure that you have installed a compatible version of the CUDA toolkit for your GPU and that it's properly configured.
Install compatible cuDNN library: Make sure that you have installed a compatible version of the cuDNN library for your CUDA toolkit version. You can check the cuDNN version by running the following code in a Python environment:
goimport torch
print(torch.backends.cudnn.version())
Make sure that you have installed a compatible version of the cuDNN library for your CUDA toolkit version.
Upgrade PyTorch: If you're using an older version of PyTorch, try upgrading to a newer version. Newer versions of PyTorch may have better compatibility with your system and GPU.
Check driver version: Make sure that you have installed the latest GPU driver for your system. Outdated drivers can cause compatibility issues with PyTorch and prevent it from using the GPU.
I hope these solutions help you resolve the issue. If you continue to experience the same issue, please let me know and I'll do my best to assist you further.