Troubleshooting CUDA Availability for PyTorch: Installation and Version Compatibility Guide
The article walks through diagnosing why PyTorch cannot access the GPU, reinstalling CUDA, selecting matching PyTorch builds, adjusting versions, and verifying that CUDA becomes available for accelerated training.
When switching from TensorFlow to PyTorch the author encountered a slow training process because the GPU was not being utilized, showing an "AssertionError: CUDA unavailable, invalid device 0 requested".
They first verified CUDA availability with import torch; print(torch.cuda.is_available()) , which returned False, confirming the issue.
To resolve it they reinstalled CUDA, checked the installed CUDA version via nvidia-smi (CUDA 10.0), and selected matching PyTorch builds. Using pip they attempted pip3 install torch1.9.0+cu101 torchvision0.10.0+cu101 torchaudio=0.9.0 -f https://download.pytorch.org/whl/torch_stable.html , but version mismatches caused errors.
They then tried conda installations with specific CUDA‑101 builds, and when those failed switched to the Tsinghua mirror command conda install -i https://pypi.tuna.tsinghua.edu.cn/simple torch===1.8.1 torchvision===0.9.1 -f https://download.pytorch.org/whl/torch_stable.html , which succeeded.
After installation they verified the torch and torchvision versions (torch 1.8.1, torchvision 0.9.1) and consulted the PyTorch previous‑versions page to match CUDA 10.0 with a compatible PyTorch build, eventually upgrading CUDA to 10.1 to meet the minimum requirement.
Finally they retested torch.cuda.is_available() and confirmed that CUDA was now usable, completing the setup.
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