A very specific error!
The "Torch is not able to use GPU" error in Table Diffusion can be frustrating. Here are some steps you can try to resolve the issue:
1. Check your CUDA version and Torch installation
Make sure that: * Your CUDA version matches the one required by PyTorch (usually specified in requirements.txt or setup.py). You can check your CUDA version using nvcc --version. * You have installed the correct version of PyTorch (pip install torch torchvision) and its dependencies.
2. Verify GPU availability
Run:
command
| nvidia-smi |
to ensure that your system recognizes a compatible NVIDIA GPU. If you don't see any GPUs listed, it might be due to a driver issue or an incompatible CUDA version.
3. Update PyTorch with the correct backend
Try running:
| python |
This will attempt to use your GPU for computations.
4. Check Table Diffusion code
Review the setup.py or requirements.txt file of the project you're working on, and ensure that: * The correct PyTorch version is specified. * There are no conflicting dependencies (e.g., multiple versions of Torch).
5. Try a different CUDA device
If your system has multiple GPUs, try running with a specific GPU index using torch.device("cuda:1" if torch.cuda.is_available() else "cpu"). This can help identify whether the issue is related to a particular GPU.
6. Reinstall PyTorch and dependencies
As a last resort: * Uninstall PyTorch (pip uninstall torch torchvision) * Remove any cached packages (pip cache purge or pip install --force-reinstall) * Install PyTorch again with the correct version (pip install torch torchvision==<version>)
If none of these steps resolve the issue, please provide more details about your environment (OS, CUDA version, Torch version), and I'll be happy to help you investigate further!