trained on GPU, but inference on CPU

[jinkoo2]

I've trained a model on a GPU. Exported the model as a zip and transferred to a PC with only CPU. I installed cpu-only pytorch bud getting this error on running nnUNet_predict. is this because this model was trained on a GPU? It seems still looking for CUDA... any way to fix this? Thanks.

Traceback (most recent call last):
File "/home/jk/projects/nnunet_scripts/_venv/bin/nnUNetv2_predict", line 8, in
sys.exit(predict_entry_point())
File "/home/jk/projects/nnunet_scripts/_nnUNet/nnunetv2/inference/predict_from_raw_data.py", line 866, in predict_entry_point
predictor.predict_from_files(args.i, args.o, save_probabilities=args.save_probabilities,
File "/home/jk/projects/nnunet_scripts/_nnUNet/nnunetv2/inference/predict_from_raw_data.py", line 258, in predict_from_files
return self.predict_from_data_iterator(data_iterator, save_probabilities, num_processes_segmentation_export)
File "/home/jk/projects/nnunet_scripts/_nnUNet/nnunetv2/inference/predict_from_raw_data.py", line 351, in predict_from_data_iterator
for preprocessed in data_iterator:
File "/home/jk/projects/nnunet_scripts/_nnUNet/nnunetv2/inference/data_iterators.py", line 117, in preprocessing_iterator_fromfiles
[i.pin_memory() for i in item.values() if isinstance(i, torch.Tensor)]
File "/home/jk/projects/nnunet_scripts/_nnUNet/nnunetv2/inference/data_iterators.py", line 117, in
[i.pin_memory() for i in item.values() if isinstance(i, torch.Tensor)]
NotImplementedError: Could not run 'aten::_pin_memory' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_pin_memory' is only available for these backends: [Meta, NestedTensorCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastXPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].