@my3bikaht Thanks. I already use data.to(device) and mode.to(device) to load them into CUDA GPU , assuming data are the training set loaded by DataLoader and model is the network. But I don’t know what might be the cause of the extra load on CPU. Can you explain further about the solution to the problem?
PS:model_inference is the extra minutes caused by turning on the Pytorch Log.