Hi, I am using a set of 1D data for training and I noticed that GPU usage is quite low (<5%) and training takes very long time to finish. I profiled my code (following instruction here: https://www.sagivtech.com/2017/09/19/optimizing-pytorch-training-code/) and found that 73% of the computation time is spent on loading data. I wonder if it is possible to load all data into GPU memory to speed up training, and tried to include
pin_memory=True in my code, but it told me “cannot pin ‘torch.cuda.FloatTensor’ only CPU memory can be pinned”. Does anyone have idea how I should do this?