Performance, Pytorch, Deep Learning, Artificial Intelligence, Tensorboard.
train_loader = torch.utils.data.DataLoader(train_set, batch_size=256, shuffle=True, num_workers=8, pin_memory=True) Last, we define our training step with the PyTorch Profiler configured to our desire: device = torch.device("cuda:0") model = Net().cuda(device) criterion = MaskedLoss().cuda(device)