Load data into GPU directly using PyTorch
In training loop, I load a batch of data into CPU and then transfer it to GPU:

import torch.utils as utils

train_loader = utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=4, pin_memory=True)

for inputs, labels in train_loader:
    inputs, labels = inputs.to(device), labels.to(device)


This way of loading data is very time-consuming. Any way to directly load data into GPU without transfer step ?

You can load all the data to in tensor than move it yo GPU memory.(assuming that you have enough memory) When you need it use the one inside the tensor which is already at GPU memory. Hope it helps.
