PYTHON

pytorch dataloader

DataLoader(dataset, batch_size=1, shuffle=False, sampler=None,
           batch_sampler=None, num_workers=0, collate_fn=None,
           pin_memory=False, drop_last=False, timeout=0,
           worker_init_fn=None, *, prefetch_factor=2,
           persistent_workers=False)

pytorch dataloader to device

You can modify the collate_fn to handle several items at once:

from torch.utils.data.dataloader import default_collate

device = torch.device('cuda:0')  # or whatever device/cpu you like

# the new collate function is quite generic
loader = DataLoader(demo, batch_size=50, shuffle=True, 
                    collate_fn=lambda x: tuple(x_.to(device) for x_ in default_collate(x)))
Note that if you want to have multiple workers for the dataloader, you'll need to add

torch.multiprocessing.set_start_method('spawn')
after your if __name__ == '__main__' (see this issue).

Having said that, it seems like using pin_memory=True in your DataLoader would be much more efficient. Have you tried this option?
See memory pinning for more information.

pytorch dataloader

DataLoader(dataset, batch_size=1, shuffle=False, sampler=None,
           batch_sampler=None, num_workers=0, collate_fn=None,
           pin_memory=False, drop_last=False, timeout=0,
           worker_init_fn=None, *, prefetch_factor=2,
           persistent_workers=False)