You can start by taking a look at the default dataset classes: torch.utils.data — PyTorch 1.8.1 documentation 4
and seeing if your data fits the map style of iterable style abstraction. The map style is usually a straightforward abstraction for many datasets as you only need to define an __getitem__ and a __len__ function. Once you have a usable dataset, using a dataloader torch.utils.data.dataloader — PyTorch 1.8.1 documentation will handle the parallelization and loading in memory for you.