Hello ptrblck, thanks for the quick reply. I create a custom Dataset class (similar to the one from this tutorial 8 )
I then store the dataloader by doing,
dataloader = DataLoader(dataset, batch_size=batch_size,
                            shuffle=True, num_workers=cores, drop_last = True)
if save:
        torch.save(Loader, os.getcwd()+"/labelled/"+savename)

If I cannot save my dataloader this way, how could I achieve my aim? (storing the transformed images to avoid the time it takes to transform + for reproducibility)
Best,
Luis