In PyTorch (and TensorFlow for that matter) how data augmentation works is that it’s done on the fly as part of image preprocessing (and preferably run in parallel on the CPU while the model is training on the GPU). This means that you do not need to run any augmentation and then store on disk, actually this is not recommended, although I guess this would be possible to do if you really want to.
This 7 PyTorch tutorial is oftentimes referenced on this forum that could be worth checking out, it’s really great both to get a deeper understanding of custom dataloading but also insight in best practices to perform data augmentation. If you would like to see a minimal example of how performing data augmentation on the fly in PyTorch looks like you can see a code example I’ve created here 7