I misunderstood the question then, sorry.
Could you explain, how this workflow would look like:

I know that I can perform transform ‘on the fly’ but I need to create the augment the dataset and then train the complete dataset.
Is there a way to augment data and save augmented ‘dataLoader’ in separate files, use another process to load the saved ‘dataloaders’ and train the network with all the examples?

Usually you would lazily load the data in the Dataset.__getitem__ method and apply the transformation.
I don’t really understand the point of storing the DataLoader (or Dataset).
If you want to increase the number of returned samples, you could multiply the number of sample by 3000 in Dataset.__len__.