When training a model, we aim to process data in batches, shuffle data at each epoch to avoid over fitting, and leverage Python's multiprocessing for data fetching through multiple workers.
This is a companion discussion topic for the original entry at https://www.reduct.store/blog/ai/datastreaming/pytorch/implement-database-data-streaming-pytorch