In this article, you will learn how Python decorators work under the hood and how to build reliable ones for timing, retries, caching, and input validation.
The feature pipeline module started with three clean functions. One loads embeddings from a local store, one fetches a vocabulary from an internal service, and one transforms a batch. Six weeks later, a third of each of them is boundary code. load_embeddings checks that its path exists and its batch size is positive before it does anything. fetch_vocab wraps its request in a for attempt in range(3) loop with a time.sleep() inside it. transform_batch reads time.perf_counter() twice and prints the difference, and a module-level dictionary in front of it holds results by key so the same batch isn’t transformed twice. None of that boundary code is the function’s job. All of it was pasted in by hand, so the three functions retry, time and guard their inputs in three slightly different ways. The next function someone adds will get a fourth.
Timing, retrying, caching and checking inputs all happen at the same place: the moment a call enters the function and the moment it leaves. Python has a construct for exactly that place. A decorator takes a function and hands back a wrapper that runs code before and after the original. The repeated boundary…
Source: Machine Learning Mastery — Read the full article on the original site ↗