liveBook · Manning

Queue

As explained in Deep Learning with PyTorch, Second Edition

A first-in, first-out structure that stores incoming work items until a worker processes them.

A queue stores incoming work items until a worker processes them, usually in arrival order. In model serving, requests enter an inference queue while a background worker collects and processes them. section 17.1.4

Batching starts when the queue is full or when its oldest request reaches a maximum waiting time. A thread-safe queue lets the API continue accepting requests while inference runs. section 17.1.4

Restaurant orders

Treat requests as kitchen tickets: collect enough tickets, or wait until one has waited too long, then process them together while new orders continue arriving. section 17.1.4

How Deep Learning with PyTorch, Second Edition covers it

How deep it goes
Explains batching thresholds, thread safety, worker interaction, and shutdown lifecycle, but not queue implementation internals.
Examples
Uses a restaurant-order analogy, queue.Queue setup, FastAPI requests, and concurrent curl calls as concrete examples.
What it assumes
Assumes familiarity with FastAPI request handling and model inference while demonstrating queue setup within that deployment.
What you can do after
Operate/configure it through Python’s queue.Queue to connect an inference endpoint with a batching model worker.
How it connects
Connects queues with request batching, background worker threads, streaming responses, asynchronous generators, and graceful shutdown.

Also explained in

Compare how 24 books explain queue →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with PyTorch, Second Edition. The section links point to the parts of the book it draws on.