3 Step Tutorial to Performance Test ML Serving APIs using Locust and FastAPI

A step-by-step tutorial to use Locust to load test a (pre-trained) image classifier model served using FastAPI. In my previous tutorial, we journeyed through building end-points to serve a machine learning (ML) model for an image classifier through an image classifier app, in 4 steps using Python and FastAPI. In this follow-up tutorial, we will focus on load/performance testing our end-points using Locust. If you have followed my last tutorial on serving a pre-trained image classifier model from TensorFlow Hub using FastAPI, then you can directly jump to Step 2 of this tutorial. In the app.py file, implement the /predict/tf/ end-point using FastAPI.