In this lesson, you will learn how to convert a pre-trained ResNetV2-50 model using PyTorch Image Models (TIMM) to ONNX, analyze its structure, and test inference using ONNX Runtime. We'll also compare inference speed and model size against standard PyTorch execution to highlight why ONNX is better suited for lightweight AI inference. This prepares the model for integration with FastAPI and Docker, ensuring environment consistency before deploying to AWS Lambda.
The Java Virtual Machine (JVM) is a marvel of engineering, optimized for long-running, high-performance applications. Its just-in-time (JIT) compiler analyzes code as it runs, making sophisticated optimizations to deliver incredible peak performance. But this strength becomes a weakness in a serverless model. When a Lambda function starts cold, the JVM must go through its entire initialization process: loading classes, verifying bytecode and beginning the slow warm-up of the JIT compiler. This can take several seconds - an eternity for a latency-sensitive workflow.
Cold starts in AWS Lambda refer to the additional latency introduced when initializing a new execution environment for a function invoked after a period of inactivity.
AWS Lambda allows for concurrent execution of programs, leveraging Goroutines for non-blocking I/O bound tasks, enhancing performance similar to Node.js asynchronous handling.
