How to Run Inference with UDP on Roboflow Inference

Running inference on vision models in real-time is crucial for applications such as sports broadcasts, and Roboflow Inference, an open-source solution, offers a way to achieve this efficiently using UDP instead of HTTP. UDP inference allows models to continue processing without being blocked by dropped or slow packets, ensuring smooth operation. Roboflow Inference supports this out of the box and provides a standard API, modular implementations, and a model registry for easy switching between models. The guide outlines the process for setting up a UDP inference system using Docker, starting with downloading the Roboflow Inference UDP Docker image and configuring both the receiving and inference servers. This setup allows for real-time processing of webcam streams or video files, providing predictions that can be used for application logic or post-processing purposes. The use of UDP in this context offers a more efficient way to handle inference requests, making it ideal for scenarios where latency is a critical factor.