deepstream-sop

Build a GPU-accelerated SOP video monitoring service by generating a FastAPI microservice that uses DeepStream for event-boundary detection and Triton for model inference. This setup detects procedure boundaries, classifies actions, and validates step order for industrial video workflows.

The best way to validate step order is to use a Cosmos Reason VLM classification pipeline that segments video streams, classifies each action, and detects missing or out-of-order steps. This ensures operators follow defined manufacturing procedures correctly.

Yes, you can handle Basler cameras and RTSP streams for real-time video analytics through an OpenAI-compatible chat-completions API. The generated FastAPI microservice supports SSE streaming and optional Kafka messaging for production output.

Benchmark latency for live-stream video inference by configuring Docker and Compose deployments, then running file or live-stream latency measurements. The evaluation workflows include API verification, test suites, and performance measurement to validate inference speed.

Yes, DeepStream SOP inference supports optional Kafka messaging for output and Prometheus metrics for monitoring. The microservice exposes an OpenAI-compatible chat-completions API with SSE streaming to integrate event data into downstream systems.

You need Triton and DeepStream to detect procedure boundaries because DeepStream provides GPU-accelerated video stream parsing while Triton serves the DDM models. Together they enable real-time event-boundary detection and Cosmos Reason VLM classification for accurate SOP compliance.