deepstream-sop

# DeepStream SOP Inference Microservice Skill This skill guides AI coding assistants in building, extending, and debugging the **NVIDIA DeepStream SOP (Standard Operating Procedure) Inference Microservice** — a GPU-accelerated pipeline for temporal action detection and VLM-based SOP compliance monitoring on industrial video feeds. **Reference repository**: https://github.com/NVIDIA/sop-monitoring-blueprints/tree/main/microservices/sop-inference-bp **Local reference code**: `sop-inference-bp/` directory (from a local clone of the repository) --- ## Models Model-agnostic at both inference stages — swap via env var (and Triton dir for GEBD). | Stage | Role | Model class | Default | Swap via | |------|------|-------------|---------|----------| | Stage 1 (CV) | Per-frame boundary scoring → chunk segmentation | **Generic Event Boundary Detection (GEBD)** | **DDM** ([MCG-NJU/DDM](https://github.com/MCG-NJU/DDM)) via Triton Python backend | Replace `triton_model_repo/<model>/` + `DDM_MODEL_PATH` (§ 5) | | Stage 3 (VLM) | Per-chunk action classification | Vision-language model via vLLM | **Cosmos Reason 1 7B** (Reason 2 also supported) | Set `VLLM_MODEL_PATH` to a different HF ID or local path | "GEBD" = swappable Stage-1 slot; "DDM" = the default architecture (terms used interchangeably). **Chunking is selectable per request** (§ 2): default `ddm-net` uses GEBD; `uniform` produces fixed-length chunks and **bypasses Stage-1 GEBD** (§ 3, § 6). DDM temporal window is configurabl...