deepstream-sop

name	deepstream-sop
description	Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger for its internals: SOPVideoProcessor, DeepStream GEBD model (e.g. DDM) via Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.
owner	[e-mail]
service	deepstream-sop
version	1.0.0
license	CC-BY-4.0 AND Apache-2.0
reviewed	2026-04-08
metadata	{"author":"Wind Yuan <[e-mail]>","tags":["deepstream","sop","vlm","triton","gpu"],"languages":["python"],"frameworks":["deepstream","triton","fastapi"],"domain":"video-analytics"}

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) 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 configurable via FRAMES_PER_SIDE / SEQUENCE_BATCH (§ 4, § 5), with optional TensorRT (§ 5).

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Architecture Overview

Runs in a Docker container (nvds-action-sop) alongside a Kafka container. Full diagram: references/sop_architecture.svg.

Data flow through the 4-stage SOPVideoProcessor pipeline (per-request):

Input Sources                    Docker Container: nvds-action-sop
─────────────                    ──────────────────────────────────────────────────
Video Files ──┐                  FastAPI Server (port 8300)
RTSP Streams ─┤── base64/       ├─ /v1/chat/completions → SOPProcessManager
Basler Camera ┘   file/rtsp/       │
                  camera           │ ModelInitializer: VLM first, then DDM dummy pipeline
                                   │ 4 Thread Pools: cv(32), clip(32), vlm(64), vlm_req(64)
                                   │
                                   ▼ SOPVideoProcessor (per-request)
                                   ┌────────────────────────────────────────────────┐
                                   │ Stage 1: DeepStream Pipeline (GPU)             │
                                   │   Source → nvstreammux → tee1                  │
                                   │    ├─[inference] queue1 → nvdspreprocess       │
                                   │    │  → nvinferserver (Triton CAPI + DDM)      │
                                   │    │  → InferOutputTensorParser → score_queue  │
                                   │    ├─[frames]  queue3 → nvvideoconvert         │
                                   │    │  → capsfilter → appsink                   │
                                   │    │  → DecodedFrameRetriever → frame_queue    │
                                   │    └─[RTSP out] queue → convert → H.264 enc    │  (optional, § 18)
                                   │       → rtppay → udpsink → RTSPServer (§ 18)   │  opt-in only
                                   │              │ boundary scores                 │
                                   │              ▼                                 │
                                   │ Stage 2: Clip Post-Process                     │
                                   │   Boundary detection → chunk segmentation      │
                                   │              │ video frames + timestamps        │
                                   │              ▼                                 │
                                   │ Stage 3: VLM Inference                         │
                                   │   Embedded vLLM (Cosmos Reason 1/2)            │
                                   │   Frame sampling at VLM_FPS → classification   │
                                   │              │ action labels                    │
                                   │              ▼                                 │
                                   │ Stage 4: SOP Checker                           │
                                   │   Sequence validation → missing/misordered     │
                                   │              │ chunk results                    │
                                   │              ▼                                 │
                                   │         final_queue                            │
                                   └────────────────────────────────────────────────┘
                                          │
Output                                    ▼
──────                             ┌─────────────────┐
SSE Stream (chat.completion.chunk) │ Kafka Messages   │
Non-streaming (chat.completion)    │ (JSON/Protobuf)  │
Prometheus metrics (/v1/metrics)   └────────┬────────┘
                                            ▼
                                   Docker Container: kafka
                                   (apache/kafka:3.7.0)