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)

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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)

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Section Index

Each section is a standalone file in references/ — load only what your task needs.

§	File	Responsibility
1	skill_01_fastapi_endpoints.md	FastAPI endpoints, server init, Prometheus metrics
2	skill_02_pydantic_schemas.md	Request/response Pydantic models (api_types.py)
3	skill_03_deepstream_pipeline.md	DeepStream pyservicemaker pipeline, tensor parser, dummy pipeline
4	skill_04_config_templates.md	nvdspreprocess / nvinferserver config templates + rendering
5	skill_05_triton_ddm_model.md	Triton model repo, config.pbtxt, model.py, ddm_net.py
5b	skill_05b_custom_postprocess.md	C++ postprocess plugin, Makefile, IOptions API
6	skill_06_sop_process_manager.md	SOPProcessManager, SOPVideoProcessor, VLLMInference, Kafka
6b	skill_06b_sop_checker.md	SOP sequence and checker compliance: MissingNumberDetector, SopCheckerCache, SopCheckerRequest/Response
7	skill_07_sse_streaming.md	SSE generator, stream response formatting, dummy test mode
8	skill_08_basler_camera.md	Basler camera support, Pylon SDK, emulation, formats
9	skill_09_docker_build_deploy.md	Docker build, deploy, .env configuration
10	skill_10_test_suite.md	Test suite coverage, assertions, running tests
11	skill_11_env_variables.md	All environment variables reference
12	skill_12_evaluation_workflow.md	End-to-end eval workflow: static checks, build, launch, tests, API/camera/Kafka checks, report
13	skill_13_verification_curl.md	Verification steps and curl examples
14	skill_14_implementation_checklist.md	Implementation checklist: file copy list, generated files, Docker prereqs, verification
15	skill_15_latency_measurement.md	TTFC and C2C latency measurement for file input via SSE streaming
16	skill_16_message_schema.md	Kafka message schema selection (JSON default vs NvProtoSchema) and extending messages with custom data
17	skill_17_camera_latency_measurement.md	Camera / live-stream chunk_e2e latency measurement using internal pipeline timestamps
18	skill_18_rtsp_streaming_output.md	OPT-IN RTSP streaming output: tee1-tap re-stream, RTSPStreamingServer, SW_ENCODER toggle. Generate only when user explicitly requests RTSP

For end-to-end evaluation, read § 12 first; load build/test/curl/latency/camera/Kafka as needed.

§ 18 is opt-in — generate only when the user explicitly requests RTSP output; otherwise skip § 18 and the RTSP_* rules below.

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Key Files Map

The full source-to-target file mapping lives in skill_14_implementation_checklist.md:

• Files copied verbatim from references/ (non-trivial algorithms — cycle detection, qwen_vl_utils preprocessing, DeepStream IOptions API, protobuf sources) with the rationale per file.
• Files copied as adaptable templates (Dockerfile, compose.yaml, Triton config and model.py, ddm_net.py, Pylon emulation config, etc.).
• Files generated from skill sections — each annotated with the Critical Rules below that the generation must follow exactly.
• Docker build prerequisites and post-build verification checklist.

Config files (nvds_preprocess_template.txt, nvds_inference_template.txt, vlm_prompts.txt) are used as-is from configs/.

When skill_06b is loaded, read configs/actions.json from the project root and run the § 6b-G generation workflow to produce nvds_action_detector/missing_number_detector.py. If configs/actions.json is absent or invalid, fall back to copying the reference file.

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Critical Rules

Each rule's full detail lives in the linked skill_NN_*.md reference file.

Tag	Rule summary	Details in
MANAGER_INIT_IN_MAIN	SOPProcessManager init in main() before uvicorn.run() — not inside lifespan()	skill_01_fastapi_endpoints.md
NAMED_KWARGS	create_video_processor() uses named kwargs; camera args as separate kwargs	skill_06_sop_process_manager.md
LIVE_REQUIRES_STREAM_TRUE	stream: true required for live inputs (RTSP / camera)	skill_08_basler_camera.md
VLM_DISABLED_DISABLES_SOP_CHECKER	DISABLE_VLM_INFERENCE=true auto-disables SOP checker at import	skill_06_sop_process_manager.md
CHUNK_PARAMS_MAX_LENGTH	ChunkParams.max_length_sec = 10s internal; 60s API default	skill_06_sop_process_manager.md
VLM_WARMUP_BEFORE_DDM	ModelInitializer: VLM warmup FIRST, then CV dummy pipeline	skill_06_sop_process_manager.md
VLM_WARMUP_3_FRAMES	VLM warmup needs 3 frames (torch.zeros) — Qwen3VL hangs on < 3	skill_06_sop_process_manager.md
THREAD_POOL_SIZES	4 thread pools: cv(32), clip(32), vlm_inference(64), vlm_request(64)	skill_06_sop_process_manager.md
MEDIA_INFO_PYMEDIAINFO	Media info via pymediainfo; live sources set fps=30/duration=inf directly	skill_06_sop_process_manager.md
CAMERA_EMULATION_PYLON_CAMEMU	PYLON_CAMEMU=1 for camera emulation (serial 0815-0000)	skill_08_basler_camera.md
DEEPSTREAM_LIB_HIDE	DeepStream lib hide trick: rename lib → lib.tmp during gst-plugin-pylon build	skill_08_basler_camera.md
VLM_REAL_GPU_FRAMES	VLM uses real GPU frames via DecodedFrameRetriever; never torch.zeros for inference	skill_06_sop_process_manager.md
BUFFER_RETRIEVER_STATIC_BASE	DecodedFrameRetriever MUST inherit BufferRetriever statically via super().__init__(); runtime __class__.__bases__ mutation hangs pipeline.attach()	skill_06_sop_process_manager.md
FRAME_RETRIEVER_PRIORITY	create_inference_pipeline: frame_retriever= kwarg takes priority over frame_queue	skill_03_deepstream_pipeline.md
MUX_ORIGINAL_RESOLUTION	nvstreammux uses original resolution (not 224); pass mux_width/mux_height from get_media_info() (probe live RTSP for non-camera inputs; camera path unaffected)	skill_03_deepstream_pipeline.md, skill_06_sop_process_manager.md
FILE_URI_NO_DOUBLE_PREFIX	create_inference_pipeline file source: check file_path.startswith("file://") before prepending — API passes file:// URLs directly	skill_03_deepstream_pipeline.md
CLEANUP_ON_DISCONNECT	Pipeline cleanup on client disconnect via trigger_stop_processors in try/finally	skill_07_sse_streaming.md
UNIFIED_CLIP_POST_PROCESS	Unified clip_post_process() for file + live; stop() puts None in _score_queue	skill_06_sop_process_manager.md
ABORT_INFLIGHT_VLM	Abort in-flight VLM requests on stop() via llm.abort(req_id)	skill_06_sop_process_manager.md
LOGGER_EXPORT_GET_LOGGER	ds_logger.py must export get_logger	skill_06_sop_process_manager.md
KAFKA_USE_CREATE_PRODUCER	Kafka: use create_producer() from messager.py; no Messager class	skill_06_sop_process_manager.md
USER_PROMPT_PRIORITY	User request text takes priority over VLM_PROMPT_PATH file; {"type":"text"} in the request overrides the config-file prompt	skill_06_sop_process_manager.md
EVAL_USE_CONFIG_PROMPT	Eval/latency requests omit request text by default so the VLM uses VLM_PROMPT_PATH	skill_12_evaluation_workflow.md, skill_13_verification_curl.md, skill_15_latency_measurement.md, skill_17_camera_latency_measurement.md
CHUNK_SCHEMA_FIELD_NAMES	Chunk schema: chunk_idx, cv_boundary_score, checker_result; summary chunk_idx=-1	skill_06_sop_process_manager.md
SEQUENTIAL_FRAME_DRAIN	Drain decoded_frame_queue (FIFO, shared across chunks) in a SINGLE thread and submit VLM per chunk incrementally; parallel drain steals frames → 0-frame chunks / wrong VLM input	skill_06_sop_process_manager.md
WALL_CLOCK_BEFORE_GPU	DecodedFrameRetriever.consume(): capture wall_clock_entry = time.time() BEFORE GPU dlpack; queue 3-tuple (timestamp, wall_clock_entry, tensor)	skill_06_sop_process_manager.md, skill_17_camera_latency_measurement.md
CHUNK_E2E_PIPELINE_TIMESTAMPS	Write pipeline_chunk_end_timestamp (last frame wall_clock) and pipeline_vlm_ready_timestamp (tm_e2e.now()) into chunk_info for camera latency (§ 17)	skill_06_sop_process_manager.md, skill_17_camera_latency_measurement.md
VLM_INFERENCE_REQUIRED_KWARGS	Every VLLMInference.inference() call must pass video_fps, system_prompt, max_completion_tokens	skill_06_sop_process_manager.md
UNIFORM_CHUNKING_BYPASSES_DDM	chunking_options.algorithm="uniform" → fixed-length chunks; create_inference_pipeline(uniform_chunk=True) skips DDM but keeps tee1 fanout; Stage 2 uses uniform_clip_post_process	skill_02_pydantic_schemas.md, skill_03_deepstream_pipeline.md, skill_06_sop_process_manager.md
DDM_TEMPORAL_CONFIGURABLE	SLIDING_WINDOWS_SIZE = 2*FRAMES_PER_SIDE + SEQUENCE_BATCH rendered into preprocess/nvinferserver (no hard-coded 18); Triton config.pbtxt sequence dim -1	skill_04_config_templates.md, skill_05_triton_ddm_model.md
DDM_TRT_OPTIONAL_PATH	DDM_TRT_OPTIMIZATION=true runs DDM via TensorRT (per-thread contexts, fixed batch = SEQUENCE_BATCH); PyTorch fallback; never both. PyTorch is default	skill_05_triton_ddm_model.md
DDM_TRT_STREAM_ORDERING	DDMTensorRTEngine.infer(): wait_stream(current) → execute_async_v3 → torch.cuda.synchronize(device) (NOT per-stream). Per-stream sync leaves TRT aux-stream work in flight → gst-CV SIGSEGV (NVBug 6289256)	skill_05_triton_ddm_model.md
METADATA_LICENSE_FROM_FILE	/v1/metadata reads licenseInfo from DS_SOP_LICENSE_PATH (default /opt/nvidia/nvds_sop/license.txt); never hard-code license text	skill_01_fastapi_endpoints.md
CAMERA_EMULATION_FRAMES_RGB	Pylon emulation PNGs must be explicit 3-channel RGB (matches Emulation_0815-0000.pfs PixelFormat=RGB8Packed); generate via nvvideoconvert ! videoconvert ! "video/x-raw,format=RGB" ! pngenc	skill_08_basler_camera.md
COMPOSE_ENV_PASSTHROUGH	docker compose only substitutes ${VAR} references; every runtime env var must be explicitly listed under environment: to reach the container.	skill_09_docker_build_deploy.md

The four RTSP_* rules below apply only when the optional RTSP streaming-output feature (§ 18) is requested. They do not apply to the default build — skip them if the user did not ask for RTSP output.

| RTSP_OUTPUT_TAPS_TEE1 | RTSP output branch links from the existing tee1 (added after the main inference link) only when rtsp_port is present. | skill_18_rtsp_streaming_output.md | | RTSP_LEAKY_QUEUE_TINY | RTSP branch queue must be leaky=2 + tiny cap (max-size-buffers=2) to prevent backpressure and NVMM pool exhaustion. | skill_18_rtsp_streaming_output.md | | RTSP_KEYINT_MAX_30 | RTSP H.264 encoder must set key-int-max=30 (and B-frames disabled) to allow downstream seeking. | skill_18_rtsp_streaming_output.md | | RTSP_ENCODER_FALLBACK | Select software/hardware H.264 encoder based on SW_ENCODER with MJPEG fallback. | skill_18_rtsp_streaming_output.md |

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