DeepStream Development Skill

When this skill is active, ALWAYS read the relevant reference documents before generating code. Do NOT rely on memory - the reference documents contain critical details about exact property names, correct API usage, and common pitfalls.

SDK and Architecture Quick Reference

DeepStream SDK Version Requirements

• GStreamer: 1.24.2
• NVIDIA Driver: 590+
• CUDA: 13.1
• TensorRT: 10.14.1.48
• Platforms: Ubuntu 24.04 (x86_64 and ARM64/Jetson)

Typical Pipeline Flow

Source → Stream Muxer → Inference → [Tracker] → OSD → Renderer

Components in [brackets] are optional -- only add them when the user explicitly requests them.

| Stage | Role | Key Element(s) | Required? | |-------|------|-----------------|-----------| | Source | Input from files, RTSP, cameras | nvurisrcbin (preferred), nvmultiurisrcbin, filesrc | Yes | | Stream Muxer | Batches streams for inference | nvstreammux | Yes | | Inference | TensorRT model execution | nvinfer, nvinferserver | Yes | | Tracker | Multi-object tracking across frames | nvtracker | Only if requested | | OSD | Draws bounding boxes, labels, overlays | nvosdbin | Yes (for visualization) | | Renderer | Display or save output | nveglglessink, nv3dsink, filesink | Yes |

Memory Model

DeepStream uses NVIDIA Video Memory Manager (NVMM) for zero-copy GPU buffer transfers. Caps strings use memory:NVMM to indicate GPU memory (e.g., video/x-raw(memory:NVMM), format=NV12).

Critical Rules

1. Only Add Requested Components: Do NOT add pipeline elements the user did not ask for.

• Tracker (nvtracker): Only add when the user explicitly requests tracking or object IDs across frames
• Secondary GIEs: Only add when the user requests classification or attribute extraction
• Analytics (nvdsanalytics): Only add when the user requests line crossing, ROI counting, etc.
• Message broker (nvmsgbroker/nvmsgconv): Only add when the user requests Kafka/cloud messaging
• When in doubt, build the minimal working pipeline and let the user ask for additions

1. Default to nvurisrcbin for Sources: When the user says "camera", "stream", "video", or provides a file path:

• Always use nvurisrcbin -- it handles RTSP, HTTP, and local files (file://) transparently
• Only use filesrc + qtdemux + parser when the user explicitly needs raw file source control
• For RTSP/live sources, also set live-source=1 on nvstreammux and sync=0 on the sink
• Convert local paths to URI: "file://" + os.path.abspath(path)

1. Metadata Iteration: Use .frame_items and .object_items (returns iterators, NOT lists)

• NEVER use len() on these - iterate to count
• Iterator can only be consumed once

1. Request Pad Syntax: Use "sink_%u" template, NEVER literal pad names

python pipeline.link(("decoder", "mux"), ("", "sink_%u")) # CORRECT # pipeline.link(("decoder", "mux"), ("", "sink_0")) # WRONG - will fail

1. Platform Detection for Sinks:

python import platform sink_type = "nv3dsink" if platform.processor() == "aarch64" else "nveglglessink"

1. Buffer Cloning: Always clone buffers for async processing

python tensor = buffer.extract(0).clone() # CRITICAL

1. Queue Types:

• queue.Queue → Use with threading.Thread
• multiprocessing.Queue → Use with multiprocessing.Process
• Using wrong type causes silent data loss!

1. nvinfer Config Format:

• YAML: Use property: section (NOT model:), key: value with space after colon
• INI: Use [property] section, key=value with equals sign
• Section MUST be named property

1. nvmsgbroker is a SINK: Cannot have downstream elements - use tee to split pipeline

1. ALL Sinks Need async=0 for Tee Splits or Dynamic Sources: CRITICAL for state transitions

python # When using tee splits OR dynamic sources, ALL sinks MUST have async=0 pipeline.add("nveglglessink", "sink", { "sync": 0, "qos": 0, "async": 0 # CRITICAL - prevents state transition deadlock }) Symptom if missing: Pipeline stays in PAUSED state, no video displays.

1. Built-in Probe Attachment: measure_fps_probe can only be attached to processing elements (e.g., nvinfer, nvosdbin), NOT to sink elements. Attaching to a sink raises RuntimeError: Probe failure.

1. Dynamic ONNX Models Require infer-dims: When the ONNX model has dynamic input shapes (e.g., exported with dynamic=True in Ultralytics YOLO, or with dynamic batch/height/width axes), you MUST add infer-dims=C;H;W to the nvinfer config. Without it, TensorRT sees -1 for dynamic dimensions and fails with setDimensions: Error Code 3. Common values:

• YOLO models (640 input): infer-dims=3;640;640
• Models with 416 input: infer-dims=3;416;416
• Models with 1280 input: infer-dims=3;1280;1280

1. Ultralytics YOLO Output Format Depends on Model Generation — newer models (v10+/v26+) output post-NMS results; older models (v8/v11) output raw pre-NMS tensors. The custom parser and cluster-mode must match the actual output:

| Model generation | Output tensor shape | Fields | cluster-mode | |------------------|--------------------|---------------------------------|----------------| | v8 / v11 | [batch, 84, 8400] | [features(4+80), anchors] — raw cx/cy/w/h + class scores, no NMS | 2 (NMS) | | v10 / v26+ | [batch, 300, 6] | [max_det, (x1,y1,x2,y2,conf,cls)] — already post-NMS, pixel coords | 4 (none) |

How to identify at runtime: log inferDims.d[0] and inferDims.d[1] inside the custom parser.

• d={84, 8400} → pre-NMS (v8/v11 style)
• d={300, 6} → post-NMS (v10/v26+ style)

Symptom of mismatch: If cluster-mode: 2 is used with a post-NMS [N, 6] output, bounding boxes appear shifted by 45° or 135° from the actual objects (DeepStream's NMS incorrectly re-processes already-final coordinates). If you see tilted or rotated boxes, also check the OBB / rotation_angle note in references/nvinfer_config.md: for non-OBB models, value-initialize NvDsInferObjectDetectionInfo with obj{} and keep rotation_angle = 0; plain NvDsInferObjectDetectionInfo obj; leaves fields uninitialized.

1. Virtual Environment Must Include pyservicemaker: pyservicemaker is installed system-wide but is NOT accessible from a standard Python virtual environment. When a task requires a venv (e.g., for model download/conversion pip dependencies), always install pyservicemaker and pyyaml inside the venv. The venv setup in generated code and README must always include:

bash python3 -m venv venv source venv/bin/activate pip install /opt/nvidia/deepstream/deepstream/service-maker/python/pyservicemaker*.whl pyyaml pip install -r requirements.txt # other dependencies Symptom if missing: ModuleNotFoundError: No module named 'pyservicemaker' when running the app inside the venv.

Key Paths

• Models: /opt/nvidia/deepstream/deepstream/samples/models/
• Primary Detector: /opt/nvidia/deepstream/deepstream/samples/models/Primary_Detector/resnet18_trafficcamnet_pruned.onnx
• Tracker lib: /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so
• Kafka lib: /opt/nvidia/deepstream/deepstream/lib/libnvds_kafka_proto.so
• Sample configs: /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/

Reference Documents

IMPORTANT: Always read these documents for complete details. Do NOT generate code from memory.

| Document | Use When | |----------|----------| | references/gstreamer_plugins.md | Looking up plugin properties, ALL properties listed | | references/service_maker_api.md | Using Pipeline/Flow API, metadata access, probes, EventMessageUserMetadata | | references/use_cases_pipelines.md | Building pipelines: simple playback, multi-inference, cascaded GIE | | references/streaming_sources.md | Ingesting local files, HTTP MP4, HLS, MPEG-DASH, or RTSP sources with nvurisrcbin | | references/kafka_messaging.md | Kafka/message broker setup, nvmsgconv/nvmsgbroker config, msg2p-newapi | | references/best_practices.md | Design patterns, common pitfalls, anti-patterns | | references/buffer_apis.md | BufferProvider/Feeder (injection), BufferRetriever/Receiver (extraction) | | references/media_extractor_advanced.md | MediaExtractor, MediaChunk, FrameSampler | | references/utilities_config.md | PerfMonitor, EngineFileMonitor, SourceConfig, SensorInfo, SmartRecordConfig | | references/nvinfer_config.md | nvinfer config file format, ALL parameters | | references/tracker_config.md | nvtracker config, NvDCF/IOU/DeepSORT/NvSORT | | references/troubleshooting.md | Error messages and solutions | | references/rest_api_dynamic.md | REST API, dynamic source add/remove, nvmultiurisrcbin | | references/metamux_config.md | nvdsmetamux config, parallel multi-model inference, metadata merging, source ID filtering | | references/docker_containers.md | Docker images, Dockerfile examples, pyservicemaker install, container run commands | | references/nvds_msgapi_adapter.md | Building custom protocol adapters: nvds_msgapi |

Quick Error Reference

| Error | Solution | |-------|----------| | iterator has no len() | Iterate to count, don't use len() | | pad template not found | Use "sink_%u" not "sink_0" | | Queue data loss | Use multiprocessing.Queue with Process | | Config parse failed | Use property: not model: in YAML | | is-classifier deprecation warning | Use network-type: 1 instead of is-classifier: 1 for classifiers; omit both for detectors | | min-boxes unknown key warning | Use minBoxes (camelCase) in class-attrs-* sections, not min-boxes | | Secondary GIE inactive | Set process-mode: 2, check operate-on-gie-id | | Tee/dynamic source stuck PAUSED | Set async: 0 on ALL sink elements | | RTSP no data/reconnecting | Test URL with ffplay, check credentials | | RuntimeError: Probe failure | measure_fps_probe cannot attach to sink elements; use nvinfer or nvosdbin instead | | setDimensions negative dims / engine build failed | Add infer-dims=C;H;W for dynamic ONNX models (e.g., infer-dims=3;640;640) | | No module named 'pyservicemaker' in venv | pip install /opt/nvidia/deepstream/deepstream/service-maker/python/pyservicemaker*.whl pyyaml inside the venv | | AttributeError: object has no attribute 'obj_label' | Use obj_meta.label not obj_meta.obj_label in pyservicemaker (C API name differs from Python binding) |

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