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name deepstream-dev
description NVIDIA DeepStream SDK 9.0 development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.
owner NVIDIA CORPORATION
service deepstream
version 1.1.0
reviewed 2026-04-24
license CC-BY-4.0 AND Apache-2.0

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 9.0 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

  2. 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)

  3. 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

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

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

  5. Platform Detection for Sinks:

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

  6. Buffer Cloning: Always clone buffers for async processing

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

  7. Queue Types:

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

  8. 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

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

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

    # 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.

  11. 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.

  12. 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

  13. 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: 
    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 (DeepStream 9.0)

  • 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/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

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)