--- name: deepstream-dev-2ed608ac9289 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 ```python pipeline.link(("decoder", "mux"), ("", "sink_%u")) # CORRECT # pipeline.link(("decoder", "mux"), ("", "sink_0")) # WRONG - will fail ``` 5. **Platform Detection for Sinks**: ```python import platform sink_type = "nv3dsink" if platform.processor() == "aarch64" else "nveglglessink" ``` 6. **Buffer Cloning**: Always clone buffers for async processing ```python 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 ```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. 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. 14. **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 (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](references/gstreamer_plugins.md) | Looking up plugin properties, ALL properties listed | | [references/service_maker_api.md](references/service_maker_api.md) | Using Pipeline/Flow API, metadata access, probes, EventMessageUserMetadata | | [references/use_cases_pipelines.md](references/use_cases_pipelines.md) | Building pipelines: simple playback, multi-inference, cascaded GIE | | [references/kafka_messaging.md](references/kafka_messaging.md) | Kafka/message broker setup, nvmsgconv/nvmsgbroker config, msg2p-newapi | | [references/best_practices.md](references/best_practices.md) | Design patterns, common pitfalls, anti-patterns | | [references/buffer_apis.md](references/buffer_apis.md) | BufferProvider/Feeder (injection), BufferRetriever/Receiver (extraction) | | [references/media_extractor_advanced.md](references/media_extractor_advanced.md) | MediaExtractor, MediaChunk, FrameSampler | | [references/utilities_config.md](references/utilities_config.md) | PerfMonitor, EngineFileMonitor, SourceConfig, SensorInfo, SmartRecordConfig | | [references/nvinfer_config.md](references/nvinfer_config.md) | nvinfer config file format, ALL parameters | | [references/tracker_config.md](references/tracker_config.md) | nvtracker config, NvDCF/IOU/DeepSORT/NvSORT | | [references/troubleshooting.md](references/troubleshooting.md) | Error messages and solutions | | [references/rest_api_dynamic.md](references/rest_api_dynamic.md) | REST API, dynamic source add/remove, nvmultiurisrcbin | | [references/metamux_config.md](references/metamux_config.md) | nvdsmetamux config, parallel multi-model inference, metadata merging, source ID filtering | | [references/docker_containers.md](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) | --- Source attribution: yo-steven Original project: https://github.com/yo-steven/skills-exploration-20260522/blob/6e1127ffb7690aa8038b172d0ae7cc720754870d/skills/deepstream/deepstream-dev/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice SPDX-License-Identifier: Apache-2.0 AND CC-BY-4.0 Copyright (c) 2026 NVIDIA Corporation This project is licensed under a dual license: - Apache License 2.0 (for code and configuration) - Creative Commons Attribution 4.0 International (CC BY 4.0) (for documentation) You may use the contents of this repository under the terms of either license, or both, as applicable to the type of content. ================================================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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For the full legal text, see: https://creativecommons.org/licenses/by/4.0/legalcode # Mega AI Hub resource metadata Creator / publisher: yo-steven Original project: https://github.com/yo-steven/skills-exploration-20260522/blob/6e1127ffb7690aa8038b172d0ae7cc720754870d/skills/deepstream/deepstream-dev/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering Roles: Backend Developer, Data Engineer, ML / AI Engineer Specialties: API, Backend Tools: Gstreamer, Kafka, Tensorrt, Nvidia Deepstream Sdk Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 36a1aa01366af260a1324f7ccd80fdb911814b43ff2a845eaaf0b2a20c5a4678