Deepstream Dev
NVIDIA DeepStream SDK 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.
- Skill ID
- nvidia/skills/deepstream-dev
- Publisher
- nvidia
- Repository
- skills
- Installs
- 0
- Files
- 21
- License
- CC-BY-4.0 AND Apache-2.0
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
nvidia/skills/deepstream-devInstalls these files- BENCHMARK.md
- SKILL.md
- evals/evals.json
- references/best_practices.md
- references/buffer_apis.md
- references/docker_containers.md
- references/gstreamer_plugins.md
- references/kafka_messaging.md
- references/media_extractor_advanced.md
- references/metamux_config.md
- references/nvds_msgapi_adapter.md
- references/nvinfer_config.md
- references/rest_api_dynamic.md
- references/service_maker_api.md
- references/streaming_sources.md
- references/tracker_config.md
- references/troubleshooting.md
- references/use_cases_pipelines.md
- references/utilities_config.md
- skill-card.md
- skill.oms.sig
What this skill tells the agent
DeepStream Development Skill
This skill requires access to all of the reference documents listed in the references/ directory below. Ensure they are available before executing the workflow.
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 → RendererComponents 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
- 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
- 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=1onnvstreammuxandsync=0on the sink - Convert local paths to URI:
"file://" + os.path.abspath(path)
- Metadata Iteration: Use
.frame_itemsand.object_items(returns iterators, NOT lists)
- NEVER use
len()on these - iterate to count - Iterator can only be consumed once
- 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``
- Platform Detection for Sinks: ``
python import platform sink_type = "nv3dsink" if platform.processor() == "aarch64" else "nveglglessink"``
- For WSL2 Ubuntu 24 Docker, this default selection must be overridden.
- WSL2 + Ubuntu 24 Docker: If
/proc/versioncontainsmicrosoftorwsland/etc/os-releasehasVERSION_ID="24.04", the generated app must never create a display branch or display sink (nveglglessink,nv3dsink, etc.), even if the prompt asks for display. Do not rely on a--no-displayflag for this case. Generate encoded MP4 output only (nvv4l2h264enc->h264parse->mp4mux/qtmux->filesink) and make the default run path write the annotated video file. In the generatedREADME.md, explicitly explain that WSL2 Ubuntu 24 Docker is MP4-output-only because display sinks are disabled by a known issue. If the user explicitly requested display, add an inline code comment and README note explaining:Display requested but disabled due to WSL2 Ubuntu 24 Docker limitation — MP4 output generated instead. - Non-WSL targets: Do not add WSL-specific behavior or WSL limitation text to generated apps or READMEs. Use the normal platform display sink selection above.
- Buffer Cloning: Always clone buffers for async processing ``
python tensor = buffer.extract(0).clone() # CRITICAL``
- Queue Types:
queue.Queue→ Use withthreading.Threadmultiprocessing.Queue→ Use withmultiprocessing.Process- Using wrong type causes silent data loss!
- nvinfer Config Format:
- YAML: Use
property:section (NOTmodel:),key: valuewith space after colon - INI: Use
[property]section,key=valuewith equals sign - Section MUST be named
property
- nvmsgbroker is a SINK: Cannot have downstream elements - use
teeto split pipeline
- 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.
- Built-in Probe Attachment:
measure_fps_probecan only be attached to processing elements (e.g.,nvinfer,nvosdbin), NOT to sink elements. Attaching to a sink raisesRuntimeError: Probe failure.
- Dynamic ONNX Models Require `infer-dims`: When the ONNX model has dynamic input shapes (e.g., exported with
dynamic=Truein Ultralytics YOLO, or with dynamic batch/height/width axes), you MUST addinfer-dims=C;H;Wto the nvinfer config. Without it, TensorRT sees-1for dynamic dimensions and fails withsetDimensions: 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
- 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-modemust 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.
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