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.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondeepstream-devExecute the skills CLI command in your project's root directory to begin installation:
Fetches deepstream-dev from nvidia/skills and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate deepstream-dev. Access via /deepstream-dev in your agent's command palette.
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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 |
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.
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 |
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).
Only Add Requested Components: Do NOT add pipeline elements the user did not ask for.
nvtracker): Only add when the user explicitly requests tracking or object IDs across framesnvdsanalytics): Only add when the user requests line crossing, ROI counting, etc.nvmsgbroker/nvmsgconv): Only add when the user requests Kafka/cloud messagingDefault to nvurisrcbin for Sources: When the user says "camera", "stream", "video", or provides a file path:
nvurisrcbin -- it handles RTSP, HTTP, and local files (file://) transparentlyfilesrc + qtdemux + parser when the user explicitly needs raw file source controllive-source=1 on nvstreammux and sync=0 on the sink"file://" + os.path.abspath(path)Metadata Iteration: Use .frame_items and .object_items (returns iterators, NOT lists)
len() on these - iterate to countRequest 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
Platform Detection for Sinks:
import platform
sink_type = "nv3dsink" if platform.processor() == "aarch64" else "nveglglessink"
Buffer Cloning: Always clone buffers for async processing
tensor = buffer.extract(0).clone() # CRITICAL
Queue Types:
queue.Queue → Use with threading.Threadmultiprocessing.Queue → Use with multiprocessing.Processnvinfer Config Format:
property: section (NOT model:), key: value with space after colon[property] section, key=value with equals signpropertynvmsgbroker is a SINK: Cannot have downstream elements - use tee to split pipeline
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.
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.
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:
infer-dims=3;640;640infer-dims=3;416;416infer-dims=3;1280;1280Ultralytics 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.
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
ModuleNotFoundError: No module named 'pyservicemaker' when running the app inside the venv./opt/nvidia/deepstream/deepstream/samples/models//opt/nvidia/deepstream/deepstream/samples/models/Primary_Detector/resnet18_trafficcamnet_pruned.onnx/opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so/opt/nvidia/deepstream/deepstream/lib/libnvds_kafka_proto.so/opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/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 |
| 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) |
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
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💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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deepstream-dev reduced setup friction for our internal harness; good balance of opinion and flexibility.
deepstream-dev is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend deepstream-dev for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
deepstream-dev reduced setup friction for our internal harness; good balance of opinion and flexibility.
deepstream-dev is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for deepstream-dev matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: deepstream-dev is the kind of skill you can hand to a new teammate without a long onboarding doc.
deepstream-dev reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for deepstream-dev matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in deepstream-dev — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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