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name: deepstream-yolo description: "Routes DeepStream-Yolo deployment, model conversion, multi-GIE, and INT8 workflows for supported YOLO-family models." disable-model-invocation: true metadata: disco-role: operating license: MIT

DeepStream-Yolo

Use this skill for DeepStream YOLO deployment work: build the custom parser library, choose the right config template, convert weights to ONNX, wire multiple GIEs, and prepare INT8 calibration runs.

Start here

1. Read references/repo-provenance.md if you need to know whether this skill still matches the current checkout.
2. Run scripts/check-deepstream-toolchain.sh before any build or runtime attempt.
3. If you want to inspect or edit the bundled runtime tree first, run scripts/stage-runtime-tree.sh --output-dir ./deepstream-yolo-runtime.
4. Read references/model-family-matrix.md before changing a config template.
5. Use the sub-skill that matches the task family:
   • sub-skills/deployment/SKILL.md for single-model build/run work.
   • sub-skills/model-conversion/SKILL.md for exporter selection and ONNX conversion.
   • sub-skills/multi-gie/SKILL.md for multiple primary/secondary detectors.
   • sub-skills/int8-benchmarking/SKILL.md for calibration and benchmark tuning.

What this skill covers

• DeepStream app configuration for one detector.
• Custom parser library builds in nvdsinfer_custom_impl_Yolo.
• Model-family-specific config templates and their special knobs.
• Ultralytics-family exporter scripts and labels generation.
• Multi-GIE folder scaffolding and plugin-version updates.
• INT8 calibration image lists, OpenCV build switch, and benchmark notes.

Shared references

• references/installation.md — read when you need the host prerequisites, CUDA_VER mapping, or a quick reminder that DeepStream runtime is separate from the Python inspection environment.
• references/runtime-assets.md — read when you need to know where the bundled parser source, configs, and multi-GIE layout image live.
• scripts/stage-runtime-tree.sh — stage the bundled parser source and configs into a fresh runtime tree before running or editing them.
• scripts/build-nvdsinfer-plugin.sh — stage the bundled runtime tree and build the custom parser library on a DeepStream host.
• references/model-family-matrix.md — read when you need to match a model family to a config template or understand the important special knobs.
• references/troubleshooting.md — read when build, runtime, export, or calibration errors need a cross-cutting fix.
• references/repo-routing-metadata.json — used by the repo-skill router during import and discovery.
• references/repo-provenance.md — read when checking staleness or refreshing this skill.

Runtime expectations

• The generated skill is self-contained; do not depend on the original source checkout being preserved.
• DeepStream runtime commands need an NVIDIA DeepStream host or container; this generated skill can still document the workflow even when that SDK is absent on the current machine.
• The bundled assets/ tree contains the parser source, build files, config templates, and multi-GIE illustration needed for self-contained runtime staging.
• The Ultralytics exporter path was inspected in a temporary Python environment with torch, ultralytics, onnx, onnxslim, and onnxruntime installed.

Route summary

deployment

Use this for:

• building nvdsinfer_custom_impl_Yolo
• choosing and editing a single-model config_infer_primary*.txt
• wiring deepstream_app_config.txt for one detector
• troubleshooting DeepStream build and runtime issues
• understanding how custom models and labels fit into the pipeline

model-conversion

Use this for:

• converting Ultralytics-family .pt checkpoints to ONNX
• deciding which exporter script to use
• generating labels.txt
• understanding which upstream repos are still reference-only in this skill

multi-gie

Use this for:

• duplicating the repo layout for multiple detectors
• changing YOLOLAYER_PLUGIN_VERSION
• wiring secondary-gie entries and operate-on-gie-id
• moving engine files into the correct gieN/ folder

int8-benchmarking

Use this for:

• enabling INT8 calibration support with OPENCV=1
• preparing calibration.txt and INT8_CALIB_IMG_PATH
• reading the benchmark table and tuning NMS values
• deciding when calibration is blocked by missing OpenCV dev headers

Quick reminders

• Set CUDA_VER before building.
• Use the config template that matches the model family, not just the checkpoint name.
• Keep labels.txt synchronized with the exporter output or the class list.
• Do not treat CPU-only inspection as proof that DeepStream runtime works.