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name: deployment description: "Routes single-model DeepStream-Yolo build, config editing, and runtime deployment tasks for one detector." disable-model-invocation: true metadata: disco-role: operating license: MIT

Deployment

Use this sub-skill when the task is about one DeepStream YOLO pipeline end to end: building the custom inference library, choosing the right config template, editing the DeepStream app config, and diagnosing build or runtime failures.

Trigger phrases

• build the DeepStream-Yolo library
• run one YOLO model in DeepStream
• edit config_infer_primary*.txt
• edit deepstream_app_config.txt
• custom model on deepstream-app
• why does the pipeline not start
• how do I map labels or class counts

Include here

• CUDA_VER selection for the chosen DeepStream release.
• make -C nvdsinfer_custom_impl_Yolo ... build flow.
• deepstream_app_config.txt and single primary-gie wiring.
• Custom models that already have .onnx or .cfg / .weights artifacts.
• Model-family config differences that affect a single detector.
• Cross-cutting deployment troubleshooting.

Exclude or route elsewhere

• Exporting checkpoints to ONNX: use model-conversion.
• Multiple primary/secondary detectors: use multi-gie.
• INT8 calibration tuning and benchmark interpretation: use int8-benchmarking.
• Repo maintenance, import/export to other agents, or refresh logic.

How to use this route

1. Read references/workflows.md for the full single-model flow.
2. Read references/configuration.md before editing any config template.
3. Run scripts/check-deepstream-toolchain.sh to see whether the host is ready for build/runtime work.
4. Run scripts/build-nvdsinfer-plugin.sh --output-dir ./deepstream-yolo-runtime once CUDA_VER is set; the helper stages the bundled parser source and configs for you.
5. Launch deepstream-app from the staged runtime tree, not from the original repository checkout.
6. Use references/troubleshooting.md if the build or runtime fails.

Key decisions

• If the model is still a checkpoint, route to model-conversion first.
• If the model is already an ONNX file or Darknet pair, this sub-skill owns the DeepStream wiring.
• If the same app needs two or more detectors, route to multi-gie instead of piling secondary configuration into this route.

What a future agent should be able to do here

• Identify the correct config template for the model family.
• Set the right batch-size, network-mode, num-detected-classes, and parser / engine creator names.
• Distinguish Darknet-style custom-network-config from ONNX-style onnx-file configs.
• Recognize whether the failure is a build issue, a model/template mismatch, or a host prerequisite gap.

Common failure signals

• CUDA_VER is not set
• deepstream-app: command not found
• Missing or mismatched labels.txt
• Wrong num-detected-classes
• Engine file created for the wrong batch size or precision
• GLib or GStreamer runtime issues on the host

Linked helpers

• scripts/check-deepstream-toolchain.sh — probe build/runtime readiness.
• scripts/build-nvdsinfer-plugin.sh — wrapper for the custom library build.
• references/workflows.md — the single-model step-by-step flow.
• references/configuration.md — the config key table and model-family notes.
• references/troubleshooting.md — deployment-specific symptoms and fixes.