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name: deployment description: "Export DAMO-YOLO models to ONNX or TensorRT, plan partial INT8 quantization, and diagnose deployment backend dependencies." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

DAMO-YOLO deployment

Use this sub-skill when the task involves DAMO-YOLO ONNX export, TensorRT engine export or evaluation, partial INT8 quantization, OpenVINO/TensorRT benchmark preparation, or deployment dependency troubleshooting.

Route training, fine-tuning, and COCO dataset setup to the training sub-skill. Route image/video/camera inference with an already-created engine to the inference sub-skill.

First actions

1. Identify the target artifact: raw ONNX, end-to-end ONNX with NMS, TensorRT FP32/FP16/INT8 engine, TensorRT evaluation, OpenVINO conversion, or partial quantization.
2. Read Deployment workflows for supported paths and when to stop at ONNX.
3. Read Deployment CLI reference for flag meanings and source-equivalent commands.
4. Run scripts/check_deploy_env.py before installing or debugging optional backends.
5. Use scripts/export_onnx_safe.py when you need a generated-skill-owned ONNX exporter that imports the installed damo package and does not call repo-local converter scripts.
6. If anything fails, use Deployment troubleshooting.

Bundled helper scripts

• scripts/check_deploy_env.py: reports availability of damo, PyTorch/CUDA, ONNX, ONNX Runtime, ONNX simplifier, TensorRT, CUDA Python/PyCUDA, and pytorch_quantization.
• scripts/export_onnx_safe.py: self-contained ONNX export helper adapted from the source converter flow. It requires a config, checkpoint, and output path; it supports raw or end-to-end ONNX export but does not build .trt engines. The helper calls torch.onnx.export(..., dynamo=False) so it does not require onnxscript on PyTorch builds whose default exporter uses it.

Operating rules

• Keep model config, checkpoint, image size, batch size, and class count aligned. Most deployment failures are shape or class-count mismatches.
• Use raw ONNX (--benchmark or no --end2end) when measuring backbone/neck/head latency without NMS.
• Use --end2end --ort for ONNX Runtime NMS export; use --end2end without --ort only when the downstream TensorRT parser/runtime supports the selected NMS plugin family.
• TensorRT engine build/eval requires a TensorRT runtime stack; the construction environment verified CUDA but did not install TensorRT, PyCUDA, or pytorch_quantization.
• Partial INT8 quantization is an advanced TensorRT workflow. It needs calibration images, pytorch_quantization, model-type-specific sensitivity lists (tiny, small, medium), and enough GPU memory.