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name: deployment description: "Plan InternImage DCNv3, CUDA, TensorRT, and mmdeploy deployment workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

InternImage deployment

Use this sub-skill when the task is about InternImage backend readiness, DCNv3 operator build diagnosis, ONNX/TensorRT export planning, mmdeploy custom operators, or CUDA/TensorRT compatibility across classification, detection, and segmentation.

Do not use this sub-skill for task-specific training, evaluation, model-family selection, dataset layout, or demo interpretation unless the blocker is deployment/backend related. Route classification model usage to the classification sub-skill, object/instance detection usage to the detection sub-skill, and semantic segmentation usage to the segmentation sub-skill.

Fast route

1. Identify the workflow and export target:

   • classification-onnx: PyTorch classification checkpoint to ONNX.
   • classification-trt: classification ONNX plus TensorRT engine and consistency check.
   • detection-trt: MMDetection deployment through mmdeploy/TensorRT.
   • segmentation-trt: MMSegmentation deployment through mmdeploy/TensorRT.

2. Read references/environment-matrix.md for the minimum Python/CUDA/OpenMMLab/mmdeploy compatibility checklist.

3. Read references/tensorrt-and-dcnv3.md before any DCNv3 source build, prebuilt wheel choice, ONNX export, or TensorRT/mmdeploy plan.

4. Generate a safe dry-run command template with:

   python sub-skills/deployment/scripts/build_export_command.py --help
   python sub-skills/deployment/scripts/build_export_command.py detection-trt --model-name mask_rcnn_internimage_t_fpn_1x_coco
   

5. If anything fails or prerequisites are uncertain, use references/troubleshooting.md before recommending a build or export run.

Operating rules

• Treat export/build commands as explicit user-approved actions. The bundled script only prints templates; it never downloads, builds, imports OpenMMLab, or launches TensorRT.
• Distinguish a CUDA runtime that PyTorch can use from the CUDA toolkit/nvcc needed by DCNv3 and mmdeploy custom-op compilation.
• Do not claim TensorRT/mmdeploy verification unless the environment has TensorRT, CUDNN, mmdeploy, a compatible PyTorch CUDA wheel, compiled DCNv3, and the mmdeploy TensorRT backend op for mmdeploy::TRTDCNv3.
• Prefer a matching prebuilt DCNv3 wheel when available for the target PyTorch/CUDA combination; use source build only when nvcc/toolkit and ABI compatibility are explicit.
• Keep deployment plans placeholder-safe: use <INTERNIMAGE_REPO>, <checkpoint.pth>, <MMDEPLOY_DIR>, <TENSORRT_DIR>, and <CUDNN_DIR> until the user provides concrete paths.

Bundled materials

• references/tensorrt-and-dcnv3.md - DCNv3 operator surfaces, mmdeploy/TensorRT custom-op build sequence, source-script decisions, and export command semantics.
• references/environment-matrix.md - compatibility matrix for CPU guidance, CUDA runtime/toolkit, OpenMMLab pins, mmdeploy/TensorRT, and current verification limits.
• references/troubleshooting.md - failure-mode triage for missing nvcc, Cuda is not availabel, missing custom ops, import/version conflicts, ONNX/TensorRT errors, and export input mistakes.
• scripts/build_export_command.py - standalone dry-run command builder for classification ONNX, classification TensorRT, detection TensorRT, and segmentation TensorRT.