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name: deployment-and-export description: "Exports PaddleDetection models and plans Paddle Inference, Python/C++, Serving, Lite, ONNX, FastDeploy, TensorRT, and benchmark deployment workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Deployment and Export

Use this route to export a trained model, inspect exported artifacts, run Paddle Inference deployment, prepare Serving/Lite/ONNX/FastDeploy paths, or plan TensorRT/benchmark checks.

Workflow

1. Start from a validated training config and weights file/URL. Confirm the model family supports the target backend.
2. Build an export command with scripts/build_export_deploy_command.py. Export should produce infer_cfg.yml, model.pdmodel, model.pdiparams, and model.pdiparams.info in the model directory.
3. Inspect the exported directory with scripts/inspect_inference_model.py before deploying.
4. For Python Paddle Inference, choose --device=CPU/GPU/XPU, --run_mode=paddle/trt_fp32/trt_fp16/trt_int8, input source, threshold, batch size, MKLDNN or TensorRT options.
5. For Serving, Lite, ONNX, FastDeploy, C++, or vendor backends, read the corresponding reference and verify the external runtime before running conversion/build commands.
6. Use scripts/convert_infer_cfg_to_json.py for the small Paddle Lite config conversion helper.

References

• references/export-and-conversion.md: export flags, ONNX conversion, benchmark, and quantization notes.
• references/python-inference.md: deploy/python runtime inputs and options.
• references/serving-and-edge.md: Serving, Lite, C++, FastDeploy, and vendor backend boundaries.
• references/troubleshooting.md: exported artifact, runtime, and backend failures.