trt-onnx-quickstart

trt-onnx-quickstart is an MLOps & model serving skill for AI agents, published by NVIDIA in the trt-samples-for-hackathon-cn repository on GitHub. Skillspace rates trt-onnx-quickstart D (59/100) and it passed the safety scan; the rating is based on static analysis only.

Build and verify a TensorRT engine from a Hugging Face model ID or ONNX file, with numerical parity checked against ONNX Runtime. Use when the user imports a non-LLM model to TensorRT, needs a verified engine from ONNX, hits trtexec "unsupported operator", must verify the engine matches ONNX numerically, debugs a polygraphy parity failure (large max abs diff at FP16), or configures multi-input dynamic shapes. Triggers: convert ONNX to TensorRT, Hugging Face to TensorRT, trtexec onnx, trtexec unsupported operator, optimum-cli export, polygraphy parity check, polygraphy run --trt --onnxrt, parity check failed, max abs diff, verify engine matches ONNX, --minShapes, dynamic shapes trtexec, multi-input shape profile, FP16 engine, INT64 warning. Adjacent skills: `trt-torch-quickstart` (PyTorch frontend), `trt-cpp-runtime-quickstart` (C++ engine load). LLM token generation belongs in TensorRT-LLM, not here.

Grade

D

Quality

59 / 100

Safety

Passed

GitHub stars

1,662

Unproven: rated by static analysis only, not yet compared head-to-head.

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