Introduction

This toolchain captures TensorRT network creation and build parameters at runtime via a shim, then deterministically replays them to reproduce an engine build. Use it to debug or reproduce builds independent of the originating framework.

Prerequisites

• TensorRT installed (ensure you know the absolute path to its lib and bin directories)

• libtensorrt_shim.so available in your TensorRT lib directory

• tensorrt_player available in your TensorRT bin directory

Quick start: Capture

TORCHTRT_ENABLE_TENSORRT_API_CAPTURE=1 python test.py

You should see shim.json and shim.bin generated in /tmp/torch_tensorrt_{current_user}/shim.

Replay: Build the engine from the capture

Use tensorrt_player to replay the captured build without the original framework:

tensorrt_player -j /absolute/path/to/shim.json -o /absolute/path/to/output_engine

This produces a serialized TensorRT engine at output_engine.

Validate the engine

Run the engine with trtexec:

trtexec --loadEngine=/absolute/path/to/output_engine

Notes

• Ensure the libnvinfer.so used by the shim matches the TensorRT version in your environment.

• If multiple TensorRT versions are installed, prefer absolute paths as shown above.

• Currently, it is not supported to capture multiple engines, in case of graph break, only the first engine will be captured.