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4 changes: 3 additions & 1 deletion examples/preprocessing/README.md
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<!--
# Copyright 2021-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# Copyright 2021-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
Expand Down Expand Up @@ -48,6 +48,8 @@ Run onnx_exporter.py to convert ResNet50 PyTorch model to ONNX format. Width and

**3. Build a TensorRT engine for the ONNX model**

`trtexec` is no longer included in the default Triton Inference Server container, so run this step in the PyTorch container from step 1 (or any other container that provides `trtexec`), not in the Triton server container used in step 4.

Set the arguments for enabling fp16 precision --fp16. To enable dynamic shapes use --minShapes, --optShapes, and maxShapes with --explicitBatch:

trtexec --onnx=model.onnx --saveEngine=./model_repository/resnet50_trt/1/model.plan --explicitBatch --minShapes=input:1x3x224x224 --optShapes=input:1x3x224x224 --maxShapes=input:256x3x224x224 --fp16
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