From 35ed39a944bbc11fa658281840a917fff8270b85 Mon Sep 17 00:00:00 2001
From: "M. Chornyi" <99709299+mc-nv@users.noreply.github.com>
Date: Wed, 21 Oct 2026 17:44:20 +0000
Subject: [PATCH] docs: Note trtexec is not in the default Triton container

---
 examples/preprocessing/README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/examples/preprocessing/README.md b/examples/preprocessing/README.md
index 81ea6923..a74acda4 100644
--- a/examples/preprocessing/README.md
+++ b/examples/preprocessing/README.md
@@ -1,5 +1,5 @@
 <!--
-# 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
@@ -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