Issue Description
I’m working with NVIDIA PeopleNet ResNet34 INT8 for object detection and need
help building a TensorRT engine from the encrypted .etlt model file. I’ve
attempted multiple approaches but encountered tooling compatibility issues.
Environment Details
Hardware:
- GPU: NVIDIA A100 80GB PCIe (Compute Capability 8.0)
- Platform: Azure NC96ads_A100_v4
Software:
- OS: Ubuntu 22.04.5 LTS
- Driver: 535.247.01
- CUDA: 12.2
- TensorRT: 10.13.0.35
Model Files:
- resnet34_peoplenet_int8.etlt (86 MB)
- resnet34_peoplenet_int8_calib.txt (9.4 KB)
What I’ve Tried
- TensorRT Python API - Cannot parse encrypted .etlt format
- Existing tao-converter - Requires TensorRT 8.x, I have 10.13
- DeepStream 7.0 Docker - Container environment complexities
What I Need
Option A (Preferred): Pre-built TensorRT engine file
Option B: Guidance on correct tao-converter version for TensorRT 10.13
Build Parameters
tao-converter
-k nvidia_tlt
-d 3,544,960
-o output_cov/Sigmoid,output_bbox/BiasAdd
-e resnet34_peoplenet_int8.engine
-t int8
-c resnet34_peoplenet_int8_calib.txt
-i nchw
-m 1
models/resnet34_peoplenet_int8.etlt
Questions
- Can someone provide a pre-built engine for A100 + TRT 10.13?
- What’s the correct tao-converter version for TensorRT 10.13?
- Should I convert .etlt to ONNX first?
Context
Production object detection pipeline for real-time video analytics. Pipeline is
ready - just blocked on TensorRT engine file.
Any help would be greatly appreciated! 🙏
Tags: deepstream, tensorrt, peoplenet, tao-toolkit, engine-build