Practical overview
When it fits
Use it when developing an application that needs a C++ or Python interface around NVIDIA TensorRT inference, especially when a documented model example or object-detection workflow fits the task.
Who it is for
- C++ developers integrating TensorRT inference into applications.
- Python developers using the documented model conversion and inference interfaces.
- Teams evaluating example workflows for YOLO, face analysis, pose estimation or related model tasks.
Documented uses
- Run YOLOv5 or YOLOX inference on images and retrieve object-detection boxes.
- Use documented TensorRT workflows for listed models such as RetinaFace, SCRFD, ArcFace, AlphaPose and CenterNet.
- Compile models for FP32, FP16 or INT8 deployment using the library's interfaces.
- Build a Linux C++ target or enable the Python interface with the documented build steps.
Requirements & limits
The project is a public GitHub repository with an MIT license.
- The Linux setup instructions specify CUDA 10.2, cuDNN 8.2.2.26, TensorRT 8.0.1.6 for CUDA 10.2 and Protocol Buffers 3.11.4.
- The README documents both C++ and Python interfaces, but the sample inference paths differ: the C++ example loads an existing TensorRT model file, while the Python YOLO example includes ONNX-to-TensorRT compilation.
- The README lists a Windows setup route with dependency instructions in a separate README.
- The README's performance report uses a specific GPU, software stack, input sizes and test procedure; its figures are specific to that setup.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Build the Linux C++ YOLO target
Configure the documented Linux dependencies and compile the yolo target.
Before you start
- CUDA 10.2
- cuDNN 8.2.2.26, including development headers and runtime library
- TensorRT 8.0.1.6 for CUDA 10.2
- Protocol Buffers 3.11.4 for the ONNX parser
- An NVIDIA GPU compute capability value to configure in Makefile or CMakeLists.txt
Steps
Configure paths for cuDNN, CUDA, TensorRT and Protocol Buffers.
Set the GPU compute capability in Makefile or CMakeLists.txt.
For CMake, run
mkdir build && cd buildand thencmake ...Run the command below from the documented build setup.
make yolo -j8
Expected outcome
The documented Linux build command compiles the yolo target.
Guide limitations
- The instructions specify particular dependency versions; they do not establish compatibility with other versions.
Compile the library's Python interface on Linux
Enable and install the Python interface using the Linux build configuration.
Before you start
- The Linux build dependencies and configuration described in the project README
Steps
Set
use_python := truein the Makefile, or setHAS_PYTHON ONin CMakeLists.txt.Run the command below.
make pyinstall -j8
Expected outcome
The README identifies the compiled Python library at `python/pytrt/libpytrtc.so`.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Tensorrt Pro | Run YOLOv5 or YOLOX inference on images and retrieve object-detection boxes. | Docker, Linux | The project is a public GitHub repository with an MIT license. |
| Hyperpose | Customize pose-estimation architectures and datasets. | Docker, Linux, macOS, Windows | |
| Multi Task Nlp | Define multiple tasks and their data and model settings in a YAML file. | Linux, macOS, Windows | The project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- shouxieai/tensorRT_Pro · Official homepage title · retained in its source language