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YoloDotNet.ExecutionProvider.Cuda 1.1.0

Information

YoloDotNet uses modular execution providers to run inference on different hardware backends. Each provider targets a specific platform or accelerator and may require additional system-level dependencies such as runtimes, drivers, or SDKs.

Installing the NuGet package alone is not always sufficient — proper setup depends on the selected provider and the target system.
This document describes the installation, requirements, and usage of the CUDA & TensorRT execution provider.

Core Library Requirement

All execution providers require the core YoloDotNet package, which contains the shared inference pipeline, models, and APIs.

NuGet Package

dotnet add package YoloDotNet

Execution Provider - CUDA and TensorRT

The CUDA & TensorRT execution provider enables GPU-accelerated inference on NVIDIA GPUs using ONNX Runtime’s CUDA backend.
Optionally, NVIDIA TensorRT can be enabled to further optimize models for maximum throughput and ultra-low latency.

⚠️ Note
This execution provider is supported on Windows and Linux only.
CUDA and TensorRT are not available on macOS.

Requirements

• CUDA Toolkit 12.x
• cuDNN 9.x
• Windows or Linux (x64)

Important
This execution provider depends on native CUDA and cuDNN libraries.
Installing the NuGet package alone is not sufficient — system-level dependencies must be installed correctly.

Installation (Windows)

• CUDA

  Download and install the following from NVIDIA’s official websites:

  • CUDA Toolkit 12.x
  • cuDNN 9.x

  After installing cuDNN, locate the folder containing the cuDNN DLL files. This is typically:

  C:\Program Files\NVIDIA\CUDNN\v9.x\bin\v12.x
  

  (Replace v9.x and v12.x with the versions installed on your system)

  Add cuDNN to the System PATH

  1. Copy the full folder path to your cuDNN bin\v12.x folder

  2. Search Edit the system environment variables in Windows search and select it.

  3. Click Environment Variables.

  4. Under System variables, select Path and click Edit.

  5. Click New and paste the copied cuDNN path.

  6. Click OK to save and close all dialogs.

  7. Reboot your system.

• TensorRT (optional)

  TensorRT is NVIDIA’s high-performance inference engine and can significantly improve performance by optimizing models for your specific GPU.

  1. Download the TensorRT 10.13.3 release for CUDA 12.x.

  2. Extract the archive to a folder on your system.

  3. Locate the lib folder inside the extracted TensorRT folder.

  4. Copy the full path to this lib folder.

  5. Add the path to your system's PATH environment variable (same process as described in the CUDA installation steps).

  6. Reboot your system.

Installation (Linux)

• CUDA

  1. Install CUDA Toolkit 12.x for your Linux distribution

  2. Install cuDNN 9.x for your Linux distribution

  3. Reboot your system

• TensorRT (optional)

  1. Download the TensorRT 10.13.3 release for CUDA 12.x.

  2. Follow NVIDIA’s TensorRT installation instructions for Linux.

NuGet Package

dotnet add package YoloDotNet.ExecutionProvider.Cuda

YoloDotNet.ExecutionProvider.Cuda v1.1 requires YoloDotNet version 4.1.

Usage Example:

using YoloDotNet;
using YoloDotNet.ExecutionProvider.Cuda;

using var yolo = new Yolo(new YoloOptions
{
 ExecutionProvider = new CudaExecutionProvider(
 model: "path/to/model.onnx",

 // GPU device index (default: 0)
 gpuId: 0,

 // Optional TensorRT configuration for maximum performance
 trtConfig: new TensorRt
 {
 Precision = TrtPrecision.FP16,
 EngineCachePath = "path/to/cache/folder",
 EngineCachePrefix = "MyCachePrefix"
 }
 ),

 // ...other options
});

// See the TensorRT demo project for advanced configuration options.

Notes & Recommendations

• Use CUDA only if you want simple GPU acceleration with minimal setup.
• Enable TensorRT if you need maximum performance and are comfortable managing engine caches.
• TensorRT engine generation happens once per model and configuration and is cached for subsequent runs.
• CUDA and TensorRT are not supported on macOS.