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WindowsML

README.md

Windows ML Samples

This repository contains comprehensive samples demonstrating how to use Windows ML and ONNX Runtime for machine learning inference on Windows. These samples emphasize Windows ML integration with various Windows technologies via the Windows App SDK. Samples for the ONNX Runtime can be found in this repository.

Overview

Windows ML enables high-performance, reliable inferencing of machine learning models on Windows devices. These samples demonstrate key concepts including:

  • Execution Provider Selection - Automatic discovery and acquisition of execution providers for hardware-accelerated inference
  • Model Compilation - Optimize models for specific hardware during first run
  • Windows App SDK Deployment Types - Use models in a variety of different Windows App SDK deployment modes (e.g., self-contained, framework-based deployment)

Prerequisites

  • Windows 11 PC running version 24H2 (build 26100) or greater
  • Visual Studio 2022 with C++ and .NET workloads
  • Windows App SDK 1.8.1 or later
  • Python 3.10-3.13 for Python samples on x64 and ARM64 devices

Sample Categories

C++ Samples

Sample Description Key Features
CppConsoleDesktop Basic C++ console application EP discovery, command-line options, model compilation
CppConsoleDesktop.FrameworkDependent Framework-dependent deployment variant Shared runtime, smaller deployment footprint
CppConsoleDesktop.SelfContained Self-contained deployment variant Standalone deployment, no runtime dependencies
CppConsoleDll DLL usage pattern WindowsML in shared library, memory management
CppResnetBuildDemo ResNet model demo from the Windows ML session at Build 2025 Model conversion, EP compilation, detailed tutorial

C++ ABI Samples

Sample Description Key Features
cpp-abi Direct ABI implementation using raw COM interfaces Automatic ABI header generation, no projections

C# Samples

Console Applications

Sample Description Key Features
CSharpConsoleDesktop Basic C# console application Shared helper usage, command-line interface
ResnetBuildDemoCS ResNet model demo from the Windows ML session at Build 2025 Model conversion, EP compilation, detailed tutorial

GUI Applications

Sample UI Framework Description
cs-wpf WPF Image classification example
cs-winforms Windows Forms Image classification example
cs-winui WinUI 3 Image classification example

Python Samples

Sample Description Key Features
SqueezeNetPython Python image classification WinML Python bindings, batch image processing

Common Workflow

Most samples follow this pattern:

  1. Initialize Environment - Create ONNX Runtime environment
  2. Register Execution Providers - Discover and register available hardware accelerators
  3. Load Model - Load ONNX model, optionally compile for target hardware
  4. Preprocess Input - Convert images to model input format
  5. Run Inference - Execute model and get predictions
  6. Process Results - Apply softmax and display top predictions

Model Files

Samples use these pre-trained models:

  • SqueezeNet - Lightweight image classification (included)
  • ResNet-50 - High-accuracy image classification (requires AI Toolkit conversion)

Hardware Support

These samples detect and utilize the CPU, GPU, and NPU.

Diagnostics

Resource Description
capture-logs Scripts and profiles for capturing Windows ML diagnostic logs (docs)

Getting Help