WindowsML

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 the following workloads:
  • Desktop development with C++ (required for C++ samples)
  • .NET desktop development (required for C# samples)
• Windows App SDK 2.1.3 or later
• CMake 3.21 or later (required for CMake samples)
• NuGet CLI (nuget.exe on PATH, required for CMake samples)
• 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

C++ CMake Samples

Sample	Description	Key Features
WinMLEpCatalog	CMake-based EP catalog sample using the WinML C API	CMake build system, automatic NuGet restore, EP discovery and registration

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

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

• Windows ML Documentation
• ONNX Runtime Documentation
• ONNX Runtime Samples
• Windows App SDK Documentation
• Report Issues