Plugin.Maui.ML 1.2.0

Plugin.Maui.ML

A comprehensive .NET MAUI plugin that provides ML inference capabilities with support for multiple backends including ONNX Runtime, CoreML, and platform-native acceleration.

👁 Example

👁 NuGet Version
👁 GitHub Actions Workflow Status

Architecture

graph TB
 A["IMLInfer Interface"] --> B["OnnxRuntimeInfer"]
 A --> C["CoreMLInfer (iOS/macOS)"]
 A --> D["MLKitInfer (Android) - Coming Soon"]
 A --> E["WindowsMLInfer (Windows) - Coming Soon"]
 
 B --> F["Cross-Platform ONNX Models"]
 C --> G["Apple Vision/CoreML Models"]
 D --> H["TFLite/MLKit Models"]
 E --> I["Windows ML Models"]
 
 B -.->|Uses| J["CoreML EP (iOS/macOS)"]
 B -.->|Uses| K["NNAPI EP (Android)"]
 B -.->|Uses| L["DirectML EP (Windows)"]
 
 style A fill:#4CAF50,stroke:#2E7D32,color:#fff
 style B fill:#2196F3,stroke:#1565C0,color:#fff
 style C fill:#FF9800,stroke:#E65100,color:#fff
 style D fill:#9C27B0,stroke:#4A148C,color:#fff
 style E fill:#00BCD4,stroke:#006064,color:#fff

Legend:

• Green: Unified interface for all backends
• Blue: ONNX Runtime (cross-platform, default)
• Orange: CoreML native (iOS/macOS only)
• Purple: ML Kit/TensorFlow Lite (Android - coming soon)
• Cyan: Windows ML (Windows - coming soon)
• Dotted arrows: Execution provider integration

Features

• 🧠 Multiple ML Backends: ONNX Runtime, CoreML (iOS/macOS), ML Kit (Android - coming soon)
• 🏗️ Platform-Specific Optimizations: Leverages native ML acceleration on each platform
  • iOS/macOS: CoreML execution provider & Apple Neural Engine
  • Android: NNAPI execution provider
  • Windows: DirectML execution provider
• 📱 Cross-Platform: Supports Android, iOS, macOS Catalyst, and Windows
• 🔧 Dependency Injection: Easy integration with .NET DI container
• 🛠️ Utility Classes: Helper classes for tensor operations and model management
• 📦 NuGet Package: Available as a ready-to-use NuGet package
• 🔍 Git LFS Support: Proper handling of large ML model files
• 🧪 Comprehensive Tests: Full unit test coverage

Installation

<PackageReference Include="Plugin.Maui.ML" Version="1.0.0" />

Quick Start

1. Register the Service

// In your MauiProgram.cs
public static class MauiProgram
{
 public static MauiApp CreateMauiApp()
 {
 var builder = MauiApp.CreateBuilder();
 builder
 .UseMauiApp<App>()
 .ConfigureFonts(fonts =>
 {
 fonts.AddFont("OpenSans-Regular.ttf", "OpenSansRegular");
 });

 // Register ML services with platform-optimized defaults
 builder.Services.AddMauiML(config =>
 {
 config.EnablePerformanceLogging = true;
 config.MaxConcurrentInferences = 2;
 });

 // Or specify a backend explicitly
 // builder.Services.AddMauiML(MLBackend.CoreML); // iOS/macOS only

 return builder.Build();
 }
}

2. Use in Your Code

public class MainPage : ContentPage
{
 private readonly IMLInfer _mlService;

 public MainPage(IMLInfer mlService)
 {
 _mlService = mlService;
 InitializeComponent();
 }

 private async void OnPredictClicked(object sender, EventArgs e)
 {
 try
 {
 // Check which backend is being used
 Console.WriteLine($"Using backend: {_mlService.Backend}");

 // Load your model (ONNX or platform-specific format)
 await _mlService.LoadModelFromAssetAsync("my-model.onnx");

 // Prepare input tensors
 var inputData = new float[] { 1.0f, 2.0f, 3.0f, 4.0f };
 var inputTensor = TensorHelper.CreateTensor(inputData, new int[] { 1, 4 });
 var inputs = new Dictionary<string, Tensor<float>>
 {
 ["input"] = inputTensor
 };

 // Run inference
 var results = await _mlService.RunInferenceAsync(inputs);

 // Process results
 foreach (var output in results)
 {
 var outputData = TensorHelper.ToArray(output.Value);
 Console.WriteLine($"Output '{output.Key}': [{string.Join(", ", outputData)}]");
 }
 }
 catch (Exception ex)
 {
 await DisplayAlert("Error", ex.Message, "OK");
 }
 }
}

Getting ONNX Models

Before using Plugin.Maui.ML, you'll need ONNX model files. You can:

1. Download pre-converted ONNX models from Hugging Face Model Hub
2. Convert existing models (PyTorch, TensorFlow, etc.) to ONNX format

We provide the HFOnnxTool to make this easy:

# Download a model that already has ONNX files
cd tools/HFOnnxTool
dotnet run -- fetch --repo sentence-transformers/all-MiniLM-L6-v2 --output ./models

# Or convert a model to ONNX format
dotnet run -- convert --repo d4data/biomedical-ner-all --task token-classification --output ./onnx

📖 See the for detailed instructions on:

• Using the inspect, fetch, and convert commands
• Working with specific models like biomedical NER and sentence transformers
• Python setup for model conversion
• Troubleshooting common issues

Backend Support

Available Backends

Backend Selection

// Automatic platform-optimized selection (recommended)
builder.Services.AddMauiML();

// Explicit backend
builder.Services.AddMauiML(MLBackend.CoreML); // iOS/macOS only

// Configuration-based
builder.Services.AddMauiML(config =>
{
 config.PreferredBackend = MLBackend.CoreML; // Falls back to default if not available
});

// Runtime selection
IMLInfer onnxInfer = new OnnxRuntimeInfer();
#if IOS || MACCATALYST
IMLInfer coreMLInfer = new CoreMLInfer();
#endif

Model Format Support

See for detailed information about backends, model conversion, and performance optimization.

API Reference

IMLInfer Interface

The main interface for ML inference operations.

Properties

• MLBackend Backend { get; } - Get the current backend type
• bool IsModelLoaded { get; } - Check if a model is currently loaded

Methods

• Task LoadModelAsync(string modelPath, CancellationToken cancellationToken = default)

  • Load a model from a file path

• Task LoadModelAsync(Stream modelStream, CancellationToken cancellationToken = default)

  • Load a model from a stream

• Task LoadModelFromAssetAsync(string assetName, CancellationToken cancellationToken = default)

  • Load a model from MAUI assets

• Task<Dictionary<string, Tensor<float>>> RunInferenceAsync(Dictionary<string, Tensor<float>> inputs, CancellationToken cancellationToken = default)

  • Run inference with float inputs

• Task<Dictionary<string, Tensor<float>>> RunInferenceLongInputsAsync(Dictionary<string, Tensor<long>> inputs, CancellationToken cancellationToken = default)

  • Run inference with Int64 inputs (e.g., token IDs)

• Dictionary<string, NodeMetadata> GetInputMetadata()

  • Get input metadata for the loaded model

• Dictionary<string, NodeMetadata> GetOutputMetadata()

  • Get output metadata for the loaded model

• void UnloadModel()

  • Dispose of the loaded model and release resources

TensorHelper Utility Class

Helper utilities for working with tensors.

Methods

• static Tensor<float> CreateTensor(float[] data, int[] dimensions)

  • Create a tensor from a float array

• static Tensor<float> CreateTensor(float[,] data)

  • Create a tensor from a 2D float array

• static Tensor<float> CreateTensor(float[,,] data)

  • Create a tensor from a 3D float array

• static float[] ToArray(Tensor<float> tensor)

  • Convert tensor to float array

• static string GetShapeString(Tensor<float> tensor)

  • Get tensor shape as a string

• static Tensor<float> Reshape(Tensor<float> tensor, int[] newDimensions)

  • Reshape a tensor to new dimensions

• static Tensor<float> Normalize(Tensor<float> tensor)

  • Normalize tensor values to 0-1 range

• static Tensor<float> Softmax(Tensor<float> tensor)

  • Apply softmax function to tensor

Configuration

Configure the ML services with MLConfiguration:

builder.Services.AddMauiML(config =>
{
 config.UseTransientService = false; // Use singleton (default)
 config.EnablePerformanceLogging = true;
 config.MaxConcurrentInferences = Environment.ProcessorCount;
 config.DefaultModelAssetPath = "models/default-model.onnx";
 config.PreferredBackend = MLBackend.CoreML; // Optional: specify preferred backend
});

Configuration Options

• PreferredBackend: Preferred ML backend (default: null = platform default)
• UseTransientService: Whether to use transient service lifetime (default: false, uses singleton)
• EnablePerformanceLogging: Enable performance logging (default: false)
• MaxConcurrentInferences: Maximum number of concurrent inference operations (default: processor count)
• DefaultModelAssetPath: Default model asset path (default: null)

Platform-Specific Features

Android

• NNAPI Support: Automatic Neural Network API acceleration on supported devices (Android 8.1+)
• Asset Loading: Load models directly from Android assets folder
• Check Capabilities: PlatformMLInfer.IsNnapiAvailable()

iOS/macOS

• CoreML Integration: Automatic CoreML acceleration via ONNX Runtime
• Pure CoreML Support: Use native .mlmodel files with CoreMLInfer
• Neural Engine: Support for Apple Neural Engine on A12+ and M1+ chips
• Bundle Resources: Load models from iOS/macOS app bundles
• Check Capabilities: PlatformMLInfer.IsNeuralEngineAvailable()

Windows

• DirectML Support: Hardware acceleration via DirectML on compatible GPUs
• Package Resources: Load models from Windows app packages
• Check Capabilities: PlatformMLInfer.IsDirectX12Available(), PlatformMLInfer.GetSystemInfo()

Examples

Check out the sample projects in the samples/ directory:

• ConsoleApp: Basic console application demonstrating plugin usage
• MauiSample: Full MAUI app with UI examples
• Comprehensive examples showing different model types and backends

Testing

Run the comprehensive test suite:

dotnet test tests/Plugin.Maui.ML.Tests/

The test suite includes:

• Unit tests for all core functionality
• Backend-specific tests
• Platform-specific integration tests
• Performance benchmarks
• Memory leak detection

Performance Considerations

• Use singleton service lifetime for better performance (default)
• Backend Selection:
  • ONNX Runtime: Best for cross-platform compatibility
  • CoreML: Best for iOS/macOS-only apps needing maximum performance
• Hardware Acceleration: Automatically enabled on all platforms
• Memory Management: Use UnloadModel() when done with a model
• Async Operations: All inference operations are async to avoid blocking UI

Performance Tips

// Check backend capabilities
#if IOS
if (Platforms.iOS.PlatformMLInfer.IsNeuralEngineAvailable())
{
 Console.WriteLine("Running on Apple Neural Engine!");
}
#endif

// Profile inference time
var sw = Stopwatch.StartNew();
var result = await _mlService.RunInferenceAsync(inputs);
sw.Stop();
Console.WriteLine($"Inference took: {sw.ElapsedMilliseconds}ms");

Documentation

• - Detailed guide on backends and model conversion
• - Guide for downloading and converting Hugging Face models to ONNX
• API Reference - Complete API documentation
• - Sample code and applications

Troubleshooting

Common Issues

1. Model Loading Errors

   • Ensure the model file path is correct
   • Verify the model format matches the backend (ONNX for OnnxRuntime, .mlmodel for CoreML)
   • Check file permissions

2. Backend Not Available

   • Some backends are platform-specific
   • Check _mlService.Backend to see what's being used
   • Use MLPlugin.Default for automatic platform selection

3. Memory Issues

   • Use UnloadModel() when done with a model
   • Consider using transient services for memory-sensitive scenarios
   • Monitor memory usage with large models

4. Platform-Specific Issues

   • iOS: Ensure CoreML execution provider is available (iOS 11+)
   • Android: Check NNAPI availability with IsNnapiAvailable()
   • Windows: Verify DirectML support with IsDirectX12Available()

Contributing

We welcome contributions! Please see our for details.

License

This project is licensed under the MIT License - see the file for details.

Acknowledgments

• Built on top of Microsoft ONNX Runtime
• CoreML support via Apple's Core ML framework
• Inspired by the .NET MAUI community plugins
• Thanks to all contributors and users