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ExecuTorch ML inference for Flutter using dart:ffi. Supports Android, iOS, macOS, Linux, Windows, and Web.

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ExecuTorch Flutter #

A Flutter plugin package using ExecuTorch to allow model inference on Android, iOS, macOS, Windows, Linux, and Web platforms.

📦 pub.dev | 🌐 Live Demo | 🔧 Example App

Overview #

ExecuTorch Flutter provides a simple Dart API for loading and running ExecuTorch models (.pte files) in your Flutter applications. The package handles all native platform integration, providing you with a straightforward interface for on-device machine learning inference.

Features #

  • ✅ Cross-Platform Support: Android (API 23+), iOS (13.0+), macOS (11.0+), Windows, Linux, and Web
  • ✅ Type-Safe API: dart:ffi bindings with type-safe Dart wrapper classes
  • ✅ Async Operations: Non-blocking model loading and inference execution
  • ✅ Multiple Models: Support for concurrent model instances
  • ✅ Error Handling: Structured exception handling with clear error messages
  • ✅ Backend Support: XNNPACK, CoreML, MPS, Vulkan backends
  • ✅ Live Camera: Real-time inference with camera stream support

Library Size by Backend #

The native library size varies depending on which backends are enabled.

📊 Download Release Size Comparison (SVG) | Download Debug Size Comparison (SVG) | JSON Report

Size reports are auto-generated for each release. Download the SVG files to view detailed size breakdowns by platform and backend.

Installation #

Requirements:

  • Flutter 3.38+ (first version with native assets hooks support)

Add to your pubspec.yaml:

dependencies:
  executorch_flutter: ^0.2.1

Basic Usage #

The package provides a simple, intuitive API that matches native ExecuTorch patterns:

1. Load a Model #

import 'package:executorch_flutter/executorch_flutter.dart';

// Recommended: Load from Flutter assets (works on all platforms including web)
final model = await ExecuTorchModel.loadFromAsset('assets/models/model.pte');

2. Run Inference #

// Prepare input tensor
final inputTensor = TensorData(
  shape: [1, 3, 224, 224],
  dataType: TensorType.float32,
  data: yourImageBytes,
);

// Run inference
final outputs = await model.forward([inputTensor]);

// Process outputs
for (var output in outputs) {
  print('Output shape: ${output.shape}');
  print('Output type: ${output.dataType}');
}

// Clean up when done
await model.dispose();

3. Loading Models #

From Assets (Recommended - works on all platforms including web):

// Simply use the asset path - no manual file copying needed
final model = await ExecuTorchModel.loadFromAsset('assets/models/model.pte');
final outputs = await model.forward([inputTensor]);
await model.dispose();

From Bytes (works on all platforms):

import 'package:flutter/services.dart' show rootBundle;

final byteData = await rootBundle.load('assets/models/model.pte');
final model = await ExecuTorchModel.loadFromBytes(byteData.buffer.asUint8List());
final outputs = await model.forward([inputTensor]);
await model.dispose();

From File Path (native platforms only):

// Only available on Android, iOS, macOS, Windows, Linux - not on web
final model = await ExecuTorchModel.load('/path/to/model.pte');
final outputs = await model.forward([inputTensor]);
await model.dispose();

Complete Examples #

See the example/ directory for a full working application:

  • Unified Model Playground - Complete app with MobileNet classification and YOLO object detection, supporting both static images and live camera

Supported Model Formats #

  • ExecuTorch (.pte): Optimized PyTorch models converted to ExecuTorch format
  • Input Types: float32, int8, int32, uint8 tensors
  • Model Size: Tested with models up to 500MB

📖 Need to export your PyTorch models? See the Official ExecuTorch Export Guide for converting PyTorch models to ExecuTorch format with platform-specific optimizations.

Platform Requirements #

Android #

  • Minimum SDK: API 23 (Android 6.0)
  • Architectures: arm64-v8a, armeabi-v7a, x86_64, x86
  • Supported Backends: XNNPACK, Vulkan (opt-in)

iOS #

  • Minimum Version: iOS 13.0+
  • Architectures: arm64 (device), x86_64 + arm64 (simulator)
  • Supported Backends: XNNPACK, CoreML, Vulkan (opt-in, via MoltenVK)

macOS #

  • Minimum Version: macOS 11.0+ (Big Sur)
  • Architectures: arm64 (Apple Silicon), x86_64 (Intel)
  • Supported Backends: XNNPACK, CoreML, MPS (arm64 only), Vulkan (opt-in, via MoltenVK)

Windows #

  • Minimum Version: Windows 10+
  • Architecture: x64
  • Supported Backends: XNNPACK, Vulkan (opt-in)

Linux #

  • Minimum Version: Ubuntu 20.04+ or equivalent
  • Architectures: x64, arm64
  • Supported Backends: XNNPACK, Vulkan (opt-in)

Web #

  • Status: Supported via WebAssembly
  • Runtime: WebAssembly (Wasm) with XNNPACK backend
  • Supported Backends: XNNPACK (Wasm SIMD)

Web Performance

Web with XNNPACK is ~6-10x slower than native, but fully functional for interactive use.

Platform Backend YOLO11n Inference Total (E2E)
Native (macOS/iOS/Android) XNNPACK ~50-100ms ~150-200ms
Web XNNPACK (Wasm SIMD) ~622ms ~855ms

Web Performance Breakdown (YOLO11n):

Stage Time % of Total
Preprocessing ~154ms 18%
Inference ~622ms 73%
Postprocessing ~79ms 9%

When to use Web:

  • ✅ Demos and prototyping
  • ✅ Interactive inference (sub-second response)
  • ✅ Accessibility (no app install required)
  • ❌ Real-time camera inference
  • ❌ High-throughput batch processing

Web Setup

  1. Run the setup script to copy required JavaScript and Wasm files:
dart run executorch_flutter:setup_web
  1. Add the script tag to your web/index.html (before the Flutter bootstrap script):
<head>
  <!-- ... other head elements ... -->

  <!-- ExecuTorch Wasm wrapper - must load before Flutter -->
  <script src="js/executorch_wrapper.js"></script>
</head>
<body>
  <script src="flutter_bootstrap.js" async></script>
</body>
  1. Use XNNPACK models - Web uses the same XNNPACK-exported models as native platforms.

Note: File-based model loading is not supported on web - models are loaded from bytes or remote URLs.

Platform Configuration #

When adding executorch_flutter to an existing Flutter project, you may need to update the minimum deployment targets. If you see build errors mentioning platform versions, follow these steps:

iOS Deployment Target (iOS 13.0+) #

If you get an error like: The package product 'executorch-flutter' requires minimum platform version 13.0 for the iOS platform

Update using Xcode (Recommended):

  1. Open your Flutter project in Xcode:
    • Navigate to your project folder
    • Open ios/Runner.xcworkspace (NOT the .xcodeproj file)
  2. In Xcode's left sidebar, click on Runner (the blue project icon at the top)
  3. Make sure Runner is selected under "TARGETS" (not under "PROJECT")
  4. Click the Build Settings tab at the top
  5. In the search bar, type: iOS Deployment Target
  6. You'll see "iOS Deployment Target" with a version number (like 13.0)
  7. Click on the version number and change it to 13.0
  8. Close Xcode

macOS Deployment Target (macOS 11.0+) #

If you get an error like: The package product 'executorch-flutter' requires minimum platform version 11.0 for the macOS platform

Update using Xcode (Recommended):

  1. Open your Flutter project in Xcode:
    • Navigate to your project folder
    • Open macos/Runner.xcworkspace (NOT the .xcodeproj file)
  2. In Xcode's left sidebar, click on Runner (the blue project icon at the top)
  3. Make sure Runner is selected under "TARGETS" (not under "PROJECT")
  4. Click the Build Settings tab at the top
  5. In the search bar, type: macOS Deployment Target
  6. You'll see "macOS Deployment Target" with a version number (like 10.15)
  7. Click on the version number and change it to 11.0
  8. Close Xcode

Verification #

After updating deployment targets, clean and rebuild:

# Clean build artifacts
flutter clean

# Get dependencies
flutter pub get

# Build for your target platform
flutter build ios --debug --no-codesign  # For iOS
flutter build macos --debug               # For macOS
flutter build apk --debug                 # For Android

Build Configuration #

The package supports build-time configuration through Flutter's hooks system in your app's pubspec.yaml. This allows you to customize the build behavior without modifying package code.

Configuration Options #

Add the following to your app's pubspec.yaml (not the package):

hooks:
  user_defines:
    executorch_flutter:
      # Enable debug logging and use Debug prebuilt binaries
      debug: true

      # Build mode: "prebuilt" (default) or "source"
      build_mode: "prebuilt"

      # ExecuTorch source version - for source builds (default: "1.0.1")
      executorch_version: "1.0.1"

      # Prebuilt release version - for prebuilt downloads (default: "1.0.1.21")
      prebuilt_version: "1.0.1.21"

      # Backend selection (platform-specific defaults apply)
      backends:
        - xnnpack
        - coreml
        - mps

Available Options #

Option Type Default Description
debug bool false Enables native debug logging and selects Debug prebuilt binaries (useful for debugging crashes)
build_mode string "prebuilt" "prebuilt" downloads pre-compiled binaries (fast, recommended). "source" builds from source (slower, requires Python 3.8+ with pyyaml)
executorch_version string "1.0.1" ExecuTorch source version (for source builds)
prebuilt_version string "1.0.1.21" Prebuilt release version (for prebuilt downloads)
backends list Platform-specific List of backends to enable. Options: xnnpack, coreml, mps, vulkan, qnn

Backend Defaults by Platform #

If backends is not specified, the following defaults are used:

Platform Default Backends
Android xnnpack
iOS xnnpack, coreml
macOS xnnpack, coreml, mps
Windows xnnpack
Linux xnnpack

Note: Vulkan is available but opt-in only due to runtime initialization issues on some platforms and UBO size limits on certain Android devices. To enable Vulkan, explicitly add it to your backends list in pubspec.yaml.

Prebuilt Binaries #

Prebuilt binaries are maintained by the package maintainer and hosted at executorch_native. The prebuilt mode downloads these pre-compiled libraries automatically during the Flutter build process.

Available prebuilt configurations:

  • Release and Debug builds for each platform
  • Platform-specific backend combinations (see table above)

Request new backends or platforms: If you need a backend or platform configuration that isn't currently available in prebuilt mode, please open an issue on the executorch_native repository.

Environment Variables #

You can also override configuration via environment variables:

Variable Description
EXECUTORCH_BUILD_MODE Override build mode ("prebuilt" or "source")
EXECUTORCH_CACHE_DIR Custom cache directory for downloaded/built artifacts
EXECUTORCH_DISABLE_DOWNLOAD Set to "1" to skip prebuilt download (requires EXECUTORCH_INSTALL_DIR)
EXECUTORCH_INSTALL_DIR Path to local ExecuTorch installation

Example Configurations #

Debug build with verbose logging:

hooks:
  user_defines:
    executorch_flutter:
      debug: true

Build from source with specific backends:

hooks:
  user_defines:
    executorch_flutter:
      build_mode: "source"
      backends:
        - xnnpack
        - vulkan

Use a specific prebuilt version:

hooks:
  user_defines:
    executorch_flutter:
      prebuilt_version: "1.0.1.21"

Advanced Usage #

Preprocessing Strategies #

The example app demonstrates three preprocessing approaches for common model types:

Hardware-accelerated preprocessing using Flutter Fragment Shaders:

  • Performance: ~75ms on web, comparable to OpenCV on native
  • Platform Support: All platforms including web
  • Dependencies: None (native Flutter APIs)
  • Use Case: Real-time camera inference, high frame rates, web apps

📖 Complete GPU Preprocessing Tutorial - Step-by-step guide with GLSL shader examples

Reference implementations: example/lib/processors/shaders/

2. OpenCV Preprocessing

High-performance C++ library preprocessing:

  • Performance: High-performance (very close to GPU on native)
  • Platform Support: Native platforms only (not available on web)
  • Dependencies: opencv_dart package
  • Use Case: Advanced image processing, computer vision operations

See OpenCV Processors for implementations.

3. CPU Preprocessing (image library)

Pure Dart image processing:

  • Performance: ~560ms on web, slower than GPU/OpenCV
  • Platform Support: All platforms
  • Dependencies: image package
  • Use Case: Simple preprocessing, debugging, fallback option

See the example app for complete processor implementations using the strategy pattern.

Example Application #

The example/ directory contains a comprehensive demo app showcasing:

  • Unified Model Playground - Main playground supporting multiple model types
    • MobileNet V3 image classification
    • YOLO object detection (v5, v8, v11)
    • Static image and live camera modes
    • Reactive settings (thresholds, top-K, preprocessing providers)
    • Performance monitoring and metrics

Converting PyTorch Models to ExecuTorch #

To use your PyTorch models with this package, convert them to ExecuTorch format (.pte files).

📖 Official ExecuTorch Export Guide: PyTorch ExecuTorch Documentation

Key Resources:

Example App Models:

Models are automatically downloaded from GitHub on first use. To export models manually:

# Export all models with all available backends
cd models/python
python3 main.py

This will:

  • ✅ Export MobileNet V3 (all backends: XNNPACK, CoreML, MPS, Vulkan)
  • ✅ Export YOLO11n, YOLOv8n, YOLOv5n (all backends)
  • ✅ Generate labels files
  • ✅ Generate index.json for dynamic model discovery

Model Hosting:

Models are hosted in a separate repository for faster cloning:

  • Repository: executorch_flutter_models
  • Models are downloaded and cached locally on first use
  • index.json provides model metadata (size, hash, platforms)

Development Status #

This project is actively developed following these principles:

  • Test-First Development: Comprehensive testing before implementation
  • Platform Parity: Consistent behavior across Android and iOS
  • Performance-First: Optimized for mobile device constraints
  • Documentation-Driven: Clear examples and API documentation

API Reference #

Core Classes #

ExecuTorchModel

The primary class for model management and inference.

// Load from Flutter asset bundle (recommended - all platforms including web)
static Future<ExecuTorchModel> loadFromAsset(String assetPath)

// Load from bytes (all platforms including web)
static Future<ExecuTorchModel> loadFromBytes(Uint8List modelBytes)

// Load from file path (native platforms only - Android, iOS, macOS, Windows, Linux)
static Future<ExecuTorchModel> load(String filePath)

// Execute inference (matches native module.forward())
Future<List<TensorData>> forward(List<TensorData> inputs)

// Release model resources
Future<void> dispose()

// Check if model is disposed
bool get isDisposed

Native API Mapping:

  • Android (Kotlin): Module.load() → module.forward()
  • iOS/macOS (Swift): Module() + load("forward") → module.forward()

TensorData

Input/output tensor representation:

final tensor = TensorData(
  shape: [1, 3, 224, 224],           // Tensor dimensions
  dataType: TensorType.float32,      // Data type (float32, int32, int8, uint8)
  data: Uint8List(...),              // Raw bytes
  name: 'input_0',                   // Optional tensor name
);

BackendQuery

Query available hardware acceleration backends at runtime. Use this to check which backends are compiled into the library and filter models accordingly.

import 'package:executorch_flutter/executorch_flutter.dart';

// Check if a specific backend is available
final hasVulkan = BackendQuery.isAvailable(Backend.vulkan);
final hasCoreML = BackendQuery.isAvailable(Backend.coreml);

// Get list of all available backends
final backends = BackendQuery.available;
print('Available backends: ${backends.map((b) => b.displayName).join(", ")}');

// Example: Filter models by available backends
if (BackendQuery.isAvailable(Backend.vulkan)) {
  // Load Vulkan-optimized model
  model = await ExecuTorchModel.loadFromAsset('assets/models/model_vulkan.pte');
} else {
  // Fallback to XNNPACK (always available)
  model = await ExecuTorchModel.loadFromAsset('assets/models/model_xnnpack.pte');
}

Backend enum values:

Backend Display Name Platforms
Backend.xnnpack XNNPACK All (including Web)
Backend.coreml CoreML iOS, macOS
Backend.mps Metal Performance Shaders macOS
Backend.vulkan Vulkan Android, iOS, macOS, Windows, Linux

Exception Hierarchy #

ExecuTorchException              // Base exception
├── ExecuTorchModelException     // Model loading/lifecycle errors
├── ExecuTorchInferenceException // Inference execution errors
├── ExecuTorchValidationException // Tensor validation errors
├── ExecuTorchMemoryException    // Memory/resource errors
├── ExecuTorchIOException        // File I/O errors
└── ExecuTorchPlatformException  // Platform communication errors

Contributing #

Contributions are welcome! Please see our Contributing Guide for:

  • Development setup and prerequisites
  • Integration testing workflow
  • Code standards and PR process
  • Platform-specific guidelines

License #

MIT License - see LICENSE file for details.

Support #

For issues and questions:

Roadmap #

See our Roadmap for planned features and improvements, including:

  • Additional model type examples (segmentation, pose estimation)
  • Additional backend support (QNN)
  • Performance optimizations and more

Experimental: Vulkan Backend #

⚠️ Work in Progress: The Vulkan backend is experimental and under active development. We encourage testing and feedback to improve stability.

Current Status #

Vulkan support is available as an opt-in backend on Android, iOS, macOS, Windows, and Linux. While prebuilt binaries with Vulkan are available, there are known issues being actively investigated:

Known Issues:

  • macOS/iOS: Vulkan is not functional on Apple platforms. MoltenVK (Vulkan-to-Metal translation) crashes during tensor allocation due to Metal texture descriptor validation failures. Use CoreML or MPS backends instead.
  • Android: Some devices (e.g., Pixel 10 Pro) may experience UBO (Uniform Buffer Object) size limit issues with certain models.
  • General: Runtime initialization may fail on some configurations.

How to Enable Vulkan #

Add Vulkan to your backends list in pubspec.yaml:

hooks:
  user_defines:
    executorch_flutter:
      backends:
        - xnnpack
        - vulkan  # Opt-in to Vulkan

Testing and Reporting Bugs #

We welcome testers! If you'd like to help improve Vulkan support:

  1. Enable Vulkan in your app's pubspec.yaml as shown above
  2. Test with Vulkan-exported models (models exported specifically for the Vulkan backend)
  3. Report issues at GitHub Issues with:
    • Device/platform information
    • Model being used
    • Full error message or crash log
    • Steps to reproduce

Model Export for Vulkan #

To use Vulkan, you need models exported specifically for the Vulkan backend:

cd models/python
python3 main.py export --vulkan

Or download Vulkan models from the executorch_flutter_models repository (files ending with _vulkan.pte).

Apple Platforms (macOS/iOS) #

Vulkan is not supported on Apple platforms. While MoltenVK provides Vulkan-to-Metal translation, ExecuTorch's Vulkan compute backend uses features that fail Metal's texture descriptor validation, causing crashes during model initialization.

Recommended alternatives for Apple platforms:

  • CoreML: Best for neural network inference on Apple devices
  • MPS (Metal Performance Shaders): GPU acceleration on macOS (arm64 only)
  • XNNPACK: Reliable CPU-based inference on all platforms

Production Recommendation #

Do not use Vulkan in production apps yet. Native crashes (SIGKILL, SIGABRT) cannot be caught by Dart's try/catch mechanism, so there's no reliable way to implement fallback logic when Vulkan fails.

For production apps, use XNNPACK which is stable across all platforms:

// Stable approach for production
final model = await ExecuTorchModel.loadFromAsset('assets/models/model_xnnpack.pte');

For testing Vulkan (development/experimentation only):

// Only use Vulkan for testing - may crash on some devices
if (BackendQuery.isAvailable(Backend.vulkan)) {
  // WARNING: If Vulkan crashes, the app will terminate
  final model = await ExecuTorchModel.loadFromAsset('assets/models/model_vulkan.pte');
}

Built with ❤️ for the Flutter and PyTorch communities.

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Metadata

ExecuTorch ML inference for Flutter using dart:ffi. Supports Android, iOS, macOS, Linux, Windows, and Web.

Repository (GitHub)
View/report issues
Contributing

Topics

#machine-learning #pytorch #ai #ffi #mobile

Documentation

API reference

License

MIT (license)

Dependencies

code_assets, ffi, flutter, flutter_web_plugins, hooks, image, logging, meta, native_toolchain_cmake, path_provider

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