# Coreml

Integrate Core ML models in iOS apps for on-device machine learning inference.

- Repository: https://github.com/dpearson2699/swift-ios-skills (1266 stars)
- Category: Data and ML
- License: see repository
- Last updated: 2026-07-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add dpearson2699/swift-ios-skills --skill coreml -g -a claude-code -y`
Codex: `npx skills add dpearson2699/swift-ios-skills --skill coreml -g -a codex -y`
Cursor: `npx skills add dpearson2699/swift-ios-skills --skill coreml -g -a cursor -y`
Gemini CLI: `npx skills add dpearson2699/swift-ios-skills --skill coreml -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add dpearson2699/swift-ios-skills`

## Description

Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Core ML Swift Integration

Load, configure, and run Core ML models in iOS apps. This skill covers the
Swift side: model loading, prediction, MLTensor, profiling, and deployment.

> **Scope boundary:** Python-side model conversion, optimization (quantization,
> palettization, pruning), and framework selection live in the `apple-on-device-ai`
> skill. This skill owns Swift integration only.

See [references/coreml-swift-integration.md](references/coreml-swift-integration.md) for complete code patterns including
actor-based caching, batch inference, image preprocessing, and testing.

## Contents

- [Loading Models](#loading-models)
- [Model Configuration](#model-configuration)
- [Making Predictions](#making-predictions)
- [MLTensor (iOS 18+)](#mltensor-ios-18)
- [Working with MLMultiArray](#working-with-mlmultiarray)
- [Image Preprocessing](#image-preprocessing)
- [Multi-Model Pipelines](#multi-model-pipelines)
- [Vision Integration](#vision-integration)
- [Performance Profiling](#performance-profiling)
- [Model Deployment](#model-deployment)
- [Memory Management](#memory-management)
- [Common Mistakes](#common-mistakes)
- [Review Checklist](#review-checklist)
- [References](#references)

## Loading Models

### Auto-Generated Classes

When you add a `.mlmodel` or `.mlpackage` to an app target, Xcode generates a Swift
class with typed input/output. Use this whenever possible.

```swift
import CoreML

let config = MLModelConfiguration()
config.computeUnits = .all

let model = try MyImageClassifier(configuration: config)
```

### Manual Loading

Load from a URL when the model is downloaded at runtime or stored outside the
bundle.

```swift
let modelURL = Bundle.main.url(
    forResource: "MyModel", withExtension: "mlmodelc"
)!
let model = try MLModel(contentsOf: modelURL, configuration: config)
```

### Async Loading (iOS 15+)

Load models without blocking the main thread. Prefer this for large models.

The rest: https://github.com/dpearson2699/swift-ios-skills/blob/HEAD/skills/coreml/SKILL.md