Coreml
Integrate Core ML models in iOS apps for on-device machine learning inference.
Install
npx skills add dpearson2699/swift-ios-skills --skill coreml -g -a claude-code -y- Repository
- dpearson2699/swift-ios-skills1.3k
- Category
- Data and ML
- Updated
- 3mo ago
- License
- See repository
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
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.
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-aiskill. This skill owns Swift integration only.
See references/coreml-swift-integration.md for complete code patterns including actor-based caching, batch inference, image preprocessing, and testing.
Contents
- Loading Models
- Model Configuration
- Making Predictions
- MLTensor (iOS 18+)
- Working with MLMultiArray
- Image Preprocessing
- Multi-Model Pipelines
- Vision Integration
- Performance Profiling
- Model Deployment
- Memory Management
- Common Mistakes
- Review Checklist
- 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.
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.
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.
