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 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.

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.

swift
let model = try await MLModel.load(
    contentsOf: modelURL,
    configuration: config
)

Compile at Runtime (iOS 16+)

Compile a .mlpackage or .mlmodel to .mlmodelc on device. Useful for models downloaded from a server. Do this once per model version, not on every launch.

swift
let compiledURL = try await MLModel.compileModel(at: packageURL)
let model = try await MLModel.load(contentsOf: compiledURL, configuration: config)

Cache the compiled URL -- recompiling on every launch is a bug. Copy compiledURL to a persistent location (e.g., Application Support). When reviewing runtime-loaded models, call out both facts together: async MLModel.compileModel(at:) is iOS 16+, and compiled models must be cached so the app does not recompile on every launch.

Model Configuration

MLModelConfiguration controls compute units, GPU access, and model parameters.

Compute Units Decision Table

| Value | Uses | When to Choose | |---|---|---| | .all | CPU + GPU + Neural Engine | Default. Let the system decide. | | .cpuOnly | CPU | Deterministic tests, CPU-only fallbacks, or constrained work after profiling shows accelerator policy, contention, thermal state, or energy budget is the limiting factor. | | .cpuAndGPU | CPU + GPU | Need GPU but model has ops unsupported by ANE. | | .cpuAndNeuralEngine (iOS 16+) | CPU + Neural Engine | Best energy efficiency for compatible models. |

swift
let config = MLModelConfiguration()
config.computeUnits = .cpuAndNeuralEngine

// Optional fallback for constrained work after profiling and policy review
config.computeUnits = .cpuOnly

Configuration Properties

swift
let config = MLModelConfiguration()
config.computeUnits = .all
config.allowLowPrecisionAccumulationOnGPU = true // faster, slight precision loss

Making Predictions

With Auto-Generated Classes

The generated class provides typed input/output structs.

swift
let model = try MyImageClassifier(configuration: config)
let input = MyImageClassifierInput(image: pixelBuffer)
let output = try model.prediction(input: input)
print(output.classLabel)        // "golden_retriever"
print(output.classLabelProbs)   // ["golden_retriever": 0.95, ...]

With MLDictionaryFeatureProvider

Use when inputs are dynamic or not known at compile time.

swift
let inputFeatures = try MLDictionaryFeatureProvider(dictionary: [
    "image": MLFeatureValue(pixelBuffer: pixelBuffer),
    "confidence_threshold": MLFeatureValue(double: 0.5),
])
let output = try model.prediction(from: inputFeatures)
let label = output.featureValue(for: "classLabel")?.stringValue

Prediction Inside Async Workflows

MLModel.prediction(...) is synchronous. In async pipelines, keep model loading async, then run prediction from an actor or non-main task without adding await to the prediction call.

swift
let output = try model.prediction(from: inputFeatures)

Batch Prediction

Process multiple inputs in one call for better throughput.

swift
let batchInputs = try MLArrayBatchProvider(array: inputs.map { input in
    try MLDictionaryFeatureProvider(dictionary: ["image": MLFeatureValue(pixelBuffer: input)])
})
let batchOutput = try model.predictions(fromBatch: batchInputs)
for i in 0..<batchOutput.count {
    let result = batchOutput.features(at: i)
    print(result.featureValue(for: "classLabel")?.stringValue ?? "unknown")
}

Use predictions(fromBatch:) when batching without explicit MLPredictionOptions. Use predictions(from:options:) only when passing both an MLBatchProvider and MLPredictionOptions; predictions(from:) by itself is not the no-options batch API.

Validate a representative single input before batching. Then verify batch output count/order, feature types, domain invariants, and agreement with the single-input result. On failure, fix the deterministic input, shape, model, or configuration issue before rerunning fixtures and physical-device profiling.

Stateful Prediction (iOS 18+)

Use MLState for models that maintain state across predictions (sequence models, LLMs, audio accumulators). Create state once and pass it to each prediction call.

swift
let state = model.makeState()

// Each synchronous prediction carries forward the internal model state
for frame in audioFrames {
    let input = try MLDictionaryFeatureProvider(dictionary: [
        "audio_features": MLFeatureValue(multiArray: frame)
    ])
    let output = try model.prediction(from: input, using: state)
    let classification = output.featureValue(for: "label")?.stringValue
}

MLState is Sendable, but Sendable does not make one state safe for concurrent inference. Predictions using the same state must be serialized; do not read or write state buffers while a prediction is in flight. Call model.makeState() for each independent concurrent stream. If you need MLPredictionOptions, iOS 18+ also provides the async prediction(from:using:options:) overload; the same one-in-flight-per-state rule still applies.

MLTensor (iOS 18+)

MLTensor is a Swift-native multidimensional array for pre/post-processing. Operations run lazily -- call await tensor.shapedArray(of:) to materialize results.

swift
import CoreML

// Creation
let tensor = MLTensor([1.0, 2.0, 3.0, 4.0])
let zeros = MLTensor(zeros: [3, 224, 224], scalarType: Float.self)

// Reshaping
let reshaped = tensor.reshaped(to: [2, 2])

// Math operations
let softmaxed = tensor.softmax(alongAxis: -1)
let centered = tensor - tensor.mean()

// Interop with MLShapedArray / MLMultiArray
let shaped = await tensor.shapedArray(of: Float.self)
let multiArray = try MLMultiArray(shaped)
let shapedAgain = MLShapedArray<Float>(multiArray)

Do not invent MLTensor APIs for statistics or bridging. Avoid examples such as MLTensor(multiArray), tensor.std(), tensor.standardDeviation(), direct lazy-buffer access, or synchronous extraction; perform unsupported DSP/statistics outside the tensor pipeline or with source-confirmed tensor operations.

Working with MLMultiArray

MLMultiArray is the primary data exchange type for non-image model inputs and outputs. Use it when the auto-generated class expects array-type features.

swift
// Create a 3D array: [batch, sequence, features]
let array = try MLMultiArray(shape: [1, 128, 768], dataType: .float32)

// Write values
for i in 0..<128 {
    array[[0, i, 0] as [NSNumber]] = NSNumber(value: Float(i))
}

// Read values
let value = array[[0, 0, 0] as [NSNumber]].floatValue

let data: [Float] = [1.0, 2.0, 3.0]
let shaped = MLShapedArray(scalars: data, shape: [3])
let fromShaped = try MLMultiArray(shaped)

See references/coreml-swift-integration.md for advanced MLMultiArray patterns including NLP tokenization and audio feature extraction.

Image Preprocessing

Image models expect CVPixelBuffer input. Use CGImage conversion for photos from the camera or photo library. Vision's VNCoreMLRequest handles this automatically; manual conversion is needed only for direct MLModel prediction.

Load Image Preprocessing for the complete checked CVPixelBuffer conversion and additional normalization or cropping patterns.

Multi-Model Pipelines

Chain models when preprocessing or postprocessing requires a separate model.

swift
// Sequential inference: preprocessor -> main model -> postprocessor
let preprocessed = try preprocessor.prediction(from: rawInput)
let mainOutput = try mainModel.prediction(from: preprocessed)
let finalOutput = try postprocessor.prediction(from: mainOutput)

For Xcode-managed pipelines, use the pipeline model type in the .mlpackage. Each sub-model runs on its optimal compute unit.

Vision Integration

Use Vision to run Core ML image models with automatic image preprocessing (resizing, normalization, color space, orientation).

Modern: CoreMLRequest (iOS 18+)

swift
import Vision
import CoreML

let model = try MLModel(contentsOf: modelURL, configuration: config)
let request = CoreMLRequest(model: .init(model))
let results = try await request.perform(on: cgImage)

if let classification = results.first as? ClassificationObservation {
    print("\(classification.identifier): \(classification.confidence)")
}

Legacy: VNCoreMLRequest

swift
let vnModel = try VNCoreMLModel(for: model)
let request = VNCoreMLRequest(model: vnModel) { request, error in
    guard let results = request.results as? [VNRecognizedObjectObservation] else { return }
    for observation in results {
        let label = observation.labels.first?.identifier ?? "unknown"
        let confidence = observation.labels.first?.confidence ?? 0
        let boundingBox = observation.boundingBox // normalized coordinates
        print("\(label): \(confidence) at \(boundingBox)")
    }
}
request.imageCropAndScaleOption = .scaleFill

let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer)
try handler.perform([request])

> For complete Vision framework patterns (text recognition, barcode detection, > document scanning), see the vision-framework skill.

Performance Profiling

MLComputePlan (iOS 17.4+)

Inspect which compute device each operation will use before running predictions. Load MLComputePlan Detailed Usage for model-structure traversal, device usage, and estimated-cost inspection.

Instruments

Use the Core ML instrument template in Instruments to profile:

• Model load time
• Prediction latency (per-operation breakdown)
• Compute device dispatch (CPU/GPU/ANE per operation)
• Memory allocation

Run outside the debugger for accurate results (Xcode: Product > Profile).

Model Deployment

Bundle small offline-critical models. Prefer Background Assets for new large or updateable assets; keep On-Demand Resources only for existing ODR projects. Compile downloaded source models once, persist the .mlmodelc by version, and test load, first/repeated prediction, lifecycle transitions, and memory on the lowest supported physical device. Load deployment patterns for implementation details.

Memory Management

• Unload on background: Release model references when the app enters background

to free GPU/ANE memory. Reload on foreground return.

• Share model instances: Never create multiple MLModel instances from the same

compiled model. Use an actor to provide shared access.

• Monitor memory pressure: Large models (>100 MB) can trigger memory warnings.

Register for UIApplication.didReceiveMemoryWarningNotification and release cached models when under pressure.

See references/coreml-swift-integration.md for an actor-based model manager with lifecycle-aware loading and cache eviction.

Common Mistakes

DON'T: Load models on the main thread. DO: Use MLModel.load(contentsOf:configuration:) async API or load on a background actor. Why: Large models can take seconds to load, freezing the UI.

DON'T: Ignore MLFeatureValue type mismatches between input and model expectations. DO: Match types exactly -- use MLFeatureValue(pixelBuffer:) for images, not raw data. Why: Type mismatches cause cryptic runtime crashes or silent incorrect results.

DON'T: Create a new MLModel instance for every prediction. DO: Load once and reuse. Use an actor to manage the model lifecycle. Why: Model loading allocates significant memory and compute resources.

DON'T: Skip error handling for model loading and prediction. DO: Catch errors and provide fallback behavior when the model fails. Why: Models can fail to load on older devices or when resources are constrained.

DON'T: Assume all operations run on the Neural Engine. DO: Use MLComputePlan (iOS 17.4+) to verify device dispatch per operation. Why: Unsupported operations fall back to CPU, which may bottleneck the pipeline.

DON'T: Process images manually before passing to Vision + Core ML. DO: Use CoreMLRequest (iOS 18+) or VNCoreMLRequest (legacy) to let Vision handle preprocessing. Why: Vision handles orientation, scaling, and pixel format conversion correctly.

Review Checklist

• ] Model loaded asynchronously (not blocking main thread)
• ] MLModelConfiguration.computeUnits set appropriately for use case
• ] Model instance reused across predictions (not recreated each time)
• ] Auto-generated class used when available (typed inputs/outputs)
• ] Error handling for model loading and prediction failures
• ] Compiled model cached persistently if compiled at runtime
• ] Image inputs use Vision pipeline (CoreMLRequest iOS 18+ or VNCoreMLRequest) for correct preprocessing
• ] MLComputePlan checked to verify compute device dispatch (iOS 17.4+)
• ] Batch predictions used when processing multiple inputs
• ] Model size appropriate for deployment strategy (bundle, Background Assets, ODR)
• ] Memory tested on target devices (especially older devices with less RAM)
• ] Predictions run outside debugger for accurate performance measurement

References

• Patterns and code: references/coreml-swift-integration.md
• Model conversion and optimization (Python-side): covered in the apple-on-device-ai skill
• Apple docs: Core ML |

MLModel | MLTensor | MLComputePlan | Background Assets