Complete implementation patterns for loading, configuring, and running Core ML models in Swift. All patterns target iOS 26+ with Swift 6.3, backward-compatible to iOS 14 unless noted.

Actor-Based Model Loading and Caching
Auto-Generated Class Usage
Manual MLFeatureProvider
Async Prediction Patterns
MLBatchProvider for Batch Inference
Stateful Predictions with MLState (iOS 18+)
Image Preprocessing
MLMultiArray Creation and Manipulation
MLTensor Advanced Operations
Vision + Core ML Pipelines
NaturalLanguage Integration
MLComputePlan Detailed Usage (iOS 17.4+)
Background Loading and Memory Management
Error Handling Patterns
Testing Patterns

Actor-Based Model Loading and Caching

Use an actor to manage model lifecycle, prevent concurrent loading, and cache compiled models persistently.

import CoreML

actor ModelManager {
    private var loadedModels: [String: MLModel] = [:]
    private let cacheDirectory: URL

    init() {
        let appSupport = FileManager.default.urls(
            for: .applicationSupportDirectory, in: .userDomainMask
        ).first!
        cacheDirectory = appSupport.appendingPathComponent("CompiledModels", isDirectory: true)
        try? FileManager.default.createDirectory(at: cacheDirectory, withIntermediateDirectories: true)
    }

    func model(named name: String, configuration: MLModelConfiguration = .init()) async throws -> MLModel {
        if let cached = loadedModels[name] {
            return cached
        }

        let compiledURL = try await compiledModelURL(for: name)
        let model = try await MLModel.load(contentsOf: compiledURL, configuration: configuration)
        loadedModels[name] = model
        return model
    }

    func unloadModel(named name: String) {
        loadedModels.removeValue(forKey: name)
    }

    func unloadAll() {
        loadedModels.removeAll()
    }

    private func compiledModelURL(for name: String) async throws -> URL {
        // Check for pre-compiled model in cache
        let cachedURL = cacheDirectory.appendingPathComponent("\(name).mlmodelc")
        if FileManager.default.fileExists(atPath: cachedURL.path) {
            return cachedURL
        }

        // Check for pre-compiled model in bundle
        if let bundledCompiledURL = Bundle.main.url(forResource: name, withExtension: "mlmodelc") {
            return bundledCompiledURL
        }

        // Compile from .mlpackage and cache
        guard let packageURL = Bundle.main.url(forResource: name, withExtension: "mlpackage") else {
            throw ModelManagerError.modelNotFound(name)
        }
        let tempCompiledURL = try await MLModel.compileModel(at: packageURL)

        // Move compiled model to persistent cache
        if FileManager.default.fileExists(atPath: cachedURL.path) {
            try FileManager.default.removeItem(at: cachedURL)
        }
        try FileManager.default.moveItem(at: tempCompiledURL, to: cachedURL)
        return cachedURL
    }
}

enum ModelManagerError: Error {
    case modelNotFound(String)
    case predictionFailed(String)
}

Usage with SwiftUI

@MainActor
@Observable
final class ClassifierViewModel {
    var classLabel: String = ""
    var confidence: Double = 0
    var isLoading = false
    var errorMessage: String?

    private let modelManager = ModelManager()

    func classify(image: CGImage) async {
        isLoading = true
        defer { isLoading = false }

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

            let model = try await modelManager.model(named: "ImageClassifier", configuration: config)

            let pixelBuffer = try image.toPixelBuffer(width: 224, height: 224)
            let input = try MLDictionaryFeatureProvider(dictionary: [
                "image": MLFeatureValue(pixelBuffer: pixelBuffer),
            ])
            let output = try await model.prediction(from: input)

            classLabel = output.featureValue(for: "classLabel")?.stringValue ?? "Unknown"
            confidence = output.featureValue(for: "classLabelProbs")?
                .dictionaryValue[classLabel]?
                .doubleValue ?? 0
        } catch {
            errorMessage = "Classification failed: \(error.localizedDescription)"
        }
    }
}

Auto-Generated Class Usage

When you add a .mlpackage or .mlmodelc to your Xcode project, Xcode generates a Swift class with typed inputs and outputs.

import CoreML

// Xcode generates: MyImageClassifier, MyImageClassifierInput, MyImageClassifierOutput

// Synchronous prediction with generated types
func classifyWithGeneratedClass(pixelBuffer: CVPixelBuffer) throws -> (label: String, confidence: Double) {
    let config = MLModelConfiguration()
    config.computeUnits = .all

    let classifier = try MyImageClassifier(configuration: config)
    let input = MyImageClassifierInput(image: pixelBuffer)
    let output = try classifier.prediction(input: input)

    let topLabel = output.classLabel
    let topConfidence = output.classLabelProbs[topLabel] ?? 0
    return (topLabel, topConfidence)
}

Accessing the Underlying MLModel

// Get the underlying MLModel from a generated class
let classifier = try MyImageClassifier(configuration: config)
let mlModel = classifier.model

// Useful for Vision integration
let vnModel = try VNCoreMLModel(for: mlModel)

Manual MLFeatureProvider

Implement MLFeatureProvider when you need custom input construction.

import CoreML

final class CustomImageInput: MLFeatureProvider {
    let image: CVPixelBuffer
    let confidenceThreshold: Double

    var featureNames: Set<String> {
        ["image", "confidence_threshold"]
    }

    func featureValue(for featureName: String) -> MLFeatureValue? {
        switch featureName {
        case "image":
            return MLFeatureValue(pixelBuffer: image)
        case "confidence_threshold":
            return MLFeatureValue(double: confidenceThreshold)
        default:
            return nil
        }
    }

    init(image: CVPixelBuffer, confidenceThreshold: Double = 0.5) {
        self.image = image
        self.confidenceThreshold = confidenceThreshold
    }
}

// Usage
let input = CustomImageInput(image: pixelBuffer, confidenceThreshold: 0.7)
let output = try model.prediction(from: input)

Async Prediction Patterns

Single Prediction with Swift Concurrency (iOS 17+)

actor PredictionService {
    private let model: MLModel

    init(model: MLModel) {
        self.model = model
    }

    func predict(input: MLFeatureProvider) async throws -> MLFeatureProvider {
        try await model.prediction(from: input)
    }
}

Streaming Predictions

func classifyFrames(_ frames: AsyncStream<CVPixelBuffer>) async throws -> AsyncStream<String> {
    let model = try await ModelManager().model(named: "Classifier")

    return AsyncStream { continuation in
        Task {
            for await frame in frames {
                let input = try MLDictionaryFeatureProvider(dictionary: [
                    "image": MLFeatureValue(pixelBuffer: frame),
                ])
                let output = try await model.prediction(from: input)
                let label = output.featureValue(for: "classLabel")?.stringValue ?? "unknown"
                continuation.yield(label)
            }
            continuation.finish()
        }
    }
}

MLBatchProvider for Batch Inference

import CoreML

func batchClassify(images: [CVPixelBuffer], model: MLModel) throws -> [(label: String, confidence: Double)] {
    let batchInputs = try MLArrayBatchProvider(array: images.map { buffer in
        try MLDictionaryFeatureProvider(dictionary: [
            "image": MLFeatureValue(pixelBuffer: buffer),
        ])
    })

    let batchOutput = try model.predictions(from: batchInputs)

    var results: [(String, Double)] = []
    for i in 0..<batchOutput.count {
        let features = batchOutput.features(at: i)
        let label = features.featureValue(for: "classLabel")?.stringValue ?? "unknown"
        let probs = features.featureValue(for: "classLabelProbs")?.dictionaryValue ?? [:]
        let confidence = (probs[label] as? NSNumber)?.doubleValue ?? 0
        results.append((label, confidence))
    }
    return results
}

Stateful Predictions with MLState (iOS 18+)

MLState enables models that maintain internal state across predictions. This is essential for sequence models (text generation, audio classification, time-series) where each prediction depends on previous context.

import CoreML

/// Audio classification that accumulates context over time
actor AudioClassifier {
    private let model: MLModel
    private var state: MLState?

    init(model: MLModel) {
        self.model = model
    }

    /// Start a new classification session
    func beginSession() {
        state = model.makeState()
    }

    /// Classify the next audio frame using accumulated state
    func classify(audioFeatures: MLMultiArray) async throws -> String {
        guard let state else {
            throw ClassifierError.noActiveSession
        }

        let input = try MLDictionaryFeatureProvider(dictionary: [
            "audio_features": MLFeatureValue(multiArray: audioFeatures)
        ])

        let output = try await model.prediction(from: input, using: state)
        return output.featureValue(for: "label")?.stringValue ?? "unknown"
    }

    /// End the session and release state
    func endSession() {
        state = nil
    }
}

enum ClassifierError: Error {
    case noActiveSession
}

Key Rules for MLState

Not Sendable: Use MLState from a single actor or task. Do not share across concurrency domains.
Independent streams: Call model.makeState() per stream when processing multiple concurrent sequences (e.g., multiple audio channels).
Resettable: Create a new state to reset accumulated context. There is no explicit reset method -- just discard the old state and create fresh.
Memory: State holds model-specific internal buffers. Release it when the session ends to free memory.

Image Preprocessing

CVPixelBuffer from CGImage

import CoreVideo
import CoreGraphics

func createPixelBuffer(from cgImage: CGImage, width: Int, height: Int) throws -> CVPixelBuffer {
    let attrs: [CFString: Any] = [
        kCVPixelBufferCGImageCompatibilityKey: true,
        kCVPixelBufferCGBitmapContextCompatibilityKey: true,
    ]

    var pixelBuffer: CVPixelBuffer?
    let status = CVPixelBufferCreate(
        kCFAllocatorDefault,
        width, height,
        kCVPixelFormatType_32ARGB,
        attrs as CFDictionary,
        &pixelBuffer
    )
    guard status == kCVReturnSuccess, let buffer = pixelBuffer else {
        throw ImageError.pixelBufferCreationFailed
    }

    CVPixelBufferLockBaseAddress(buffer, [])
    defer { CVPixelBufferUnlockBaseAddress(buffer, []) }

    guard let context = CGContext(
        data: CVPixelBufferGetBaseAddress(buffer),
        width: width,
        height: height,
        bitsPerComponent: 8,
        bytesPerRow: CVPixelBufferGetBytesPerRow(buffer),
        space: CGColorSpaceCreateDeviceRGB(),
        bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue
    ) else {
        throw ImageError.contextCreationFailed
    }

    context.draw(cgImage, in: CGRect(x: 0, y: 0, width: width, height: height))
    return buffer
}

enum ImageError: Error {
    case pixelBufferCreationFailed
    case contextCreationFailed
}

For CIImage sources, use CIContext.render(_:to:) into a CVPixelBuffer created with kCVPixelFormatType_32BGRA.

MLMultiArray Creation and Manipulation

import CoreML

let array = try MLMultiArray(shape: [1, 3, 224, 224], dataType: .float32)
for i in 0..<array.count { array[i] = NSNumber(value: Float.random(in: 0...1)) }

let values: [Float] = [1.0, 2.0, 3.0, 4.0, 5.0]
let mlArray = try MLMultiArray(values)

// Access elements
let element = array[[0, 0, 112, 112] as [NSNumber]].floatValue

// Convert to Swift array
func toFloatArray(_ multiArray: MLMultiArray) -> [Float] {
    let pointer = multiArray.dataPointer.assumingMemoryBound(to: Float.self)
    return Array(UnsafeBufferPointer(start: pointer, count: multiArray.count))
}

let featureValue = MLFeatureValue(multiArray: array)

MLTensor Advanced Operations (iOS 18+)

import CoreML

// Creation patterns
let tensor1D = MLTensor([1.0, 2.0, 3.0, 4.0])
let zeros = MLTensor(zeros: [3, 224, 224], scalarType: Float.self)
let ones = MLTensor(ones: [2, 2], scalarType: Float.self)

// Reshaping
let reshaped = tensor1D.reshaped(to: [2, 2])
let expanded = tensor1D.expandingShape(at: 0)   // [1, 4]

// Arithmetic and reduction
let sum = tensor1D + ones.reshaped(to: [4])
let mean = tensor1D.mean()
let argmax = tensor1D.argmax()

// Activation functions
let softmaxed = tensor1D.softmax()

// Interop with MLMultiArray
let multiArray = try MLMultiArray([1.0, 2.0, 3.0])
let fromMultiArray = MLTensor(multiArray)
let shaped = tensor1D.shapedArray(of: Float.self) // MLShapedArray

// Concatenation
let a = MLTensor([1.0, 2.0])
let b = MLTensor([3.0, 4.0])
let concatenated = MLTensor(concatenating: [a, b], alongAxis: 0)

// Normalization pattern (e.g., ImageNet preprocessing)
func normalize(_ tensor: MLTensor, mean: [Float], std: [Float]) -> MLTensor {
    let meanTensor = MLTensor(mean)
    let stdTensor = MLTensor(std)
    return (tensor - meanTensor) / stdTensor
}

Vision + Core ML Pipelines

Modern API (iOS 18+)

import Vision
import CoreML

@MainActor
@Observable
final class ObjectDetectionViewModel {
    var detections: [Detection] = []
    var isProcessing = false

    struct Detection: Identifiable {
        let id = UUID()
        let label: String
        let confidence: Float
        let boundingBox: CGRect
    }

    func detect(in image: CGImage) async {
        isProcessing = true
        defer { isProcessing = false }

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

            let detector = try MyObjectDetector(configuration: config)
            let request = CoreMLRequest(model: .init(detector.model))

            let results = try await request.perform(on: image)
            detections = results.compactMap { observation in
                guard let object = observation as? RecognizedObjectObservation,
                      let topLabel = object.labels.first else { return nil }
                return Detection(
                    label: topLabel.identifier,
                    confidence: topLabel.confidence,
                    boundingBox: object.boundingBox
                )
            }
        } catch {
            detections = []
        }
    }
}

func classifyImage(_ image: CGImage) async throws -> [(label: String, confidence: Float)] {
    let classifier = try MyImageClassifier(configuration: .init())
    let request = CoreMLRequest(model: .init(classifier.model))

    let results = try await request.perform(on: image)
    return results.compactMap { observation in
        guard let classification = observation as? ClassificationObservation else { return nil }
        return (classification.identifier, classification.confidence)
    }
}

Legacy API (Pre-iOS 18)

import Vision
import CoreML

func detectLegacy(in image: CGImage) async throws -> [VNRecognizedObjectObservation] {
    let config = MLModelConfiguration()
    config.computeUnits = .all

    let detector = try MyObjectDetector(configuration: config)
    let vnModel = try VNCoreMLModel(for: detector.model)

    let request = VNCoreMLRequest(model: vnModel)
    request.imageCropAndScaleOption = .scaleFill

    let handler = VNImageRequestHandler(cgImage: image)
    return try await Task.detached {
        try handler.perform([request])
        return request.results as? [VNRecognizedObjectObservation] ?? []
    }.value
}

func classifyImageLegacy(_ image: CGImage) async throws -> [(label: String, confidence: Float)] {
    let classifier = try MyImageClassifier(configuration: .init())
    let vnModel = try VNCoreMLModel(for: classifier.model)
    let request = VNCoreMLRequest(model: vnModel)
    request.imageCropAndScaleOption = .centerCrop

    let handler = VNImageRequestHandler(cgImage: image)
    return try await Task.detached {
        try handler.perform([request])
        guard let results = request.results as? [VNClassificationObservation] else { return [] }
        return results.prefix(5).map { ($0.identifier, $0.confidence) }
    }.value
}

NaturalLanguage Integration

Use NLModel to load Core ML models trained for NLP tasks.

import NaturalLanguage

func analyzeSentiment(text: String) throws -> (label: String, confidence: Double)? {
    let modelURL = Bundle.main.url(forResource: "SentimentClassifier", withExtension: "mlmodelc")!
    let nlModel = try NLModel(contentsOf: modelURL)

    guard let label = nlModel.predictedLabel(for: text) else { return nil }
    let hypotheses = nlModel.predictedLabelHypotheses(for: text, maximumCount: 1)
    let confidence = hypotheses[label] ?? 0
    return (label, confidence)
}

MLComputePlan Detailed Usage (iOS 17+)

import CoreML

func profileModel(at url: URL) async throws {
    let config = MLModelConfiguration()
    config.computeUnits = .all
    let computePlan = try await MLComputePlan.load(contentsOf: url, configuration: config)

    guard case let .program(program) = computePlan.modelStructure,
          let mainFunction = program.functions["main"] else {
        print("Model is not an ML program or has no main function")
        return
    }

    for operation in mainFunction.block.operations {
        let opName = operation.operatorName
        if let deviceUsage = computePlan.deviceUsage(for: operation) {
            print("  \(opName): \(deviceUsage.preferredComputeDevice)")
        }
        if let cost = computePlan.estimatedCost(of: operation) {
            print("    Estimated weight: \(cost.weight)")
        }
    }
}

Interpreting MLComputePlan Results

Device	Meaning	Action
Neural Engine	Best efficiency and speed for supported ops	Ideal -- no changes needed
GPU	Runs on Metal GPU	Good for large matrix ops
CPU	Fallback for unsupported operations	Investigate if many ops fall here

If many critical operations fall back to CPU, try .cpuAndGPU compute units, check for unsupported ANE operations, or re-convert with a different deployment target.

Background Model Loading and App Lifecycle

Manage model loading and memory across app lifecycle transitions.

@MainActor
@Observable
final class AppModelState {
    var isModelReady = false
    private let modelManager = ModelManager()

    func warmup() async {
        do {
            let config = MLModelConfiguration()
            config.computeUnits = .all
            _ = try await modelManager.model(named: "MainClassifier", configuration: config)
            isModelReady = true
        } catch {
            isModelReady = false
        }
    }

    func handleBackground() async {
        await modelManager.unloadAll()
        isModelReady = false
    }
}

// SwiftUI integration
@main
struct MyApp: App {
    @State private var modelState = AppModelState()
    @Environment(\.scenePhase) private var scenePhase

    var body: some Scene {
        WindowGroup {
            ContentView()
                .environment(modelState)
                .task { await modelState.warmup() }
                .onChange(of: scenePhase) { _, newPhase in
                    Task {
                        if newPhase == .background {
                            await modelState.handleBackground()
                        } else if newPhase == .active {
                            await modelState.warmup()
                        }
                    }
                }
        }
    }
}

Error Handling

import CoreML

func loadAndPredict(modelName: String, input: MLFeatureProvider) async -> MLFeatureProvider? {
    let config = MLModelConfiguration()
    config.computeUnits = .all

    do {
        guard let url = Bundle.main.url(forResource: modelName, withExtension: "mlmodelc") else {
            print("Model \(modelName) not found in bundle")
            return nil
        }
        let model = try await MLModel.load(contentsOf: url, configuration: config)
        return try await model.prediction(from: input)
    } catch {
        print("Model error: \(error)")
        return nil
    }
}

Common Error Types

Error	Cause	Fix
`MLModel` file not found	Wrong bundle path or missing target membership	Verify file is in correct target
Compilation failure	Corrupted `.mlpackage` or unsupported ops	Re-export from coremltools
Input shape mismatch	Wrong image dimensions or tensor shape	Match model's expected input shape
Out of memory	Model too large for device	Use smaller model or `.cpuOnly` compute
Compute unit fallback	Ops unsupported on requested device	Use `.all` or check `MLComputePlan`

Testing Patterns

import Testing
import CoreML

struct ModelLoadingTests {
    @Test func loadModelSucceeds() async throws {
        let config = MLModelConfiguration()
        config.computeUnits = .cpuOnly // CPU for test stability
        let model = try MyImageClassifier(configuration: config)
        #expect(model.model.modelDescription.inputDescriptionsByName.count > 0)
    }

    @Test func predictionReturnsValidOutput() async throws {
        let config = MLModelConfiguration()
        config.computeUnits = .cpuOnly

        let model = try MyImageClassifier(configuration: config)
        let input = try createTestInput(width: 224, height: 224)
        let output = try model.prediction(input: input)

        #expect(!output.classLabel.isEmpty)
        #expect(output.classLabelProbs.values.allSatisfy { $0 >= 0 && $0 <= 1 })
    }

    @Test func predictionLatencyUnderThreshold() async throws {
        let config = MLModelConfiguration()
        config.computeUnits = .all

        let model = try MyImageClassifier(configuration: config)
        let input = try createTestInput(width: 224, height: 224)

        _ = try model.prediction(input: input) // Warm up

        let start = ContinuousClock.now
        for _ in 0..<10 {
            _ = try model.prediction(input: input)
        }
        let avgMs = (ContinuousClock.now - start) / 10

        #expect(avgMs < .milliseconds(50), "Average prediction time \(avgMs) exceeds 50ms")
    }
}

private func createTestPixelBuffer(width: Int, height: Int) throws -> CVPixelBuffer {
    var pixelBuffer: CVPixelBuffer?
    let status = CVPixelBufferCreate(
        kCFAllocatorDefault, width, height,
        kCVPixelFormatType_32ARGB, nil, &pixelBuffer
    )
    guard status == kCVReturnSuccess, let buffer = pixelBuffer else {
        throw ImageError.pixelBufferCreationFailed
    }
    return buffer
}

Memory Management Best Practices

Unload on background. Unload models when scenePhase == .background and reload on return to foreground. iOS reclaims memory aggressively.
Use .cpuOnly for background processing. GPU and Neural Engine may not be available in background execution contexts.
Prefer compiled models. .mlmodelc loads faster and uses less transient memory than compiling .mlpackage at runtime.
Share model instances. Use an actor (like ModelManager above) to ensure only one instance of each model exists.
Release batch providers promptly. Large MLArrayBatchProvider instances hold references to all input data.

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CHANGELOG.md

README.md

tile.json

dpearson2699/swift-ios-skills

coreml-swift-integration.mdskills/coreml/references/

Core ML Swift Integration Reference

Contents

Actor-Based Model Loading and Caching

Usage with SwiftUI

Auto-Generated Class Usage

Accessing the Underlying MLModel

Manual MLFeatureProvider

Async Prediction Patterns

Single Prediction with Swift Concurrency (iOS 17+)

Streaming Predictions

MLBatchProvider for Batch Inference

Stateful Predictions with MLState (iOS 18+)

Key Rules for MLState

Image Preprocessing

CVPixelBuffer from CGImage

MLMultiArray Creation and Manipulation

MLTensor Advanced Operations (iOS 18+)

Vision + Core ML Pipelines

Modern API (iOS 18+)

Legacy API (Pre-iOS 18)

NaturalLanguage Integration

MLComputePlan Detailed Usage (iOS 17+)

Interpreting MLComputePlan Results

Background Model Loading and App Lifecycle

Error Handling

Common Error Types

Testing Patterns

Memory Management Best Practices

dpearson2699/swift-ios-skills

coreml-swift-integration.md.css-3qkkll{font-size:var(--chakra-font-sizes-sm);font-weight:var(--chakra-font-weights-normal);color:var(--chakra-colors-gray-300);}skills/coreml/references/

Core ML Swift Integration Reference

Contents

Actor-Based Model Loading and Caching

Usage with SwiftUI

Auto-Generated Class Usage

Accessing the Underlying MLModel

Manual MLFeatureProvider

Async Prediction Patterns

Single Prediction with Swift Concurrency (iOS 17+)

Streaming Predictions

MLBatchProvider for Batch Inference

Stateful Predictions with MLState (iOS 18+)

Key Rules for MLState

Image Preprocessing

CVPixelBuffer from CGImage

MLMultiArray Creation and Manipulation

MLTensor Advanced Operations (iOS 18+)

Vision + Core ML Pipelines

Modern API (iOS 18+)

Legacy API (Pre-iOS 18)

NaturalLanguage Integration

MLComputePlan Detailed Usage (iOS 17+)

Interpreting MLComputePlan Results

Background Model Loading and App Lifecycle

Error Handling

Common Error Types

Testing Patterns

Memory Management Best Practices

coreml-swift-integration.mdskills/coreml/references/