coreml

name

coreml

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.

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. Target iOS 26+ with Swift 6.3, backward-compatible to iOS 14 unless noted.

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.

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.

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.

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.

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

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

Configuration Properties

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.

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.

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.

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

Batch Prediction

Process multiple inputs in one call for better throughput.

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.

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.

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.

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.

// 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.

import CoreVideo

func createPixelBuffer(from cgImage: CGImage, width: Int, height: Int) -> CVPixelBuffer? {
    var pixelBuffer: CVPixelBuffer?
    let attrs: [CFString: Any] = [
        kCVPixelBufferCGImageCompatibilityKey: true,
        kCVPixelBufferCGBitmapContextCompatibilityKey: true,
    ]
    CVPixelBufferCreate(kCFAllocatorDefault, width, height,
                        kCVPixelFormatType_32ARGB, attrs as CFDictionary, &pixelBuffer)

    guard let buffer = pixelBuffer else { return nil }
    CVPixelBufferLockBaseAddress(buffer, [])
    let context = CGContext(
        data: CVPixelBufferGetBaseAddress(buffer),
        width: width, height: height,
        bitsPerComponent: 8, bytesPerRow: CVPixelBufferGetBytesPerRow(buffer),
        space: CGColorSpaceCreateDeviceRGB(),
        bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue
    )
    context?.draw(cgImage, in: CGRect(x: 0, y: 0, width: width, height: height))
    CVPixelBufferUnlockBaseAddress(buffer, [])
    return buffer
}

For additional preprocessing patterns (normalization, center-cropping), see references/coreml-swift-integration.md.

Multi-Model Pipelines

Chain models when preprocessing or postprocessing requires a separate model.

// 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+)

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

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.

let computePlan = try await MLComputePlan.load(
    contentsOf: modelURL, configuration: config
)
guard case let .program(program) = computePlan.modelStructure else { return }
guard let mainFunction = program.functions["main"] else { return }

for operation in mainFunction.block.operations {
    let deviceUsage = computePlan.deviceUsage(for: operation)
    let estimatedCost = computePlan.estimatedCost(of: operation)
    print("\(operation.operatorName): \(String(describing: deviceUsage?.preferred))")
}

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 vs Downloaded Assets

Strategy	Pros	Cons
Bundle in app	Instant availability, works offline	Increases app download size
Background Assets	Preferred for large or updateable model assets	Requires asset-pack setup
On-demand resources	Smaller initial download for existing ODR apps	Legacy technology; prefer Background Assets for new work
CloudKit / server	Maximum flexibility	Requires network, longer setup

Size Considerations

• For iOS/iPadOS 18+, App Store Connect lists a 4 GB thinned app bundle limit and 8 GB thinned ODR asset-pack limit.
• Prefer Background Assets for new large or updateable model assets; keep ODR guidance for existing projects that already use it.
• Pre-compile to .mlmodelc to skip on-device compilation
• For downloaded .mlmodel or .mlpackage files, compile once with MLModel.compileModel(at:), move the resulting .mlmodelc out of Core ML's temporary location, and cache it by model version.
• Validate memory and performance on physical target devices, especially the lowest-memory supported device. Check model load, first prediction, repeated predictions, background/foreground transitions, and low-memory behavior.

For Background Assets, make the asset pack locally available, resolve the model URL, then load the compiled model with MLModel.load(contentsOf:configuration:).

// Existing On-Demand Resources project
let request = NSBundleResourceRequest(tags: ["ml-model-v2"])
try await request.beginAccessingResources()
let modelURL = Bundle.main.url(forResource: "LargeModel", withExtension: "mlmodelc")!
let model = try await MLModel.load(contentsOf: modelURL, configuration: config)
// Call request.endAccessingResources() when done

Memory Management

• Unload on background: Release model references when the app enters background to free GPU/ANE memory. Reload on foreground return.
• Choose compute units by context: use .all by default. Consider .cpuOnly only when profiling or app policy shows accelerator contention, thermal state, energy budget, deterministic testing, or a legitimate background execution constraint makes CPU the right tradeoff.
• 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: Recompile .mlpackage to .mlmodelc on every app launch. DO: Compile once with MLModel.compileModel(at:) and cache the compiled URL persistently. Why: Compilation is expensive. Cache the .mlmodelc in Application Support.

DON'T: Hardcode .cpuOnly unless you have a specific reason. DO: Use .all and let the system choose the optimal compute unit. Why: .all enables Neural Engine and GPU, which are faster and more energy-efficient.

DON'T: Claim GPU or Neural Engine are categorically unavailable for all background-adjacent work. DO: Treat background execution as policy-, mode-, contention-, thermal-, and energy-dependent, and profile the actual workload on device. Why: Apps may be suspended, throttled, or limited by their background mode; .cpuOnly is a tradeoff, not a universal requirement.

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