coreml（coreml 命令行）

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 drag a .mlpackage or .mlmodelc into Xcode, it 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 16+)

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

Compile a .mlpackage or .mlmodel to .mlmodelc on device. Useful for models downloaded from a server.

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

Cache the compiled URL -- recompiling on every launch wastes time. Copy compiledURL to a persistent location (e.g., Application Support).

Model Configuration

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

Compute Units Decision Table

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

// Allow low-priority background inference
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

Async Prediction (iOS 17+)

let output = try await 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(from: batchInputs)
for i in 0..<batchOutput.count {
    let result = batchOutput.features(at: i)
    print(result.featureValue(for: "classLabel")?.stringValue ?? "unknown")
}

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

State is not Sendable -- use it from a single actor or task. Call model.makeState() to create independent state for concurrent streams.

MLTensor (iOS 18+)

MLTensor is a Swift-native multidimensional array for pre/post-processing. Operations run lazily -- call .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()
let normalized = (tensor - tensor.mean()) / tensor.standardDeviation()

// Interop with MLMultiArray
let multiArray = try MLMultiArray([1.0, 2.0, 3.0, 4.0])
let fromMultiArray = MLTensor(multiArray)
let backToArray = tensor.shapedArray(of: Float.self)

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

// Create from data pointer for zero-copy interop
let data: [Float] = [1.0, 2.0, 3.0]
let fromData = try MLMultiArray(dataPointer: UnsafeMutableRawPointer(mutating: data),
                                 shape: [3],
                                 dataType: .float32,
                                 strides: [1])

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): \(deviceUsage?.preferredComputeDevice ?? "unknown")")
}

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 On-Demand Resources

Size Considerations

• App Store limit: 4 GB for app bundle
• Cellular download limit: 200 MB (can request exception)
• Use ODR tags for models > 50 MB
• Pre-compile to .mlmodelc to skip on-device compilation

// On-demand resource loading
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.
• Use .cpuOnly for background tasks: Background processing cannot use GPU or ANE; setting .cpuOnly avoids silent fallback and resource contention.
• 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: 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 vs 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 | MLComputePlan