Following this instruction to install jax (https://developer.apple.com/metal/jax/), I still encountered this error: RuntimeError: This version of jaxlib was built using AVX instructions, which your CPU and/or operating system do not support. This error is frequently encountered on macOS when running an x86 Python installation on ARM hardware. In this case, try installing an ARM build of Python. Otherwise, you may be able work around this issue by building jaxlib from source. How to fix it?

Xcode Version 16.0 (16A242d) iOS18 - Swift There seems to be a behavior change on iOS18 when using AppShortcuts and AppIntents to pass string parameters. After Siri prompts for a string property requestValueDialog, if the user makes a statement the string is passed. If the user's statement is a question, however, the string is not sent to the AppIntent and instead Siri attempts to answer that question. Example Code: struct MyAppNameShortcuts: AppShortcutsProvider { @AppShortcutsBuilder static var appShortcuts: [AppShortcut] { AppShortcut( intent: AskQuestionIntent(), phrases: [ "Ask \(.applicationName) a question", ] ) } } struct AskQuestionIntent: AppIntent { static var title: LocalizedStringResource = .init(stringLiteral: "Ask a question") static var openAppWhenRun: Bool = false static var parameterSummary: some ParameterSummary { Summary("Search for \(\.$query)") } @Dependency private var apiClient: MockApiClient @Parameter(title: "Query", requestValueDialog: .init(stringLiteral: "What would you like to ask?")) var query: String // perform is not called if user asks a question such as "What color is the moon?" in response to requestValueDialog // iOS 17, the same string is passed though @MainActor func perform() async throws -> some IntentResult & ProvidesDialog & ShowsSnippetView { print("Query is: \(query)") let queryResult = try await apiClient.askQuery(queryString: query) let dialog = IntentDialog( full: .init(stringLiteral: queryResult.answer), supporting: .init(stringLiteral: "The answer to \(queryResult.question) is...") ) let view = SiriAnswerView(queryResult: queryResult) return .result(dialog: dialog, view: view) } } Given the above mock code: iOS17: Hey Siri Ask (AppName) a question Siri responds "What would you like to ask?" Say "What color is the moon?" String of "What color is the moon?" is passed to the AppIntent iOS18: Hey Siri Ask (AppName) a question Siri responds "What would you like to ask?" Say "What color is the moon?" Siri answers the question "What color is the moon?" Follow above steps again and instead reply "Moon" "Moon" is passed to AppIntent Basically any interrogative string parameters seem to be intercepted and sent to Siri proper rather than the provided AppIntent in iOS 18

Yesterday after updating to iOS 18.1 I joined the Apple Intelligence waitlist on my iPhone 15 Pro. About an hour later I noticed that it had the message "Support for processing Apple Intelligence on device is downloading." A day later it is still displaying the same message. I have strong wi-fi, I'm plugged in to power with full battery, and there are 750gb available in storage. From what I have been able to find online, this isn't the typical user experience and that it probably isn't going to complete the process at this point. Any advice on how to proceed and get Apple Intelligence installed and working would be greatly appreciated.

I just updated to 18.1 beta 4 and the only AI features I can use are the new Siri and photo cleanup. The rest are unavailable. Apple intelligence takes up 589mb on my iphone

Hi, I'm trying to personalize the Detect animal poses in Vision example (WWDC 23). Detect animal poses in Vision After some tests I saw that the landmarks and connection drawings work only if I do not ignore the safe area, if I ignore it (removing the toggle) or use the app on the iPad the drawings are no longer applied correctly. In the example GeometryReader is used to detect the size of the view: ... ZStack { GeometryReader { geo in AnimalSkeletonView(animalJoint: animalJoint, size: geo.size) } }.frame(maxWidth: .infinity) ... struct AnimalSkeletonView: View { // Get the animal joint locations. @StateObject var animalJoint = AnimalPoseDetector() var size: CGSize var body: some View { DisplayView(animalJoint: animalJoint) if animalJoint.animalBodyParts.isEmpty == false { // Draw the skeleton of the animal. // Iterate over all recognized points and connect the joints. ZStack { ZStack { // left head if let nose = animalJoint.animalBodyParts[.nose] { if let leftEye = animalJoint.animalBodyParts[.leftEye] { Line(points: [nose.location, leftEye.location], size: size) .stroke(lineWidth: 5.0) .fill(Color.orange) } } ... } } } } } // Create a transform that converts the pose's normalized point. struct Line: Shape { var points: [CGPoint] var size: CGSize func path(in rect: CGRect) -> Path { let pointTransform: CGAffineTransform = .identity .translatedBy(x: 0.0, y: -1.0) .concatenating(.identity.scaledBy(x: 1.0, y: -1.0)) .concatenating(.identity.scaledBy(x: size.width, y: size.height)) var path = Path() path.move(to: points[0]) for point in points { path.addLine(to: point) } return path.applying(pointTransform) } } Looking online I saw that it was recommended to change the property cameraView.previewLayer.videoGravity from: cameraView.previewLayer.videoGravity = .resizeAspectFill to: cameraView.previewLayer.videoGravity = .resizeAspect but it doesn't work for me. Could you help me understand where I'm wrong? Thanks!

hey just curious if apple intelligence will be available on iPhone 15 Plus as well??? in october or is there a way that iPhone 15 Plus owners can join apple intelligence’s wait lists or something??? please let me know !😫

I want my confidence of model is worked according to the when I detected the object by real time camera with help of ml model in android its gives me different results with different confidence as like 75, 40,30,95 not range 95 to 100 but when I used same model in ios its will give me range above 95 of any case. so what will be reason do you think

I need to add AI Image Playground in my iOS app with UIKit, as per WWDC 2024 introduce new AI Image Playground API, I didn't find any official document yet, So how can add it ?

Hello, I‘m using DockKit within my SwiftUI Application with GetStream. Before updating to iOS 18 yesterday the custom Tracking using DockKit worked like a charm, but After updating it stopped working unexpectedly. What‘s more curious: using the official GetStream Video Calls Application it works on iOS18 still, but Not within my Application. I can confirm, that my iPhone is still paired and I can receive logs about the current docking State and everything seems fine. Any suggestions what I‘m missing here?

As a user, when viewing a photo or image, I want to be able to tell Siri, “add this to ”, similar to example from the WWDC presentation where a photo is added to a note in the notes app. Is this... possible with app domains as they are documented? I see domains like open-file and open-photo, but I don't know if those are appropriate for this kind of functionality?

When I try to run basically any CoreML model using MLPredictionOptions.outputBackings , inference throws the following error: 2024-09-11 15:36:00.184740-0600 run_demo[4260:64822] [coreml] Unrecognized ANE execution priority (null) 2024-09-11 15:36:00.185380-0600 run_demo[4260:64822] *** Terminating app due to uncaught exception 'NSInvalidArgumentException', reason: 'Unrecognized ANE execution priority (null)' *** First throw call stack: ( 0 CoreFoundation 0x000000019812cec0 __exceptionPreprocess + 176 1 libobjc.A.dylib 0x0000000197c12cd8 objc_exception_throw + 88 2 CoreFoundation 0x000000019812cdb0 +[NSException exceptionWithName:reason:userInfo:] + 0 3 CoreML 0x00000001a1bf6504 _ZN12_GLOBAL__N_141espressoPlanPriorityFromPredictionOptionsEP19MLPredictionOptions + 264 4 CoreML 0x00000001a1bf68c0 -[MLNeuralNetworkEngine _matchEngineToOptions:error:] + 236 5 CoreML 0x00000001a1be254c __62-[MLNeuralNetworkEngine predictionFromFeatures:options:error:]_block_invoke + 68 6 libdispatch.dylib 0x0000000197e20658 _dispatch_client_callout + 20 7 libdispatch.dylib 0x0000000197e2fcd8 _dispatch_l *** Terminating app due to uncaught exception 'NSInvalidArgumentException', reason: 'Unrecognized ANE execution priority (null)' *** First throw call stack: ( 0 CoreFoundation 0x000000019812cec0 __exceptionPreprocess + 176 1 libobjc.A.dylib 0x0000000197c12cd8 objc_exception_throw + 88 2 CoreFoundation 0x000000019812cdb0 +[NSException exceptionWithName:reason:userInfo:] + 0 3 CoreML 0x00000001a1bf6504 _ZN12_GLOBAL__N_141espressoPlanPriorityFromPredictionOptionsEP19MLPredictionOptions + 264 4 CoreML 0x00000001a1bf68c0 -[MLNeuralNetworkEngine _matchEngineToOptions:error:] + 236 5 CoreML 0x00000001a1be254c __62-[MLNeuralNetworkEngine predictionFromFeatures:options:error:]_block_invoke + 68 6 libdispatch.dylib 0x0000000197e20658 _dispatch_client_callout + 20 7 libdispatch.dylib 0x0000000197e2fcd8 _dispatch_lane_barrier_sync_invoke_and_complete + 56 8 CoreML 0x00000001a1be2450 -[MLNeuralNetworkEngine predictionFromFeatures:options:error:] + 304 9 CoreML 0x00000001a1c9e118 -[MLDelegateModel _predictionFromFeatures:usingState:options:error:] + 776 10 CoreML 0x00000001a1c9e4a4 -[MLDelegateModel predictionFromFeatures:options:error:] + 136 11 libMLBackend_coreml.dylib 0x00000001002f19f0 _ZN6CoreML8runModelENS_5ModelERNSt3__16vectorIPvNS1_9allocatorIS3_EEEES7_ + 904 12 libMLBackend_coreml.dylib 0x00000001002c56e8 _ZZN8ModelImp9runCoremlEPN2ML7Backend17ModelIoBindingImpEENKUlvE_clEv + 120 13 libMLBackend_coreml.dylib 0x00000001002c1e40 _ZNSt3__110__function6__funcIZN2ML4Util10WorkThread11runInThreadENS_8functionIFvvEEEEUlvE_NS_9allocatorIS8_EES6_EclEv + 40 14 libMLBackend_coreml.dylib 0x00000001002bc3a4 _ZZN2ML4Util10WorkThreadC1EvENKUlvE_clEv + 160 15 libMLBackend_coreml.dylib 0x00000001002bc244 _ZNSt3__114__thread_proxyB7v160006INS_5tupleIJNS_10unique_ptrINS_15__thread_structENS_14default_deleteIS3_EEEEZN2ML4Util10WorkThreadC1EvEUlvE_EEEEEPvSC_ + 52 16 libsystem_pthread.dylib 0x0000000197fd32e4 _pthread_start + 136 17 libsystem_pthread.dylib 0x0000000197fce0fc thread_start + 8 ) libc++abi: terminating due to uncaught exception of type NSException Interestingly, if I don't use MLPredictionOptions to set pre-allocated output backings, then inference appears to run as expected. A similar issue seems to have been discussed and fixed here: https://developer.apple.com/forums/thread/761649 , however I'm seeing this issue on a beta build that I downloaded today (Sept 11 2024). Will this be fixed? Any advice would be greatly appreciated. Thanks

I've got Apple AI working on my iPhone 15 pro max, SIRI 2.0 working as expected, however I don't seem to have the below options for Apple AI working / appearing. AI in mail AI in notes Clean up just stuck on downloading in photos Not sure if my setup is wrong or it's just not available for me yet

Hello everyone, I have a PyTorch model that outputs an image. I converted this model to CoreML using coremltools, and the resulting CoreML model can be used in my iOS project to perform inference using the MLModel's prediction function, which returns a result of type CVPixelBuffer. I want to avoid allocating memory every time I call the prediction function. Instead, I would like to use a pre-allocated buffer. I noticed that MLModel provides an overloaded prediction function that accepts an MLPredictionOptions object. This object has an outputBackings member, which allows me to pass a pre-allocated CVPixelBuffer. However, when I attempt to do this, I encounter the following error: Copy from tensor to pixel buffer (pixel_format_type: BGRA, image_pixel_type: BGR8, component_dtype: INT, component_pack: FMT_32) is not supported. Could someone point out what I might be doing wrong? How can I make MLModel use my pre-allocated CVPixelBuffer instead of creating a new one each time? Here is the Python code I used to convert the PyTorch model to CoreML, where I specified the color_layout as coremltools.colorlayout.BGR: def export_ml(model, resolution="640x360"): ml_path = f"model.mlpackage" print("exporting ml model") width, height = map(int, resolution.split('x')) img0 = torch.randn(1, 3, height, width) img1 = torch.randn(1, 3, height, width) traced_model = torch.jit.trace(model, (img0, img1)) input_shape = ct.Shape(shape=(1, 3, height, width)) output_type_img = ct.ImageType(name="out", scale=1.0, bias=[0, 0, 0], color_layout=ct.colorlayout.BGR) ml_model = ct.convert( traced_model, inputs=[input_type_img0, input_type_img1], outputs=[output_type_img] ) ml_model.save(ml_path) Here is the Swift code in my iOS project that calls the MLModel's prediction function: func prediction(image1: CVPixelBuffer, image2: CVPixelBuffer, model: MLModel) -> CVPixelBuffer? { let options = MLPredictionOptions() guard let outputBuffer = outputBacking else { fatalError("Failed to create CVPixelBuffer.") } options.outputBackings = ["out": outputBuffer] // Perform the prediction guard let prediction = try? model.prediction(from: RifeInput(img0: image1, img1: image2), options: options) else { Log.i("Failed to perform prediction") return nil } // Extract the result guard let cvPixelBuffer = prediction.featureValue(for: "out")?.imageBufferValue else { Log.i("Failed to get results from the model") return nil } return cvPixelBuffer } Here is the code I used to create the outputBacking: let attributes: [String: Any] = [ kCVPixelBufferCGImageCompatibilityKey as String: true, kCVPixelBufferCGBitmapContextCompatibilityKey as String: true, kCVPixelBufferWidthKey as String: Int(640), kCVPixelBufferHeightKey as String: Int(360), kCVPixelBufferIOSurfacePropertiesKey as String: [:] ] let status = CVPixelBufferCreate(kCFAllocatorDefault, 640, 360, kCVPixelFormatType_32BGRA, attributes as CFDictionary, &outputBacking) guard let outputBuffer = outputBacking else { fatalError("Failed to create CVPixelBuffer.") } Any help or guidance would be greatly appreciated! Thank you!

Hi, I have an existing app with AppEntities defined, that works on iOS16 and iOS17. The AppEntities also have EntityPropertyQuery defined, so they work as 'find intents'. I want to use the new @AssistantEntity on iOS18, while supporting the previous versions. What's the best way to do this? For e.g. I have a 'person' AppEntity: @available(iOS 16.0, macOS 13.0, watchOS 9.0, tvOS 16.0, *) struct CJLogAppEntity: AppEntity { static var defaultQuery = CJLogAppEntityQuery() .... } struct CJLogAppEntityQuery: EntityPropertyQuery { ... } How do I adopt this with @AssistantEntity(schema: .journal.entry) for iOS18, while maintaining compatibility with iOS16 and 17?

We have an application that receives a message (through MQTT) from an external system to snap a photo, runs a CoreML vision request on the image, and then sends the results back. The customer has 100s of devices and recently on a couple of those devices (13 pros), the customer encountered an issue in which the devices were not responding in time. There was no crash, just some individual inferences were slowed down. The device performs 1000s of requests per day. Upon further evaluation of the request before and after in the device logs, I noticed that Apple loads the following default 2024-09-04 13:18:31.310401 -0400 ProcessName Processing image for reference: *** default 2024-09-04 13:18:31.403606 -0400 ProcessName Found matching service: H1xANELoadBalancer default 2024-09-04 13:18:31.403646 -0400 ProcessName Found matching service: H11ANEIn default 2024-09-04 13:18:31.403661 -0400 ProcessName Found ANE device :1 default 2024-09-04 13:18:31.403681 -0400 ProcessName Total num of devices 1 default 2024-09-04 13:18:31.403681 -0400 ProcessName (Single-ANE System) Opening H11ANE device at index 0 default 2024-09-04 13:18:31.403681 -0400 ProcessName H11ANEDevice::H11ANEDeviceOpen, usage type: 1 In a good scenario (above), these actions will performed very quickly (in a split second). The app doesn't do anything until coreml inference result is returned. In the bad scenario (below), there is a delay of about 4 seconds from app passing the control to vision request and then getting the response back (leading to timeouts with the customer) default 2024-09-04 13:19:08.777468 -0400 ProcessName Processing image for reference: ZZZ default 2024-09-04 13:19:12.199758 -0400 ProcessName Found matching service: H1xANELoadBalancer default 2024-09-04 13:19:12.199800 -0400 ProcessName Found matching service: H11ANEIn default 2024-09-04 13:19:12.199812 -0400 ProcessName Found ANE device :1 default 2024-09-04 13:19:12.199832 -0400 ProcessName Total num of devices 1 default 2024-09-04 13:19:12.199834 -0400 ProcessName (Single-ANE System) Opening H11ANE device at index 0 default 2024-09-04 13:19:12.199834 -0400 ProcessName H11ANEDevice::H11ANEDeviceOpen, usage type: 1 The logs are in order, I haven't removed anything. The code is fairly simple, it's just running a vision request without doing much. Has anyone encountered this before?

Xcode Version: Version 15.2 (15C500b) com.github.apple.coremltools.source: torch==1.12.1 com.github.apple.coremltools.version: 7.2 Compute: Mixed (Float16, Int32) Storage: Float16 The input to the mlpackage is MultiArray (Float16 1 × 1 × 544 × 960) The flexibility is: 1 × 1 × 544 × 960 | 1 × 1 × 384 × 640 | 1 × 1 × 736 × 1280 | 1 × 1 × 1088 × 1920 I tested this on iPhone XR, iPhone 11, iPhone 12, iPhone 13, and iPhone 14. On all devices except the iPhone 11, the model runs correctly on the NPU. However, on the iPhone 11, the model runs on the CPU instead. Here is the CoreMLTools conversion code I used: mlmodel = ct.convert(trace, inputs=[ct.TensorType(shape=input_shape, name="input", dtype=np.float16)], outputs=[ct.TensorType(name="output", dtype=np.float16, shape=output_shape)], convert_to='mlprogram', minimum_deployment_target=ct.target.iOS16 )

I am checking actual movement on iOS18.1 beta 3 devices, but the following items are not functioning. Image Playground Image Wand Genmoji Please let me know the following Are the above 3 items available on iOS18.1 beta 3? If available, are there any other operations other than enabling Apple Intelligece that are required to use the features?

I am searching for a method to remove background from a video. it can be from camera Session fileOutput url or from photo library. I was able to accomplish live preview of removed background with the depth data and some metal framework code from the example Enhancing Live Video by Leveraging TrueDepth Camera Data. However I count figure out a way to save this as a video so that I can upload it. Also this method is using over 150% of cpu ( Xcode cpu usage ), which seems to be quite a lot and the device is getting heated up so fast and drops the frames when It hot. I also found something similar from GitHub using CoreML example by Dmitry Voitekh which only uses less than 40% cpu. Any information regarding this will be helpful. Objective : Remove Background from video and save it

When I use CoreML to infer a w8a8 model on iPhone 15 (iOS 18 beta 8), the model uses CPU inference instead of ANE, which results in slower inference speed. The model I am using is from the coremltools documentation, which indicates that on iOS 17, quantized models can run on ANE properly and achieve faster speeds. How can I make the quantized model run correctly on ANE to achieve the desired inference speed? To reproduce this issue, you can download the Weight & Activation quantized model from the following link: https://apple.github.io/coremltools/docs-guides/source/opt-quantization-perf.html.

We have a code that crashed The crash stack is as follows Thread 26 Crashed: 0 CoreFoundation 0x0000000198b0569c CFRelease + 44 1 CoreFoundation 0x0000000198b12334 __CFBasicHashRehash + 1172 2 CoreFoundation 0x0000000198b015dc __CFBasicHashAddValue + 100 3 CoreFoundation 0x0000000198b232e4 CFDictionarySetValue + 208 4 Foundation 0x00000001979b0378 _getStringAtMarker + 464 5 Foundation 0x00000001979b016c _NSXPCSerializationStringForObject + 56 6 Foundation 0x00000001979cec4c __44-[NSXPCDecoder _decodeArrayOfObjectsForKey:]_block_invoke + 52 7 Foundation 0x00000001979ceb90 _NSXPCSerializationIterateArrayObject + 208 8 Foundation 0x00000001979cda7c -[NSXPCDecoder _decodeArrayOfObjectsForKey:] + 240 9 Foundation 0x00000001979cd1bc -[NSDictionary(NSDictionary) initWithCoder:] + 176 10 Foundation 0x00000001979ae6e8 _decodeObject + 1264 11 Foundation 0x00000001979cec4c __44-[NSXPCDecoder _decodeArrayOfObjectsForKey:]_block_invoke + 52 12 Foundation 0x00000001979ceb90 _NSXPCSerializationIterateArrayObject + 208 13 Foundation 0x00000001979cda7c -[NSXPCDecoder _decodeArrayOfObjectsForKey:] + 240 14 Foundation 0x00000001979cd1a4 -[NSDictionary(NSDictionary) initWithCoder:] + 152 15 Foundation 0x00000001979ae6e8 _decodeObject + 1264 16 Foundation 0x00000001979ad030 -[NSXPCDecoder _decodeObjectOfClasses:atObject:] + 148 17 Foundation 0x0000000197a0a7f0 _NSXPCSerializationDecodeTypedObjCValuesFromArray + 892 18 Foundation 0x0000000197a0a1f8 _NSXPCSerializationDecodeInvocationArgumentArray + 412 19 Foundation 0x0000000197a0866c -[NSXPCDecoder __decodeXPCObject:allowingSimpleMessageSend:outInvocation:outArguments:outArgumentsMaxCount:outMethodSignature:outSelector:isReply:replySelector:] + 700 20 Foundation 0x0000000197a61078 -[NSXPCDecoder _decodeReplyFromXPCObject:forSelector:] + 76 21 Foundation 0x0000000197a5f690 -[NSXPCConnection _decodeAndInvokeReplyBlockWithEvent:sequence:replyInfo:] + 252 22 Foundation 0x0000000197a63664 __88-[NSXPCConnection _sendInvocation:orArguments:count:methodSignature:selector:withProxy:]_block_invoke_5 + 188 23 Foundation 0x0000000197a08058 -[NSXPCConnection _sendInvocation:orArguments:count:methodSignature:selector:withProxy:] + 2244 24 CoreFoundation 0x0000000198b19d88 ___forwarding___ + 1016 25 CoreFoundation 0x0000000198b198d0 _CF_forwarding_prep_0 + 96 26 AppleNeuralEngine 0x00000001e912ab1c -[_ANEDaemonConnection loadModel:sandboxExtension:options:qos:withReply:] + 332 27 AppleNeuralEngine 0x00000001e912a674 __44-[_ANEClient doLoadModel:options:qos:error:]_block_invoke + 360 28 libdispatch.dylib 0x00000001a0a21dd4 _dispatch_client_callout + 20 29 libdispatch.dylib 0x00000001a0a312c4 _dispatch_lane_barrier_sync_invoke_and_complete + 56 30 AppleNeuralEngine 0x00000001e9129ef0 -[_ANEClient doLoadModel:options:qos:error:] + 500 31 Espresso 0x00000001a7e02034 Espresso::ANERuntimeEngine::compiler::build_segment(std::__1::shared_ptr<Espresso::abstract_batch> const&, int, Espresso::net_compiler_segment_based::segment_t const&) + 3736 32 Espresso 0x00000001a7e010cc Espresso::net_compiler_segment_based::build(std::__1::shared_ptr<Espresso::abstract_batch> const&, int, int) + 384 33 Espresso 0x00000001a7df02a4 Espresso::ANERuntimeEngine::compiler::build(std::__1::shared_ptr<Espresso::abstract_batch> const&, int, int) + 120 34 Espresso 0x00000001a7e1b3a4 Espresso::net::__build(std::__1::shared_ptr<Espresso::abstract_batch> const&, int, int) + 360 35 Espresso 0x00000001a7e178e0 Espresso::abstract_context::compute_batch_sync(void (std::__1::shared_ptr<Espresso::abstract_batch> const&) block_pointer) + 112 36 Espresso 0x00000001a7e198b8 EspressoLight::espresso_plan::prepare_compiler_if_needed() + 3208 37 Espresso 0x00000001a7e183f4 EspressoLight::espresso_plan::prepare() + 1712 38 Espresso 0x00000001a7da8e78 espresso_plan_build_with_options + 300 39 Espresso 0x00000001a7da8d30 espresso_plan_build + 44 40 CoreML 0x00000001b346645c -[MLNeuralNetworkEngine rebuildPlan:error:] + 536 41 CoreML 0x00000001b3464294 -[MLNeuralNetworkEngine _setupContextAndPlanWithConfiguration:usingCPU:reshapeWithContainer:error:] + 3132 42 CoreML 0x00000001b34797a0 -[MLNeuralNetworkEngine initWithContainer:configuration:error:] + 196 43 CoreML 0x00000001b347962c +[MLNeuralNetworkEngine loadModelFromCompiledArchive:modelVersionInfo:compilerVersionInfo:configuration:error:] + 164 44 CoreML 0x00000001b34792a0 +[MLLoader _loadModelWithClass:fromArchive:modelVersionInfo:compilerVersionInfo:configuration:error:] + 144 45 CoreML 0x00000001b3478c64 +[MLLoader _loadModelFromArchive:configuration:modelVersion:compilerVersion:loaderEvent:useUpdatableModelLoaders:loadingClasses:error:] + 532 46 CoreML 0x00000001b34650c8 +[MLLoader _loadWithModelLoaderFromArchive:configuration:loaderEvent:useUpdatableModelLoaders:error:] + 424 47 CoreML 0x00000001b3474bc8 +[MLLoader _loadModelFromArchive:configuration:loaderEvent:useUpdatableModelLoaders:error:] + 460 48 CoreML 0x00000001b347a024 +[MLLoader _loadModelFromAssetAtURL:configuration:loaderEvent:error:] + 244 49 CoreML 0x00000001b3479cbc +[MLLoader loadModelFromAssetAtURL:configuration:error:] + 104 50 CoreML 0x00000001b347ac2c -[MLModelAsset load:] + 564 51 CoreML 0x00000001b347a9c4 -[MLModelAsset modelWithError:] + 24 52 CoreML 0x00000001b347a7b4 +[MLModel modelWithContentsOfURL:configuration:error:] + 172 53 CoreML 0x00000001b37afbc4 +[MLModel modelWithContentsOfURL:error:] + 76 Core code MLModel* model = nil; NSError *error = nil; @try { model = [MLModel modelWithContentsOfURL:modelURL error:&error]; } @catch (NSException *exception) { model = nil; return Ret_OperationErr_InvalidInit; } Two question: What does this stack mean? I added @ try @ catch, why is it still crashing?