The following code taken from keras.io produces the error InternalError: Exception encountered when calling GPT2Tokenizer.call(). ... 2 root error(s) found. (0) INTERNAL: stream cannot wait for itself Macos on Macbook, M2 Max. Setting the optimizer to "Adam" does not help. import keras_nlp # version 0.15 causal_lm = keras_nlp.models.GPT2CausalLM.from_preset("gpt2_base_en") causal_lm.compile(sampler="greedy") # the next call produces the error causal_lm.generate(["Keras is a"])

Where does the processing power to enact certain AI capabilities come from? Is it hosted on the originating device? Or does the device send contents of originating information to Apple assets to process and give product to end user? e.g. If I ask AI to summarize an email will it send the contents of the email to an Apple AI asset to process it and give the summary to the originating device.

I signed up for apple intelligence on my IPad Air m1 and then updated my phone today. It tells me that I’ve already been put on a waitlist i didn’t even join? And it’s been stuck on that for 2 days now

i have iphone 15 pro max on ios 18.1 I tried to join Apple Intelligence about three weeks ago, but I'm still stuck on the waitlist. I've already tried everything recommended by Apple, including changing my region and Siri's language to English (US). Can anyone help me figure out how to solve this issue?

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?

Hi everyone ! I'm getting random crashes when I'm using the Speech Recognizer functionality in my app. This is an old bug (for 8 years on Apple Forums) and I will really appreciate if anyone from Apple will be able to find a fix for this crashes. Can anyone also help me please to understand what could I do to keep the Speech Recognizer functionality still available in my app, but to avoid this crashes (if there is any other native library available or a CocoaPod library). Here is my code and also the crash log for it. Code: func startRecording() { startStopRecordBtn.setImage(UIImage(#imageLiteral(resourceName: "microphone_off")), for: .normal) if UserDefaults.standard.bool(forKey: Constants.darkTheme) { commentTextView.textColor = .white } else { commentTextView.textColor = .black } commentTextView.isUserInteractionEnabled = false recordingLabel.text = Constants.recording if recognitionTask != nil { recognitionTask?.cancel() recognitionTask = nil } let audioSession = AVAudioSession.sharedInstance() do { try audioSession.setCategory(AVAudioSession.Category.record) try audioSession.setMode(AVAudioSession.Mode.measurement) try audioSession.setActive(true, options: .notifyOthersOnDeactivation) } catch { showAlertWithTitle(message: Constants.error) } recognitionRequest = SFSpeechAudioBufferRecognitionRequest() let inputNode = audioEngine.inputNode guard let recognitionRequest = recognitionRequest else { fatalError(Constants.error) } recognitionRequest.shouldReportPartialResults = true recognitionTask = speechRecognizer?.recognitionTask(with: recognitionRequest, resultHandler: { (result, error) in var isFinal = false if result != nil { self.commentTextView.text = result?.bestTranscription.formattedString isFinal = (result?.isFinal)! } if error != nil || isFinal { self.audioEngine.stop() inputNode.removeTap(onBus: 0) self.recognitionRequest = nil self.recognitionTask = nil self.startStopRecordBtn.isEnabled = true } }) let recordingFormat = inputNode.outputFormat(forBus: 0) inputNode.installTap(onBus: 0, bufferSize: 1024, format: recordingFormat) {[weak self] (buffer: AVAudioPCMBuffer, when: AVAudioTime) in // CRASH HERE self?.recognitionRequest?.append(buffer) } audioEngine.prepare() do { try audioEngine.start() } catch { showAlertWithTitle(message: Constants.error) } } Here is the crash log: Thanks for very much for reading this !

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

in iOS 15, on stopSpeaking of AVSpeechSynthesizer, didFinish delegate method getting called instead of didCancel which is working fine in iOS 14 and below version.

Adding the openAppWhenRun property to an AppIntent for a ControlWidgetButton causes the following error when the control is tapped in Control Center: Unknown NSError The operation couldn’t be completed. (LNActionExecutorErrorDomain error 2018.) Here’s the full ControlWidget and AppIntent code that causes the errorerror: Should controls be able to open apps after the AppIntent runs, or is this a bug?

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?

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

Recently, deep learning model have been getting larger, and sometimes loading models has become a bottleneck. I download the .mlpackage format CoreML from the internet and need to use compileModelAtURL to convert the .mlpackage into an .mlmodelc, then call modelWithContentsOfURL to convert the .mlmodelc into a handle. Generally, generating a handle with modelWithContentsOfURL is very slow. I noticed from WWDC 2023 that it is possible to cache the compiled results (see https://developer.apple.com/videos/play/wwdc2023/10049/?time=677, which states "This compilation includes further optimizations for the specific compute device and outputs an artifact that the compute device can run. Once complete, Core ML caches these artifacts to be used for subsequent model loads."). However, it seems that I couldn't find how to cache in the documentation.

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?

Hello, I’m currently working on Tiny ML or ML on Edge using the Google Colab platform. Due to the exhaust of my compute unit’s free usage, I’m being prompted to pay. I’ve been considering leveraging the GPU capabilities of my iPad M1 and Intel-based Mac. Both devices utilize Thunderbolt ports capable of sharing connections up to 30GB/s. Since I’m primarily using a classification model, extensive GPU usage isn’t necessary. I’m looking for assistance or guidance on utilizing the iPad’s processor as an eGPU on my Mac, possibly through an API or Apple technology. Any help would be greatly appreciated!

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

With iOS 18, Writing Tools are enabled for text fields all over the system. But under the hood, this uses Apple's on device LLM to summarize a piece of text. Is there any kind of Swift API to access this LLM summarization feature for pieces of text that I provide to the API? Instead of forcing the user to select the text.