x = tf.Variable(tf.ones(3)) x[1].assign(5) Above code results in: tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation ResourceStridedSliceAssign: Could not satisfy explicit device specification '/job:localhost/replica:0/task:0/device:GPU:0' because no supported kernel for GPU devices is available. Colocation Debug Info: Colocation group had the following types and supported devices: Root Member(assigned_device_name_index_=1 requested_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' assigned_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' resource_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' supported_device_types_=[CPU] possible_devices_=[] ResourceStridedSliceAssign: CPU _Arg: GPU CPU Colocation members, user-requested devices, and framework assigned devices, if any: ref (_Arg) framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0 ResourceStridedSliceAssign (ResourceStridedSliceAssign) /job:localhost/replica:0/task:0/device:GPU:0 Op: ResourceStridedSliceAssign Node attrs: ellipsis_mask=0, Index=DT_INT32, T=DT_FLOAT, shrink_axis_mask=1, end_mask=0, begin_mask=0, new_axis_mask=0 Registered kernels: device='XLA_CPU_JIT'; Index in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BOOL, DT_QINT8, DT_QUINT8, DT_QINT32, DT_BFLOAT16, DT_UINT16, DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64, DT_FLOAT8_E5M2, DT_FLOAT8_E4M3FN, DT_INT4, DT_UINT4] device='DEFAULT'; T in [DT_INT32] device='CPU'; T in [DT_UINT64] device='CPU'; T in [DT_INT64] device='CPU'; T in [DT_UINT32] device='CPU'; T in [DT_UINT16] device='CPU'; T in [DT_INT16] device='CPU'; T in [DT_UINT8] device='CPU'; T in [DT_INT8] device='CPU'; T in [DT_INT32] device='CPU'; T in [DT_HALF] device='CPU'; T in [DT_BFLOAT16] device='CPU'; T in [DT_FLOAT] device='CPU'; T in [DT_DOUBLE] device='CPU'; T in [DT_COMPLEX64] device='CPU'; T in [DT_COMPLEX128] device='CPU'; T in [DT_BOOL] device='CPU'; T in [DT_STRING] device='CPU'; T in [DT_RESOURCE] device='CPU'; T in [DT_VARIANT] device='CPU'; T in [DT_QINT8] device='CPU'; T in [DT_QUINT8] device='CPU'; T in [DT_QINT32] device='CPU'; T in [DT_FLOAT8_E5M2] device='CPU'; T in [DT_FLOAT8_E4M3FN] [[{{node ResourceStridedSliceAssign}}]] [Op:ResourceStridedSliceAssign] name: strided_slice/_assign I am starting to regret my Macbook purchase. There are so many issues with tensorflow-metal: ADAM is slow Inconsistent values with CPU And now this, I saw a post regarding this but that was one year old. So, Macbooks are not even good for learning anymore?

When the input dimension is 600w, the operator runs on ANE. But when the input shape is 100w or 200w, this operator can only run on the CPU. The data dimension has decreased, but it does not run on ANE. What is the reason for this and what are the ways to avoid it

As the title suggests I am using AVAudioEngine for SpeechRecognition input & AVAudioPlayer for sound output. Apple says in this talk https://developer.apple.com/videos/play/wwdc2019/510 that the setVoiceProcessingEnabled function very usefully cancels the output from speaker to the mic. I set voiceProcessing on the Input and output nodes. It seems to work however the volume is low, even when the system volume is turned up. Any solution to this would be much appreciated.

What is the accuracy and resolution of the angles measured using Vision?

I am trying to parse text from an image, split it into words and store the words in a String array. Additionally I want to store the bounding box of each recognized word. My code works but for some reason the bounding boxes of words that are not separated by a space but by an apostrophe come out wrong. Here is the complete code of my VNRecognizeTextRequestHander: let request = VNRecognizeTextRequest { request, error in guard let observations = request.results as? [VNRecognizedTextObservation] else { return } // split recognized text into words and store each word with corresponding observation let wordObservations = observations.flatMap { observation in observation.topCandidates(1).first?.string.unicodeScalars .split(whereSeparator: { CharacterSet.letters.inverted.contains($0) }) .map { (observation, $0) } ?? [] } // store recognized words as strings recognizedWords = wordObservations.map { (observation, word) in String(word) } // calculate bounding box for each word recognizedWordRects = wordObservations.map { (observation, word) in guard let candidate = observation.topCandidates(1).first else { return .zero } let stringRange = word.startIndex..<word.endIndex guard let rect = try? candidate.boundingBox(for: stringRange)?.boundingBox else { return .zero } let bottomLeftOriginRect = VNImageRectForNormalizedRect(rect, Int(captureRect.width), Int(captureRect.height)) // adjust coordinate system to start in top left corner let topLeftOriginRect = CGRect(origin: CGPoint(x: bottomLeftOriginRect.minX, y: captureRect.height - bottomLeftOriginRect.height - bottomLeftOriginRect.minY), size: bottomLeftOriginRect.size) print("BoundingBox for word '\(String(word))': \(topLeftOriginRect)") return topLeftOriginRect } } And here's an example for what's happening. When I'm processing the following image: the code above produces the following output: BoundingBox for word 'In': (23.00069557577264, 5.718113962610181, 45.89460636656961, 32.78087073878238) BoundingBox for word 'un': (71.19064286904202, 6.289275587192936, 189.16024359557852, 34.392966621800475) BoundingBox for word 'intervista': (71.19064286904202, 6.289275587192936, 189.16024359557852, 34.392966621800475) BoundingBox for word 'del': (262.64622870703477, 8.558512219726875, 54.733978711037985, 32.79967358237818) Notice how the bounding boxes of the words 'un' and 'intervista' are exactly the same. This happens consistently for words that are separated by an apostrophe. Why is that? Thank you for any help Elias

I'm following Apple WWDC video (https://developer.apple.com/videos/play/wwdc2021/10037/) about how to create a recommendation model. But I'm getting this error when I run the project on that like of code from their tutorial. "Column keywords has element of unsupported type Dictionary<String, Double>." Here is the block of code took from the transcript of WWDC video that cause me issue: func featuresFromMealAndKeywords(meal: String, keywords: [String]) -> [String: Double] { // Capture interactions between content (the dish keywords) and context (meal) by // adding a copy of each keyword modified to include the meal. let featureNames = keywords + keywords.map { meal + ":" + $0 } // For each keyword, create an entry in a dictionary of features with a value of 1.0. return featureNames.reduce(into: [:]) { features, name in features[name] = 1.0 } } var trainingKeywords: [[String: Double]] = [] var trainingTargets: [Double] = [] for item in userPurchasedItems { // Add in the positive example. trainingKeywords.append( featuresFromMealAndKeywords(meal: item.meal, keywords: item.keywords)) trainingTargets.append(1.0) // Add in the negative example. let negativeKeywords = allKeywords.subtracting(item.keywords) trainingKeywords.append( featuresFromMealAndKeywords(meal: item.meal, keywords: Array(negativeKeywords))) trainingTargets.append(-1.0) } // Create the training data. var trainingData = DataFrame() trainingData.append(column: Column(name: "keywords" contents: trainingKeywords)) trainingData.append(column: Column(name: "target", contents: trainingTargets)) // Create the model. let model = try MLLinearRegressor(trainingData: trainingData, targetColumn: "target") Did DataFrame implementation changed since then and doesn't support Dictionary anymore? I'm at lost right now on how to reproduce their example.

Running the sample Python keras-ocr example on M3 Max returns incorrect results if tensorflow-metal is installed. Code Example: https://keras-ocr.readthedocs.io/en/latest/examples/using_pretrained_models.html Note: https://upload.wikimedia.org/wikipedia/commons/e/e8/FseeG2QeLXo.jpg not found. Line commented out. Without tensorflow-metal (Correct results): ['toodstande', 's', 'somme', 'srny', 'squadron', 'ds', 'quentn', 'snhnen', 'bnpnone', 'sasne', 'taing', 'yeoms', 'sry', 'the', 'royal', 'wessex', 'yeomanry', 'regiment', 'yeomanry', 'wests', 'south', 'the', 'now', 'recruiting', 'arm', 'blon', 'wxybsqipsacomodn', 'email', '438300', '01722'] ['banana', 'union', 'no', 'no', 'software', 'patents'] With tensorflow-metal (Incorrect results): ['sddoooo', '', 'eamnooss', 'xynrr', 'daanues', 'idd', 'innee', 'iiiinus', 'tnounppanab', 'inla', 'ppnt', 'mmnooexyy', 'yyr', 'ehhtt', 'laayvyoorr', 'xeseww', 'rinamoevy', 'tnemiger', 'yrnamoey', 'sstseww', 'htuwlos', 'fefeahit', 'wwoniia', 'turceedrr', 'ymmrira', 'atate', 'prasbyxwr', 'liamme', '00338803144', '22277100'] ['annnaab', 'noolinnu', 'oon', 'oon', 'wttffoos', 'sttneettaap'] Logs: With tensorflow-metal (Incorrect results) (.venv) <REDACTED> % pip3 install -U tensorflow-metal Collecting tensorflow-metal Using cached tensorflow_metal-1.1.0-cp311-cp311-macosx_12_0_arm64.whl.metadata (1.2 kB) Requirement already satisfied: wheel~=0.35 in ./.venv/lib/python3.11/site-packages (from tensorflow-metal) (0.42.0) Requirement already satisfied: six>=1.15.0 in ./.venv/lib/python3.11/site-packages (from tensorflow-metal) (1.16.0) Using cached tensorflow_metal-1.1.0-cp311-cp311-macosx_12_0_arm64.whl (1.4 MB) Installing collected packages: tensorflow-metal Successfully installed tensorflow-metal-1.1.0 (.venv) <REDACTED> % python3 keras-ocr-bug.py Looking for <REDACTED>/.keras-ocr/craft_mlt_25k.h5 2023-12-16 22:05:05.452493: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M3 Max 2023-12-16 22:05:05.452532: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 64.00 GB 2023-12-16 22:05:05.452545: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 24.00 GB 2023-12-16 22:05:05.452591: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2023-12-16 22:05:05.452609: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) WARNING:tensorflow:From <REDACTED>/.venv/lib/python3.11/site-packages/tensorflow/python/util/dispatch.py:1260: resize_bilinear (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.image.resize(...method=ResizeMethod.BILINEAR...)` instead. Looking for <REDACTED>/.keras-ocr/crnn_kurapan.h5 2023-12-16 22:05:07.526354: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled. 1/1 [==============================] - 1s 855ms/step 2/2 [==============================] - 1s 140ms/step ['sddoooo', '', 'eamnooss', 'xynrr', 'daanues', 'idd', 'innee', 'iiiinus', 'tnounppanab', 'inla', 'ppnt', 'mmnooexyy', 'yyr', 'ehhtt', 'laayvyoorr', 'xeseww', 'rinamoevy', 'tnemiger', 'yrnamoey', 'sstseww', 'htuwlos', 'fefeahit', 'wwoniia', 'turceedrr', 'ymmrira', 'atate', 'prasbyxwr', 'liamme', '00338803144', '22277100'] ['annnaab', 'noolinnu', 'oon', 'oon', 'wttffoos', 'sttneettaap'] Logs: Valid results, without tensorflow-metal (.venv) <REDACTED> % pip3 uninstall tensorflow-metal Found existing installation: tensorflow-metal 1.1.0 Uninstalling tensorflow-metal-1.1.0: Would remove: <REDACTED>/.venv/lib/python3.11/site-packages/tensorflow-plugins/* <REDACTED>/.venv/lib/python3.11/site-packages/tensorflow_metal-1.1.0.dist-info/* Proceed (Y/n)? Y Successfully uninstalled tensorflow-metal-1.1.0 (.venv) <REDACTED> % python3 keras-ocr-bug.py Looking for <REDACTED>/.keras-ocr/craft_mlt_25k.h5 WARNING:tensorflow:From <REDACTED>/.venv/lib/python3.11/site-packages/tensorflow/python/util/dispatch.py:1260: resize_bilinear (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.image.resize(...method=ResizeMethod.BILINEAR...)` instead. Looking for <REDACTED>/.keras-ocr/crnn_kurapan.h5 1/1 [==============================] - 7s 7s/step 2/2 [==============================] - 1s 71ms/step ['toodstande', 's', 'somme', 'srny', 'squadron', 'ds', 'quentn', 'snhnen', 'bnpnone', 'sasne', 'taing', 'yeoms', 'sry', 'the', 'royal', 'wessex', 'yeomanry', 'regiment', 'yeomanry', 'wests', 'south', 'the', 'now', 'recruiting', 'arm', 'blon', 'wxybsqipsacomodn', 'email', '438300', '01722'] ['banana', 'union', 'no', 'no', 'software', 'patents']

Hi, I think many of us would love to be able to use our GPUs for Jax on the new Apple Silicon devices, but currently, the Jax-metal plugin is, for all effects and purposes, broken. Is it still under active development? Is there a planned release for a new version? thanks!

I seem to be having some trouble running the example app from the WWDC 2023 session on 3D Body Pose Detection (this one). I'm getting an issue about the request revision being incompatible, I tried searching the API documentation for any configuration that has been changed or introduced but to no avail. I also couldn't find much online for it. Is this a known issue? Or am I doing something wrong? Error in question: Unable to perform the request: Error Domain=com.apple.Vision Code=16 "VNDetectHumanBodyPose3DRequest does not support VNDetectHumanBodyPose3DRequestRevision1" UserInfo={NSLocalizedDescription=VNDetectHumanBodyPose3DRequest does not support VNDetectHumanBodyPose3DRequestRevision1}. Code Snippet: guard let assetURL = fileURL else { return } let request = VNDetectHumanBodyPose3DRequest() self.fileURL = assetURL let requestHandler = VNImageRequestHandler(url: assetURL) do { try requestHandler.perform([request]) if let returnedObservation = request.results?.first { Task { @MainActor in self.humanObservation = returnedObservation } } } catch { print("Unable to perform the request: \(error).") } Thank you for any and all advice!

Hi, I have been trying to connect my microphone on my reason studio for days now without any outcome. So I was asked to download ASIO Driver on my mac. I realised that I have an IAC driver. I need help on downloading the Asio driver and wish to know if there will be a problem running it with the IAC driver. I also just upgraded without knowing from ventura to sonoma. Am using an audio iinterface(Focusrite scarlett solo to connect to the reason application and my mic is nt1-a rode but I can get a sound but cannot record. Will be overwhelmed if I can get help from here. Thanks

Hello, I followed the instructions provided here: https://developer.apple.com/metal/tensorflow-plugin/ and while trying to run the example I am getting following error: otFoundError: dlopen(/Users/nedimhadzic/venv-metal/lib/python3.11/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Symbol not found: __ZN10tensorflow16TensorShapeProtoC1ERKS0_ Referenced from: <C62E0AB4-567E-3E14-8F96-9F07A746C4DC> /Users/nedimhadzic/venv-metal/lib/python3.11/site-packages/tensorflow-plugins/libmetal_plugin.dylib Expected in: <FFF31651-3926-3E79-A442-143B7156FB13> /Users/nedimhadzic/venv-metal/lib/python3.11/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so tensorflow: 2.15.0 tensorlow-metal: 1.0.0 macos: 14.2.1 Intel CPU and AMD Radeon Pro 5500M Any idea? Regards, Nedim

Currently in South Korea, due to my personal experiences with what seems like warranty but isn't, and the operation of a ruthless Genius Bar, I feel compelled to purchase the officially released Vision Pro. I'd like to discuss with other developers here about their thoughts on the release schedule. The product launched in the USA in February, but I'm curious about the months following for the secondary and tertiary launch countries. Naturally, we'll know the launch is imminent when local staff are summoned to the headquarters for training. However, the urgency for localized services, development, and personal purchase is growing on my mind.

Hi Apple Team, We have a technical query regarding one feature- Audio Recognition and Live captioning. We are developing an app for deaf community to avoid communication barriers. We want to know if there is any possibility to recognize the sound from other applications in an iPhone and show live captions in our application (based on iOS).

I created a word tagging model in CreateML and am trying to make predictions with it using the following code: let text = "$30.00 7/1/2023" let model = TaggingModel() let input = TaggingModelInput(text: text) guard let output = try? model.prediction(input: input) else { fatalError("Unexpected runtime error.") } However, the output separates "$" and "30.00" as separate tokens as well as "7", "/", "1", "/", etc. Is there any way to make sure prices and dates get grouped together and to simply separate tokens based on whitespace? Any help is appreciated!

Here is my environment : python==3.9.0 tensorflow==2.9.0 os==Sonoma 14.2 (23C64) Error : Translated Report (Full Report Below) Process: Python [10330] Path: /Library/Frameworks/Python.framework/Versions/3.9/Resources/Python.app/Contents/MacOS/Python Identifier: org.python.python Version: 3.9.0 (3.9.0) Code Type: X86-64 (Translated) Parent Process: Python [8039] Responsible: Terminal [779] User ID: 501 Date/Time: 2023-12-30 22:31:38.4916 +0530 OS Version: macOS 14.2 (23C64) Report Version: 12 Anonymous UUID: F7E462E7-6380-C3DA-E2EC-5CF01A61D195 Sleep/Wake UUID: 50F32A2D-8CFA-4117-8048-D9CF76E24F26 Time Awake Since Boot: 29000 seconds Time Since Wake: 2193 seconds System Integrity Protection: enabled Notes: PC register does not match crashing frame (0x0 vs 0x10CEDE6D9) Crashed Thread: 0 Dispatch queue: com.apple.main-thread Exception Type: EXC_BAD_INSTRUCTION (SIGILL) Exception Codes: 0x0000000000000001, 0x0000000000000000 Termination Reason: Namespace SIGNAL, Code 4 Illegal instruction: 4 Terminating Process: exc handler [10330] Error Formulating Crash Report: PC register does not match crashing frame (0x0 vs 0x10CEDE6D9) Thread 0 Crashed:: Dispatch queue: com.apple.main-thread 0 _cpu_feature_guard.so 0x10cede6d9 _GLOBAL__sub_I_cpu_feature_guard.cc + 9 1 dyld 0x2026a3fca invocation function for block in dyld4::Loader::findAndRunAllInitializers(dyld4::RuntimeState&) const::$_0::operator()() const + 182 2 dyld 0x2026e5584 invocation function for block in dyld3::MachOAnalyzer::forEachInitializer(Diagnostics&, dyld3::MachOAnalyzer::VMAddrConverter const&, void (unsigned int) block_pointer, void const*) const + 133 3 dyld 0x2026d9913 invocation function for block in dyld3::MachOFile::forEachSection(void (dyld3::MachOFile::SectionInfo const&, bool, bool&) block_pointer) const + 543 4 dyld 0x20268707f dyld3::MachOFile::forEachLoadCommand(Diagnostics&, void (load_command const*, bool&) block_pointer) const + 249 5 dyld 0x2026d8adc dyld3::MachOFile::forEachSection(void (dyld3::MachOFile::SectionInfo const&, bool, bool&) block_pointer) const + 176 6 dyld 0x2026db104 dyld3::MachOFile::forEachInitializerPointerSection(Diagnostics&, void (unsigned int, unsigned int, bool&) block_pointer) const + 116 7 dyld 0x2026e52ba dyld3::MachOAnalyzer::forEachInitializer(Diagnostics&, dyld3::MachOAnalyzer::VMAddrConverter const&, void (unsigned int) block_pointer, void const*) const + 390 8 dyld 0x2026a0cfc dyld4::Loader::findAndRunAllInitializers(dyld4::RuntimeState&) const + 222 9 dyld 0x2026a65cb dyld4::JustInTimeLoader::runInitializers(dyld4::RuntimeState&) const + 21 10 dyld 0x2026a0ef1 dyld4::Loader::runInitializersBottomUp(dyld4::RuntimeState&, dyld3::Array<dyld4::Loader const*>&) const + 181 11 dyld 0x2026a4040 dyld4::Loader::runInitializersBottomUpPlusUpwardLinks(dyld4::RuntimeState&) const::$_1::operator()() const + 98 12 dyld 0x2026a0f87 dyld4::Loader::runInitializersBottomUpPlusUpwardLinks(dyld4::RuntimeState&) const + 93 13 dyld 0x2026bdc65 dyld4::APIs::dlopen_from(char const*, int, void*) + 935 14 _ctypes.cpython-39-darwin.so 0x10ac20962 py_dl_open + 162 15 Python 0x10b444f2d cfunction_call + 125 16 Python 0x10b40625d _PyObject_MakeTpCall + 365 17 Python 0x10b4dc8fc call_function + 876 18 Python 0x10b4d9e2b _PyEval_EvalFrameDefault + 25371 19 Python 0x10b4dd563 _PyEval_EvalCode + 2611 20 Python 0x10b4069b1 _PyFunction_Vectorcall + 289 21 Python 0x10b4060b5 _PyObject_FastCallDictTstate + 293 22 Python 0x10b406c98 _PyObject_Call_Prepend + 152 23 Python 0x10b4601e5 slot_tp_init + 165 24 Python 0x10b45b699 type_call + 345 ... Thread 1:: com.apple.rosetta.exceptionserver 0 runtime 0x7ff7fffaf294 0x7ff7fffab000 + 17044 Thread 2:: /Reaper 0 ??? 0x7ff8aa35ea78 ??? 1 libsystem_kernel.dylib 0x7ff819da46fa kevent + 10 2 libzmq.5.dylib 0x10bf038f6 zmq::kqueue_t::loop() + 278 3 libzmq.5.dylib 0x10bf31a59 zmq::worker_poller_base_t::worker_routine(void) + 25 4 libzmq.5.dylib 0x10bf7854c thread_routine(void*) + 300 5 libsystem_pthread.dylib 0x7ff819ddf202 _pthread_start + 99 6 libsystem_pthread.dylib 0x7ff819ddabab thread_start + 15 Thread 3:: /0 0 ??? 0x7ff8aa35ea78 ??? 1 libsystem_kernel.dylib 0x7ff819da46fa kevent + 10 2 libzmq.5.dylib 0x10bf038f6 zmq::kqueue_t::loop() + 278 3 libzmq.5.dylib 0x10bf31a59 zmq::worker_poller_base_t::worker_routine(void) + 25 4 libzmq.5.dylib 0x10bf7854c thread_routine(void*) + 300 5 libsystem_pthread.dylib 0x7ff819ddf202 _pthread_start + 99 6 libsystem_pthread.dylib 0x7ff819ddabab thread_start + 15 Thread 4: 0 ??? 0x7ff8aa35ea78 ??? 1 libsystem_kernel.dylib 0x7ff819da46fa kevent + 10 2 select.cpython-39-darwin.so 0x10ab95dc3 select_kqueue_control + 915 3 Python 0x10b40f11f method_vectorcall_FASTCALL + 335 4 Python 0x10b4dc86c call_function + 732 5 Python 0x10b4d9d72 _PyEval_EvalFrameDefault + 25186 6 Python 0x10b4dd563 _PyEval_EvalCode + 2611 7 Python 0x10b4069b1 _PyFunction_Vectorcall + 289 ...

Application is getting Crashed: AXSpeech EXC_BAD_ACCESS KERN_INVALID_ADDRESS 0x000056f023efbeb0 Crashed: AXSpeech 0 libobjc.A.dylib 0x4820 objc_msgSend + 32 1 libsystem_trace.dylib 0x6c34 _os_log_fmt_flatten_object + 116 2 libsystem_trace.dylib 0x5344 _os_log_impl_flatten_and_send + 1884 3 libsystem_trace.dylib 0x4bd0 _os_log + 152 4 libsystem_trace.dylib 0x9c48 _os_log_error_impl + 24 5 TextToSpeech 0xd0a8c _pcre2_xclass_8 6 TextToSpeech 0x3bc04 TTSSpeechUnitTestingMode 7 TextToSpeech 0x3f128 TTSSpeechUnitTestingMode 8 AXCoreUtilities 0xad38 -[NSArray(AXExtras) ax_flatMappedArrayUsingBlock:] + 204 9 TextToSpeech 0x3eb18 TTSSpeechUnitTestingMode 10 TextToSpeech 0x3c948 TTSSpeechUnitTestingMode 11 TextToSpeech 0x48824 AXAVSpeechSynthesisVoiceFromTTSSpeechVoice 12 TextToSpeech 0x49804 AXAVSpeechSynthesisVoiceFromTTSSpeechVoice 13 Foundation 0xf6064 __NSThreadPerformPerform + 264 14 CoreFoundation 0x37acc CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE0_PERFORM_FUNCTION + 28 15 CoreFoundation 0x36d48 __CFRunLoopDoSource0 + 176 16 CoreFoundation 0x354fc __CFRunLoopDoSources0 + 244 17 CoreFoundation 0x34238 __CFRunLoopRun + 828 18 CoreFoundation 0x33e18 CFRunLoopRunSpecific + 608 19 Foundation 0x2d4cc -[NSRunLoop(NSRunLoop) runMode:beforeDate:] + 212 20 TextToSpeech 0x24b88 TTSCFAttributedStringCreateStringByBracketingAttributeWithString 21 Foundation 0xb3154 NSThread__start + 732 com.livingMedia.AajTakiPhone_issue_3ceba855a8ad2d1af83655803dc13f70_crash_session_9081fa41ced440ae9a57c22cb432f312_DNE_0_v2_stacktrace.txt 22 libsystem_pthread.dylib 0x24d4 _pthread_start + 136 23 libsystem_pthread.dylib 0x1a10 thread_start + 8

I am sending CVPixelBuffers to the input of the DeepLabV3 MLModel. I am of the understanding that it requires pixel color format 32ARGB or 32RGBA. Correct? Can 32BRGA be input? CVPixelBuffers support 32BRGA and OpenCV as well. Please note, I want to use the MLModel as trained. Neither 32RGBA no 32ARGB are supported for type CVPixelBuffer. 32ARGB: An unsupported runtime error occurs with the configuration as follows... func configureOutput() { videoOutput.setSampleBufferDelegate(self, queue: bufferQueue) videoOutput.alwaysDiscardsLateVideoFrames = true videoOutput.videoSettings = [String(kCVPixelBufferPixelFormatTypeKey): kCMPixelFormat_32ARGB]. 32RGBA: "Cannot find 'kCMPixelFormat_32rgba' in scope." The app process: Video captured pixelBuffers are sent to c++ code where openCV operations are done, creating up to 3 smaller Mats which are then converted back into pixel buffers in the Objective-C. These converted PixedBuffer are used in three ways. All are sent to the MLModel for image segmentation to identify people; the files may be sent to the photo library; or may simply be viewed on the screen. I need a color format that can support all these down stream operations/pipelines.

Hi, In Xcode 14 I was able to train linear regression models with Create ML using large CSV files (I tested on about 30000 items and 5 features): However, in Xcode 15 (I tested on 15.0.1 and 15.1), the training continuously stays in the "Processing" state: When using a dataset with 900 items, everything works fine. I filed a feedback for this issue: FB13516799. Does anybody else have this issue / can reproduce it?

I want to use it in the code