Hello, I got a brand new MacBook M3 Pro and trying to configure Tensorflow w/ GPU support. I followed instructions provided at https://developer.apple.com/metal/tensorflow-plugin/ step by step. Unfortunately, even after creating/recreating/installing/uninstalling TensorFlow the problem is not getting resolved as Python crashes. I cannot get past that point to try Jupyter notebook. Here is the error ask the versions in "tf" environment. I already spent entire Saturday yesterday and so far no progress. Can someone tell me what is going on? Python 3.11.7 (main, Dec 4 2023, 18:10:11) [Clang 15.0.0 (clang-1500.1.0.2.5)] on darwin Type "help", "copyright", "credits" or "license" for more information. import tensorflow as tf 2024-01-07 11:44:04.893581: F tensorflow/c/experimental/stream_executor/stream_executor.cc:743] Non-OK-status: stream_executor::MultiPlatformManager::RegisterPlatform( std::move(cplatform)) status: INTERNAL: platform is already registered with name: "METAL" [1] 1797 abort /opt/homebrew/bin/python3 ❯ python -m pip list | grep tensorflow tensorflow 2.15.0 tensorflow-estimator 2.15.0 tensorflow-io-gcs-filesystem 0.34.0 tensorflow-macos 2.15.0 tensorflow-metal 1.1.0 ❯ python --version Python 3.11.7 OS is Sonoma 14.2.1 Thanks Sohail

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?

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

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 ...

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).

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.

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

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

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 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!

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']

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

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

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.

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

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?

I'm building up a data frame for the sole purpose of using that lovely textual grid output. I'm getting output without any issue, but I'm trying to sort out how I might apply a formatter to a specific column so that print(dataframeInstance) "just works" nicely. In my use case, I'm running a function, collecting its output - appending that into a frame, and then using TabularData to get a nice output in a unit test, so I can see the patterns within the output. I found https://developer.apple.com/documentation/tabulardata/column/description(options:), but wasn't able to find any way to "pre-bind" that to a dataframe Column when I was creating it. (I have some double values that get a bit "excessive" in length due to the joys of floating point rounding) Is there a way of setting a formatter on a column at creation time, or after (using a property) that could basically use the same pattern as that description method above?

I created a macOS 14 VM using https://github.com/s-u/macosvm which uses the Virtualization Framework. I want to check if I can use paravirtualized graphics for tensorflow workloads. I followed the steps from https://developer.apple.com/metal/tensorflow-plugin/ but when I run the script from step 4. Verify, I get a segmentation fault (see below). Did anyone try to get this kind of GPU compute in a VM and succeed? /Users/teuf/venv-metal/lib/python3.9/site-packages/urllib3/__init__.py:34: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020 warnings.warn( 2023-11-20 07:41:11.723578: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple Paravirtual device 2023-11-20 07:41:11.723620: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 10.00 GB 2023-11-20 07:41:11.723626: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 0.50 GB 2023-11-20 07:41:11.723700: 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. 2023-11-20 07:41:11.723968: 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>) zsh: segmentation fault python3 ./tensorflow-test.py Thread 0 Crashed:: Dispatch queue: metal gpu stream 0 MPSCore 0x1999598f8 MPSDevice::GetMPSLibrary_DoNotUse(MPSLibraryInfo const*) + 92 1 MPSCore 0x19995c544 0x199927000 + 218436 2 MPSCore 0x19995c908 0x199927000 + 219400 3 MetalPerformanceShadersGraph 0x1fb696a58 0x1fb583000 + 1129048 4 MetalPerformanceShadersGraph 0x1fb6f0cc8 0x1fb583000 + 1498312 5 MetalPerformanceShadersGraph 0x1fb6ef2dc 0x1fb583000 + 1491676 6 MetalPerformanceShadersGraph 0x1fb717ea0 0x1fb583000 + 1658528 7 MetalPerformanceShadersGraph 0x1fb717ce4 0x1fb583000 + 1658084 8 MetalPerformanceShadersGraph 0x1fb6edaac 0x1fb583000 + 1485484 9 MetalPerformanceShadersGraph 0x1fb7a85e0 0x1fb583000 + 2250208 10 MetalPerformanceShadersGraph 0x1fb7a79f0 0x1fb583000 + 2247152 11 MetalPerformanceShadersGraph 0x1fb6602b4 0x1fb583000 + 905908 12 MetalPerformanceShadersGraph 0x1fb65f7b0 0x1fb583000 + 903088 13 libmetal_plugin.dylib 0x1156dfdcc invocation function for block in metal_plugin::runMPSGraph(MetalStream*, MPSGraph*, NSDictionary*, NSDictionary*) + 164 14 libdispatch.dylib 0x18e79b910 _dispatch_client_callout + 20 15 libdispatch.dylib 0x18e7aacc4 _dispatch_lane_barrier_sync_invoke_and_complete + 56 16 libmetal_plugin.dylib 0x1156dfd14 metal_plugin::runMPSGraph(MetalStream*, MPSGraph*, NSDictionary*, NSDictionary*) + 108 17 libmetal_plugin.dylib 0x115606634 metal_plugin::MPSStatelessRandomUniformOp<float>::ProduceOutput(metal_plugin::OpKernelContext*, metal_plugin::Tensor*) + 876 18 libmetal_plugin.dylib 0x115607620 metal_plugin::MPSStatelessRandomOpBase::Compute(metal_plugin::OpKernelContext*) + 620 19 libmetal_plugin.dylib 0x1156061f8 void metal_plugin::ComputeOpKernel<metal_plugin::MPSStatelessRandomUniformOp<float>>(void*, TF_OpKernelContext*) + 44 20 libtensorflow_framework.2.dylib 0x10b807354 tensorflow::PluggableDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) + 148 21 libtensorflow_framework.2.dylib 0x10b7413e0 tensorflow::(anonymous namespace)::SingleThreadedExecutorImpl::Run(tensorflow::Executor::Args const&) + 2100 22 libtensorflow_framework.2.dylib 0x10b70b820 tensorflow::FunctionLibraryRuntimeImpl::RunSync(tensorflow::FunctionLibraryRuntime::Options, unsigned long long, absl::lts_20230125::Span<tensorflow::Tensor const>, std::__1::vector<tensorflow::Tensor, std::__1::allocator<tensorflow::Tensor>>*) + 420 23 libtensorflow_framework.2.dylib 0x10b715668 tensorflow::ProcessFunctionLibraryRuntime::RunMultiDeviceSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long long, std::__1::vector<std::__1::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::__1::allocator<std::__1::variant<tensorflow::Tensor, tensorflow::TensorShape>>>*, std::__1::function<absl::lts_20230125::Status (tensorflow::ProcessFunctionLibraryRuntime::ComponentFunctionData const&, tensorflow::ProcessFunctionLibraryRuntime::InternalArgs*)>) const + 1336 24 libtensorflow_framework.2.dylib 0x10b71a8a4 tensorflow::ProcessFunctionLibraryRuntime::RunSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long long, absl::lts_20230125::Span<tensorflow::Tensor const>, std::__1::vector<tensorflow::Tensor, std::__1::allocator<tensorflow::Tensor>>*) const + 848 25 libtensorflow_cc.2.dylib 0x2801b5008 tensorflow::KernelAndDeviceFunc::Run(tensorflow::ScopedStepContainer*, tensorflow::EagerKernelArgs const&, std::__1::vector<std::__1::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::__1::allocator<std::__1::variant<tensorflow::Tensor, tensorflow::TensorShape>>>*, tsl::CancellationManager*, std::__1::optional<tensorflow::EagerFunctionParams> const&, std::__1::optional<tensorflow::ManagedStackTrace> const&, tsl::CoordinationServiceAgent*) + 572 26 libtensorflow_cc.2.dylib 0x28016613c tensorflow::EagerKernelExecute(tensorflow::EagerContext*, absl::lts_20230125::InlinedVector<tensorflow::TensorHandle*, 4ul, std::__1::allocator<tensorflow::TensorHandle*>> const&, std::__1::optional<tensorflow::EagerFunctionParams> const&, tsl::core::RefCountPtr<tensorflow::KernelAndDevice> const&, tensorflow::GraphCollector*, tsl::CancellationManager*, absl::lts_20230125::Span<tensorflow::TensorHandle*>, std::__1::optional<tensorflow::ManagedStackTrace> const&) + 452 27 libtensorflow_cc.2.dylib 0x2801708ec tensorflow::ExecuteNode::Run() + 396 28 libtensorflow_cc.2.dylib 0x2801b0118 tensorflow::EagerExecutor::SyncExecute(tensorflow::EagerNode*) + 244 29 libtensorflow_cc.2.dylib 0x280165ac8 tensorflow::(anonymous namespace)::EagerLocalExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) + 2580 30 libtensorflow_cc.2.dylib 0x2801637a8 tensorflow::DoEagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) + 416 31 libtensorflow_cc.2.dylib 0x2801631e8 tensorflow::EagerOperation::Execute(absl::lts_20230125::Span<tensorflow::AbstractTensorHandle*>, int*) + 132

Hello, I'm trying to train a MLImageClassifier dataset using Swift using the function MLImageClassifier.train. It doesn't change the dataset size (I have the same problem with a smaller one), but when the train reaches the 9 completedUnitCount of 10, even if the CPU usage is still high, seems to happen a soft lock that doesn't never brings the model to its completion (or error). The dataset is made of jpg images, using the CreateML app doesn't appear any problem during the training. There is any known issue with CreateML training APIs about part 9 of the process? There is any information about this part of the training job? Thank you