Hi there,I’m a Computer Science student and I have a MacBook Pro 2019 and I’m thinking in buying a new Mac either a Mac Studio or a MacBook Pro but I want to use it for ML. I’m now doing a segmentation model and I’m wondering if I could use Core Ml or the Apple Neural Engine in the new M3 chips to train it, I’m now using colab and tensorflow to create the model but it’s not doing the job, I’m falling short of Cuda memory. Thanks :)

Hi, all. I've been writing various computational functions using Metal. However, in the following operation functions, unlike + and *, there is an accuracy issue in the / operation. This is a function that divides a matrix of shape [n, x, y] and a scalar [1]. When compared to numpy or torch, if I change the operator of the above function to * or + instead of /, I can get completely the same results, but in the case of /, there is a difference in the mean of more than 1e-5. (For reference, this was written with reference to the metal kernel code in llama.cpp) kernel void kernel_div_single_f16( device const half * src0, device const half * src1, device half * dst, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, constant int64_t & ne03, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { const int64_t i03 = tgpig.z; const int64_t i02 = tgpig.y; const int64_t i01 = tgpig.x; const uint offset = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; for (int i0 = tpitg.x; i0 < ne00; i0 += ntg.x) { dst[offset + i0] = src0[offset+i0] / *src1; } } My mac book is, Macbork Pro(16, 2021) / macOS 12.5 / Apple M1 Pro. Are there any issues related to Div? Thanks in advance for your reply.

I create an app that allow user tap on button in widget but when app is running it's got an error when tap: Could not find an intent with identifier AppIntentsIdentifier , mangled TypeName: Optional("Somethings")

Controls' actions use App Intents in iOS 18. However, when executing App Intents from Controls, alert dialogs such as IntentDialog can't be used. This makes it unclear how to display errors when trying to create Controls that do not launch the app. Is there any way to handle this?

Enhance Dialogue not showing for me as an option anywhere .I’m using an Apple TV 4K 1st Generation running IOS 18 public beta ,JBL soundbar and LG OLED all have eARC.

"Last year, I upgraded to an M2 Max laptop, expecting that tensorflow-metal would facilitate effective local prototyping utilizing the Apple Silicon's capabilities. It has been quite some time since tensorflow-metal was last updated, and there appear to be several unresolved issues noted by the community here. I've personally observed the following behavior with my setup: Without tensorflow-metal: import tensorflow as tf for _ in range(10): print(tf.random.normal((3,)).numpy()) [-1.4213976 0.08230731 -1.1260201 ] [ 1.2913705 -0.47693467 -1.2886043 ] [ 0.09144169 -1.0892165 0.9313669 ] [ 1.1081179 0.9865657 -1.0298151] [ 0.03328908 -0.00655857 -0.02662632] [-1.002391 -1.1873596 -1.1168724] [-1.2135247 -1.2823236 -1.0396363] [-0.03492929 -0.9228362 0.19147137] [-0.59353966 0.502279 0.80000925] [-0.82247525 -0.13076428 0.99579334] With tensorflow-metal: import tensorflow as tf for _ in range(10): print(tf.random.normal((3,)).numpy()) [ 1.0031303 0.8095635 -0.0610961] [-1.3544159 0.7045493 0.03666191] [-1.3544159 0.7045493 0.03666191] [-1.3544159 0.7045493 0.03666191] [-1.3544159 0.7045493 0.03666191] [-1.3544159 0.7045493 0.03666191] [-1.3544159 0.7045493 0.03666191] [-1.3544159 0.7045493 0.03666191] [-1.3544159 0.7045493 0.03666191] [-1.3544159 0.7045493 0.03666191] Given these observations, it seems there may be an issue with the randomness of tf.random.normal when using tensorflow-metal. My current setup includes MacOS 14.5, tensorflow 2.14.1, and tensorflow-macos 2.14.1. I am interested in understanding if there are known solutions or workarounds for this behavior. Furthermore, could anyone provide an update on whether tensorflow-metal is still being actively developed, or if alternative approaches are recommended for utilizing the GPU capabilities of this hardware?

My application is being supported by both iOS and iPadOS platforms. I would like to add AppIntents only on iOS and not on iPadOS. I have not found any blogs related to it. Is it possible? If so, May I know how can we do it? If not, what is the best practice to avoid showing Siri shortcuts on iPad.

Hello everybody, I am running into an error with BNNS.NormalizationLayer. It appears to only work with .vector, and matrix shapes throws layerApplyFail during training. Inference doesn't throw but the output stays the same. How to correctly use BNNS.NormalizationLayer with matrix shapes? How to debug layerApplyFail exception? Thanks let array: [Float32] = [ 01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13, 14, 15, 16, 17, 18, ] // let inputShape: BNNS.Shape = .vector(6 * 3) // works let inputShape: BNNS.Shape = .matrixColumnMajor(6, 3) let input = BNNSNDArrayDescriptor.allocateUninitialized(scalarType: Float32.self, shape: inputShape) let output = BNNSNDArrayDescriptor.allocateUninitialized(scalarType: Float32.self, shape: inputShape) let beta = BNNSNDArrayDescriptor.allocate(repeating: Float32(0), shape: inputShape, batchSize: 1) let gamma = BNNSNDArrayDescriptor.allocate(repeating: Float32(1), shape: inputShape, batchSize: 1) let activation: BNNS.ActivationFunction = .identity let layer = BNNS.NormalizationLayer(type: .layer(normalizationAxis: 0), input: input, output: output, beta: beta, gamma: gamma, epsilon: 1e-12, activation: activation)! let layerInput = BNNSNDArrayDescriptor.allocate(initializingFrom: array, shape: inputShape) let layerOutput = BNNSNDArrayDescriptor.allocateUninitialized(scalarType: Float32.self, shape: inputShape) // try layer.apply(batchSize: 1, input: layerInput, output: layerOutput, for: .inference) // No throw try layer.apply(batchSize: 1, input: layerInput, output: layerOutput, for: .training) _ = layerOutput.makeArray(of: Float32.self) // All zeros when .inference

I am opening the Siri shortcut screen from the viewDidLoad method, as follows: override func viewDidLoad() { super.viewDidLoad() // Present the Siri Shortcut screen to add Card Payment Intent let viewController = INUIAddVoiceShortcutViewController(shortcut: INShortcut(intent: self.cardPaymentIntent)!) viewController.modalPresentationStyle = .pageSheet // Setting Delegate viewController.delegate = self self.present(viewController, animated: true, completion: nil) } // Delegate Method Conformance :: INUIAddVoiceShortcutViewControllerDelegate @available(iOS 12.0, *) func addVoiceShortcutViewController(_ controller: INUIAddVoiceShortcutViewController, didFinishWith voiceShortcut: INVoiceShortcut?, error: Error?) { controller.dismiss(animated: true, completion: nil) // The issue is here. Whether we add the or Dismiss the Siri shortcut screen without adding it, this delegate gets called. } @available(iOS 12.0, *) func addVoiceShortcutViewControllerDidCancel(_ controller: INUIAddVoiceShortcutViewController) { controller.dismiss(animated: true, completion: nil) } // Card Payment Intent public var cardPaymentIntent: CardPaymentIntent { let intent = CardPaymentIntent() intent.suggestedInvocationPhrase = NSLocalizedString("Pay my credit card", comment: "") return intent } Whenever I present the siri shortcut screen, either I add the shortcut or dismiss the screen without adding. In both cases , the shortcut is added. And this method is called every time func addVoiceShortcutViewController(_ controller: INUIAddVoiceShortcutViewController, didFinishWith voiceShortcut: INVoiceShortcut?, error: Error?) Any solution ? while I dismiss the screen, i want it not to be added into the shortcut

I'm trying to create an App Shortcut so that users can interact with one of my app's features using Siri. I would like to be able to turn this shortcut on or off at runtime using a feature toggle. Ideally, I would be able to do something like this. struct MyShortcuts: AppShortcutsProvider { static var appShortcuts: [AppShortcut] { // This shortcut is always available AppShortcut( intent: AlwaysAvailableIntent(), phrases: ["Show my always available intent with \(.applicationName)"], shortTitle: "Always Available Intent", systemImageName: "infinity" ) // This shortcut is only available when "myCoolFeature" is available if FeatureProvider.shared.isAvailable("myCoolFeature") { AppShortcut( intent: MyCoolFeatureIntent(), phrases: ["Show my cool feature in \(.applicationName)"], shortTitle: "My Cool Feature Intent", systemImageName: "questionmark" ) } } } However, this does not work because the existing buildOptional implementation is limited to components of type (any _AppShortcutsContentMarker & _LimitedAvailabilityAppShortcutsContentMarker)?. All other attempts at making appShortcuts dynamic have resulted in shortcuts not working at all. I've tried: Creating a makeAppShortcuts method that returns [AppShortcut] and invoking this method from within the appShortcuts Extending AppShortcutsBuilder to support a buildOptional block isn't restricted to a component type of (any _AppShortcutsContentMarker & _LimitedAvailabilityAppShortcutsContentMarker)? Extending AppShortcutsBuilder to support buildArray and using compactMap(_:) to return an empty array when the feature is disabled I haven't used SiriKit before but it appears that shortcut suggestions were set at runtime by invoking setShortcutSuggestions(_:), meaning that what I'm trying to do would be possible. I'm not against using SiriKit if I have to but my understanding is that the App Intents framework is meant to be a replacement for SiriKit, and the prompt within Xcode to replace older custom intents with App Intents indicates that that is indeed the case. Is there something obvious that I'm just missing or is this simply not possible with the App Intent framework? Is the App Intent framework not meant to replace SiriKit and should I just use that instead?

I've been attempting to install tf metal on my computer so that I can use GPUs instead of CPUs. I have tf macOS installed already, and I am fully updated with pip and tf. I'm currently 2 months into building and training a tf CNN, and I'm at the point where training a single epoch for my network will take a week (I have a lot of data that I need to use). I desperately need to use GPUs but am stuck with CPUs for now. I can't get access to a cluster, so the best I can do is continue to use my M2 MacBook. Is there any other way I can install TF metal? Is there a way I can use GPUs (rather than CPUs) when using TF if I can't get install metal? I keep getting this error message: "ERROR: Could not find a version that satisfies the requirement tensorflow-metal (from versions: none) ERROR: No matching distribution found for tensorflow-metal" I looked on apple forums, tried to download it from GitHub (the page is down), and anything else I could think of and/or find on the internet to help, but it still isn't installing. I've used the following commands and still no luck: python -m pip install tensorflow-metal pip install https://github.com/apple/tensorflow_metal/releases/download/v0.5.0/tensorflow_metal-0.5.0-py3-none-any.whl pip install tensorflow-metal pip3 install tensorflow-metal SYSTEM_VERSION_COMPAT=0 python -m pip install tensorflow-metal SYSTEM_VERSION_COMPAT=0 pip install tensorflow-macos tensorflow-metal conda install -c anaconda tensorflow-gpu Any help would be appreciated! Thanks so much!

Hi, I have a 128Gb 2024 M2 iPad Air. I upgraded to iPadOS 18 Beta 3 & I’m not seeing the option in settings to join the waitlist for the Apple Intelligence? Is this just an option in settings they’re gradually rolling out to devices? I know my iPad is eligible.

Hi, I am working with AppIntents, and have created an OpenIntent with a target using a MyAppContact AppEntity that I have created. This works fine when running from Shortcuts because it pops up a list of options from the 'suggestedEntities` method. But It doesn't work well when using with Siri. It invokes the AppIntent, but keeps repeatedly asking for the value of the 'target' entity, which you can't really pass in with voice. What's the workaround here? Can an OpenIntent be activated by voice as well?

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

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?

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!

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?

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.

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"])

VNRecognizeTextRequest2 did not recognize the upside down text of English text. VNRecognizeTextRequest3 can recognize the text even if English text is upside down. Till iOS 17, I can select VNRecognizeTextRequest2 or VNRecognizeTextRequest3 in my code which is minimum build is iOS16 when I need upside down text detection required.. But on iOS18, even if I set the VNRecognizeTextRequest2 in my code, result seems to be based on the VNRecognizeTextRequest3 because upside down text is detected. VNRecognizeTextRequest2 was deplicant on iOS18, I know. How can I recognize the observation result is upside down or not? Are there any solution with VNRecognizeTextRequest3?