We are experiencing a major issue with the native .version1 of the SoundAnalysis framework in iOS 18, which has led to all our user not having recordings. Our core feature relies heavily on sound analysis in the background, and it previously worked flawlessly in prior iOS versions. However, in the new iOS 18, sound analysis stops working in the background, triggering a critical warning. Details of the issue: We are using SoundAnalysis to analyze background sounds and have enabled the necessary background permissions. We are using the latest XCode A warning now appears, and sound analysis fails in the background. Below is the warning message we are encountering: Warning Message: Execution of the command buffer was aborted due to an error during execution. Insufficient Permission (to submit GPU work from background) [Espresso::handle_ex_plan] exception=Espresso exception: "Generic error": Insufficient Permission (to submit GPU work from background) (00000006:kIOGPUCommandBufferCallbackErrorBackgroundExecutionNotPermitted); code=7 status=-1 Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1). CoreML prediction failed with Error Domain=com.apple.CoreML Code=0 "Failed to evaluate model 0 in pipeline" UserInfo={NSLocalizedDescription=Failed to evaluate model 0 in pipeline, NSUnderlyingError=0x30330e910 {Error Domain=com.apple.CoreML Code=0 "Failed to evaluate model 1 in pipeline" UserInfo={NSLocalizedDescription=Failed to evaluate model 1 in pipeline, NSUnderlyingError=0x303307840 {Error Domain=com.apple.CoreML Code=0 "Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1)." UserInfo={NSLocalizedDescription=Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1).}}}}} We urgently need guidance or a fix for this, as our application’s main functionality is severely impacted by this background permission error. Please let us know the next steps or if this is a known issue with iOS 18.

When building MLModel, it is set to use NPU. It seems that GPU is used during inference, but it crashes during Compile. The stack is as follows：

Hey everyone, I've been updating my code to take advantage of the new Vision API for text recognition in macOS 15. I'm noticing some very odd behavior though, it seems like in general the new Vision API consistently produces worse results than the old API. For reference here is how I'm setting up my request. var request = RecognizeTextRequest() request.recognitionLevel = getOCRMode() // generally accurate request.usesLanguageCorrection = !disableLanguageCorrection // generally true request.recognitionLanguages = language.split(separator: ",").map { Locale.Language(identifier: String($0)) } // generally 'en' let observations = try? await request.perform(on: image) as [RecognizedTextObservation] Then I will process the results and just get the top candidate, which as mentioned above, typically is of worse quality then the same request formed with the old API. Am I doing something wrong here?

Hi! I recently updated to the latest 18.2 Beta version of iOS on my iPhone 15 Pro Max. Could you please guide me on how to locate and utilize the Image Search feature powered by Apple Intelligence? Just a little detail: I went on YouTube and the instruction was to hold the camera action button on the iPhone 16 and image search appears. So far, I haven’t been able to replicate these results on my iPhone 15 Pro Max. This is a great capability and I’d really like to try it out. “Live long and prosper.” -Spock -Jordan

When I pressed an early access a few days ago and when I check it it still says we will notify you when it is ready can apple please fix this problem with image playground

Itself been 4-5 days my Image playground has showing the “Downloading Support for Image Playground “

I click Join the Apple Intelligence Waitlist and chose Join Waitlist, but no show Joined list

I just got my new iPhone 16 Pro and upgraded to the 18.2 developer beta 4. I've set both Siri and the device language to English (United States), but the Apple Intelligence feature still doesn’t appear in my settings.

Hi Apple Developer Community, I’m exploring ways to fine-tune the SNSoundClassifier to allow users of my iOS app to personalize the model by adding custom sounds or adjusting predictions. While Apple’s WWDC session on sound classification explains how to train from scratch, I’m specifically interested in using SNSoundClassifier as the base model and building/fine-tuning on top of it. Here are a few questions I have: 1. Fine-Tuning on SNSoundClassifier: Is there a way to fine-tune this model programmatically through APIs? The manual approach using macOS, as shown in this documentation is clear, but how can it be done dynamically - within the app for users or in a cloud backend (AWS/iCloud)? Are there APIs or classes that support such on-device/cloud-based fine-tuning or incremental learning? If not directly, can the classifier’s embeddings be used to train a lightweight custom layer? Training is likely computationally intensive and drains too much on battery, doing it on cloud can be right way but need the right apis to get this done. A sample code will do good. 2. Recommended Approach for In-App Model Customization: If SNSoundClassifier doesn’t support fine-tuning, would transfer learning on models like MobileNetV2, YAMNet, OpenL3, or FastViT be more suitable? Given these models (SNSoundClassifier, MobileNetV2, YAMNet, OpenL3, FastViT), which one would be best for accuracy and performance/efficiency on iOS? I aim to maintain real-time performance without sacrificing battery life. Also it is important to see architecture retention and accuracy after conversion to CoreML model. 3. Cost-Effective Backend Setup for Training: Mac EC2 instances on AWS have a 24-hour minimum billing, which can become expensive for limited user requests. Are there better alternatives for deploying and training models on user request when s/he uploads files (training data)? 4. TensorFlow vs PyTorch: Between TensorFlow and PyTorch, which framework would you recommend for iOS Core ML integration? TensorFlow Lite offers mobile-optimized models, but I’m also curious about PyTorch’s performance when converted to Core ML. 5. Metrics: Metrics I have in mind while picking the model are these: Publisher, Accuracy, Fine-Tuning capability, Real-Time/Live use, Suitability of iPhone 16, Architectural retention after coreML conversion, Reasons for unsuitability, Recommended use case. Any insights or recommended approaches would be greatly appreciated. Thanks in advance!

Hello, I am exploring real-time object detection, and its replacement/overlay with another shape, on live video streams for an iOS app using Core ML and Vision frameworks. My target is to achieve high-speed, real-time detection without noticeable latency, similar to what’s possible with PageFault handling and Associative Caching in OS, but applied to video processing. Given that this requires consistent, real-time model inference, I’m curious about how well the Neural Engine or GPU can handle such tasks on A-series chips in iPhones versus M-series chips (specifically M1 Pro and possibly M4) in MacBooks. Here are a few specific points I’d like insight on: Hardware Suitability: How feasible is it to perform real-time object detection with Core ML on the Neural Engine (i.e., can it maintain low latency)? Would the M-series chips (e.g., M1 Pro or newer) offer a tangible benefit for this type of task compared to the A-series in mobile devices? Which A- and M- chips would be minimum feasible recommendation for such task. Performance Expectations: For continuous, live video object detection, what would be the expected frame rate or latency using an optimized Core ML model? Has anyone benchmarked such applications, and is the M-series required to achieve smooth, real-time processing? Differences Across Apple Hardware: How does performance scale between the A-series Neural Engine and M-series GPU and Neural Engine? Is the M-series vastly superior for real-time Core ML tasks like object detection on live video feeds? If anyone has attempted live object detection on these chips, any insights on real-time performance, limitations, or optimizations would be highly appreciated. Please refer: Apple APIs Thank you in advance for your help!

I was installing TensorFlow metal in the environment called "arm64_tf'" in anaconda using command line "python -m pip install tensorflow-metal" in terminal and it shows : ERROR: Could not find a version that satisfies the requirement tensorflow-metal (from versions: none) ERROR: No matching distribution found for tensorflow-metal I have already tried using " conda install -c anaconda libffi" but it still doesn't work is there a solution ? Thanks apologies for my bad English

Submited as : FB16052050 I am looking to adopt Machine Learning in a more granular manner, going beyond just using pre-built Metal, Core ML, or Create ML approaches. Specifically, I want to train models using Open Python PyTorch libraries, as these offer greater flexibility compared to Apple's native tools. However, these PyTorch APIs are primarily optimised for NVIDIA GPUs (or TPUs), not Apple's M3 or Apple Neural Engine (ANE). My goal is to train the models locally without resorting to cloud-based solutions for training or inference, and to then convert the models into Core ML format for deployment on Apple hardware. This would allow me to leverage Apple's hardware acceleration (via ANE, Metal, and MPS) while maintaining control over the training process in PyTorch. I want to know: What are my options for training models in PyTorch on local hardware (Apple M3 or equivalent), and how can I ensure that the PyTorch model can eventually be converted to Core ML without losing flexibility in model training and customisation? How can I perform training in PyTorch and avoid being restricted to inference-only workflows as Core ML typically allows? Is it possible to use the training capabilities of PyTorch and still get the performance benefits of Apple's hardware for both training and inference? What are the best practices or tools to ensure that my training pipeline in PyTorch is compatible with Apple's hardware constraints and optimised for local execution? I'm seeking a practical, cloud-free approach on Apple Hardware only that allows me to train models in PyTorch (keeping control over the training process) while ensuring that they can be deployed efficiently using Core ML on Apple hardware.

Hello all, I'm working on a project that involves listening to a person speak off of a script and I want to stop then restart the recognitionTask between sections so I don't run afoul of keeping the recognitionTask running for longer than it needs to. Also, I'd like to be able to flush the current input between sections so the input from the previous section doesn't roll over into the next one. This is based on the sample code for SFSpeechRecognizer so there's a chance I might be misunderstanding something. private func restartRecording() { let inputNode = audioEngine.inputNode audioEngine.stop() inputNode.removeTap(onBus: 0) recognitionRequest?.endAudio() recordingStarted = false recognitionTask?.cancel() do { try startRecording() } catch { print("Oopsie.") } } Here's my code. When I run it, the recognition task doesn't restart. Any ideas?

I am working to add Spotlight indexing for my app entities as discussed in WWDC24's video "What's New in App Intents". That video goes over the IndexedEntity protocol and the integration with Spotlight via CSSearchableItemAttributeSet. What I'm seeing though does not match the video. In the video, the presenter goes through the sort of progressive approach you can take to getting this data into Spotlight starting with the basics and then expanding to include more support depending on how much the developer wants to do. What I'm seeing is that if you conform to IndexedEntity, your entities will appear in Spotlight using the name derived from public var displayRepresentation: DisplayRepresentation So, that works. Name appears... BUT the next part of the video goes into how to expand your implementation with more metadata for Spotlight via CSSearchableItemAttributeSet. The issue I'm seeing is that once that's implemented, the items disappear from Spotlight, almost like that implementation is overriding the base implementation in a way that no longer functions. My expectation is that an item with custom attributes would use them in Spotlight as appropriate, not disappear from search, i.e. what's shown in the video should work. I've got a sample project here: https://hanchor.s3.amazonaws.com/misc/IndexingTest.zip To reproduce with the sample: Build and run. Indexing is setup in the init() method so it will just run. Go to Spotlight and search for 'Huntersblau', a string included in the content set. At this point you should see a result - good! Stop the app and go back and uncomment the var attributeSet: CSSearchableItemAttributeSet implementation in IndexingTestApp.swift. This will provide custom attributes to Spotlight. Repeat steps 1 and 2 - you'll see now, it no longer appears in the search results - when CSSearchableItemAttributeSet is implemented, the item drops out of Spotlight.

I'm trying to determine the best practice for handling if Image Playground is available but not installed or simply not supported. If ImagePlaygroundViewController.isAvailable is true, I will just display a button to start an Image Playground session. If it is false, does that mean ImagePlayground is supported but not installed? If it's supported and not installed, instead of a button to launch it, I want to display something like "Enable Apple Intelligence in Settings" or, better yet, a button that opens the Intelligence settings. Is that possible? But if it is on a system that doesn't support it, of course, I don't want to instruct the user to enable it. How can I determine if a device cannot install Image Playground? I read that Apple Intelligence requires iPhone 15 Pro, iPhone 15 Pro Max, and all iPhone 16 models, and no mention of the M1 iPad Pro, yet Image Playground runs on my M1 iPad Pro. What are the hardware requirements for Image Playground?

My app was rejected because of this error below but I cannot find any documentation on a key related to Image Playground. My app is set to minimum of 18.2 already. Rejection Message: The UIRequiredDeviceCapabilities key in the Info.plist is set in such a way that the app will not install on iPhone running iOS 18.1.1 Next Steps To resolve this issue, check the UIRequiredDeviceCapabilities key to verify that it contains only the attributes required for the app features or the attributes that must not be present on the device. Attributes specified by a dictionary should be set to true if they are required and false if they must not be present on the device. Resources Learn more about the UIRequiredDeviceCapabilities key.

I cannot find the hardware requirements for Image Playground documented anywhere. I'm also not sure if they are identical to devices that support Apple Intelligence. On the App Store, the only requirement listed for Image Playground is iOS 18.2. Not knowing the requirements is an issue because I need to be able to clearly state the requirements for the feature in my app description. Also, I'm sure my mother's current iPad is too old, but I'm not sure what models support it if I were to buy her a new one.

Hey Chat, I'm researching personality analysis using LLMs, and I'm curious about whether Apple’s AI can be allowed access to your messages, Instagram DMs, and similar communications to perform a personality analysis based on your writing style. If anyone has insights on this, I would greatly appreciate your input. Thx a ton

Hi, as showed in the course I created the PyTorch model sample and want to export / convert this model o a CoreML iOS Model using the coremltools. Input is a 224x224 image and output is a image classification (3 different classes) I am using coremltools for this with this code: import coremltools as ct modelml = ct.convert( scripted_model, inputs=[ct.ImageType(shape=(1,3,224,244))] ) I have a working iOS App code which performs with another model which was created using Microsoft Azure Vision. The PyTorch exported model is loaded and a prediction is performed, but I am getting this error: Foundation.MonoTouchException: Objective-C exception thrown. Name: NSInvalidArgumentException Reason: -[VNCoreMLFeatureValueObservation identifier]: unrecognized selector sent to instance 0x2805dd3b0 When I check the exported model with Xcode and compare it with another model which is working with the sample iOS App code (created and exported from Microsoft Azure) I can see that the input (for image classification using the device camera) seems ok and is equal, but the output is totally different. (see screenshots) The working model has two outputs: loss => Dictionary (String => Double) classLabel => String My exported model using coremltools just has one export: MultiArray(Float32) (name var_1620, I think this is the last feature layer output of the EfficentNetB2) How do I change my model or my coremltools export to get the correct output for the prediction ? I read the coreml documentation (https://coremltools.readme.io/docs/pytorch-conversion) and tried some GitHub samples. But I never get the correct output. How do I export the PyTorch model so that the output is correct and the prediction will work ? Best Marco

I’m trying to use a Decimal as a @Property in my AppEntity, but using the following code shows me a compiler error. I’m using Xcode 16.1. The documentation notes the following: You can use the @Parameter property wrapper with common Swift and Foundation types: Primitives such as Bool, Int, Double, String, Duration, Date, Decimal, Measurement, and URL. Collections such as Array and Set. Make sure the collection’s elements are of a type that’s compatible with IntentParameter. Everything works fine for other primitives as bools, strings and integers. How do I use the Decimal though? Code struct MyEntity: AppEntity { var id: UUID @Property(title: "Amount") var amount: Decimal // … } Compiler Error This error appears at the line of the @Property definition: Generic class 'EntityProperty' requires that 'Decimal' conform to '_IntentValue'