It seems to be that Swift has more APIs implemented than the C++ interface (especially APIs found in the MLXNN and MLXOptimize folders). Is there any intention to implement more APIs for neural networks and training them in the future?

Hi Apple Engineers, I am experiencing a potential memory management bug with CoreML on M1 Mac (32GB Unified Memory). When processing long video files (approx. 12,000 frames) using a CoreML execution provider, the system often completes the 'Analysing' phase but fails to transition into 'Processing'. It simply exits silently or hits an import error (scipy). However, if I split the same task into small 20-frame segments, it works perfectly at high speeds (~40 FPS). This suggests the hardware is capable, but there is an issue with memory fragmentation or resource cleanup during long-running CoreML sessions. Is there a way to force a VRAM/Unified Memory flush via CLI, or is this a known limitation for large frame indexing?

hello, Do you have any information on the handling of sparse matrix with MPS and PyTorch? release date? ...

Hi everyone 👋 I'd like to use coremltools to see how well a model performs on a remote device as part of a CI/CD pipeline. According to the Core ML Tools "Debugging and Performance Utilities" guide, remote devices must be in a "connected" state in order for coremltools to install the ModelRunner application. The devices in our system have a "paired" state, and I'm unable to set the them as "connected." The only way I know how to connect a device is to physically plug it in to a computer and open Xcode. I don't have physical access to the devices in the CI/CD system, and the host computer that interacts with them doesn't have Xcode installed. Here are some questions I've been looking into and would love some help answering: Has anyone managed to use the coremltools performance utilities in a similar system? Can you put a device in a "connected" state if you don't have physical access to the device and if you only have access to Xcode command line tools and not the Xcode app? Is it at all possible to install the coremltools ModelRunner application on a "paired" device, for example, by manually building the app and installing it with devicectl? Would other utilities, such as the MLModelBenchmarker work as expected if the app is installed this way? Thank you!

Hi everyone, I believe I’ve encountered a potential bug or a hardware alignment limitation in the Core ML Framework / ANE Runtime specifically affecting the new Stateful API (introduced in iOS 18/macOS 15). The Issue: A Stateful mlprogram fails to run on the Apple Neural Engine (ANE) if the state tensor dimensions (specifically the width) are not a multiple of 32. The model works perfectly on CPU and GPU, but fails on ANE both during runtime and when generating a Performance Report in Xcode. Error Message in Xcode UI: "There was an error creating the performance report Unable to compute the prediction using ML Program. It can be an invalid input data or broken/unsupported model." Observations: Case A (Fails): State shape = (1, 3, 480, 270). Prediction fails on ANE. Case B (Success): State shape = (1, 3, 480, 256). Prediction succeeds on ANE. This suggests an internal memory alignment or tiling issue within the ANE driver when handling Stateful buffers that don't meet the 32-pixel/element alignment. Reproduction Code (PyTorch + coremltools): import torch.nn as nn import coremltools as ct import numpy as np class RNN_Stateful(nn.Module): def __init__(self, hidden_shape): super(RNN_Stateful, self).__init__() # Simple conv to update state self.conv1 = nn.Conv2d(3 + hidden_shape[1], hidden_shape[1], kernel_size=3, padding=1) self.conv2 = nn.Conv2d(hidden_shape[1], 3, kernel_size=3, padding=1) self.register_buffer("hidden_state", torch.ones(hidden_shape, dtype=torch.float16)) def forward(self, imgs): self.hidden_state = self.conv1(torch.cat((imgs, self.hidden_state), dim=1)) return self.conv2(self.hidden_state) # h=480, w=255 causes ANE failure. w=256 works. b, ch, h, w = 1, 3, 480, 255 model = RNN_Stateful((b, ch, h, w)).eval() traced_model = torch.jit.trace(model, torch.randn(b, 3, h, w)) mlmodel = ct.convert( traced_model, inputs=[ct.TensorType(name="input_image", shape=(b, 3, h, w), dtype=np.float16)], outputs=[ct.TensorType(name="output", dtype=np.float16)], states=[ct.StateType(wrapped_type=ct.TensorType(shape=(b, ch, h, w), dtype=np.float16), name="hidden_state")], minimum_deployment_target=ct.target.iOS18, convert_to="mlprogram" ) mlmodel.save("rnn_stateful.mlpackage") Steps to see the error: Open the generated .mlpackage in Xcode 16.0+. Go to the Performance tab and run a test on a device with ANE (e.g., iPhone 15/16 or M-series Mac). The report will fail to generate with the error mentioned above. Environment: OS: macOS 15.2 Xcode: 16.3 Hardware: M4 Has anyone else encountered this 32-pixel alignment requirement for StateType tensors on ANE? Is this a known hardware constraint or a bug in the Core ML runtime? Any insights or workarounds (other than manual padding) would be appreciated.

Hi, I am a new IOS developer, trying to learn to integrate the Apple Foundation Model. my set up is: Mac M1 Pro MacOS 26 Beta Version 26.0 beta 3 Apple Intelligence & Siri --> On here is the code, func generate() { Task { isGenerating = true output = "⏳ Thinking..." do { let session = LanguageModelSession( instructions: """ Extract time from a message. Example Q: Golfing at 6PM A: 6PM """) let response = try await session.respond(to: "Go to gym at 7PM") output = response.content } catch { output = "❌ Error:, \(error)" print(output) } isGenerating = false } and I get these errors guardrailViolation(FoundationModels.LanguageModelSession.GenerationError.Context(debugDescription: "Prompt may contain sensitive or unsafe content", underlyingErrors: [Asset com.apple.gm.safety_embedding_deny.all not found in Model Catalog])) Can you help me get through this?

With the release of the newest version of tahoe and MLX supporting RDMA. Is there a documentation link to how to utilizes the libdrma dylib as well as what functions are available? I am currently assuming it mostly follows the standard linux infiniband library but I would like the apple specific details.

We are developing Apple AI for foreign markets and adapting it for iPhone models 17 and above. When the system language and Siri language are not the same—for example, if the system is in English and Siri is in Chinese—it can cause a situation where Apple AI cannot be used. So, may I ask if there are any other reasons that could cause Apple AI to be unavailable within the app, even if it has been enabled?

When the system language and Siri language are not the same, Apple AI may not be usable. For example, if the system is in English and Siri is in Chinese, it may cause Apple AI to not work. May I ask if there are other reasons why the app still cannot be used internally even after enabling Apple AI?

Hi team, I’m exploring the Model Context Protocol (MCP), which is used to connect LLMs/AI agents to external tools in a structured way. It's becoming a common standard for automation and agent workflows. Before I go deeper, I want to confirm: Does Apple currently provide any official MCP server, API surface, or SDK on iOS/macOS? From what I see, only third-party MCP servers exist for iOS simulators/devices, and Apple’s own frameworks (Foundation Models, Apple Intelligence) don’t expose MCP endpoints. Is there any chance Apple might introduce MCP support—or publish recommended patterns for safely integrating MCP inside apps or developer tools? I would like to see if I can share my app's data to the MCP server to enable other third-party apps/services to integrate easily

I'm using Vision framework (DetectFaceLandmarksRequest) with the same code and the same test image to detect face landmarks. On iOS 18 everything works as expected: detected face landmarks align with the face correctly. But when I run the same code on devices with iOS 26, the landmark coordinates are outside the [0,1] range, which indicates they are out of face bounds. Fun fact: the old VNDetectFaceLandmarksRequest API works very well without encountering this issue How I get face landmarks: private let faceRectangleRequest = DetectFaceRectanglesRequest(.revision3) private var faceLandmarksRequest = DetectFaceLandmarksRequest(.revision3) func detectFaces(in ciImage: CIImage) async throws -> FaceTrackingResult { let faces = try await faceRectangleRequest.perform(on: ciImage) faceLandmarksRequest.inputFaceObservations = faces let landmarksResults = try await faceLandmarksRequest.perform(on: ciImage) ... } How I show face landmarks in SwiftUI View: private func convert( point: NormalizedPoint, faceBoundingBox: NormalizedRect, imageSize: CGSize ) -> CGPoint { let point = point.toImageCoordinates( from: faceBoundingBox, imageSize: imageSize, origin: .upperLeft ) return point } At the same time, it works as expected and gives me the correct results: region is FaceObservation.Landmarks2D.Region let points: [CGPoint] = region.pointsInImageCoordinates( imageSize, origin: .upperLeft ) After that, I found that the landmarks are normalized relative to the unalignedBoundingBox. However, I can’t access it in code. Still, using these values for the bounding box works correctly. Things I've already tried: Same image input Tested multiple devices on iOS 26.2 -> always wrong. Tested multiple devices on iOS 18.7.1 -> always correct. Environment: macOS 26.2 Xcode 26.2 (17C52) Real devices, not simulator Face Landmarks iOS 18 Face Landmarks iOS 26

I am using macOS Tahoe on Xcode 26.

Hi, I'm testing DockKit with a very simple setup: I use VNDetectFaceRectanglesRequest to detect a face and then call dockAccessory.track(...) using the detected bounding box. The stand is correctly docked (state == .docked) and dockAccessory is valid. I'm calling .track(...) with a single observation and valid CameraInformation (including size, device, orientation, etc.). No errors are thrown. To monitor this, I added a logging utility – track(...) is being called 10–30 times per second, as recommended in the documentation. However: the stand does not move at all. There is no visible reaction to the tracking calls. Is there anything I'm missing or doing wrong? Is VNDetectFaceRectanglesRequest supported for DockKit tracking, or are there hidden requirements? Would really appreciate any help or pointers – thanks! That's my complete code: extension VideoFeedViewController: AVCaptureVideoDataOutputSampleBufferDelegate { func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) { guard let frame = CMSampleBufferGetImageBuffer(sampleBuffer) else { return } detectFace(image: frame) func detectFace(image: CVPixelBuffer) { let faceDetectionRequest = VNDetectFaceRectanglesRequest() { vnRequest, error in guard let results = vnRequest.results as? [VNFaceObservation] else { return } guard let observation = results.first else { return } let boundingBoxHeight = observation.boundingBox.size.height * 100 #if canImport(DockKit) if let dockAccessory = self.dockAccessory { Task { try? await trackRider( observation.boundingBox, dockAccessory, frame, sampleBuffer ) } } #endif } let imageResultHandler = VNImageRequestHandler(cvPixelBuffer: image, orientation: .up) try? imageResultHandler.perform([faceDetectionRequest]) func combineBoundingBoxes(_ box1: CGRect, _ box2: CGRect) -> CGRect { let minX = min(box1.minX, box2.minX) let minY = min(box1.minY, box2.minY) let maxX = max(box1.maxX, box2.maxX) let maxY = max(box1.maxY, box2.maxY) let combinedWidth = maxX - minX let combinedHeight = maxY - minY return CGRect(x: minX, y: minY, width: combinedWidth, height: combinedHeight) } #if canImport(DockKit) func trackObservation(_ boundingBox: CGRect, _ dockAccessory: DockAccessory, _ pixelBuffer: CVPixelBuffer, _ cmSampelBuffer: CMSampleBuffer) throws { // Zähle den Aufruf TrackMonitor.shared.trackCalled() let invertedBoundingBox = CGRect( x: boundingBox.origin.x, y: 1.0 - boundingBox.origin.y - boundingBox.height, width: boundingBox.width, height: boundingBox.height ) guard let device = captureDevice else { fatalError("Kamera nicht verfügbar") } let size = CGSize(width: Double(CVPixelBufferGetWidth(pixelBuffer)), height: Double(CVPixelBufferGetHeight(pixelBuffer))) var cameraIntrinsics: matrix_float3x3? = nil if let cameraIntrinsicsUnwrapped = CMGetAttachment( sampleBuffer, key: kCMSampleBufferAttachmentKey_CameraIntrinsicMatrix, attachmentModeOut: nil ) as? Data { cameraIntrinsics = cameraIntrinsicsUnwrapped.withUnsafeBytes { $0.load(as: matrix_float3x3.self) } } Task { let orientation = getCameraOrientation() let cameraInfo = DockAccessory.CameraInformation( captureDevice: device.deviceType, cameraPosition: device.position, orientation: orientation, cameraIntrinsics: cameraIntrinsics, referenceDimensions: size ) let observation = DockAccessory.Observation( identifier: 0, type: .object, rect: invertedBoundingBox ) let observations = [observation] guard let image = CMSampleBufferGetImageBuffer(sampleBuffer) else { print("no image") return } do { try await dockAccessory.track(observations, cameraInformation: cameraInfo) } catch { print(error) } } } #endif func clearDrawings() { boundingBoxLayer?.removeFromSuperlayer() boundingBoxSizeLayer?.removeFromSuperlayer() } } } } @MainActor private func getCameraOrientation() -> DockAccessory.CameraOrientation { switch UIDevice.current.orientation { case .portrait: return .portrait case .portraitUpsideDown: return .portraitUpsideDown case .landscapeRight: return .landscapeRight case .landscapeLeft: return .landscapeLeft case .faceDown: return .faceDown case .faceUp: return .faceUp default: return .corrected } }

Apple's Image Playground primarily performs image generation on-device, but can use secure Private Cloud Compute for more complex requests that require larger models. Private Cloud Compute (PCC) For more complex tasks that require greater computational power than the device can provide, Image Playground leverages Apple's Private Cloud Compute. This system extends the privacy and security of the device to the cloud: Secure Environment: PCC runs on Apple silicon servers and uses a secure enclave to protect data, ensuring requests are processed in a verified, secure environment. No Data Storage: Data is never stored or made accessible to Apple when using PCC; it is used only to fulfill the specific request. Independent Verification: Independent experts are able to inspect the code running on these servers to verify Apple's privacy promises.

v3 was released 2 years ago but developers are unable to convert models created with Keras v3 to CoreML

Hi everyone, I’m working on an iOS app that uses a Core ML model to run live image recognition. I’ve run into a persistent issue with the mlpackage not being turned into a swift class. This following error is in the code, and in carDetection.mlpackage, it says that model class has not been generated yet. The error in the code is as follows: What I’ve tried: Verified Target Membership is checked for carDetectionModel.mlpackage Confirmed the file is listed under Copy Bundle Resources (and removed from Compile Sources) Cleaned the build folder (Shift + Cmd + K) and rebuilt Renamed and re-added the .mlpackage file Restarted Xcode and re-added the file Logged bundle contents at runtime, but the .mlpackage still doesn’t appear The mlpackage is in Copy bundle resources, and is not in the compile sources. I just don't know why a swift class is not being generated for the mlpackage. Could someone please give me some guidance on what to do to resolve this issue? Sorry if my error is a bit naive, I'm pretty new to iOS app development

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

Hello, Are there any plans to compile a python 3.13 version of tensorflow-metal? Just got my new Mac mini and the automatically installed version of python installed by brew is python 3.13 and while if I was in a hurry, I could manage to get python 3.12 installed and use the corresponding tensorflow-metal version but I'm not in a hurry. Many thanks, Alan

I have an app that stores lots of data that is of interest to the user. Analogies would be the Photos apps or the Health app. I'm trying to use the Foundation Models framework to allow users to surface information they find interesting using natural language, for example, "Tell me about the widgets from yesterday" or "Tell me about the widgets for the last 3 days". Specifically, I'm trying to get a date range passed down to the Tool so that I can pull the relevant widgets from the database in the call function. What is the right way to set up the Arguments to get at a date range?

Hello, I am interested in using jax-metal to train ML models using Apple Silicon. I understand this is experimental. After installing jax-metal according to https://developer.apple.com/metal/jax/, my python code fails with the following error JaxRuntimeError: UNKNOWN: -:0:0: error: unknown attribute code: 22 -:0:0: note: in bytecode version 6 produced by: StableHLO_v1.12.1 My issue is identical to the one reported here https://github.com/jax-ml/jax/issues/26968#issuecomment-2733120325, and is fixed by pinning to jax-metal 0.1.1., jax 0.5.0 and jaxlib 0.5.0. Thank you!