I have been attempting to achieve the early access image creation (iPad iOS 18.2) and it has taken days, I still don’t have it and feel I never will. Someone heeelelllppppp!

I have an issue with AI writing tools, certain applications, such as LinkedIn, function effectively, while others, like Instagram and WhatsApp, lack the writing tools option.

I'm experiencing issues with the Core ML Async API, as it doesn't seem to be working correctly. It consistently hangs during the "03 performInference, after get smallInput, before prediction" part, as shown in the attached: log1.txt log2.txt Below is my code. Could you please advise on how I should modify it? private func createFrameAsync(for sampleBuffer: CMSampleBuffer ) { guard let pixelBuffer = sampleBuffer.imageBuffer else { return } Task { print("**** createFrameAsync before performInference") do { try await runModelAsync(on: pixelBuffer) } catch { print("Error processing frame: \(error)") } print("**** createFrameAsync after performInference") } } func runModelAsync(on pixelbuffer: CVPixelBuffer) async { print("01 performInference, before resizeFrame") guard let data = metalResizeFrame(sourcePixelFrame: pixelbuffer, targetSize: MTLSize.init(width: InputWidth, height: InputHeight, depth: 1), resizeMode: .scaleToFill) else { os_log("Preprocessing failed", type: .error) return } print("02 performInference, after resizeFrame, before get smallInput") let input = model_smallInput(input: data) print("03 performInference, after get smallInput, before prediction") if let prediction = try? await mlmodel!.model.prediction(from: input) { print("04 performInference, after prediction, before get result") var results: [Float] = [] let output = prediction.featureValue(for: "output")?.multiArrayValue if let bufferPointer = try? UnsafeBufferPointer<Float>(output!) { results = Array(bufferPointer) } print("05 performInference, after get result, before setRenderData") let localResults = results await MainActor.run { ScreenRecorder.shared .setRenderDataNormalized( screenImage: pixelbuffer, depthData: localResults ) } print("06 performInference, after setRenderData") } }

I have already updated a few days ago and requested for Genmoji, Image wand and Image playground yet it is still in pending. Really want it to work.

stuck on "early access requested". i feel like since we're developing testers we shouldn't have to go through this process...

Hey, has anyone figured out how the “Persons” list in Genmoji/Playground actually works? I’ve had a strange experience so far. When I first got access during Beta 2, the list randomly included about 10–15 people, even though my photo library contains many more recognizable faces. To try fixing this, I started naming faces in the Photos app, hoping they’d be added to the Genmoji/Playground list, but nothing changed. Then, after updating to Beta 3, it added just 2–3 of the people I had named. Encouraged, I spent about an hour naming all the faces in my library. But a few hours later, the list unexpectedly removed around 10 people, leaving me with fewer than I had initially. I’ve also read that leaving the phone locked and plugged into power should help sort people in the library, but that hasn’t worked for me yet. Anyone else experienced this or found a way to make it work? Thanks!

Hi folks, I'm trying to import data to train a model and getting the above error. I'm using the latest Xcode, have double checked the formatting in the annotations file, and used jpgrepair to remove any corruption from the data files. Next step is to try a different dataset, but is this a particular known error? (Or am I doing something obviously wrong?) 2019 Intel Mac, Xcode 15.4, macOS Sonoma 14.1.1 Thanks

Hi, I am not been able to use new memory create function post 18.1 beta update. Below is the error / response showing.

Hi, I am trying to create a multi label image classifier model using CreateML (the one included in Xcode 16.1). However, my annoations.json file won't get accepted by the app. I get the following error: annotations.json file contains field "Index 0" that is not of type String Here is a JSON example which results in said error: [ { "image": "image1.jpg", "annotations": [ { "label": "car-license-plate", "coordinates": { "x": 160, "y": 108, "width": 190, "height": 200 } } ] }, { "image": "image2.jpg", "annotations": [ { "label": "car-license-plate", "coordinates": { "x": 250, "y": 150, "width": 100, "height": 98 } } ] } ]

https://developer.apple.com/machine-learning/models/ Adding the DepthAnythingV2SmallF16.mlpackage to a new project in Xcode 16.1 and invoking the class crashes the app. Anyone else having the same issue? I tried Xcode 16.2 beta and it has the same response. Code import UIKit import CoreML class ViewController : UIViewController { override func viewDidLoad() { super.viewDidLoad() // Do any additional setup after loading the view. do { // Use a default model configuration. let defaultConfig = MLModelConfiguration() // app crashes here let model = try? DepthAnythingV2SmallF16( configuration: defaultConfig ) } catch { // } } } Response /AppleInternal/Library/BuildRoots/4b66fb3c-7dd0-11ef-b4fb-4a83e32a47e1/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphExecutable.mm:129: failed assertion Error: unhandled platform for MPSGraph serialization' `

I need to add AI Image Playground in my iOS app with UIKit, as per WWDC 2024 introduce new AI Image Playground API, I didn't find any official document yet, So how can add it ?

I am working on adding indexing to my App Entities via IndexedEntity. I already, separately index my content via Spotlight. Watching 'What's New in App Intents', this is covered well but I have a question. Do I need to implement both CSSearchableItem's associateAppEntity AND also a custom implementation of attributeSet in my IndexedEntity conformance? It seems duplicative but I can't tell from the video if you're supposed to do both or just one or the other.

So when I was on the Settings app. I couldn’t see it, but I updated it and I don’t it why don’t is this a glitch please fix it your friend Isaiah.

I am trying to create a Pipeline with 3 sub-models: a Feature Vectorizer -> a NN regressor converted from PyTorch -> a Feature Extractor (to convert the output tensor to a Double value). The pipeline works fine when I use just a Vectorizer and an Extractor, this is the code: vectorizer = models.feature_vectorizer.create_feature_vectorizer( input_features=["windSpeed", "theoreticalPowerCurve", "windDirection"], # Multiple input features output_feature_name="input" ) preProc_spec = vectorizer[0] ct.utils.convert_double_to_float_multiarray_type(preProc_spec) extractor = models.array_feature_extractor.create_array_feature_extractor( input_features=[("input",datatypes.Array(3,))], # Multiple input features output_name="output", extract_indices = 1 ) ct.utils.convert_double_to_float_multiarray_type(extractor) pipeline_network = pipeline.PipelineRegressor ( input_features = ["windSpeed", "theoreticalPowerCurve", "windDirection"], output_features=["output"] ) pipeline_network.add_model(preProc_spec) pipeline_network.add_model(extractor) ct.utils.convert_double_to_float_multiarray_type(pipeline_network.spec) ct.utils.save_spec(pipeline_network.spec,"Final.mlpackage") This model works ok. I created a regression NN using PyTorch and converted to Core ML either import torch import torch.nn as nn class TurbinePowerModel(nn.Module): def __init__(self): super().__init__() self.linear1 = nn.Linear(3, 4) self.activation1 = nn.ReLU() #self.linear2 = nn.Linear(5, 4) #self.activation2 = nn.ReLU() self.output = nn.Linear(4, 1) def forward(self, x): #x = F.normalize(x, dim = 0) x = self.linear1(x) x = self.activation1(x) # x = self.linear2(x) # x = self.activation2(x) x = self.output(x) return x def forward_inference(self, windSpeed,theoreticalPowerCurve,windDirection): input_tensor = torch.tensor([windSpeed, theoreticalPowerCurve, windDirection], dtype=torch.float32) return self.forward(input_tensor) model = torch.load('TurbinePowerRegression-1layer.pt', weights_only=False) import coremltools as ct print(ct.__version__) import pandas as pd from sklearn.preprocessing import StandardScaler df = pd.read_csv('T1_clean.csv',delimiter=';') X = df[['WindSpeed','TheoreticalPowerCurve','WindDirection']] y = df[['ActivePower']] scaler = StandardScaler() X = scaler.fit_transform(X) y = scaler.fit_transform(y) X_tensor = torch.tensor(X, dtype=torch.float32) y_tensor = torch.tensor(y, dtype=torch.float32) traced_model = torch.jit.trace(model, X_tensor[0]) mlmodel = ct.convert( traced_model, inputs=[ct.TensorType(name="input", shape=X_tensor[0].shape)], classifier_config=None # Optional, for classification tasks ) mlmodel.save("TurbineBase.mlpackage") This model has a Multiarray(Float 32 3) as input and a Multiarray(Float32 1) as output. When I try to include it in the middle of the pipeline (Adjusting the output and input types of the other models accordingly), the process runs ok, but I have the following error when opening the generated model on Xcode: What's is missing on the models. How can I set or adjust this metadata properly? Thanks!!!

Using an iPhone 15 Pro. I'm in Australia on iOS 18.2 Beta 1 but there's no mention of Apple Intelligence anywhere in the phone. Settings looks identical without the menu for AI, there's no playground image generation. It's as if my phone doesn't know it exists

Recently, I'm trying to deploy some third-party LLM to Apple devices. The methodoloy is similar to https://github.com/Anemll/Anemll. The biggest issue I'm having now is the runtime memory usage. When there are multiple functions in a model (mlpackage or mlmodelc), the runtime memory usage for weights is somehow duplicated when I load all of them. Here's the detail: I created my multifunction mlpackage following https://apple.github.io/coremltools/docs-guides/source/multifunction-models.html I loaded each of the functions using the generated swift class: let config = MLModelConfiguration() config.computeUnits = MLComputeUnits.cpuAndNeuralEngine config.functionName = "infer_512"; let ffn1_infer_512 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_1024"; let ffn1_infer_1024 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_2048"; let ffn1_infer_2048 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) I observed that RAM usage increases linearly as I load each of the functions. Using instruments, I see that there are multiple HWX files generated and loaded, each of which contains all the weight data. My understanding of what's happening here: The CoreML framework did some MIL->MIL preprocessing before further compilation, which includes separating CPU workload from ANE workload. The ANE part of each function is moved into a separate MIL file then compile separately into a HWX file each. The problem is that the weight data of these HWX files are duplicated. Since that the weight data of LLMs is huge, it will cause out-of-memory issue on mobile devices. The improvement I'm hoping from Apple: I hope we can try to merge the processed MIL files back into one before calling ANECCompile(), so that the weights can be merged. I don't have control over that in user space and I'm not sure if that is feasible. So I'm asking for help here. Thanks.

Hi everyone, I'm working on integrating object recognition from live video feeds into my existing app by following Apple's sample code. My original project captures video and records it successfully. However, after integrating the Vision-based object detection components (VNCoreMLRequest), no detections occur, and the callback for the request is never triggered. To debug this issue, I’ve added the following functionality: Set up AVCaptureVideoDataOutput for processing video frames. Created a VNCoreMLRequest using my Core ML model. The video recording functionality works as expected, but no object detection happens. I’d like to know: How to debug this further? Which key debug points or logs could help identify where the issue lies? Have I missed any key configurations? Below is a diff of the modifications I’ve made to my project for the new feature. Diff of Changes: (Attach the diff provided above) Specific Observations: The captureOutput method is invoked correctly, but there is no output or error from the Vision request callback. Print statements in my setup function setForVideoClassify() show that the setup executes without errors. Questions: Could this be due to issues with my Core ML model compatibility or configuration? Is the VNCoreMLRequest setup incorrect, or do I need to ensure specific image formats for processing? Platform: Xcode 16.1, iOS 18.1, Swift 5, SwiftUI, iPhone 11, Darwin MacBook-Pro.local 24.1.0 Darwin Kernel Version 24.1.0: Thu Oct 10 21:02:27 PDT 2024; root:xnu-11215.41.3~2/RELEASE_X86_64 x86_64 Any guidance or advice is appreciated! Thanks in advance.

It appears that there is a size limit when training the Tabular Classification model in CreatML. When the training data is small, the training process completes smoothly after a specified period. However, as the data volume increases, the following issues occur: initially, the training process indicates that it is in progress, but after approximately 24 hours, it is automatically terminated after an hour. I am certain that this is not a manual termination by myself or others, but rather an automatic termination by the machine. This issue persists despite numerous attempts, and the only message displayed is “Training Canceled.” I would appreciate it if someone could explain the reason behind this behavior and provide a solution. Thank you for your assistance.

Some of my users are experiencing crashes on instantiation of a CoreML model I've bundled with my app. I haven't been able to reproduce the crash on any of my devices. Crashes happen across all iOS 18 releases. Seems like something internal in CoreML is causing an issue. Full stack trace: 6646631296fb42128ddc340b2d4322f7-symbolicated.crash

I'm working on a cross-platform AI app. It is a CMake project. The inference part should be built as a library separately on Windows and MacOS. On MacOS it should be built with objective-c and CoreML. Here's my step roughly: Create a XCode Project for CoreML inference and build it as static library. Models are compiled to ".mlmodelc", and codes are compile to binary ".a" lib. Create a CMake Project for the app, and use the ".a" lib built by XCode. Run the App. I initialize the CoreML model like this(just for demostration): #include "det.h" // the model header generated by xcode auto url = [[NSURL alloc] initFileURLWithPath:[NSString stringWithFormat:@"%@/%@", dir, @"det.mlmodelc"]]; auto model = [[det alloc] initWithContentsOfURL:url error:&error]; // no error The url is valid, and the initialization doesn't report any error. However, when I tried to do inference using codes like this: auto cvPixelBuffer = createCVPixelBuffer(960, 960); // util function auto preds = [model predictionFromImage:cvPixelBuffer error:NULL]; The output preds will be null and I got these errors: 2024-12-10 14:52:37.678201+0800 望言OCR[50204:5615023] [e5rt] E5RT encountered unknown exception. 2024-12-10 14:52:37.678237+0800 望言OCR[50204:5615023] [coreml] E5RT: E5RT encountered an unknown exception. (11) 2024-12-10 14:52:37.870739+0800 望言OCR[50204:5615023] H11ANEDevice::H11ANEDeviceOpen kH11ANEUserClientCommand_DeviceOpen call failed result=0xe00002e2 2024-12-10 14:52:37.870758+0800 望言OCR[50204:5615023] Device Open failed - status=0xe00002e2 2024-12-10 14:52:37.870760+0800 望言OCR[50204:5615023] (Single-ANE System) Critical Error: Could not open the only H11ANE device 2024-12-10 14:52:37.870769+0800 望言OCR[50204:5615023] H11ANEDeviceOpen failed: 0x17 2024-12-10 14:52:37.870845+0800 望言OCR[50204:5615023] H11ANEDevice::H11ANEDeviceOpen kH11ANEUserClientCommand_DeviceOpen call failed result=0xe00002e2 2024-12-10 14:52:37.870848+0800 望言OCR[50204:5615023] Device Open failed - status=0xe00002e2 2024-12-10 14:52:37.870849+0800 望言OCR[50204:5615023] (Single-ANE System) Critical Error: Could not open the only H11ANE device 2024-12-10 14:52:37.870853+0800 望言OCR[50204:5615023] H11ANEDeviceOpen failed: 0x17 2024-12-10 14:52:37.870857+0800 望言OCR[50204:5615023] [common] start: ANEDeviceOpen() failed : ret=23 : It seems that CoreML failed to find ANE device. Is there anything need to be done before we use a CoreML Model as a library in a CMake or other non-XCode project? By the way, codes like above will work on an XCode Native App with CoreML (I tested this before) . So I guess I missed some environment initializations in my non-XCode project?