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?

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

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, 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!

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!

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.

I'm finding the model is giving very jagged edges. This may be to do with the output resolution: Grayscale16Half 518 × 392. I have tried to re-convert this model on Colab but have not had much luck as this is very much out of my comfort zone. Has anyone else dealt with this? the model would be perfect if I could just overcome this issue.

Attempting to set up ComfyUI-CoreMLSuite on my Mac Studio. ComfyUI starts but no Core nodes are in the add-node-list. cloned both ComfyUI-CoreMLSuite and ml-stable-diffusion into custom_nodes and bounced the ComfyUI server. The startup complains that ml-stable-diffusion has no init.py. FileNotFoundError: [Errno 2] No such file or directory: ... /ComfyUI/custom_nodes/ml-stable-diffusion/init.py' It appears to be a show stopper. What to do?

Hello, I’m attempting to convert a TensorFlow model to CoreML using the coremltools package, but I’m encountering an error during the conversion process. The error traceback points to an issue within the Cast operation in the MIL (Model Intermediate Layer) when it tries to perform type inference: AttributeError: 'float' object has no attribute 'astype' Here is the relevant part of the error traceback: File ~/.pyenv/versions/3.10.12/lib/python3.10/site-packages/coremltools/converters/mil/mil/ops/defs/iOS15/elementwise_unary.py", line 896, in get_cast_value return input_var.val.astype(dtype=type_map[dtype_val]) I’ve tried converting a model from the yamnet-tensorflow2 repository, and this error occurs when CoreML tries to cast a float type during the conversion of certain operations. I’m currently using Python 3.10 and coremltools version 6.0.1, with TensorFlow 2.x. Has anyone encountered a similar issue or can offer suggestions on how to resolve this? I’ve also considered that this might be related to mismatches in the model’s data types, but I’m not sure how to proceed. Platform and package versions: coremltools 6.1 tensorflow 2.10.0 tensorflow-estimator 2.10.0 tensorflow-hub 0.16.1 tensorflow-io-gcs-filesystem 0.37.1 Python 3.10.12 pip 24.3.1 from ~/.pyenv/versions/3.10.12/lib/python3.10/site-packages/pip (python 3.10) 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 help or pointers would be greatly appreciated!

Hi Everyone, I'm currently facing an issue where TensorFlow is unable to detect the GPU on my M1 Mac for model training. When I run the following code to check for available GPUs: import tensorflow as tf print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) Num GPUs Available: 0 I have already applied the steps mentioned in the developer apple document. https://developer.apple.com/metal/tensorflow-plugin/ System Information: Device: M1 Mac Pro Max Python Version: 3.12.2 TensorFlow Version: 2.17.0 OS: macOS Sequoia (15.1) Questions: Is there any additional configuration required to enable GPU support on M1 Macs? Are there specific TensorFlow versions that I should be using for better compatibility? Has anyone else faced this issue, and how did you resolve it?

Hi, I found when continuously predicting with the same Core ML model in 120 FPS will be faster than in 60 FPS. I use Macbook Pro M2 and turn on ProMotion to run Core ML model prediction with a 120 FPS video, the average prediction time is 7.46ms as below: But when I turn off ProMotion, set 60 Hz refresh rate, and run Core ML model prediction with a 60 FPS video, the average prediction time is 10.91ms as below: What could be the technical explanation for these results? Is there any documentation or technical literature that addresses this behavior?

I have a model that uses a CoreML delegate, and I’m getting the following warning whenever I set the model to nil. My understanding is that CoreML is creating a cache in the app’s storage but is having issues clearing it. As a result, the app’s storage usage increases every time the model is loaded. This StackOverflow post explains the problem in detail: App Storage Size Increases with CoreML usage This is a critical issue because the cache will eventually fill up the phone’s storage: doUnloadModel:options:qos:error:: model=_ANEModel: { modelURL=file:///var/mobile/Containers/Data/Application/22DDB13E-DABA-4195-846F-F884135F37FE/tmp/F38A9824-3944-420C-BD32-78CE598BE22D-10125-00000586EFDFD7D6.mlmodelc/ : sourceURL= (null) : key={"isegment":0,"inputs":{"0_0":{"shape":[256,256,1,3,1]}},"outputs":{"142_0":{"shape":[16,16,1,222,1]},"138_0":{"shape":[16,16,1,111,1]}}} : identifierSource=0 : cacheURLIdentifier=E0CD0F44FB0417936057FC6375770CFDCCC8C698592ED412DDC9C81E96256872_C9D6E5E73302943871DC2C610588FEBFCB1B1D730C63CA5CED15D2CD5A0AC0DA : string_id=0x00000000 : program=_ANEProgramForEvaluation: { programHandle=6077141501305 : intermediateBufferHandle=6077142786285 : queueDepth=127 } : state=3 : programHandle=6077141501305 : intermediateBufferHandle=6077142786285 : queueDepth=127 : attr={ ANEFModelDescription = { ANEFModelInput16KAlignmentArray = ( ); ANEFModelOutput16KAlignmentArray = ( ); ANEFModelProcedures = ( { ANEFModelInputSymbolIndexArray = ( 0 ); ANEFModelOutputSymbolIndexArray = ( 0, 1 ); ANEFModelProcedureID = 0; } ); kANEFModelInputSymbolsArrayKey = ( "0_0" ); kANEFModelOutputSymbolsArrayKey = ( "138_0@output", "142_0@output" ); kANEFModelProcedureNameToIDMapKey = { net = 0; }; }; NetworkStatusList = ( { LiveInputList = ( { BatchStride = 393216; Batches = 1; Channels = 3; Depth = 1; DepthStride = 393216; Height = 256; Interleave = 1; Name = "0_0"; PlaneCount = 3; PlaneStride = 131072; RowStride = 512; Symbol = "0_0"; Type = Float16; Width = 256; } ); LiveOutputList = ( { BatchStride = 113664; Batches = 1; Channels = 111; Depth = 1; DepthStride = 113664; Height = 16; Interleave = 1; Name = "138_0@output"; PlaneCount = 111; PlaneStride = 1024; RowStride = 64; Symbol = "138_0@output"; Type = Float16; Width = 16; }, { BatchStride = 227328; Batches = 1; Channels = 222; Depth = 1; DepthStride = 227328; Height = 16; Interleave = 1; Name = "142_0@output"; PlaneCount = 222; PlaneStride = 1024; RowStride = 64; Symbol = "142_0@output"; Type = Float16; Width = 16; } ); Name = net; } ); } : perfStatsMask=0} was not loaded by the client.