I have exported a Pytorch model into a CoreML mlpackage file and imported the model file into my iOS project. The model is a Music Source Separation model - running prediction on audio-spectrogram blocks and returning separated audio source spectrograms. Model produces correct results vs. desktop+GPU+Python but the inference on iPhone 15 Pro Max is really, really slow. Using Xcode model Performance tool I can see that the inference isn't automatically managed between compute units - all of it runs on CPU. The Performance tool notation hints all that ops should be supported by both the GPU and Neural Engine. One thing to note, that when initializing the model with MLModelConfiguration option .cpuAndGPU or .cpuAndNeuralEngine there is an error in Xcode console: `Error(s) occurred compiling MIL to BNNS graph: [CreateBnnsGraphProgramFromMIL]: Failed to determine convolution kernel at location at /private/var/containers/Bundle/Application/2E3C4AFF-1FA4-4C95-AAE4-ECEBC0FB0BF9/mymss.app/mymss.mlmodelc/model.mil:2453:12 @ CreateBnnsGraphProgramFromMIL` Before going back hammering the model in Python, are there any tips/strategies I could try in CoreMLTools export phase or in configuring the model for prediction on iOS? My export toolchain is currently Linux with CoreMLTools v8.1, export target iOS16.

Hello, I am developing an app for the Swift Student challenge; however, I keep encountering an error when using ClassifyImageRequest from the Vision framework in Xcode: VTEST: error: perform(_:): inside 'for await result in resultStream' error: internalError("Error Domain=NSOSStatusErrorDomain Code=-1 \"Failed to create espresso context.\" UserInfo={NSLocalizedDescription=Failed to create espresso context.}") It works perfectly when testing it on a physical device, and I saw on another thread that ClassifyImageRequest doesn't work on simulators. Will this cause problems with my submission to the challenge? Thanks

Hello, I have a CoreML model and I want to convert it to a PyTorch model. Any ideas if this is possible and if so how?

While training a text classifier model with a few thousand samples completes in seconds, when using 100,000 or 1 million samples, CreateML's training time increases exponentially (to hours or days). During these hours/days, GPU usage is low and almost every CPU core is idle. When using the Swift APIs for model training, resource utilization does not increase. I'm using Xcode 16.2, macOS 15.2 on either an M2 Ultra 64 GB or an M3 Max 48 GB laptop (both using built-in SSD with ~500 GB free) running no other applications. Is there a setting I've missed to allow training to take over more of my computing resources? Is this expected of CreateML (i.e., when looking to exploit a larger corpus, I should move to other tooling)? I'd love to speed up my iteration cycle time.

I am a App designer and I am curious about what specific ML or AI Apple used to develop those features in the system. As far as I know, Apple's hand-raising detection, destination recommendations in maps, and exercise types in fitness all use ML. Are there more specific application examples of ML or AI? Does Apple have a document specifically introducing examples of specific applications of ML or AI technology in the system?

I have reinstalled everything including command line tools but the CreateML frameworks fail to install, I need the framework so that I can train my auto-categorzation model which predicts category based on descriptions. I need that framework because I want to use reviision 4. please suggest advice on how do I proceed

Hi everyone, I'm working with VNFeaturePrintObservation in Swift to compute the similarity between images. The computeDistance function allows me to calculate the distance between two images, and I want to cluster similar images based on these distances. Current Approach Right now, I'm using a brute-force approach where I compare every image against every other image in the dataset. This results in an O(n^2) complexity, which quickly becomes a bottleneck. With 5000 images, it takes around 10 seconds to complete, which is too slow for my use case. Question Are there any efficient algorithms or data structures I can use to improve performance? If anyone has experience with optimizing feature vector clustering or has suggestions on how to scale this efficiently, I'd really appreciate your insights. Thanks!

Hello, I am thinking of buying the MacBook Pro 14" with M4 Pro for ML/AI/ NLP tasks mostly. And since I have only used Windows before, I am wandering if it is compatible with libraries like "Pytorch" and "TensorFlow" etc., or people have experienced problems in installation... Thank you!

Is there any way to stop GPU work running that is scheduled using metal? Long shader calculations don't stop when application is stopped in Xcode and continue to take up GPU time and affect the display. Why is this functionality not available when Swift Tasks are able to be canceled?

Hey guys, I've been having difficulties transferring my Xcode project to a Swift playground (.swiftpm) for the Swift Student Challenge. I keep getting these errors as well as none of the views being able to find the model in scope: "TrashDetector 1.mlmodel: No predominant language detected. Set COREML_CODEGEN_LANGUAGE to preferred language." Unexpected duplicate tasks: Target 'TrashQuest' (project 'TrashQuest') has write command with output /Users/kmcph3/Library/Developer/Xcode/DerivedData/TrashQuest-glvzskunedgtakfrdmsxdoplondj/Build/Intermediates.noindex/TrashQuest.build/Debug-iphonesimulator/TrashQuest.build/0a4ef2429d66360920ddb4f16e65e233.sb I've gone through multiple post with these exact problems, but they all seem to be talking about ".playground" files due to the "Resources" folder (mind you I did try exactly what they said). Is there anyone that can help??? (Quick side note, why does it need to be a swiftpm file for the SSC??? Like why can't we just send the zip of our Xcode project??)

May i know the bundle identifier for apple intelligence?

Based on the documentation, it appears that MLTensor can be used to perform tensor operations using the ANE (Apple Neural Engine) by wrapping the tensor operations with withMLTensorComputePolicy with a MLComputePolicy initialized with MLComputeUnits.cpuAndNeuralEngine (it can also be initialized with MLComputeUnits.all to let the OS spread the load between the Neural Engine, GPU and CPU). However, when using the Instruments app, it appears that the tensor operations never get executed on the Neural Engine. It would be helpful if someone can guide me on the correct way to ensure that the Nerual Engine is used to perform the tensor operations (not as part of a CoreML model file). based on this example, I've created a simple code to try it: import Foundation import CoreML print("Starting...") let semaphore = DispatchSemaphore(value: 0) Task { await withMLTensorComputePolicy(.init(MLComputeUnits.cpuAndNeuralEngine)) { let v1 = MLTensor([1.0, 2.0, 3.0, 4.0]) let v2 = MLTensor([5.0, 6.0, 7.0, 8.0]) let v3 = v1.matmul(v2) await v3.shapedArray(of: Float.self) // is 70.0 let m1 = MLTensor(shape: [2, 3], scalars: [ 1, 2, 3, 4, 5, 6 ], scalarType: Float.self) let m2 = MLTensor(shape: [3, 2], scalars: [ 7, 8, 9, 10, 11, 12 ], scalarType: Float.self) let m3 = m1.matmul(m2) let result = await m3.shapedArray(of: Float.self) // is [[58, 64], [139, 154]] // Supports broadcasting let m4 = MLTensor(randomNormal: [3, 1, 1, 4], scalarType: Float.self) let m5 = MLTensor(randomNormal: [4, 2], scalarType: Float.self) let m6 = m4.matmul(m5) print("Done") return result; } semaphore.signal() } semaphore.wait() Here's what I get on the Instruments app: Notice how the Neural Engine line shows no usage. Ive run this test on an M1 Max MacBook Pro.

I was generating models using the code:- import Foundation import CreateML import TabularData import CoreML .... func makeTheModel(columntopredict:String,training:DataFrame,colstouse:[String],numberofmodels:Int) -> [MLLinearRegressor] { var returnmodels = [MLLinearRegressor]() var result = 0.0 for i in 0...numberofmodels { let pms = MLLinearRegressor.ModelParameters(validation: .split(strategy: .automatic)) do { let tm = try MLLinearRegressor(trainingData: training, targetColumn: columntopredict) returnmodels.append(tm) } catch let error as NSError { print("Error: \(error.localizedDescription)") } } return returnmodels } Which worked absolutely fine with Sonoma, but upon upgrading the OS to 15.3.1, it does absolutely nothing. I get no error messages, I get nothing, the code just pauses. If I look at CPU usage, as soon as it hits the line let tm = try MLLinearRegressor(trainingData: training, targetColumn: columntopredict) the CPU usage drops to 0% What am I doing wrong? Is there a flag I need to set somewhere in Xcode? This is on an M1 MacBook Pro Any help would be greatly appreciated

I have rewatched WWDC22 a few times , but still not getting full understanding how to get .mlmodel model file type from components . Example with banana ripeness is cool , but what need to be added to actually have output of .mlmodel , is somewhere full sample code for this type of modular project ? Code is from [https://developer.apple.com/videos/play/wwdc2022/10019) import CoreImage import CreateMLComponents struct ImageRegressor { static let trainingDataURL = URL(fileURLWithPath: "~/Desktop/bananas") static let parametersURL = URL(fileURLWithPath: "~/Desktop/parameters") static func train() async throws -> some Transformer<CIImage, Float> { let estimator = ImageFeaturePrint() .appending(LinearRegressor()) // File name example: banana-5.jpg let data = try AnnotatedFiles(labeledByNamesAt: trainingDataURL, separator: "-", index: 1, type: .image) .mapFeatures(ImageReader.read) .mapAnnotations({ Float($0)! }) let (training, validation) = data.randomSplit(by: 0.8) let transformer = try await estimator.fitted(to: training, validateOn: validation) try estimator.write(transformer, to: parametersURL) return transformer } } I have tried to run it in Mac OS command line type app, Swift-UI but most what I had as output was .pkg with "pipeline.json, parameters, optimizer.json, optimizer"

Keep getting error : I have tried Picker for File, Photo Library , both same results . Debugging the resize for 360x360 but still facing this error. The model I'm trying to implement is created with CreateMLComponents The process is from example of WWDC 2022 Banana Ripeness , I have used index for each .jpg . Prediction Failed: The VNCoreMLTransform request failed Is there some possible way to solve it or is error somewhere in training of model ?

I’m keep looking around documentation and some sample codes but still haven’t found example of how was used this type of Network Regressor . Does it take some special parameters to perform on ANE , what size,format of DataFrame ?

I've been trying to get some basic models to work on an M2 with tensor metal 1.2 and keras 2.15 and 2.18 and they all fail to work as expected. I'm running models copy/pasted from common tutorials like Jason Brownlee ML Mastery Object Classification tutorial using CIFAR-10. When run with the GPU I can't get any reasonable results. Under keras 2.15 the best validation accuracy ends up being around 10-15%. Under keras 2.18, the validation goes off the rails around epoch 5 with wildly low accuracy and loss values that are reported as "nan". Epoch 4/25 782/782: 19s 24ms/step - accuracy: 0.3450 - loss: 2.8925 - val_accuracy: 0.2992 - val_loss: 1.9869 Epoch 5/25 782/782: 19s 24ms/step - accuracy: 0.2553 - loss: nan - val_accuracy: 0.0000e+00 - val_loss: nan Running the same code on the CPU using keras 2.15 using tf.config.experimental.set_visible_devices([], 'GPU') yields a reasonable result with the validation accuracy around 75% as expected. Running the same code on keras 2.15 on a linux instance with just the CPU provides similar results. The tutorial can be found here: https://machinelearningmastery.com/object-recognition-convolutional-neural-networks-keras-deep-learning-library/ The only places I've deviated from the provided tutorial is using sdg = tf.keras.optimizers.legacy.SGD(learning_rate=lrate, momentum=0.9, nesterov=False) I did this at the advice of the warning: WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.SGD` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.SGD`. Is there something special that I need to do to make this work? I've followed the instructions here: https://developer.apple.com/metal/tensorflow-plugin/ I've purged the venv a few times and started from scratch, but all with similarly terrible results. Here are my platform details: Chip: Apple M2 Memory: 16 GB macOS : Sequoia 15.2 Python venv: 3.11 Jupyter Lab Version: 4.3.3 TensorFlow versions: 2.15, 2.18 tensorflow-metal: 1.2.0 Thanks for any assistance or advice.

使用MPS来加速机器学习功能，有时是否与torch会有适配性问题？

I’m trying to group my EntityPropertyQuery selection into sections as well as making it searchable. I know that the EntityStringQuery is used to perform the text search via entities(matching string: String). That works well enough and results in this modal: Though, when I’m using a DynamicOptionsProvider to section my EntityPropertyQuery, it doesn’t allow for searching anymore and simply opens the sectioned list in a menu like so: How can I combine both? I’ve seen it in other apps, but can’t figure out why my code doesn’t allow to section the results and make it searchable? Any ideas? My code (simplified) struct MyIntent: AppIntent { @Parameter(title: "Meter"), optionsProvider: MyOptionsProvider()) var meter: MyIntentEntity? // … struct MyOptionsProvider: DynamicOptionsProvider { func results() async throws -> ItemCollection<MyIntentEntity> { // Get All Data let allData = try IntentsDataHandler.shared.getEntities() // Create Arrays for Sections let fooEntities = allData.filter { $0.type == .foo } let barEntities = allData.filter { $0.type == .bar } return ItemCollection(sections: [ ItemSection("Foo", items: fooEntities), ItemSection("Bar", items: barEntities) ]) } } struct MeterIntentQuery: EntityStringQuery { // entities(for identifiers: [UUID]) and suggestedEntities() functions func entities(matching string: String) async throws -> [MyIntentEntity] { // Fetch All Data let allData = try IntentsDataHandler.shared.getEntities() // Filter Data by String let matchingData = allData.filter { data in return data.title.localizedCaseInsensitiveContains(string)) } return matchingData } }

While building an app with large language model inferencing on device, I got gibberish output. After carefully examining every detail, I found it's caused by the fused scaledDotProductAttention operation. I switched back to the discrete operations and problem solved. To reproduce the bug, please check https://github.com/zhoudan111/MPSGraph_SDPA_bug