Has anyone been able to run Tensorflow > 2.15 with Tensorflow Metal 1.1.0 on M3? I tried several times but was not successful. Seems like development on TensorFlow Metal has paused?

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!

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"

Just tried to write a very simple test of using foundation models, but it gave me the error like this "ModelManager received unentitled request. Expected entitlement com.apple.modelmanager.inference establishment of session failed with Missing entitlement: com.apple.modelmanager.inference" The simple code is listed below: let session: LanguageModelSession = LanguageModelSession() let response = try? await session.respond(to: "What is the capital of France?") print("Response: (response)") So what's the problem of this one?

We have suddenly encountered a serious issue: our local ML models are no longer being decrypted. Everything was set up according to the guide at https://developer.apple.com/documentation/coreml/generating-a-model-encryption-key and had been working in production, but yesterday we started receiving the following error: Error Domain=com.apple.CoreML Code=8 "Fetching decryption key from server failed: noEntryFound("No records found"). Make sure the encryption key was generated with correct team ID." UserInfo={NSLocalizedDescription=Fetching decryption key from server failed: noEntryFound("No records found"). Make sure the encryption key was generated with correct team ID.} We haven’t changed anything in our code. This started spontaneously affecting users of the release version as of yesterday. It also no longer works locally — we receive the same error at the moment the autogenerated function is called: class func load(configuration: MLModelConfiguration = MLModelConfiguration(), completionHandler handler: @escaping (Swift.Result<ZingPDModel, Error>) -> Void) I assume that I can generate a new key through Xcode, integrate it in place of the old one, and it might start working again. However, this won’t affect existing users until they update the app. Could the issue be on Apple’s infrastructure side?

When context window size exceeded, this error is not called (instead another error has shown up) to handle new session. LanguageModelSession.GenerationError.exceededContextWindowSize Or am I doing things wrong?

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

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?

Howdy, I'm following along with this sample: https://developer.apple.com/documentation/appintents/making-onscreen-content-available-to-siri-and-apple-intelligence I've got everything up and building. I can confirm that the userActivity modifier is associating my App Intent via EntityIdentifier but my custom Transferable representation (text) is never being called and when Siri is doing the ChatGPT handoff, it's just offering to send a screenshot which is what it does when it has no custom representation. What could I doing wrong? Where should I be looking?

Not finding a lot on the Swift Assist technology announced at WWDC 2024. Does anyone know the latest status? Also, currently I use OpenAI's macOS app and its 'Work With...' functionality to assist with Xcode development, and this is okay, certainly saves copying code back and forth, but it seems like AI should be able to do a lot more to help with Xcode app development. I guess I'm looking at what people are doing with AI in Visual Studio, Cline, Cursor and other IDEs and tools like those and feel a bit left out working in Xcode. Please let me know if there are AI tools or techniques out there you use to help with your Xcode projects. Thanks in advance!

@available(macCatalyst 18.1, *) @available(iOS 18.1, *) extension CKImageSelectionManager: ImagePlaygroundViewController.Delegate { public func imagePlaygroundViewController(_ imagePlaygroundViewController: ImagePlaygroundViewController, didCreateImageAt imageURL: URL) { } func presentImagePlayground() { let imagePlaygroundVC = ImagePlaygroundViewController() // Set delegate to self to receive the callback imagePlaygroundVC.delegate = self imagePlaygroundVC.isModalInPresentation = true // Prevents dismissal with swipe if needed self.delegate?.presentImageSelectionViewController(imagePlaygroundVC) } } This generates an error in the xcode generated swift header.

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 ?

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？

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

I'm new to Swift and was hoping the Playground would support loading adaptors. When I tried, I got a permissions error - thinking it's because it's not in the project and Playgrounds don't like going outside the project? A tutorial and some sample code would be helpful. Also some benchmarks on how long it's expected to take. Selfishly I'm on an M2 Mac Mini.

Hey everyone I'm Manish Mehta, field CTO at Centific. I recently read Apple's white paper, The Illusion of Thinking and it got me thinking about the current state of AI reasoning. Who here has read it? The paper highlights how LLMs often rely on pattern recognition rather than genuine understanding. When faced with complex tasks, their performance can degrade significantly. I was just thinking that to move beyond this problem, we need to explore approaches that combines Deeper Reasoning Architectures for true cognitive capability with Deep Human Partnership to guide AI toward better judgment and understanding. The first part means fundamentally rewiring AI to reason. This involves advancing deeper architectures like World Models, which can build internal simulations to understand real-world scenarios , and Neurosymbolic systems, which combines neural networks with symbolic reasoning for deeper self-verification. Additionally, we need to look at deep human partnership and scalable oversight. An AI cannot learn certain things from data alone, it lacks the real-world judgment an AI will never have. Among other things, deep domain expert human partners are needed to instill this wisdom , validate the AI's entire reasoning process , build its ethical guardrails , and act as skilled adversaries to find hidden flaws before they can cause harm. What do you all think? Is this focus on a deeper partnership between advanced AI reasoning and deep human judgment the right path forward? Agree? Disagree? Thanks

Our app is downloading a zip of an .mlpackage file, which is then compiled into an .mlmodelc file using MLModel.compileModel(at:). This model is then run using a VNCoreMLRequest. Two users – and this after a very small rollout - are reporting issues running the VNCoreMLRequest. The error message from their logs: Error Domain=com.apple.CoreML Code=0 "Failed to build the model execution plan using a model architecture file '/private/var/mobile/Containers/Data/Application/F93077A5-5508-4970-92A6-03A835E3291D/Documents/SKDownload/Identify-image-iOS/mobile_img_eu_v210.mlmodelc/model.mil' with error code: -5." The URL there is to a file inside the compiled model. The error is happening when the perform function of VNImageRequestHandler is run. (i.e. the model compiled without an error.) Anyone else seen this issue? Its only picked up in a few web results and none of them are directly relevant or have a fix. I know that a CoreML error Code=0 is a generic error, but does anyone know what error code -5 is? Not even sure which framework its coming from.

When I initialize a session with an existing transcript using this initializer: public convenience init(model: SystemLanguageModel = .default, guardrails: LanguageModelSession.Guardrails = .default, tools: [any Tool] = [], transcript: Transcript) The tools get ignored. I noticed that when doing that, the model never use the tools. When inspecting the transcript, I can see that the instruction entry does not have any tools available to it. I tried this for both transcripts that already include an instruction entry and ones that don't - both yielding the same result.. Is this the intended behavior / am I missing something here?

With respond() methods, the foundation model works well enough. With streamResponse() methods, the responses are very repetitive, verbose, and messy. My app with foundation model uses more than 500 MB memory on an iPad Pro when running from Xcode. Devices supporting Apple Intelligence have at least 8GB memory. Should Apple use a bigger model (using 3 ~ 4 GB memory) for better stream responses?

Hi everyone, I'm a Mac enthusiast experimenting with tensorflow-metal on my Mac Pro (2013). My question is about GPU selection in tensorflow-metal (v0.8.0), which still supports Intel-based Macs, including my machine. I've noticed that when running TensorFlow with Metal, it automatically selects a GPU, regardless of what I specify using device indices like "gpu:0", "gpu:1", or "gpu:2". I'm wondering if there's a way to manually specify which GPU should be used via an environment variable or another method. For reference, I’ve tried the example from TensorFlow’s guide on multi-GPU selection: https://www.tensorflow.org/guide/gpu#using_a_single_gpu_on_a_multi-gpu_system My goal is to explore performance optimizations by using MirroredStrategy in TensorFlow to leverage multiple GPUs: https://www.tensorflow.org/guide/distributed_training#mirroredstrategy Interestingly, I discovered that the metalcompute Python library (https://pypi.org/project/metalcompute/) allows to utilize manually selected GPUs on my system, allowing for proper multi-GPU computations. This makes me wonder: Is there a hidden environment variable or setting that allows manual GPU selection in tensorflow-metal? Has anyone successfully used MirroredStrategy on multiple GPUs with tensorflow-metal? Would a bridge between metalcompute and tensorflow-metal be necessary for this use case, or is there a more direct approach? I’d love to hear if anyone else has experimented with this or has insights on getting finer control over GPU selection. Any thoughts or suggestions would be greatly appreciated! Thanks!