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Foundation Models Adaptors for Generable output?
Is it possible to train an Adaptor for the Foundation Models to produce Generable output? If so what would the response part of the training data need to look like? Presumably, under the hood, the model is outputting JSON (or some other similar structure) that can be decoded to a Generable type. Would the response part of the training data for an Adaptor need to be in that structured format?
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165
Jun ’25
Core ML Model Prediction in 120 FPS faster than 60 FPS
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?
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563
Nov ’24
Unable to Use M1 Mac Pro Max GPU for TensorFlow Model Training
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?
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891
Nov ’24
Ways I can leverage AI when the user asks Siri, "What does this word mean"
I'm the creator of an app that helps users learn Arabic. Inside of the app users can save words, engage in lessons specific to certain grammar concepts etc. I'm looking for a way for Siri to 'suggest' my app when the user asks to define any Arabic words. There are other questions that I would like for Siri to suggest my app for, but I figure that's a good start. What framework am I looking for here? I think AppItents? I remember I played with it for a bit last year but didn't get far. Any suggestions would be great. Would the new Foundations model be any help here?
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94
Jun ’25
Is there an API to check if a Core ML compiled model is already cached?
Hello Apple Developer Community, I'm investigating Core ML model loading behavior and noticed that even when the compiled model path remains unchanged after an APP update, the first run still triggers an "uncached load" process. This seems to impact user experience with unnecessary delays. Question: Does Core ML provide any public API to check whether a compiled model (from a specific .mlmodelc path) is already cached in the system? If such API exists, we'd like to use it for pre-loading decision logic - only perform background pre-load when the model isn't cached. Has anyone encountered similar scenarios or found official solutions? Any insights would be greatly appreciated!
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181
May ’25
Issue with iOS 18.2 Beta - "Ask" Button Not Working in Visual Intelligence
Hey everyone, I recently installed the iOS 18.2 developer beta and connected my ChatGPT Premium account to use it within the Visual Intelligence feature. After taking a photo, I tried pressing the "Ask" button, but it’s completely unresponsive. I've tried troubleshooting by doing a hard reset and a regular restart, but no luck so far. Has anyone else encountered this issue? If so, do you know of any workaround or fix that might help? Thanks in advance!
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1.9k
Oct ’24
Help with TensorFlow to CoreML Conversion: AttributeError: 'float' object has no attribute 'astype'
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!
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1.1k
Nov ’24
Does the Random Number Generation (RGN) process change over different OS versions?
Hi everyone! I appreciate your help. I am a researcher and I use UMAP to cluster my data. Reproducibility is a key requirement for my field, so I set a random seed for reproducibility. After coming back to my project after some time, I do not get the same results than previously even though I am working in a virtual environment, which I did not change. When pondering about the reasons, I remembered that I upgraded my OS from Sonoma 14.1.1 to 14.5, so I was wondering whether the change in OS might cause those issues. I'm sorry if this question is obvious to developer folks, but before I downgrade my OS or create a virtual machine, any tipp is much appreciated. Thank you!
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443
Oct ’24
DepthAnything v2
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.
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624
Dec ’24
Running out of memory analyzing images with ImageRequestHandler
Hi, I'm trying to analyze images in my Photos library with the following code: func analyzeImages(_ inputIDs: [String]) { let manager = PHImageManager.default() let option = PHImageRequestOptions() option.isSynchronous = true option.isNetworkAccessAllowed = true option.resizeMode = .none option.deliveryMode = .highQualityFormat let concurrentTasks=1 let clock = ContinuousClock() let duration = clock.measure { let group = DispatchGroup() let sema = DispatchSemaphore(value: concurrentTasks) for entry in inputIDs { if let asset=PHAsset.fetchAssets(withLocalIdentifiers: [entry], options: nil).firstObject { print("analyzing asset: \(entry)") group.enter() sema.wait() manager.requestImage(for: asset, targetSize: PHImageManagerMaximumSize, contentMode: .aspectFit, options: option) { (result, info) in if let result = result { Task { print("retrieved asset: \(entry)") let aestheticsRequest = CalculateImageAestheticsScoresRequest() let fingerprintRequest = GenerateImageFeaturePrintRequest() let inputImage = result.cgImage! let handler = ImageRequestHandler(inputImage) let (aesthetics,fingerprint) = try await handler.perform(aestheticsRequest, fingerprintRequest) // save Results print("finished asset: \(entry)") sema.signal() group.leave() } } else { group.leave() } } } } group.wait() } print("analyzeImages: Duration \(duration)") } When running this code, only two requests are being processed simultaneously (due to to the semaphore)... However, if I call the function with a large list of images (>100), memory usage balloons over 1.6GB and the app crashes. If I call with a smaller number of images, the loop completes and the memory is freed. When I use instruments to look for memory leaks, it indicates no memory leaks are found, but there are 150+ VM:IOSurfaces allocated by CMPhoto, CoreVideo and CoreGraphics @ 35MB each. Shouldn't each surface be released when the task is complete?
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583
Dec ’24
Source Files from the Session number 424 WWDC2019
In the 2019 WWDC session Training Object Detection Models in Create ML a JSON file named: annotations_832_newdice_copy.json was show alongside with the images folder named: Dice Training Images Two Sets. Are these resources made available for devs ? I am looking to understand whether the 6000 annotations were needed to be done manually ? Meaning, they have annotated around 1000 images making 6 labels on each manually to achieve this source ? Video shows around 1000 images. Can someone please clarify.
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633
Dec ’24
existential any error in MLModel class
Problem I have set SWIFT_UPCOMING_FEATURE_EXISTENTIAL_ANY at Build Settings > Swift Compiler - Upcoming Features to true to support this existential any proposal. Then following errors appears in the MLModel class, but this is an auto-generated file, so I don't know how to deal with it. Use of protocol 'MLFeatureProvider' as a type must be written 'any MLFeatureProvider' Use of protocol 'Error' as a type must be written 'any Error' environment Xcode 16.0 Xcode 16.1 Beta 2 What I tried Delete cache of DerivedData and regenerate MLModel class files I also tried using DepthAnythingV2SmallF16P6.mlpackage to verify if there is a problem with my mlmodel I tried the above after setting up Swift6 in Xcode I also used coremlc to generate MLModel class files with Swift6 specified by command.
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655
Dec ’24
Apple Intelligence crashed/stopped working
Hi everyone, I’m currently using macOS Version 15.3 Beta (24D5034f), and I’m encountering an issue with Apple Intelligence. The image generation tools seem to work fine, but everything else shows a message saying that it’s “not available at this time.” I’ve tried restarting my Mac and double-checked my settings, but the problem persists. Is anyone else experiencing this issue on the beta version? Are there any fixes or settings I might be overlooking? Any help or insights would be greatly appreciated! Thanks in advance!
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749
Dec ’24
unable to run tensorflow on my machine
Hello! I've been trying to run tensorflow on my MBA M3. I previously had an Intel Mac and was able to run tensorflow without any problem. I've been working on a personal project in a directory I made on my previous Mac, that I was running through Jupyter notebook. Now every time I try to run the code, the kernel will die and I'm unsure what to do. I tried following tutorials, but every tutorial I've seen has made me create a new environment to access Jupyter Notebook, but not letting me access notebooks and files that have already been created. I tried to run this following command in terminal and received the subsequent error back. python -m pip install tensorflow-metal ERROR: Could not find a version that satisfies the requirement tensorflow-metal (from versions: none) ERROR: No matching distribution found for tensorflow-metal I've installed miniforge, Xcode, and anaconda onto my computer already and wanted some assistance.
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831
Dec ’24
VNDetectTextRectanglesRequest not detecting text rectangles (includes image)
Hi everyone, I'm trying to use VNDetectTextRectanglesRequest to detect text rectangles in an image. Here's my current code: guard let cgImage = image.cgImage(forProposedRect: nil, context: nil, hints: nil) else { return } let textDetectionRequest = VNDetectTextRectanglesRequest { request, error in if let error = error { print("Text detection error: \(error)") return } guard let observations = request.results as? [VNTextObservation] else { print("No text rectangles detected.") return } print("Detected \(observations.count) text rectangles.") for observation in observations { print(observation.boundingBox) } } textDetectionRequest.revision = VNDetectTextRectanglesRequestRevision1 textDetectionRequest.reportCharacterBoxes = true let handler = VNImageRequestHandler(cgImage: cgImage, orientation: .up, options: [:]) do { try handler.perform([textDetectionRequest]) } catch { print("Vision request error: \(error)") } The request completes without error, but no text rectangles are detected — the observations array is empty (count = 0). Here's a sample image I'm testing with: I expected VNTextObservation results, but I'm not getting any. Is there something I'm missing in how this API works? Or could it be a limitation of this request or revision? Thanks for any help!
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114
May ’25