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tensorflow-metal ReLU activation fails to clip negative values on M4 Apple Silicon
Environment: Hardware: Mac M4 OS: macOS Sequoia 15.7.4 TensorFlow-macOS Version: 2.16.2 TensorFlow-metal Version: 1.2.0 Description: When using the tensorflow-metal plug-in for GPU acceleration on M4, the ReLU activation function (both as a layer and as an activation argument) fails to correctly clip negative values to zero. The same code works correctly when forced to run on the CPU. Reproduction Script: import os import numpy as np import tensorflow as tf # weights and biases = -1 weights = [np.ones((10, 5)) * -1, np.ones(5) * -1] # input = 1 data = np.ones((1, 10)) # comment this line => GPU => get negative values # uncomment this line => CPU => no negative values # tf.config.set_visible_devices([], 'GPU') # create model model = tf.keras.Sequential([ tf.keras.layers.Input(shape=(10,)), tf.keras.layers.Dense(5, activation='relu') ]) # set weights model.layers[0].set_weights(weights) # get output output = model.predict(data) # check if negative is present print(f"min value: {output.min()}") print(f"is negative present? {np.any(output < 0)}")
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Mar ’26
Subject: Technical Report: Float32 Precision Ceiling & Memory Fragmentation in JAX/Metal Workloads on M3
Subject: Technical Report: Float32 Precision Ceiling & Memory Fragmentation in JAX/Metal Workloads on M3 To: Metal Developer Relations Hello, I am reporting a repeatable numerical saturation point encountered during sustained recursive high-order differential workloads on the Apple M3 (16 GB unified memory) using the JAX Metal backend. Workload Characteristics: Large-scale vector projections across multi-dimensional industrial datasets Repeated high-order finite-difference calculations Heavy use of jax.grad and lax.cond inside long-running loops Observation: Under these conditions, the Metal/MPS backend consistently enters a terminal quantization lock where outputs saturate at a fixed scalar value (2.0000), followed by system-wide NaN propagation. This appears to be a precision-limited boundary in the JAX-Metal bridge when handling high-order operations with cubic time-scale denominators. have identified the specific threshold where recursive high-order tensor derivatives exceed the numerical resolution of 32-bit consumer architectures, necessitating a migration to a dedicated 64-bit industrial stack. I have prepared a minimal synthetic test script (randomized vectors only, no proprietary logic) that reliably reproduces the allocator fragmentation and saturation behavior. Let me know if your team would like the telemetry for XLA/MPS optimization purposes. Best regards, Alex Severson Architect, QuantumPulse AI
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256
Mar ’26
Qwen3 VL CoreML
Looking for help with or to help with, due to the pending document enhancement, the Vibe Coders edition of cml editor. Also for more information on how to use the .mlkey whether or not my model is suppose to say IOs18 when I am planning to use it on Mac Apple Intelligence seems to think coreML is for iOS but are the capabilities extended when running NPU on the book? How to use this graph. coming in hot sorry. btw. there are 100s of feedback and crash reports sent in form me for additional info? I attached a image that might help with updating Tags
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269
Mar ’26
Apple Intelligence Naughty Naughty
When doing some exploratory research into using Apple Intelligence in our aviation-focused application, I noticed that there were several times that key phases would be marked as inappropriate. I tried to stifle these using prompts and rules but couldn't get it to take hold. I was encouraged by an Apple employee to go ahead and post this so that the AI team can use the feedback. There were several terms that triggered this warning, but the two that were most prominent were: 'Tailwind' 'JFK' or 'KJFK' (NY airport ICAO/IATA codes)
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Mar ’26
Creating powerful, efficient, and maintainable applications.
Recursive and Self-Referential Data Structures Combining recursive and self-referential data structures with frameworks like Accelerate, SwiftMacros, and utilizing SwiftUI hooks can offer significant benefits in terms of performance, maintainability, and expressiveness. Here is how Apple Intelligence breaks it down. Benefits: Natural Representation of Complex Data: Recursive structures, such as trees and graphs, are ideal for representing hierarchical or interconnected data, like file systems, social networks, and DOM trees. Simplified Algorithms: Many algorithms, such as traversals, sorting, and searching, are more straightforward and elegant when implemented using recursion. Dynamic Memory Management: Self-referential structures can dynamically grow and shrink, making them suitable for applications with unpredictable data sizes. Challenges: Performance Overhead: Recursive algorithms can lead to stack overflow if not properly optimized (e.g., using tail recursion). Self-referential structures can introduce memory management challenges, such as retain cycles. Accelerate Framework Benefits: High-Performance Computation: Accelerate provides optimized libraries for numerical and scientific computing, including linear algebra, FFT, and image processing. It can significantly speed up computations, especially for large datasets, by leveraging multi-core processors and GPU acceleration. Parallel Processing: Accelerate automatically parallelizes operations, making it easier to take advantage of modern hardware capabilities. Integration with Recursive Data: Matrix and Vector Operations: Use Accelerate for operations on matrices and vectors, which are common in recursive algorithms like those used in machine learning and physics simulations. FFT and Convolutions: Accelerate's FFT functions can be used in recursive algorithms for signal processing and image analysis. SwiftMacros Benefits: Code Generation and Transformation: SwiftMacros allow you to generate and transform code at compile time, enabling the creation of DSLs, boilerplate reduction, and optimization. Improved Compile-Time Checks: Macros can perform complex compile-time checks, ensuring code correctness and reducing runtime errors. Integration with Recursive Data: DSL for Data Structures: Create a DSL using SwiftMacros to define recursive data structures concisely and safely. Optimization: Use macros to generate optimized code for recursive algorithms, such as memoization or iterative transformations. SwiftUI Hooks Benefits: State Management: Hooks like @State, @Binding, and @Effect simplify state management in SwiftUI, making it easier to handle dynamic data. Side Effects: @Effect allows you to perform side effects in a declarative manner, integrating seamlessly with asynchronous operations. Reusable Logic: Custom hooks enable the reuse of stateful logic across multiple views, promoting code maintainability. Integration with Recursive Data: Dynamic Data Binding: Use SwiftUI's data binding to manage the state of recursive data structures, ensuring that UI updates reflect changes in the underlying data. Efficient Rendering: SwiftUI's diffing algorithm efficiently updates the UI only for the parts of the recursive structure that have changed, improving performance. Asynchronous Data Loading: Combine @Effect with recursive data structures to fetch and process data asynchronously, such as loading a tree structure from a remote server. Example: Combining All Components Imagine you're building an app that visualizes a hierarchical file system using a recursive tree structure. Here's how you might combine these components: Define the Recursive Data Structure: Use SwiftMacros to create a DSL for defining tree nodes. @macro struct TreeNode { var value: T var children: [TreeNode] } Optimize with Accelerate: Use Accelerate for operations like computing the size of the tree or performing transformations on node values. func computeTreeSize(_ node: TreeNode) -> Int { return node.children.reduce(1) { $0 + computeTreeSize($1) } } Manage State with SwiftUI Hooks: Use SwiftUI hooks to load and display the tree structure dynamically. struct FileSystemView: View { @State private var rootNode: TreeNode = loadTree() var body: some View { TreeView(node: rootNode) } private func loadTree() -> TreeNode<String> { // Load or generate the tree structure } } struct TreeView: View { let node: TreeNode var body: some View { List(node.children, id: \.value) { Text($0.value) TreeView(node: $0) } } } Perform Side Effects with @Effect: Use @Effect to fetch data asynchronously and update the tree structure. struct FileSystemView: View { @State private var rootNode: TreeNode = TreeNode(value: "/") @Effect private var loadTreeEffect: () -> Void = { // Fetch data from a server or database } var body: some View { TreeView(node: rootNode) .onAppear { loadTreeEffect() } } } By combining recursive data structures with Accelerate, SwiftMacros, and SwiftUI hooks, you can create powerful, efficient, and maintainable applications that handle complex data with ease.
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450
Mar ’26
Handling exceedingContextWindowSizeError
Reading all the docs(1) I was under the impression that handling this error is well managed... Until I hit it and found out that the recommended handling options hide a crucial fact: in the catch block you can not do anything?! It's too late - everything is lost, no way to recover... All the docs mislead me that I can apply the Transcript trick in the catch block until I realised, that there is nothing there !!! This article here(2) enlightened me on the handling of this problem, but I must say (and the author as well) - this is a hack! So my questions: is there really no way to handle this exception properly? if not, can we have the most important information - the count of the context exposed through the official API (at least the known ones)? https://developer.apple.com/documentation/Technotes/tn3193-managing-the-on-device-foundation-model-s-context-window#Handle-the-exceeding-context-window-size-error-elegantly https://zats.io/blog/making-the-most-of-apple-foundation-models-context-window/
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180
Mar ’26
How can I change the output dimensions of a CoreML model in Xcode when the outputs come from a NonMaximumSuppression layer?
After exerting a custom model with nms=True. In Xcode, the outputs show as: confidence: MultiArray (0 × 5) coordinates: MultiArray (0 × 4) I want to set fixed shapes (e.g., 100 × 5, 100 × 4), but Xcode does not allow editing—the shape fields are locked. The model graph shows both outputs come directly from a NonMaximumSuppression layer. Is it possible to set fixed output dimensions for NMS outputs in CoreML?
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Mar ’26
Unable to load a quantized Qwen 1.7B model on an iPhone SE 3
I am trying to benchmark and see if the Qwen3 1.7B model can run in an iPhone SE 3 [4 GB RAM]. My core problem is - Even with weight quantization the SE 3 is not able to load into memory. What I've tried: I am converting a Torch model to the Core ML format using coremltools. I have tried the following combinations of quantization and context length 8 bit + 1024 8 bit + 2048 4 bit + 1024 4 bit + 2048 All the above quantizations are done with dynamic shape with the default being [1,1] in the hope that the whole context length does not get allocated in memory The 4-bit model is approximately 865MB on disk The 8-bit model is approximately 1.7 GB on disk During load: With the int4 quantization the memory spikes during intitial load a lot. Could this be because many operations are converted to int8 or fp16 as core ML does not perform operations natively on int4? With int8 on the profiler the memory does not go above 2 GB (only 900 MB) but it is still not able to load as it shows the following error. 2GB is the limit where jetsam kills the app for the iPhone SE 3 E5RT: Error(s) occurred compiling MIL to BNNS graph: [CreateBnnsGraphProgramFromMIL]: BNNS Graph Compile: failed to preallocate file with error: No space left on device for path: /var/mobile/Containers/Data/Application/ 5B8BB7D2-06A6-4BAE-A042-407B6D805E7C/Library/Caches /com.tss.qwen3-coreml/ com.apple.e5rt.e5bundlecache/ 23A341/<long key>.tmp.12586_4362093968.bundle/ H14.bundle/main/main_bnns/bnns_program.bnnsir Some online sources have suggested activation quantization but I am unsure if that will have any impact on loading [as the spike is during load and not inference] The model spec also suggests that there is no dequantization happening (for e.g from 4 bit -> fp16) So I had couple of queries: Has anyone faced similar issues? What could be the reasons for the temporary memory spike during LOAD What are approaches that can be adopted to deal with this issue? Any help would be greatly appreciated. Thank you.
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Mar ’26
Translation framework use in Swift 6
I’m trying to integrate Apple’s Translation framework in a Swift 6 project with Approachable Concurrency enabled. I’m following the code here: https://developer.apple.com/documentation/translation/translating-text-within-your-app#Offer-a-custom-translation And, specifically, inside the following code .translationTask(configuration) { session in do { // Use the session the task provides to translate the text. let response = try await session.translate(sourceText) // Update the view with the translated result. targetText = response.targetText } catch { // Handle any errors. } } On the try await session.translate(…) line, the compiler complains that “Sending ‘session’ risks causing data races”. Extended error message: Sending main actor-isolated 'session' to @concurrent instance method 'translate' risks causing data races between @concurrent and main actor-isolated uses I’ve downloaded Apple’s sample code (at the top of linked webpage), it compiles fine as-is on Xcode 26.4, but fails with the same error as soon as I switch the Swift Language Mode to Swift 6 in the project. How can I fix this?
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295
Feb ’26
Parallel/Steam processing of Apple Intelligence
I have built a MAC-OS machine intelligence application that uses Apple Intelligence. A part of the application is to preprocess text. For longer text content I have implemented chunking to get around the token limit. However the application performance is now limited by the fact that Apple Intelligence is sequential in operation. This has a large impact on the application performance. Is there any approach to operate Apple Intelligence in a parallel mode or even a streaming interface. As Apple Intelligence has Private Cloud Services I was hoping to be able to send multiple chunks in parallel as that would significantly improve performance. Any suggestions would be welcome. This could also be considered a request for a future enhancement.
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Feb ’26
Core Model Editor and Params
Optimal Precision • Current Precision: Mixed (Float32, int32) • Optimal Precision: Not specified in the image, but typically involves using the most efficient data type for the model's operations to balance speed and memory usage without significant loss of accuracy. Comparison: • Mixed Precision: Utilizes both Float32 and int32 to optimize performance. Float32 provides high precision, while int32 reduces memory usage and increases computational speed. • Optimal Precision: Aimed at achieving the best trade-off between performance and accuracy, potentially using other data types like Float16 (bfloat16) for even greater efficiency in certain hardware environments. Operation Distribution • Current Distribution: • iOS18.mul: 168 • iOS18.transpose: 126 • iOS18.linear: 98 • iOS18.add: 97 • iOS18.sliceByIndex: 96 • iOS18.expandDims: 74 • iOS18.concat: 72 • iOS18.squeeze: 72 • iOS18.reshape: 67 • iOS18.layerNorm: 49 • iOS18.matmul: 48 • iOS18.gelu: 26 • iOS18.softmax: 24 • Split: 24 • conv: 1 • iOS18.conv: 1 Comparison: • Operation Count: Indicates how frequently each operation is executed. High counts for operations like mul, transpose, and linear suggest these are computationally intensive parts of the model. • Optimization Opportunities: Reducing the count of high-frequency operations or optimizing their execution can improve performance. This might involve pruning unnecessary operations, optimizing algorithms, or leveraging hardware acceleration. General Recommendations • Precision Tuning: Experiment with different precision levels to find the best balance for your specific hardware and accuracy requirements. • Operation Optimization: Focus on optimizing the most frequent operations. Techniques include using more efficient algorithms, parallelizing computations, or utilizing specialized hardware like GPUs or TPUs. • Benchmarking: Regularly benchmark the model to assess the impact of changes and ensure that optimizations lead to meaningful performance improvements. By focusing on these areas, you can potentially enhance the efficiency and performance of your ML model.
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Feb ’26
CoreML Instrument Testing Native Clawbot using FM.SyML & OAIC & Diffusion
After running performance test on my CoreML qwen3 vision, I appreciated the update where results were viewable... ON Mac it mentions Ios18 and im not sure if or how to change.. that bottle neck lead to rebuilding CoreML view. I woke up and realized I have all the pieces together... and ended up with a swift package working demo of Clawbot.. the current issue is Im trying to use gguf 3b to code it.. I have become well aware that everything I create using the big models, they soon become the default themes /layouts for everyone else simply asking for this or that (I appoligise) so here I am asking (while looking to schedule meet with dev) if its possible to speak with anyone about th 1000s of Apple Intelligence PCC, Xcode, and vision reports and feedback ive sent , in terms of just general ways I can work more efficiently without the crash... ive already build a TUI for MLX but the tools for coreML while seems promising are not intuitive, but the vision format instruction was nice to see. Anyway my question is:
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Feb ’26
Core Image for depth maps & segmentation masks: numeric fidelity issues when rendering CIImage to CVPixelBuffer (looking for Architecture suggestions)
Hello All, I’m working on a computer-vision–heavy iOS application that uses the camera, LiDAR depth maps, and semantic segmentation to reason about the environment (object identification, localization and measurement - not just visualization). Current architecture I initially built the image pipeline around CIImage as a unifying abstraction. It seemed like a good idea because: CIImage integrates cleanly with Vision, ARKit, AVFoundation, Metal, Core Graphics, etc. It provides a rich set of out-of-the-box transforms and filters. It is immutable and thread-safe, which significantly simplified concurrency in a multi-queue pipeline. The LiDAR depth maps, semantic segmentation masks, etc. were treated as CIImages, with conversion to CVPixelBuffer or MTLTexture only at the edges when required. Problem I’ve run into cases where Core Image transformations do not preserve numeric fidelity for non-visual data. Example: Rendering a CIImage-backed segmentation mask into a larger CVPixelBuffer can cause label values to change in predictable but incorrect ways. This occurs even when: using nearest-neighbor sampling disabling color management (workingColorSpace / outputColorSpace = NSNull) applying identity or simple affine transforms I’ve confirmed via controlled tests that: Metal → CVPixelBuffer paths preserve values correctly CIImage → CVPixelBuffer paths can introduce value changes when resampling or expanding the render target This makes CIImage unsafe as a source of numeric truth for segmentation masks and depth-based logic, even though it works well for visualization, and I should have realized this much sooner. Direction I’m considering I’m now considering refactoring toward more intent-based abstractions instead of a single image type, for example: Visual images: CIImage (camera frames, overlays, debugging, UI) Scalar fields: depth / confidence maps backed by CVPixelBuffer + Metal Label maps: segmentation masks backed by integer-preserving buffers (no interpolation, no transforms) In this model, CIImage would still be used extensively — but primarily for visualization and perceptual processing, not as the container for numerically sensitive data. Thread safety concern One of the original advantages of CIImage was that it is thread-safe by design, and that was my biggest incentive. For CVPixelBuffer / MTLTexture–backed data, I’m considering enforcing thread safety explicitly via: Swift Concurrency (actor-owned data, explicit ownership) Questions For those may have experience with CV / AR / imaging-heavy iOS apps, I was hoping to know the following: Is this separation of image intent (visual vs numeric vs categorical) a reasonable architectural direction? Do you generally keep CIImage at the heart of your pipeline, or push it to the edges (visualization only)? How do you manage thread safety and ownership when working heavily with CVPixelBuffer and Metal? Using actor-based abstractions, GCD, or adhoc? Are there any best practices or gotchas around using Core Image with depth maps or segmentation masks that I should be aware of? I’d really appreciate any guidance or experience-based advice. I suspect I’ve hit a boundary of Core Image’s design, and I’m trying to refactor in a way that doesn't involve too much immediate tech debt, remains robust and maintainable long-term. Thank you in advance!
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Feb ’26
tensorflow-metal fails with tensorflow > 2.18.1
Also submitted as feedback (ID: FB20612561). Tensorflow-metal fails on tensorflow versions above 2.18.1, but works fine on tensorflow 2.18.1 In a new python 3.12 virtual environment: pip install tensorflow pip install tensor flow-metal python -c "import tensorflow as tf" Prints error: Traceback (most recent call last): File "", line 1, in File "/Users//pt/venv/lib/python3.12/site-packages/tensorflow/init.py", line 438, in _ll.load_library(_plugin_dir) File "/Users//pt/venv/lib/python3.12/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Library not loaded: @rpath/_pywrap_tensorflow_internal.so Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib Reason: tried: '/Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file)
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2.7k
Feb ’26
Xcode Playground and FoundationModels
I am trying to test FoundationModels in a Swift Playground in Xcode 26.2, macOS 26.3, and am running into an issue. The following simple code generates an error: import FoundationModels @Generable struct Specifications { @Guide(description: "Search for color") var color: String } I see the following error message in the console: error: AIPlayground.playground:4:8: external macro implementation type 'FoundationModelsMacros.GenerableMacro' could not be found for macro 'Generable(description:)'; plugin for module 'FoundationModelsMacros' not found The Xcode editor does not appear to recognize the @Generable or @Guide macros, despite importing FoundationModels. What step/setting am I missing?
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Feb ’26
Apple OCR framework seems to be holding on to allocations every time it is called.
Environment: macOS 26.2 (Tahoe) Xcode 16.3 Apple Silicon (M4) Sandboxed Mac App Store app Description: Repeated use of VNRecognizeTextRequest causes permanent memory growth in the host process. The physical footprint increases by approximately 3-15 MB per OCR call and never returns to baseline, even after all references to the request, handler, observations, and image are released. ` private func selectAndProcessImage() { let panel = NSOpenPanel() panel.allowedContentTypes = [.image] panel.allowsMultipleSelection = false panel.canChooseDirectories = false panel.message = "Select an image for OCR processing" guard panel.runModal() == .OK, let url = panel.url else { return } selectedImageURL = url isProcessing = true recognizedText = "Processing..." // Run OCR on a background thread to keep UI responsive let workItem = DispatchWorkItem { let result = performOCR(on: url) DispatchQueue.main.async { recognizedText = result isProcessing = false } } DispatchQueue.global(qos: .userInitiated).async(execute: workItem) } private func performOCR(on url: URL) -> String { // Wrap EVERYTHING in autoreleasepool so all ObjC objects are drained immediately let resultText: String = autoreleasepool { // Load image and convert to CVPixelBuffer for explicit memory control guard let imageData = try? Data(contentsOf: url) else { return "Error: Could not read image file." } guard let nsImage = NSImage(data: imageData) else { return "Error: Could not create image from file data." } guard let cgImage = nsImage.cgImage(forProposedRect: nil, context: nil, hints: nil) else { return "Error: Could not create CGImage." } let width = cgImage.width let height = cgImage.height // Create a CVPixelBuffer from the CGImage var pixelBuffer: CVPixelBuffer? let attrs: [String: Any] = [ kCVPixelBufferCGImageCompatibilityKey as String: true, kCVPixelBufferCGBitmapContextCompatibilityKey as String: true ] let status = CVPixelBufferCreate( kCFAllocatorDefault, width, height, kCVPixelFormatType_32ARGB, attrs as CFDictionary, &pixelBuffer ) guard status == kCVReturnSuccess, let buffer = pixelBuffer else { return "Error: Could not create CVPixelBuffer (status: \(status))." } // Draw the CGImage into the pixel buffer CVPixelBufferLockBaseAddress(buffer, []) guard let context = CGContext( data: CVPixelBufferGetBaseAddress(buffer), width: width, height: height, bitsPerComponent: 8, bytesPerRow: CVPixelBufferGetBytesPerRow(buffer), space: CGColorSpaceCreateDeviceRGB(), bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue ) else { CVPixelBufferUnlockBaseAddress(buffer, []) return "Error: Could not create CGContext for pixel buffer." } context.draw(cgImage, in: CGRect(x: 0, y: 0, width: width, height: height)) CVPixelBufferUnlockBaseAddress(buffer, []) // Run OCR let requestHandler = VNImageRequestHandler(cvPixelBuffer: buffer, options: [:]) let request = VNRecognizeTextRequest() request.recognitionLevel = .accurate request.usesLanguageCorrection = true do { try requestHandler.perform([request]) } catch { return "Error during OCR: \(error.localizedDescription)" } guard let observations = request.results, !observations.isEmpty else { return "No text found in image." } let lines = observations.compactMap { observation in observation.topCandidates(1).first?.string } // Explicitly nil out the pixel buffer before the pool drains pixelBuffer = nil return lines.joined(separator: "\n") } // Everything — Data, NSImage, CGImage, CVPixelBuffer, VN objects — released here return resultText } `
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172
Feb ’26
tensorflow-metal ReLU activation fails to clip negative values on M4 Apple Silicon
Environment: Hardware: Mac M4 OS: macOS Sequoia 15.7.4 TensorFlow-macOS Version: 2.16.2 TensorFlow-metal Version: 1.2.0 Description: When using the tensorflow-metal plug-in for GPU acceleration on M4, the ReLU activation function (both as a layer and as an activation argument) fails to correctly clip negative values to zero. The same code works correctly when forced to run on the CPU. Reproduction Script: import os import numpy as np import tensorflow as tf # weights and biases = -1 weights = [np.ones((10, 5)) * -1, np.ones(5) * -1] # input = 1 data = np.ones((1, 10)) # comment this line => GPU => get negative values # uncomment this line => CPU => no negative values # tf.config.set_visible_devices([], 'GPU') # create model model = tf.keras.Sequential([ tf.keras.layers.Input(shape=(10,)), tf.keras.layers.Dense(5, activation='relu') ]) # set weights model.layers[0].set_weights(weights) # get output output = model.predict(data) # check if negative is present print(f"min value: {output.min()}") print(f"is negative present? {np.any(output < 0)}")
Replies
2
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436
Activity
Mar ’26
Subject: Technical Report: Float32 Precision Ceiling & Memory Fragmentation in JAX/Metal Workloads on M3
Subject: Technical Report: Float32 Precision Ceiling & Memory Fragmentation in JAX/Metal Workloads on M3 To: Metal Developer Relations Hello, I am reporting a repeatable numerical saturation point encountered during sustained recursive high-order differential workloads on the Apple M3 (16 GB unified memory) using the JAX Metal backend. Workload Characteristics: Large-scale vector projections across multi-dimensional industrial datasets Repeated high-order finite-difference calculations Heavy use of jax.grad and lax.cond inside long-running loops Observation: Under these conditions, the Metal/MPS backend consistently enters a terminal quantization lock where outputs saturate at a fixed scalar value (2.0000), followed by system-wide NaN propagation. This appears to be a precision-limited boundary in the JAX-Metal bridge when handling high-order operations with cubic time-scale denominators. have identified the specific threshold where recursive high-order tensor derivatives exceed the numerical resolution of 32-bit consumer architectures, necessitating a migration to a dedicated 64-bit industrial stack. I have prepared a minimal synthetic test script (randomized vectors only, no proprietary logic) that reliably reproduces the allocator fragmentation and saturation behavior. Let me know if your team would like the telemetry for XLA/MPS optimization purposes. Best regards, Alex Severson Architect, QuantumPulse AI
Replies
0
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0
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256
Activity
Mar ’26
Qwen3 VL CoreML
Looking for help with or to help with, due to the pending document enhancement, the Vibe Coders edition of cml editor. Also for more information on how to use the .mlkey whether or not my model is suppose to say IOs18 when I am planning to use it on Mac Apple Intelligence seems to think coreML is for iOS but are the capabilities extended when running NPU on the book? How to use this graph. coming in hot sorry. btw. there are 100s of feedback and crash reports sent in form me for additional info? I attached a image that might help with updating Tags
Replies
1
Boosts
0
Views
269
Activity
Mar ’26
Apple Intelligence Naughty Naughty
When doing some exploratory research into using Apple Intelligence in our aviation-focused application, I noticed that there were several times that key phases would be marked as inappropriate. I tried to stifle these using prompts and rules but couldn't get it to take hold. I was encouraged by an Apple employee to go ahead and post this so that the AI team can use the feedback. There were several terms that triggered this warning, but the two that were most prominent were: 'Tailwind' 'JFK' or 'KJFK' (NY airport ICAO/IATA codes)
Replies
2
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0
Views
606
Activity
Mar ’26
Error Domain=NSOSStatusErrorDomain Code=-1 "kCFStreamErrorHTTPParseFailure / kCFSocketError / kCFStreamErrorDomainCustom / kCSIdentityUnknownAuthorityErr / qErr / telGenericError / dsNoExtsMacsBug / kMovieLoadStateError / cdevGenErr: Could not parse
Can't able to run the Create ML for training and I upgraded to MacOS 26.3 beta and I have tried older and newer
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0
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254
Activity
Mar ’26
Creating powerful, efficient, and maintainable applications.
Recursive and Self-Referential Data Structures Combining recursive and self-referential data structures with frameworks like Accelerate, SwiftMacros, and utilizing SwiftUI hooks can offer significant benefits in terms of performance, maintainability, and expressiveness. Here is how Apple Intelligence breaks it down. Benefits: Natural Representation of Complex Data: Recursive structures, such as trees and graphs, are ideal for representing hierarchical or interconnected data, like file systems, social networks, and DOM trees. Simplified Algorithms: Many algorithms, such as traversals, sorting, and searching, are more straightforward and elegant when implemented using recursion. Dynamic Memory Management: Self-referential structures can dynamically grow and shrink, making them suitable for applications with unpredictable data sizes. Challenges: Performance Overhead: Recursive algorithms can lead to stack overflow if not properly optimized (e.g., using tail recursion). Self-referential structures can introduce memory management challenges, such as retain cycles. Accelerate Framework Benefits: High-Performance Computation: Accelerate provides optimized libraries for numerical and scientific computing, including linear algebra, FFT, and image processing. It can significantly speed up computations, especially for large datasets, by leveraging multi-core processors and GPU acceleration. Parallel Processing: Accelerate automatically parallelizes operations, making it easier to take advantage of modern hardware capabilities. Integration with Recursive Data: Matrix and Vector Operations: Use Accelerate for operations on matrices and vectors, which are common in recursive algorithms like those used in machine learning and physics simulations. FFT and Convolutions: Accelerate's FFT functions can be used in recursive algorithms for signal processing and image analysis. SwiftMacros Benefits: Code Generation and Transformation: SwiftMacros allow you to generate and transform code at compile time, enabling the creation of DSLs, boilerplate reduction, and optimization. Improved Compile-Time Checks: Macros can perform complex compile-time checks, ensuring code correctness and reducing runtime errors. Integration with Recursive Data: DSL for Data Structures: Create a DSL using SwiftMacros to define recursive data structures concisely and safely. Optimization: Use macros to generate optimized code for recursive algorithms, such as memoization or iterative transformations. SwiftUI Hooks Benefits: State Management: Hooks like @State, @Binding, and @Effect simplify state management in SwiftUI, making it easier to handle dynamic data. Side Effects: @Effect allows you to perform side effects in a declarative manner, integrating seamlessly with asynchronous operations. Reusable Logic: Custom hooks enable the reuse of stateful logic across multiple views, promoting code maintainability. Integration with Recursive Data: Dynamic Data Binding: Use SwiftUI's data binding to manage the state of recursive data structures, ensuring that UI updates reflect changes in the underlying data. Efficient Rendering: SwiftUI's diffing algorithm efficiently updates the UI only for the parts of the recursive structure that have changed, improving performance. Asynchronous Data Loading: Combine @Effect with recursive data structures to fetch and process data asynchronously, such as loading a tree structure from a remote server. Example: Combining All Components Imagine you're building an app that visualizes a hierarchical file system using a recursive tree structure. Here's how you might combine these components: Define the Recursive Data Structure: Use SwiftMacros to create a DSL for defining tree nodes. @macro struct TreeNode { var value: T var children: [TreeNode] } Optimize with Accelerate: Use Accelerate for operations like computing the size of the tree or performing transformations on node values. func computeTreeSize(_ node: TreeNode) -> Int { return node.children.reduce(1) { $0 + computeTreeSize($1) } } Manage State with SwiftUI Hooks: Use SwiftUI hooks to load and display the tree structure dynamically. struct FileSystemView: View { @State private var rootNode: TreeNode = loadTree() var body: some View { TreeView(node: rootNode) } private func loadTree() -> TreeNode<String> { // Load or generate the tree structure } } struct TreeView: View { let node: TreeNode var body: some View { List(node.children, id: \.value) { Text($0.value) TreeView(node: $0) } } } Perform Side Effects with @Effect: Use @Effect to fetch data asynchronously and update the tree structure. struct FileSystemView: View { @State private var rootNode: TreeNode = TreeNode(value: "/") @Effect private var loadTreeEffect: () -> Void = { // Fetch data from a server or database } var body: some View { TreeView(node: rootNode) .onAppear { loadTreeEffect() } } } By combining recursive data structures with Accelerate, SwiftMacros, and SwiftUI hooks, you can create powerful, efficient, and maintainable applications that handle complex data with ease.
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Activity
Mar ’26
Handling exceedingContextWindowSizeError
Reading all the docs(1) I was under the impression that handling this error is well managed... Until I hit it and found out that the recommended handling options hide a crucial fact: in the catch block you can not do anything?! It's too late - everything is lost, no way to recover... All the docs mislead me that I can apply the Transcript trick in the catch block until I realised, that there is nothing there !!! This article here(2) enlightened me on the handling of this problem, but I must say (and the author as well) - this is a hack! So my questions: is there really no way to handle this exception properly? if not, can we have the most important information - the count of the context exposed through the official API (at least the known ones)? https://developer.apple.com/documentation/Technotes/tn3193-managing-the-on-device-foundation-model-s-context-window#Handle-the-exceeding-context-window-size-error-elegantly https://zats.io/blog/making-the-most-of-apple-foundation-models-context-window/
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180
Activity
Mar ’26
How can I change the output dimensions of a CoreML model in Xcode when the outputs come from a NonMaximumSuppression layer?
After exerting a custom model with nms=True. In Xcode, the outputs show as: confidence: MultiArray (0 × 5) coordinates: MultiArray (0 × 4) I want to set fixed shapes (e.g., 100 × 5, 100 × 4), but Xcode does not allow editing—the shape fields are locked. The model graph shows both outputs come directly from a NonMaximumSuppression layer. Is it possible to set fixed output dimensions for NMS outputs in CoreML?
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289
Activity
Mar ’26
Unable to load a quantized Qwen 1.7B model on an iPhone SE 3
I am trying to benchmark and see if the Qwen3 1.7B model can run in an iPhone SE 3 [4 GB RAM]. My core problem is - Even with weight quantization the SE 3 is not able to load into memory. What I've tried: I am converting a Torch model to the Core ML format using coremltools. I have tried the following combinations of quantization and context length 8 bit + 1024 8 bit + 2048 4 bit + 1024 4 bit + 2048 All the above quantizations are done with dynamic shape with the default being [1,1] in the hope that the whole context length does not get allocated in memory The 4-bit model is approximately 865MB on disk The 8-bit model is approximately 1.7 GB on disk During load: With the int4 quantization the memory spikes during intitial load a lot. Could this be because many operations are converted to int8 or fp16 as core ML does not perform operations natively on int4? With int8 on the profiler the memory does not go above 2 GB (only 900 MB) but it is still not able to load as it shows the following error. 2GB is the limit where jetsam kills the app for the iPhone SE 3 E5RT: Error(s) occurred compiling MIL to BNNS graph: [CreateBnnsGraphProgramFromMIL]: BNNS Graph Compile: failed to preallocate file with error: No space left on device for path: /var/mobile/Containers/Data/Application/ 5B8BB7D2-06A6-4BAE-A042-407B6D805E7C/Library/Caches /com.tss.qwen3-coreml/ com.apple.e5rt.e5bundlecache/ 23A341/<long key>.tmp.12586_4362093968.bundle/ H14.bundle/main/main_bnns/bnns_program.bnnsir Some online sources have suggested activation quantization but I am unsure if that will have any impact on loading [as the spike is during load and not inference] The model spec also suggests that there is no dequantization happening (for e.g from 4 bit -> fp16) So I had couple of queries: Has anyone faced similar issues? What could be the reasons for the temporary memory spike during LOAD What are approaches that can be adopted to deal with this issue? Any help would be greatly appreciated. Thank you.
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260
Activity
Mar ’26
Translation framework use in Swift 6
I’m trying to integrate Apple’s Translation framework in a Swift 6 project with Approachable Concurrency enabled. I’m following the code here: https://developer.apple.com/documentation/translation/translating-text-within-your-app#Offer-a-custom-translation And, specifically, inside the following code .translationTask(configuration) { session in do { // Use the session the task provides to translate the text. let response = try await session.translate(sourceText) // Update the view with the translated result. targetText = response.targetText } catch { // Handle any errors. } } On the try await session.translate(…) line, the compiler complains that “Sending ‘session’ risks causing data races”. Extended error message: Sending main actor-isolated 'session' to @concurrent instance method 'translate' risks causing data races between @concurrent and main actor-isolated uses I’ve downloaded Apple’s sample code (at the top of linked webpage), it compiles fine as-is on Xcode 26.4, but fails with the same error as soon as I switch the Swift Language Mode to Swift 6 in the project. How can I fix this?
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295
Activity
Feb ’26
What Should the iOS Deployment Target Be Set to?
Originally, I set my iOS deployment target to 18.1, but now that I'm integrating Foundational Models, I set it to iOS 26.0. Is this ok?
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164
Activity
Feb ’26
Parallel/Steam processing of Apple Intelligence
I have built a MAC-OS machine intelligence application that uses Apple Intelligence. A part of the application is to preprocess text. For longer text content I have implemented chunking to get around the token limit. However the application performance is now limited by the fact that Apple Intelligence is sequential in operation. This has a large impact on the application performance. Is there any approach to operate Apple Intelligence in a parallel mode or even a streaming interface. As Apple Intelligence has Private Cloud Services I was hoping to be able to send multiple chunks in parallel as that would significantly improve performance. Any suggestions would be welcome. This could also be considered a request for a future enhancement.
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221
Activity
Feb ’26
What Should the iOS Deployment Target Be?
The deployment target for my app was set to iOS 18.1 originally, but now that I'm using Foundational Models framework, it has been set to iOS 26.0. Is this ok?
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245
Activity
Feb ’26
Core Model Editor and Params
Optimal Precision • Current Precision: Mixed (Float32, int32) • Optimal Precision: Not specified in the image, but typically involves using the most efficient data type for the model's operations to balance speed and memory usage without significant loss of accuracy. Comparison: • Mixed Precision: Utilizes both Float32 and int32 to optimize performance. Float32 provides high precision, while int32 reduces memory usage and increases computational speed. • Optimal Precision: Aimed at achieving the best trade-off between performance and accuracy, potentially using other data types like Float16 (bfloat16) for even greater efficiency in certain hardware environments. Operation Distribution • Current Distribution: • iOS18.mul: 168 • iOS18.transpose: 126 • iOS18.linear: 98 • iOS18.add: 97 • iOS18.sliceByIndex: 96 • iOS18.expandDims: 74 • iOS18.concat: 72 • iOS18.squeeze: 72 • iOS18.reshape: 67 • iOS18.layerNorm: 49 • iOS18.matmul: 48 • iOS18.gelu: 26 • iOS18.softmax: 24 • Split: 24 • conv: 1 • iOS18.conv: 1 Comparison: • Operation Count: Indicates how frequently each operation is executed. High counts for operations like mul, transpose, and linear suggest these are computationally intensive parts of the model. • Optimization Opportunities: Reducing the count of high-frequency operations or optimizing their execution can improve performance. This might involve pruning unnecessary operations, optimizing algorithms, or leveraging hardware acceleration. General Recommendations • Precision Tuning: Experiment with different precision levels to find the best balance for your specific hardware and accuracy requirements. • Operation Optimization: Focus on optimizing the most frequent operations. Techniques include using more efficient algorithms, parallelizing computations, or utilizing specialized hardware like GPUs or TPUs. • Benchmarking: Regularly benchmark the model to assess the impact of changes and ensure that optimizations lead to meaningful performance improvements. By focusing on these areas, you can potentially enhance the efficiency and performance of your ML model.
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110
Activity
Feb ’26
CoreML Instrument Testing Native Clawbot using FM.SyML & OAIC & Diffusion
After running performance test on my CoreML qwen3 vision, I appreciated the update where results were viewable... ON Mac it mentions Ios18 and im not sure if or how to change.. that bottle neck lead to rebuilding CoreML view. I woke up and realized I have all the pieces together... and ended up with a swift package working demo of Clawbot.. the current issue is Im trying to use gguf 3b to code it.. I have become well aware that everything I create using the big models, they soon become the default themes /layouts for everyone else simply asking for this or that (I appoligise) so here I am asking (while looking to schedule meet with dev) if its possible to speak with anyone about th 1000s of Apple Intelligence PCC, Xcode, and vision reports and feedback ive sent , in terms of just general ways I can work more efficiently without the crash... ive already build a TUI for MLX but the tools for coreML while seems promising are not intuitive, but the vision format instruction was nice to see. Anyway my question is:
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Activity
Feb ’26
Core Image for depth maps & segmentation masks: numeric fidelity issues when rendering CIImage to CVPixelBuffer (looking for Architecture suggestions)
Hello All, I’m working on a computer-vision–heavy iOS application that uses the camera, LiDAR depth maps, and semantic segmentation to reason about the environment (object identification, localization and measurement - not just visualization). Current architecture I initially built the image pipeline around CIImage as a unifying abstraction. It seemed like a good idea because: CIImage integrates cleanly with Vision, ARKit, AVFoundation, Metal, Core Graphics, etc. It provides a rich set of out-of-the-box transforms and filters. It is immutable and thread-safe, which significantly simplified concurrency in a multi-queue pipeline. The LiDAR depth maps, semantic segmentation masks, etc. were treated as CIImages, with conversion to CVPixelBuffer or MTLTexture only at the edges when required. Problem I’ve run into cases where Core Image transformations do not preserve numeric fidelity for non-visual data. Example: Rendering a CIImage-backed segmentation mask into a larger CVPixelBuffer can cause label values to change in predictable but incorrect ways. This occurs even when: using nearest-neighbor sampling disabling color management (workingColorSpace / outputColorSpace = NSNull) applying identity or simple affine transforms I’ve confirmed via controlled tests that: Metal → CVPixelBuffer paths preserve values correctly CIImage → CVPixelBuffer paths can introduce value changes when resampling or expanding the render target This makes CIImage unsafe as a source of numeric truth for segmentation masks and depth-based logic, even though it works well for visualization, and I should have realized this much sooner. Direction I’m considering I’m now considering refactoring toward more intent-based abstractions instead of a single image type, for example: Visual images: CIImage (camera frames, overlays, debugging, UI) Scalar fields: depth / confidence maps backed by CVPixelBuffer + Metal Label maps: segmentation masks backed by integer-preserving buffers (no interpolation, no transforms) In this model, CIImage would still be used extensively — but primarily for visualization and perceptual processing, not as the container for numerically sensitive data. Thread safety concern One of the original advantages of CIImage was that it is thread-safe by design, and that was my biggest incentive. For CVPixelBuffer / MTLTexture–backed data, I’m considering enforcing thread safety explicitly via: Swift Concurrency (actor-owned data, explicit ownership) Questions For those may have experience with CV / AR / imaging-heavy iOS apps, I was hoping to know the following: Is this separation of image intent (visual vs numeric vs categorical) a reasonable architectural direction? Do you generally keep CIImage at the heart of your pipeline, or push it to the edges (visualization only)? How do you manage thread safety and ownership when working heavily with CVPixelBuffer and Metal? Using actor-based abstractions, GCD, or adhoc? Are there any best practices or gotchas around using Core Image with depth maps or segmentation masks that I should be aware of? I’d really appreciate any guidance or experience-based advice. I suspect I’ve hit a boundary of Core Image’s design, and I’m trying to refactor in a way that doesn't involve too much immediate tech debt, remains robust and maintainable long-term. Thank you in advance!
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410
Activity
Feb ’26
tensorflow-metal fails with tensorflow > 2.18.1
Also submitted as feedback (ID: FB20612561). Tensorflow-metal fails on tensorflow versions above 2.18.1, but works fine on tensorflow 2.18.1 In a new python 3.12 virtual environment: pip install tensorflow pip install tensor flow-metal python -c "import tensorflow as tf" Prints error: Traceback (most recent call last): File "", line 1, in File "/Users//pt/venv/lib/python3.12/site-packages/tensorflow/init.py", line 438, in _ll.load_library(_plugin_dir) File "/Users//pt/venv/lib/python3.12/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Library not loaded: @rpath/_pywrap_tensorflow_internal.so Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib Reason: tried: '/Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/Users//pt/venv/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file)
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Activity
Feb ’26
Xcode Playground and FoundationModels
I am trying to test FoundationModels in a Swift Playground in Xcode 26.2, macOS 26.3, and am running into an issue. The following simple code generates an error: import FoundationModels @Generable struct Specifications { @Guide(description: "Search for color") var color: String } I see the following error message in the console: error: AIPlayground.playground:4:8: external macro implementation type 'FoundationModelsMacros.GenerableMacro' could not be found for macro 'Generable(description:)'; plugin for module 'FoundationModelsMacros' not found The Xcode editor does not appear to recognize the @Generable or @Guide macros, despite importing FoundationModels. What step/setting am I missing?
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Activity
Feb ’26
Warming Up Apple Intelligence
Whats to code to warm it up once? Saw this in a developer video but cannot find it. Prevent cold run within an application. Thank you in advance!
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194
Activity
Feb ’26
Apple OCR framework seems to be holding on to allocations every time it is called.
Environment: macOS 26.2 (Tahoe) Xcode 16.3 Apple Silicon (M4) Sandboxed Mac App Store app Description: Repeated use of VNRecognizeTextRequest causes permanent memory growth in the host process. The physical footprint increases by approximately 3-15 MB per OCR call and never returns to baseline, even after all references to the request, handler, observations, and image are released. ` private func selectAndProcessImage() { let panel = NSOpenPanel() panel.allowedContentTypes = [.image] panel.allowsMultipleSelection = false panel.canChooseDirectories = false panel.message = "Select an image for OCR processing" guard panel.runModal() == .OK, let url = panel.url else { return } selectedImageURL = url isProcessing = true recognizedText = "Processing..." // Run OCR on a background thread to keep UI responsive let workItem = DispatchWorkItem { let result = performOCR(on: url) DispatchQueue.main.async { recognizedText = result isProcessing = false } } DispatchQueue.global(qos: .userInitiated).async(execute: workItem) } private func performOCR(on url: URL) -> String { // Wrap EVERYTHING in autoreleasepool so all ObjC objects are drained immediately let resultText: String = autoreleasepool { // Load image and convert to CVPixelBuffer for explicit memory control guard let imageData = try? Data(contentsOf: url) else { return "Error: Could not read image file." } guard let nsImage = NSImage(data: imageData) else { return "Error: Could not create image from file data." } guard let cgImage = nsImage.cgImage(forProposedRect: nil, context: nil, hints: nil) else { return "Error: Could not create CGImage." } let width = cgImage.width let height = cgImage.height // Create a CVPixelBuffer from the CGImage var pixelBuffer: CVPixelBuffer? let attrs: [String: Any] = [ kCVPixelBufferCGImageCompatibilityKey as String: true, kCVPixelBufferCGBitmapContextCompatibilityKey as String: true ] let status = CVPixelBufferCreate( kCFAllocatorDefault, width, height, kCVPixelFormatType_32ARGB, attrs as CFDictionary, &pixelBuffer ) guard status == kCVReturnSuccess, let buffer = pixelBuffer else { return "Error: Could not create CVPixelBuffer (status: \(status))." } // Draw the CGImage into the pixel buffer CVPixelBufferLockBaseAddress(buffer, []) guard let context = CGContext( data: CVPixelBufferGetBaseAddress(buffer), width: width, height: height, bitsPerComponent: 8, bytesPerRow: CVPixelBufferGetBytesPerRow(buffer), space: CGColorSpaceCreateDeviceRGB(), bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue ) else { CVPixelBufferUnlockBaseAddress(buffer, []) return "Error: Could not create CGContext for pixel buffer." } context.draw(cgImage, in: CGRect(x: 0, y: 0, width: width, height: height)) CVPixelBufferUnlockBaseAddress(buffer, []) // Run OCR let requestHandler = VNImageRequestHandler(cvPixelBuffer: buffer, options: [:]) let request = VNRecognizeTextRequest() request.recognitionLevel = .accurate request.usesLanguageCorrection = true do { try requestHandler.perform([request]) } catch { return "Error during OCR: \(error.localizedDescription)" } guard let observations = request.results, !observations.isEmpty else { return "No text found in image." } let lines = observations.compactMap { observation in observation.topCandidates(1).first?.string } // Explicitly nil out the pixel buffer before the pool drains pixelBuffer = nil return lines.joined(separator: "\n") } // Everything — Data, NSImage, CGImage, CVPixelBuffer, VN objects — released here return resultText } `
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Activity
Feb ’26