What are the major differences in review process for AI based apps vis a vis normal apps for Apple store?

When I import starts models in Jupyter notebook, I ge the following error: ImportError: dlopen(/opt/anaconda3/lib/python3.12/site-packages/scipy/linalg/_fblas.cpython-312-darwin.so, 0x0002): Library not loaded: @rpath/liblapack.3.dylib Referenced from: <5ACBAA79-2387-3BEF-9F8E-6B7584B0F5AD> /opt/anaconda3/lib/python3.12/site-packages/scipy/linalg/_fblas.cpython-312-darwin.so Reason: tried: '/opt/anaconda3/lib/python3.12/site-packages/scipy/linalg/../../../../liblapack.3.dylib' (no such file), '/opt/anaconda3/lib/python3.12/site-packages/scipy/linalg/../../../../liblapack.3.dylib' (no such file), '/opt/anaconda3/bin/../lib/liblapack.3.dylib' (no such file), '/opt/anaconda3/bin/../lib/liblapack.3.dylib' (no such file), '/usr/local/lib/liblapack.3.dylib' (no such file), '/usr/lib/liblapack.3.dylib' (no such file, not in dyld cache). What should I do?

what am I not understanding here. in short the view loads text from the jsons descriptions and then should filter out the words. and return and display a list of most used words, debugging shows words being identified by the code but does not filter them out private func loadWordCounts() { DispatchQueue.global(qos: .background).async { let fileManager = FileManager.default guard let documentsDirectory = try? fileManager.url(for: .documentDirectory, in: .userDomainMask, appropriateFor: nil, create: false) else { return } let descriptions = loadDescriptions(fileManager: fileManager, documentsDirectory: documentsDirectory) var counts = countWords(in: descriptions) let tagsToRemove: Set<NLTag> = [ .verb, .pronoun, .determiner, .particle, .preposition, .conjunction, .interjection, .classifier ] for (word, _) in counts { let tagger = NLTagger(tagSchemes: [.lexicalClass]) tagger.string = word let (tag, _) = tagger.tag(at: word.startIndex, unit: .word, scheme: .lexicalClass) if let unwrappedTag = tag, tagsToRemove.contains(unwrappedTag) { counts[word] = 0 } } DispatchQueue.main.async { self.wordCounts = counts } } }

Is it just me or is early access image playground not available, been waiting for a little over 24hrs and still no access. (no rush for the team if there’s smth wrong) they might be busy rolling out the first few apple intelligence features (ios 18.1) public release.

I'm using the iOS 18.2 beta on my iPhone 15 Pro Max, but can't find Apple Intelligence, and the Settings app still shows the Old Siri logo.

hi, I am currently running LSTM on TensorFlow. However, when i switched from keras2 to keras3. code running time has increased 10 times -- it seems there is no GPU acceleration. Here is my code: batch size = 256 optimiser = adam activation = tanh _______________________________________________ Layer (type) Output Shape Param # ============================================= input_1 (InputLayer) [(None, 7, 16)] 0 bidirectional (Bidirection (None, 7, 320) 226560 al) bidirectional_1 (Bidirecti (None, 7, 512) 1181696 onal) bidirectional_2 (Bidirecti (None, 256) 656384 onal) dense (Dense) (None, 1) 257 ============================================== Total params: 2064897 (7.88 MB) Trainable params: 2064897 (7.88 MB) Non-trainable params: 0 (0.00 Byte) ______________________________________________ This is keras 3.6.0 + tensorflow 2.17.0 + tensorflow-metal 1.1.0 training status: Training------------ Epoch 1/200 28/681 ━━━━━━━━━━━━━━━━━━━━ 8:13 756ms/step - loss: 0.5901 - mape: 338.6876 - mse: 0.8591 This is keras 2.14.0 + tensorflow 2.14.0 + tensorflow-metal 1.1.0 training status: Training------------ Epoch 1/200 681/681 [==============================] - 37s 49ms/step - loss: 3.6345 - mape: 499038.7500 - mse: 34.4148 - val_loss: 3.5452 - val_mape: 41.7964 - val_mse: 32.0133 - lr: 0.0010 Is that because keras3 has no GPU support on macos? Apart from that, if I change LSTM activation from tanh to sigmoid in keras2, it does not have GPU support as well. My system is 15.0.1 and the code was running on python3.11 I am not sure why these happen. Thanks

Hello. I can't find anything about the SSML that is used in Apple's speech synthesis. SSML from Google, Amazon and W3C either don't work or work incorrectly. Where is Apple's documentation for their implementation of SSML?

Hi All, Is it possible to record a video using the Object Capture instead of taking a series of pictures ? Is it possible to get the bounding box coordinates of the object we capture ?

I am using Apple’s Vision framework with DetectHorizonRequest to detect the horizon in an image. Here is my code: func processHorizonImage(_ ciImage: CIImage) async { let request = DetectHorizonRequest() do { let result = try await request.perform(on: ciImage) print(result) } catch { print(error) } } After calling the perform method, I am getting result as nil. To ensure the request's correctness, I have verified the following: The input CIImage is valid and contains a visible horizon. No errors are being thrown. The relevant frameworks are properly imported. Given that my image contains a clear horizon, why am I still not getting any results? I would appreciate any help or suggestions to resolve this issue. Thank you for your support! This is the image

Almost all the functions in Accelerate are for single precision (Float) and double precision (Double) operations. However, I stumbled upon three integer arithmetic functions which operate on Int32 values. Are there any more functions in Accelerate that operate on integer values? If not, then why aren't there more functions that work with integers?

Hey everyone, I've been updating my code to take advantage of the new Vision API for text recognition in macOS 15. I'm noticing some very odd behavior though, it seems like in general the new Vision API consistently produces worse results than the old API. For reference here is how I'm setting up my request. var request = RecognizeTextRequest() request.recognitionLevel = getOCRMode() // generally accurate request.usesLanguageCorrection = !disableLanguageCorrection // generally true request.recognitionLanguages = language.split(separator: ",").map { Locale.Language(identifier: String($0)) } // generally 'en' let observations = try? await request.perform(on: image) as [RecognizedTextObservation] Then I will process the results and just get the top candidate, which as mentioned above, typically is of worse quality then the same request formed with the old API. Am I doing something wrong here?

Hi Ty for playing

WWDC 2024 mentioned that the OCR feature from the Vision framework has support for "Korean, Swedish, and Chinese", but the Swedish support does not seem to be available... Running either print(try? VNRecognizeTextRequest().supportedRecognitionLanguages()) or var ocrRequest = RecognizeTextRequest(.revision3) print(ocrRequest.supportedRecognitionLanguages) did not print out Swedish as one of the supported languages, but Korean and Chinese are. Tested on early versions of iOS 18 developer beta, and the latest version of iOS 18.1 (22B5054e).

I was working on my project and when I tried to train a model the kernel crashed, so I restarted the kernel and tried the same and still I got the same crashing issue. Then I read one of the thread having the same issue where the apple support was saying to install tensorflow-macos and tensorflow-metal and read the guide from this site: https://developer.apple.com/metal/tensorflow-plugin/ and I did so, I tried every single thing and when I tried the test code provided in the site, I got the same error, here's the code and the output. Code: import tensorflow as tf cifar = tf.keras.datasets.cifar100 (x_train, y_train), (x_test, y_test) = cifar.load_data() model = tf.keras.applications.ResNet50( include_top=True, weights=None, input_shape=(32, 32, 3), classes=100,) loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"]) model.fit(x_train, y_train, epochs=5, batch_size=64) and here's the output: Epoch 1/5 The Kernel crashed while executing code in the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details. And here's the half of log file as it was not fully coming: metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1 2024-10-06 23:30:49.894405: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 8.00 GB 2024-10-06 23:30:49.894420: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 2.67 GB 2024-10-06 23:30:49.894444: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2024-10-06 23:30:49.894460: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: ) 2024-10-06 23:30:56.701461: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled. [libprotobuf FATAL google/protobuf/message_lite.cc:353] CHECK failed: target + size == res: libc++abi: terminating due to uncaught exception of type google::protobuf::FatalException: CHECK failed: target + size == res: Please respond to this post as soon as possible as I am working on my project now and getting this error again n again. Device: Apple MacBook Air M1.

I can use BLAS and LAPACK functions via the Accelerate framework to perform vector and matrix arithmetic and linear algebra calculations. But do these functions take advantage of Apple Silicon features?

I understand we can use MPSImageBatch as input to [MPSNNGraph encodeBatchToCommandBuffer: ...] method. That being said, all inputs to the MPSNNGraph need to be encapsulated in a MPSImage(s). Suppose I have an machine learning application that trains/infers on thousands of input data where each input has 4 feature channels. Metal Performance Shaders is chosen as the primary AI backbone for real-time use. Due to the nature of encodeBatchToCommandBuffer method, I will have to create a MTLTexture first as a 2D texture array. The texture has pixel width of 1, height of 1 and pixel format being RGBA32f. The general set up will be: #define NumInputDims 4 MPSImageBatch * infBatch = @[]; const uint32_t totalFeatureSets = N; // Each slice is 4 (RGBA) channels. const uint32_t totalSlices = (totalFeatureSets * NumInputDims + 3) / 4; MTLTextureDescriptor * descriptor = [MTLTextureDescriptor texture2DDescriptorWithPixelFormat: MTLPixelFormatRGBA32Float width: 1 height: 1 mipmapped: NO]; descriptor.textureType = MTLTextureType2DArray descriptor.arrayLength = totalSlices; id<MTLTexture> texture = [mDevice newTextureWithDescriptor: descriptor]; // bytes per row is `4 * sizeof(float)` since we're doing one pixel of RGBA32F. [texture replaceRegion: MTLRegionMake3D(0, 0, 0, 1, 1, totalSlices) mipmapLevel: 0 withBytes: inputFeatureBuffers[0].data() bytesPerRow: 4 * sizeof(float)]; MPSImage * infQueryImage = [[MPSImage alloc] initWithTexture: texture featureChannels: NumInputDims]; infBatch = [infBatch arrayByAddingObject: infQueryImage]; The training/inference will be: MPSNNGraph * mInferenceGraph = /*some MPSNNGraph setup*/; MPSImageBatch * returnImage = [mInferenceGraph encodeBatchToCommandBuffer: commandBuffer sourceImages: @[infBatch] sourceStates: nil intermediateImages: nil destinationStates: nil]; // Commit and wait... // Read the return image for the inferred result. As you can see, the setup is really ad hoc - a lot of 1x1 pixels just for this sole purpose. Is there any better way I can achieve the same result while still on Metal Performance Shaders? I guess a further question will be: can MPS handle general machine learning cases other than CNN? I can see the APIs are revolved around convolution network, both from online documentations and header files. Any response will be helpful, thank you.

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!

Hi everyone, I'm working on an iOS app built in Swift using Xcode, where I'm integrating Roboflow's object detection API to extract items from grocery receipts. My goal is to identify key information (like items, total, tax, etc.) from the images of these receipts. I'm successfully sending images to the Roboflow API and receiving predictions with bounding box data, but when I attempt to extract text from the detected regions (bounding boxes), it appears that the text extraction is failing—no text is being recognized. The issue seems to be that the bounding boxes are either not properly being handled or something is going wrong in the way I process the API response. Here's a brief breakdown of what I'm doing: The image is captured, converted to base64, and sent to the Roboflow API. The API response comes back with bounding boxes for the detected elements (items, date, subtotal, etc.). The problem occurs when I try to extract the text from the image using the bounding box data—it seems like the bounding boxes are being found, but no text is returned. I suspect the issue might be happening because the app’s segue to the results view controller is triggered before the OCR extraction completes, or there might be a problem in my code handling the bounding box response. Response Data: { "inference_id": "77134cce-91b5-4600-a59b-fab74350ca06", "time": 0.09240847699993537, "image": { "width": 370, "height": 502 }, "predictions": [ { "x": 163.5, "y": 250.5, "width": 313.0, "height": 127.0, "confidence": 0.9357666373252869, "class": "Item", "class_id": 1, "detection_id": "753341d5-07b6-42a1-8926-ecbc61128243" }, { "x": 52.5, "y": 417.5, "width": 89.0, "height": 23.0, "confidence": 0.8819760680198669, "class": "Date", "class_id": 0, "detection_id": "b4681149-d538-47b1-8700-d9528bf1daa0" }, ... ] } And the log showing bounding boxes: Prediction: ["width": 313, "y": 250.5, "x": 163.5, "detection_id": 753341d5-07b6-42a1-8926-ecbc61128243, "class": Item, "height": 127, "confidence": 0.9357666373252869, "class_id": 1] No bounding box found in prediction. I've double-checked the bounding box coordinates, and everything seems fine. Does anyone have experience with using OCR alongside object detection APIs in Swift? Any help on how to ensure the bounding boxes are properly processed and used for OCR would be greatly appreciated! Also, would it help to delay the segue to the results view controller until OCR is complete? Thank you!

The metal plugin for TensorFlow had its GitHub repo taken down, and on pypi, the last update was a year ago for TF 2.14. What's the status on the metal plugin? For now it seems to work fine for TF 2.15 but what's the plan for the future?

When I use VNGenerateForegroundInstanceMaskRequest to generate the mask in the simulator by SwiftUI, there is an error "Could not create inference context". Then I add the code to make the vision by CPU: let request = VNGenerateForegroundInstanceMaskRequest() let handler = VNImageRequestHandler(ciImage: inputImage) #if targetEnvironment(simulator) if #available(iOS 18.0, *) { let allDevices = MLComputeDevice.allComputeDevices for device in allDevices { if(device.description.contains("MLCPUComputeDevice")){ request.setComputeDevice(.some(device), for: .main) break } } } else { // Fallback on earlier versions request.usesCPUOnly = true } #endif do { try handler.perform([request]) if let result = request.results?.first { let mask = try result.generateScaledMaskForImage(forInstances: result.allInstances, from: handler) return CIImage(cvPixelBuffer: mask) } } catch { print(error) } Even I force the simulator to run the code by CPU, but it still have the error: "Could not create inference context"