Hello, I am currently developing an application that requires barcode scanning using Apple’s Vision framework (VNBarcodeSymbology). I noticed that the framework supports several GS1 DataBar symbologies, such as: VNBarcodeSymbology.gs1DataBar VNBarcodeSymbology.gs1DataBarExpanded VNBarcodeSymbology.gs1DataBarLimited However, I could not find any explicit reference to support for GS1 DataBar Stacked (both regular and expanded variants). Could you confirm whether GS1 DataBar Stacked is currently supported in VisionKit's DataScannerViewController or VNBarcodeObservation? If not, are there any plans to include support for this symbology in a future iOS update? This functionality is critical for my use case, as GS1 DataBar Stacked barcodes are widely used in retail, pharmaceuticals, and logistics, where space constraints prevent the use of standard GS1 DataBar formats. I appreciate any clarification on this matter and would be happy to provide additional details if needed.

Hi, The most recent version of tensorflow-metal is only available for macosx 12.0 and python up to version 3.11. Is there any chance it could be updated with wheels for macos 15 and Python 3.12 (which is the default version supported for tensrofllow 2.17+)? I'd note that even downgrading to Python 3.11 would not be sufficient, as the wheels only work for macos 12. Thanks.

Hi everyone😊, I want to implement facial recognition into my app. I was planning to use createML's image classification, but there seams to be a lot of hassle to implement (the JSON file etc.). Are there some other easy to implement options that don't involve advanced coding. Thanks, Oliver

基于iPhone 14 Max相机，实现模型识别，并在识别对象周围画一个矩形框。宽度和高度使用激光雷达计算，并在实时更新的图像上以厘米为单位显示。 swift code

I’m building an app that generates images based on text input from a specific text field. However, I’m encountering a problem: For short prompts like "a cat and a dog", the entire string is sent to the Image Playground, even when I use the extracted method. For longer inputs, the behavior is inconsistent. Sometimes it extracts keywords correctly, but other times it doesn’t extract anything at all. Since my app relies on generating images based on the extracted keywords, this inconsistency negatively impacts the user experience in my app. How can I make sure that keywords are always extracted from the input string? Button("Generate", systemImage: "apple.intelligence") { isPresented = true } .imagePlaygroundSheet(isPresented: $isPresented, concepts: [ImagePlaygroundConcept.extracted(from: text, title: textTitle)]) { url in imageURL = url }

Hello! I have a swift program that tracks the location of a ball (through the back camera). It seems to be working fine, but the only issue is the run time, particularly my concatenate, normalize, and argmax functions, which are meant to be a 1 to 1 copy of the PyTorch argmax function and the following python lines: imgs = np.concatenate((img, img_prev, img_preprev), axis=2) imgs = imgs.astype(np.float32)/255.0 imgs = np.rollaxis(imgs, 2, 0) inp = np.expand_dims(imgs, axis=0) # used to pass into model However, I need my program to run in real time and in an ideal world, I want it to run way under real time. Below is a run down of the run times that result from my code: Starting model inference Setup took: 0.0 seconds Resize took: 0.03741896152496338 seconds Concatenation took: 0.3359949588775635 seconds Normalization took: 0.9906361103057861 seconds Model prediction took: 0.3425499200820923 seconds Argmax took: 28.17007803916931 seconds Postprocess took: 0.054128050804138184 seconds Model inference took 29.934185028076172 seconds Here are the concatenateBuffers, normalizeBuffers, and argmax functions that I use: func concatenateBuffers(_ buffers: [CVPixelBuffer?]) -> CVPixelBuffer? { guard buffers.count == 3, let first = buffers[0] else { return nil } let width = CVPixelBufferGetWidth(first) let height = CVPixelBufferGetHeight(first) let targetChannels = 9 var concatenated: CVPixelBuffer? let attrs = [kCVPixelBufferCGImageCompatibilityKey: kCFBooleanTrue] as CFDictionary CVPixelBufferCreate(kCFAllocatorDefault, width, height, kCVPixelFormatType_32BGRA, attrs, &concatenated) guard let output = concatenated else { return nil } CVPixelBufferLockBaseAddress(output, []) defer { CVPixelBufferUnlockBaseAddress(output, []) } guard let outputData = CVPixelBufferGetBaseAddress(output) else { return nil } let outputPtr = UnsafeMutablePointer<UInt8>(OpaquePointer(outputData)) // Lock all input buffers at once buffers.forEach { buffer in guard let buffer = buffer else { return } CVPixelBufferLockBaseAddress(buffer, .readOnly) } defer { buffers.forEach { CVPixelBufferUnlockBaseAddress($0!, .readOnly) } } // Process each input buffer for (frameIdx, buffer) in buffers.enumerated() { guard let buffer = buffer, let inputData = CVPixelBufferGetBaseAddress(buffer) else { continue } let inputPtr = UnsafePointer<UInt8>(OpaquePointer(inputData)) let bytesPerRow = CVPixelBufferGetBytesPerRow(buffer) let totalPixels = width * height // Process all pixels in one go for this frame for i in 0..<totalPixels { let y = i / width let x = i % width let inputOffset = y * bytesPerRow + x * 4 let outputOffset = i * targetChannels + frameIdx * 3 // BGR order to match numpy outputPtr[outputOffset] = inputPtr[inputOffset + 2] // B outputPtr[outputOffset + 1] = inputPtr[inputOffset + 1] // G outputPtr[outputOffset + 2] = inputPtr[inputOffset] // R } } return output } func normalizeBuffer(_ buffer: CVPixelBuffer?) -> MLMultiArray? { guard let input = buffer else { return nil } let width = CVPixelBufferGetWidth(input) let height = CVPixelBufferGetHeight(input) let channels = 9 CVPixelBufferLockBaseAddress(input, .readOnly) defer { CVPixelBufferUnlockBaseAddress(input, .readOnly) } guard let inputData = CVPixelBufferGetBaseAddress(input) else { return nil } let shape = [1, NSNumber(value: channels), NSNumber(value: height), NSNumber(value: width)] guard let output = try? MLMultiArray(shape: shape, dataType: .float32) else { return nil } let inputPtr = inputData.assumingMemoryBound(to: UInt8.self) let bytesPerRow = CVPixelBufferGetBytesPerRow(input) let ptr = UnsafeMutablePointer<Float>(OpaquePointer(output.dataPointer)) let totalSize = width * height for c in 0..<channels { for idx in 0..<totalSize { let h = idx / width let w = idx % width let inputIdx = h * bytesPerRow + w * channels + c ptr[c * totalSize + idx] = Float(inputPtr[inputIdx]) / 255.0 } } return output } func argmax(_ array: MLMultiArray) -> MLMultiArray? { let shape = array.shape.map { $0.intValue } guard shape.count == 3, shape[0] == 1, shape[1] == 256, shape[2] == 230400 else { return nil } guard let output = try? MLMultiArray(shape: [1, NSNumber(value: 230400)], dataType: .int32) else { return nil } let ptr = UnsafePointer<Float>(OpaquePointer(array.dataPointer)) let outputPtr = UnsafeMutablePointer<Int32>(OpaquePointer(output.dataPointer)) let channelSize = 230400 for pos in 0..<230400 { var maxValue = -Float.infinity var maxIndex: Int32 = 0 for channel in 0..<256 { let value = ptr[channel * channelSize + pos] if value > maxValue { maxValue = value maxIndex = Int32(channel) } } outputPtr[pos] = maxIndex } return output } Are there any glaring areas of inefficiencies that can be reduced to allow for under real time processing whilst following the same logic as found in the python code exactly? Would using Obj-C speed things up for some reason? Are there any tools I can use so I don't have to write these functions myself? Additionally, in the classes init, function, I tried to check the compute units being used since I feel 0.34 seconds for a singular model prediction is also far too long, but no print statements are showing for some reason: init() { guard let loadedModel = try? BallTrackerModel() else { fatalError("Could not load model") } let config = MLModelConfiguration() config.computeUnits = .all guard let configuredModel = try? BallTrackerModel(configuration: config) else { fatalError("Could not configure model") } self.model = configuredModel print("model loaded with compute units \(config.computeUnits.rawValue)") } Thanks!

I am working on a CoreML image classification model in Xcode, which takes a 299x299 image and attempts to classify hand-drawn sketches. The model was trained using Create ML and works perfectly when tested in the Create ML preview. However, when used in Xcode application, the classification results are incorrect. I have already verified that the image is correctly resized to 299x299 pixels, matching the input size of the model. The classification always returns incorrect results, even when using images that were correctly classified during training. I originally used kCVPixelFormatType_32ARGB, but I read that CoreML typically expects BGRA format. I updated my conversion function to use kCVPixelFormatType_32BGRA and CGImageAlphaInfo.premultipliedLast, but the issue persists. This makes me suspect that either the pixel format is still incorrect or that something went wrong during the .mlmodelc compilation.

We are building an app which can reads texts. It can read english and Japanese normal texts successfully. But in some cases, we need to read Japanese tategaki (vertically aligned texts). But in that times, the same code gives no output. So, is there any need to change any configuration to read Japanese tategaki? Or is it really possible to read Japanese tategaki using vision framework? lazy var detectTextRequest = VNRecognizeTextRequest { request, error in self.resStr="\n" self.words = [:] // Get OCR result guard let res = request.results as? [VNRecognizedTextObservation] else { return } // separate the words by space let text = res.compactMap({$0.topCandidates(1).first?.string}).joined(separator: " ") var n = 0 self.wordArr=[[]] self.xs = 1 self.ys = 1 var hs = 0.0 // To compare the heights of the words // To get the original axis (top most word's axis), only once for r in res { var word = r.topCandidates(1).first?.string self.words[word ?? ""] = [r.topLeft.x, r.topLeft.y] if(self.cartLabelType == 1){ if(word?.components(separatedBy: CharacterSet(charactersIn: "/／")).count ?? 0>2){ self.xs = r.topLeft.x self.ys = r.topLeft.y } } } } }

I have an image based app with albums, except in my app, albums are known as galleries. When I tried to conform my existing OpenGalleryIntent with @AssistantIntent(schema: .photos.openAlbum), I had to change my existing gallery parameter to be called target in order to fit the predefined shape of this domain. Previously, my intent was configured to display as “Open Gallery” with the description “Opens the selected Gallery” in the Shortcuts app. After conforming to the photos domain, it displays as “Open Album” with a description “Opens the Provided Album”. Shortcuts is ignoring my configured title and description now. My code builds, but with the following build warnings: Parameter argument title of a required Assistant schema intent parameter target should not be overridden Implementation of the property title of an AppIntent conforming to AssistantSchemaIntent should not be overridden Implementation of the property description of an AppIntent conforming to AssistantSchemaIntent should not be overridden Is my only option to change the concept of a Gallery inside of my app into an Album? I don't want to do this... Conceptually, my app aligns well with this domain does, but I didn't consider that conforming to the shape of an AI schema intent would also dictate exactly how it's presented to the user. FB16283840

I'm currently trying to add support for Image Playground to our apps. It seems that it's not working in an app that is "Designed for iPad" and runs on a Mac. The modal just shows a spinner and the following is logged to console: Private sandbox for com.apple.GenerativePlaygroundApp.remoteUIExtension : <none> Private sandbox for com.apple.GenerativePlaygroundApp.remoteUIExtension : <none> Private sandbox for com.apple.GenerativePlaygroundApp.remoteUIExtension : <none> Private sandbox for com.apple.GenerativePlaygroundApp.remoteUIExtension : <none> GP extension could not be loaded: Extension (platform: 2) could not be found (in update) dealloc Query controller [C32BA176-6A3E-465D-B3C5-0F8D91068B89] ImagePlaygroundViewController.isAvailable returns true, however. In a "real" Mac Catalyst app, it's working. Just not when the app is actually an iPad app. Is this a bug?

I've checked on pypi.org and it appears to only have arm64 packages, has x86 with AMD been deprecated?

In an App Playground Xcode project there is no Targets menu in the UI, When I try use the model, it says the model is not in scope. When I did it in a regular project it automatically generated a Swift Class and had no erorrs because it had a target but I see no place to add a target on an App playground.

Hello! I have a TrackNet model that I have converted to CoreML (.mlpackage) using coremltools, and the conversion process appears to go smoothly as I get the .mlpackage file I am looking for with the weights and model.mlmodel file in the folder. However, when I drag it into Xcode, it just shows up as 4 script tags (as pictured) instead of the model "interface" that is typically expected. I initially was concerned that my model was not compatible with CoreML, but upon logging the conversions, everything seems to be converted properly. I have some code that may be relevant in debugging this issue: How I use the model: model = BallTrackerNet() # this is the model architecture which will be referenced later device = self.device # cpu model.load_state_dict(torch.load("models/balltrackerbest.pt", map_location=device)) # balltrackerbest is the weights model = model.to(device) model.eval() Here is the BallTrackerNet() model itself: import torch.nn as nn import torch class ConvBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, pad=1, stride=1, bias=True): super().__init__() self.block = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=pad, bias=bias), nn.ReLU(), nn.BatchNorm2d(out_channels) ) def forward(self, x): return self.block(x) class BallTrackerNet(nn.Module): def __init__(self, out_channels=256): super().__init__() self.out_channels = out_channels self.conv1 = ConvBlock(in_channels=9, out_channels=64) self.conv2 = ConvBlock(in_channels=64, out_channels=64) self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv3 = ConvBlock(in_channels=64, out_channels=128) self.conv4 = ConvBlock(in_channels=128, out_channels=128) self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv5 = ConvBlock(in_channels=128, out_channels=256) self.conv6 = ConvBlock(in_channels=256, out_channels=256) self.conv7 = ConvBlock(in_channels=256, out_channels=256) self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv8 = ConvBlock(in_channels=256, out_channels=512) self.conv9 = ConvBlock(in_channels=512, out_channels=512) self.conv10 = ConvBlock(in_channels=512, out_channels=512) self.ups1 = nn.Upsample(scale_factor=2) self.conv11 = ConvBlock(in_channels=512, out_channels=256) self.conv12 = ConvBlock(in_channels=256, out_channels=256) self.conv13 = ConvBlock(in_channels=256, out_channels=256) self.ups2 = nn.Upsample(scale_factor=2) self.conv14 = ConvBlock(in_channels=256, out_channels=128) self.conv15 = ConvBlock(in_channels=128, out_channels=128) self.ups3 = nn.Upsample(scale_factor=2) self.conv16 = ConvBlock(in_channels=128, out_channels=64) self.conv17 = ConvBlock(in_channels=64, out_channels=64) self.conv18 = ConvBlock(in_channels=64, out_channels=self.out_channels) self.softmax = nn.Softmax(dim=1) self._init_weights() def forward(self, x, testing=False): batch_size = x.size(0) x = self.conv1(x) x = self.conv2(x) x = self.pool1(x) x = self.conv3(x) x = self.conv4(x) x = self.pool2(x) x = self.conv5(x) x = self.conv6(x) x = self.conv7(x) x = self.pool3(x) x = self.conv8(x) x = self.conv9(x) x = self.conv10(x) x = self.ups1(x) x = self.conv11(x) x = self.conv12(x) x = self.conv13(x) x = self.ups2(x) x = self.conv14(x) x = self.conv15(x) x = self.ups3(x) x = self.conv16(x) x = self.conv17(x) x = self.conv18(x) # x = self.softmax(x) out = x.reshape(batch_size, self.out_channels, -1) if testing: out = self.softmax(out) return out def _init_weights(self): for module in self.modules(): if isinstance(module, nn.Conv2d): nn.init.uniform_(module.weight, -0.05, 0.05) if module.bias is not None: nn.init.constant_(module.bias, 0) elif isinstance(module, nn.BatchNorm2d): nn.init.constant_(module.weight, 1) nn.init.constant_(module.bias, 0) Here is also the meta data of my model: [ { "metadataOutputVersion" : "3.0", "storagePrecision" : "Float16", "outputSchema" : [ { "hasShapeFlexibility" : "0", "isOptional" : "0", "dataType" : "Float32", "formattedType" : "MultiArray (Float32 1 × 256 × 230400)", "shortDescription" : "", "shape" : "[1, 256, 230400]", "name" : "var_462", "type" : "MultiArray" } ], "modelParameters" : [ ], "specificationVersion" : 6, "mlProgramOperationTypeHistogram" : { "Cast" : 2, "Conv" : 18, "Relu" : 18, "BatchNorm" : 18, "Reshape" : 1, "UpsampleNearestNeighbor" : 3, "MaxPool" : 3 }, "computePrecision" : "Mixed (Float16, Float32, Int32)", "isUpdatable" : "0", "availability" : { "macOS" : "12.0", "tvOS" : "15.0", "visionOS" : "1.0", "watchOS" : "8.0", "iOS" : "15.0", "macCatalyst" : "15.0" }, "modelType" : { "name" : "MLModelType_mlProgram" }, "userDefinedMetadata" : { "com.github.apple.coremltools.source_dialect" : "TorchScript", "com.github.apple.coremltools.source" : "torch==2.5.1", "com.github.apple.coremltools.version" : "8.1" }, "inputSchema" : [ { "hasShapeFlexibility" : "0", "isOptional" : "0", "dataType" : "Float32", "formattedType" : "MultiArray (Float32 1 × 9 × 360 × 640)", "shortDescription" : "", "shape" : "[1, 9, 360, 640]", "name" : "input_frames", "type" : "MultiArray" } ], "generatedClassName" : "BallTracker", "method" : "predict" } ] I have been struggling with this conversion for almost 2 weeks now so any help, ideas or pointers would be greatly appreciated! Let me know if any other information would be helpful to see as well. Thanks! Michael

I am trying to run TinyLlama directly using Swift Playgrounds for iOS. I have tried multiple solutions, like libraries (LLM.swift, swift-transformers, ...) which never worked due to import issues, and also tried importing an exported mlmodel. For the later, I followed the article about Llama 3.1 on CoreML. It was hard to understand how to do the inference with it, but I was able to export a mlpackage, that I then placed in a xcode project to generate the mlmodelc (compiled model) and the model class. I had to go with the first version described in the article, without optimizations, as I got errors during model loading with the flexible input shapes. I was able to run the model for one token generation. But my biggest problem is that, though the mlmodelc is only 550 MiB, th model loads 24+GiB of memory, largely exceeding what I can have on an iOS device. Is there a way to use do LLM inferences on Swift Playgrounds at a reasonable speed (even 1 token / s would be sufficient)?

Hello! I have a TrackNet model that I have converted to CoreML (.mlpackage) using coremltools, and the conversion process appears to go smoothly as I get the .mlpackage file I am looking for with the weights and model.mlmodel file in the folder. However, when I drag it into Xcode, it just shows up as 4 script tags instead of the model "interface" that is typically expected. I initially was concerned that my model was not compatible with CoreML, but upon logging the conversions, everything seems to be converted properly. I have some code that may be relevant in debugging this issue: How I use the model: model = BallTrackerNet() # this is the model architecture which will be referenced later device = self.device # cpu model.load_state_dict(torch.load("models/balltrackerbest.pt", map_location=device)) # balltrackerbest is the weights model = model.to(device) model.eval() Here is the BallTrackerNet() model itself import torch.nn as nn import torch class ConvBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, pad=1, stride=1, bias=True): super().__init__() self.block = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=pad, bias=bias), nn.ReLU(), nn.BatchNorm2d(out_channels) ) def forward(self, x): return self.block(x) class BallTrackerNet(nn.Module): def __init__(self, out_channels=256): super().__init__() self.out_channels = out_channels self.conv1 = ConvBlock(in_channels=9, out_channels=64) self.conv2 = ConvBlock(in_channels=64, out_channels=64) self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv3 = ConvBlock(in_channels=64, out_channels=128) self.conv4 = ConvBlock(in_channels=128, out_channels=128) self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv5 = ConvBlock(in_channels=128, out_channels=256) self.conv6 = ConvBlock(in_channels=256, out_channels=256) self.conv7 = ConvBlock(in_channels=256, out_channels=256) self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv8 = ConvBlock(in_channels=256, out_channels=512) self.conv9 = ConvBlock(in_channels=512, out_channels=512) self.conv10 = ConvBlock(in_channels=512, out_channels=512) self.ups1 = nn.Upsample(scale_factor=2) self.conv11 = ConvBlock(in_channels=512, out_channels=256) self.conv12 = ConvBlock(in_channels=256, out_channels=256) self.conv13 = ConvBlock(in_channels=256, out_channels=256) self.ups2 = nn.Upsample(scale_factor=2) self.conv14 = ConvBlock(in_channels=256, out_channels=128) self.conv15 = ConvBlock(in_channels=128, out_channels=128) self.ups3 = nn.Upsample(scale_factor=2) self.conv16 = ConvBlock(in_channels=128, out_channels=64) self.conv17 = ConvBlock(in_channels=64, out_channels=64) self.conv18 = ConvBlock(in_channels=64, out_channels=self.out_channels) self.softmax = nn.Softmax(dim=1) self._init_weights() def forward(self, x, testing=False): batch_size = x.size(0) x = self.conv1(x) x = self.conv2(x) x = self.pool1(x) x = self.conv3(x) x = self.conv4(x) x = self.pool2(x) x = self.conv5(x) x = self.conv6(x) x = self.conv7(x) x = self.pool3(x) x = self.conv8(x) x = self.conv9(x) x = self.conv10(x) x = self.ups1(x) x = self.conv11(x) x = self.conv12(x) x = self.conv13(x) x = self.ups2(x) x = self.conv14(x) x = self.conv15(x) x = self.ups3(x) x = self.conv16(x) x = self.conv17(x) x = self.conv18(x) # x = self.softmax(x) out = x.reshape(batch_size, self.out_channels, -1) if testing: out = self.softmax(out) return out def _init_weights(self): for module in self.modules(): if isinstance(module, nn.Conv2d): nn.init.uniform_(module.weight, -0.05, 0.05) if module.bias is not None: nn.init.constant_(module.bias, 0) elif isinstance(module, nn.BatchNorm2d): nn.init.constant_(module.weight, 1) nn.init.constant_(module.bias, 0) I have been struggling with this conversion for almost 2 weeks now so any help, ideas or pointers would be greatly appreciated! Thanks! Michael

I see the solution is simple "just change the language in the build settings" but the build settings are not a thing in an App Playground project. It also says duplicated tasks.

Hi all, I'm working on an app to classify dog breeds via CoreML, but when I try training a model using Image Feature Print v2, I get the following error: Failed to create CVPixelBufferPool. Width = 0, Height = 0, Format = 0x00000000 Strangely, when I switch back to Image Feature Print v1, the model trains perfectly fine. I've verified that there aren't any invalid or broken images in my dataset. Is there a fix for this? Thanks!

I have a smallish image classifier I've been working on using the Create ML app. For a while everything was going fine, but lately, as the dataset has gotten larger, Create ML seems to stop during the testing phase with no error or test results. You can see here that there is no score in the result box, even though there are testing started and completed messages: No error message is shown in the Create ML app, but I do see these messages in the log: default 14:25:36.529887-0500 MLRecipeExecutionService [0x6000012bc000] activating connection: mach=false listener=false peer=false name=com.apple.coremedia.videodecoder default 14:25:36.529978-0500 MLRecipeExecutionService [0x41c5d34c0] activating connection: mach=false listener=true peer=false name=(anonymous) default 14:25:36.530004-0500 MLRecipeExecutionService [0x41c5d34c0] Channel could not return listener port. default 14:25:36.530364-0500 MLRecipeExecutionService [0x429a88740] activating connection: mach=false listener=false peer=true name=com.apple.xpc.anonymous.0x41c5d34c0.peer[1167].0x429a88740 default 14:25:36.534523-0500 MLRecipeExecutionService [0x6000012bc000] invalidated because the current process cancelled the connection by calling xpc_connection_cancel() default 14:25:36.534537-0500 MLRecipeExecutionService [0x41c5d34c0] invalidated because the current process cancelled the connection by calling xpc_connection_cancel() default 14:25:36.534544-0500 MLRecipeExecutionService [0x429a88740] invalidated because the current process cancelled the connection by calling xpc_connection_cancel() error 14:25:36.558788-0500 MLRecipeExecutionService CreateWithURL:342: *** ERROR: err=24 (Too many open files) - could not open '<CFURL 0x60000079b540 [0x1fdd32240]>{string = file:///Users/kevin/Library/Mobile%20Documents/com~apple~CloudDocs/Binary%20Formations/Under%20My%20Roof/Core%20ML%20Training%20Data/Household%20Items/Output/2025.01.23_12.55.16/Test/Stove/Test480.webp, encoding = 134217984, base = (null)}' default 14:25:36.559030-0500 MLRecipeExecutionService Error: <private> default 14:25:36.559077-0500 MLRecipeExecutionService Error: <private> Of particular interest is the "Too many open files" message from MLRecipeExecutionService referencing one of the test images. There are a total of 2,555 test images, which I wouldn't think would be a very large set. The system doesn't seem to be running out of memory or anything like that. Near the end of the test run there MLRecipeExecution service had 2934 file descriptors open according to lsof. Has anyone else run into this or know of a workaround? So far I've tried rebooting and recreating the Create ML project. Currently using Create ML Version 6.1 (150.3) on macOS 15.2 (24C101) running on a Mac Studio.

import coremltools as ct from coremltools.models.neural_network import quantization_utils # load full precision model model_fp32 = ct.models.MLModel(modelPath) model_fp16 = quantization_utils.quantize_weights(model_fp32, nbits=16) model_fp16.save("reduced-model.mlmodel") I'm testing it with the model from one of Apple's source codes(GameBoardDetector), and it works fine, reduces the model size by half. But there are several problems with my model(trained on CreateML app using Full Network): Quantizing to float 16 does not work(new file gets created with reduced only 0.1mb). Quantizing to below 16 values cause errors, and no file gets created. Here are additional metadata and precisions of models. Working model's additional metadata and precision: Mine's additional metadata and precision:

Hey everyone, I am a beginner with developing and using Artificial Intelligence models. How do I integrate my createML image classification with swift. I already have have an ML model and I want to integrate it into a swiftUI app. If anyone could help, that would be great. Thank you, O3DP