Note: I posted this to the feedback assistant but haven't gotten a response for 3months =( FB13482199 I am trying to train a large image classifier. I have a training run for ~300000 images. Each image has a folder and the file names within the folders are somewhat random. 381 classes. I am on an M2 Pro, Sonoma 14.0 running CreateML Version 5.0 (121.1). I would prefer not to pursue the pytorch/HF -> coremltools route. CreateML seems to consistently crash ~25000-30000 images in during the feature extraction phase with "Unexpected Error". It does not seem to be due to an out of memory issue. I am looking for some guidance since it seems impossible to debug why this is consistently crashing. My initial assumption was that it could be due to blank/corrupt files. I do not think that is the case. I also checked if there were any special characters in the data/folders. I wasn't able to go through all, but did try some programatic regex. Don't think this is the case either. I attached the sysdiagnose results in feedback assistant after the crash happened. I did notice when going into /var/logs there was some write issue saying that Mac had written too much to disk. Note: I also tried Xcode 15.2-beta this time and the associated CoreML version. My questions: How can I fix this? How should I go about debugging CreateML errors in the future? 'Unexpected Error' - where can I go about getting the exact createml logs on my device? This is far too broad of an error statement Please let me know. As a note, I did successfully train a past model on ~100000 images. I am planning to 10-15x that if this run is successful. Please help, spent a lot of time gathering the extra data and to date have been an occasional power user of createml. Haven't heard back from Apple since December =/. I assume I'm not the only one with this problem, so looking for any instructions to hands on debug and help others. Thx!

I've been following along with "App Shortcuts" development but cannot get Siri to run my Intent. The intent on its own works in Shortcuts, along with a couple others that aren't in the AppShortcutsProvder structure. I keep getting the following two errors, but cannot figure out why this is occurring with documentation or other forum posts. No ConnectionContext found for 12909953344 Attempted to fetch App Shortcuts, but couldn't find the AppShortcutsProvider. Here are the relevant snippets of code - (1) The AppIntent definition struct SetBrightnessIntent: AppIntent { static var title = LocalizedStringResource("Set Brightness") static var description = IntentDescription("Set Glass Display Brightness") @Parameter(title: "Level") var level: Int? static var parameterSummary: some ParameterSummary { Summary("Set Brightness to \(\.$level)%") } func perform() async throws -> some IntentResult { guard let level = level else { throw $level.needsValueError("Please provide a brightness value") } if level > 100 || level <= 0 { throw $level.needsValueError("Brightness must be between 1 and 100") } // do stuff with level return .result() } } (2) The AppShortcutsProvider (defined in my iOS app target, there are no other targets) struct MyAppShortcuts: AppShortcutsProvider { static var shortcutTileColor: ShortcutTileColor = .grayBlue @AppShortcutsBuilder static var appShortcuts: [AppShortcut] { AppShortcut( intent: SetBrightnessIntent(), phrases: [ "set \(.applicationName) brightness to \(\.$level)", "set \(.applicationName) brightness to \(\.$level) percent" ], shortTitle: LocalizedStringResource("Set Glass Brightness"), systemImageName: "sun.max" ) } } Does anything here look wrong? Is there some magical key that I need to specify in Info.plist to get Siri to recognize the AppShortcutsProvider? On Xcode 16.2 and iOS 18.2 (non-beta).

In this thread, I asked about adding parameters to App Shortcuts. The conclusion that I've drawn so far is that for App Shortcuts, there cannot be any parameters in the prompt, otherwise the system cannot find the AppShortcutsProvider. While this is fine for Shortcuts and non-voice interaction, I'd like to find a way to add parameters to the prompt. Here is the scenario: My app controls a device that displays some content on "pages." The pages are defined in an AppEnum, which I use for Shortcuts integration via App Intents. The App Intent functions as expected, and is able to change the page based on the user selection within Shortcuts (or prompted if using the App Shortcut). What I'd like to do is allow the user to be able to say "Siri, open with ." So far, The closest I've come to understanding how this works is through the .intentsdefinition file you can create (and SiriKit in general), however the part that really confused me there is a button in the File Editor that says "Convert to App Intent." To me, this means that I should be able to use the app intent I've already authored and hook that into Siri, rather than making an entirely new function/code-block that does exactly the same thing. Ideally, that's what I want to do. What's the right way to define this behavior? p.s. If I had to pick an intent schema in the context of AssistantSchemas, I'd say it's closest to the "Open File" one, if that helps. I'd ultimately like to make the "pages" user-customizable so in the long run, that would be what I'd do.

When using CoreML for VAE model prediction, the prediction result shows a distorted display with no error messages. How can this issue be addressed?

We are using VNRecognizeTextRequest to detect text in documents, and we have noticed that even in some very clear and well-formatted documents, there are still instances where text blocks are missed. the live text also have the same issue.

I would like to make use of create ML to classify a motion. However, it seems it requires 2 classes at least to train or test it. What should I do as I only has 1 class (the target motion). Also, how to interpret the 'Recall' and 'F1 Score'

Hey everyone, I want to add an if statement that would do something along the lines of this: if confidence = 100% { } How could I do this? I already have a createML model. Thank you, Oliver

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

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:

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.

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 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.

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 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 (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

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.

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

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 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

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 } } } } }