Same. I've tried every possible x265 config combination i could think of

So the official toolchain showcased https://developer.apple.com/augmented-reality/tools/ and all over RealityKit, requires x86_64 prebuilt and 3.7.9, even further backwards from shipped system /usr/bin/python3? Apple should donate some hardware to whoever is maintaining the dependency that is preventing dogfooding on flagship hardware

Where is this extension's entitlements documented? https://developer.apple.com/documentation/bundleresources/entitlements

I'm getting a failure on export using the exact transformer used in this guide https://developer.apple.com/documentation/createml/creating-a-multi-label-image-classifier ComposedTransformer<ComposedTransformer<ImageReader, ImageFeaturePrint>, FullyConnectedNetworkMultiLabelClassifierModel> The transformer is not representable as a CoreML model (ImageReader). I'm guessing this is just a misleading error? In this intro with no runnable examples https://developer.apple.com/videos/play/wwdc2022/10019/ also noticed this line try estimator.write(transformer, to: parametersURL) which writes a .pkg file which I'm guessing is for instantiating the estimator

Somehow got it to export by composing the transformer explicitly, which gave a ComposedTransformer<ImageFeaturePrint, FullyConnectedNetworkMultiLabelClassifierModel>, so I'm guessing the chaining used in the documentation was never attempted to export since return type is invalid let annotatedFeatures = detectionFiles.map { AnnotatedFeature( feature: directoryURL.appending(component: $0.filename), annotation: $0.labels ) } let reader = ImageReader() let (training, validation) = annotatedFeatures.randomSplit(by: 0.8) let featurePrint = ImageFeaturePrint(revision: 2) let classifier = FullyConnectedNetworkMultiLabelClassifier<Float, String>(labels: labels) let task = featurePrint.appending(classifier) Task { let trainingImages = try await reader.applied(to: training) let validationImages = try await reader.applied(to: validation) let model = try await task.fitted( to: trainingImages, validateOn: validationImages, eventHandler: { event in debugPrint(event) } ) try! model.export(to: modelFile) }

I target 17, there is no runtime to produce 5.5" screenshots, besides literally faking it, which seems very anti-spirit and nonsensical. Apple you have sent me 3+ emails on preparing my app for vision, including how to properly "frame" screenshots. I'll get on that as soon as I'm done uploading iPhone 8 Plus assets. xoxoxo

This is happening everywhere. This is the only useful information I could find, no clue how they came up with that temp workaround, though. Seems like they might have used the red phone. https://github.com/googleads/swift-package-manager-google-mobile-ads/issues/62#issuecomment-1981219533 Re-tooling commits branching platform for path differences https://github.com/firebase/firebase-ios-sdk/pull/12517/files https://github.com/facebook/facebook-ios-sdk/issues/2353 https://github.com/google/GoogleAppMeasurement/issues/62 etc, etc.

Same, randomly. No clue how to reproduce.

I don't understand how it's being added in during torchscript conversion? I can add the new soft max with validating lossLayer, but I can't figure out how to swap/pop the original SoftmaxND. I thought del builder.layer[-1] would be enough, but NOOP. So now I'm stuck with protobuf validations that that multiple layers are consuming the same outputs

Dec 2024 update: Same, randomly. No clue how to reproduce.

Directly related https://developer.apple.com/forums/thread/771911 https://github.com/ultralytics/ultralytics/issues/5165 Somehow related, as it's really really the only other mention of this flag I can find https://developer.apple.com/forums/thread/735698 https://developer.apple.com/search/?q=MLE5Engine

After reading a bunch of related posts, it looks like it might be related to the compute units and flexible shapes and that the ANE doesn't support it. I bet disabling the MLE5Engine flag coincidentally is the equivalent of config.computeUnits = .cpuAndGPU I bet the arch running on the neural engine is creating the nonsensical outputs, where constrained to cpu/gpu is returning normal behavior. Going to test and see if I can confirm.

What's the point of even shipping CreateML if it's not capable of extracting 20,000 360x360 embeddings on a flagship M2? This has the added advantage that you will only need to do this once, and can then train multiple models, or one model over multiple iterations. C'mon man we're talking about 20k images here, no one is going to deploy a vector db for IFPv2. I've literally never even seen Create ML Components in the wild besides WWDC session classifying 50 bananas. The framework documentation is abysmal. Half the "snippets" and transformer generics in auto-generated documentation were obviously never tried in an actual compiler. lol // List all the labels. let labels = ["aloe", "cactus", "person", "pot", "window_sill"] // Compose the estimator. let estimator = ImageReader() .appending(ImageFeaturePrint()) .appending(FullyConnectedNetworkMultiLabelClassifier<Float, String>(labels: labels))

lol

This is a very confusing reply. Nowhere does OP imply they are unfamiliar with iOS Background Execution Limits. Are you implying that "Debug -> Simulate Background Fetch" should be renamed to "Debug -> DO NOT Simulate Background Fetch"? or maybe Debug -> Simulate Background Fetch on Simulator (remember this is an Xcode.app menu item, not a Simulator.app menu item) What if the question is re-framed to "How do I execute code, that normally, and by normally I mean may or may not get scheduled, with absolutely no misconceptions about the magical vibe heuristics the conclave uses to schedule said task, right now. Or said another way, how can a developer, that is developing, simulate being scheduled by the all-knowing, wise, beautiful, system scheduler? I'll sign a new agreement that affirms my task is not worthy and volunteer a earliestBeginDate of INFINITE just to get a block ran The only useful non-slop answer I have found is set breakpoint after submit() e -l objc -- (void)[[BGTaskScheduler sharedScheduler] _simulateLaunchForTaskWithIdentifier:@"framework.this.love.i"] I strongly recommend that you watch WWDC 2020 Session 10063 Background execution demystified. read https://developer.apple.com/forums/thread/775182 It’s an excellent resource.