I'm running MacOs 26 Beta 5. I noticed that I can no longer achieve 100% usage on the ANE as I could before with Apple Foundations on-device model. Has Apple activated some kind of throttling or power limiting of the ANE? I cannot get above 3w or 40% usage now since upgrading. I'm on the high power energy mode. I there an API rate limit being applied? I kave a M4 Pro mini with 64 GB of memory.

I have an existing photo generation app that i was looking to switch to this new api, to create specific images. How are people finding it to develop with in terms of ease of implementation?

JAX Metal shows 55x slower random number generation compared to NVIDIA CUDA on equivalent workloads. This makes Monte Carlo simulations and scientific computing impractical on Apple Silicon. Performance Comparison NVIDIA GPU: 0.475s for 12.6M random elements M1 Max Metal: 26.3s for same workload Performance gap: 55x slower Environment Apple M1 Max, 64GB RAM, macOS Sequoia Version 15.6.1 JAX 0.4.34, jax-metal latest Backend: Metal Reproduction Code import time import jax import jax.numpy as jnp from jax import random key = random.PRNGKey(42) start_time = time.time() random_array = random.normal(key, (50000, 252)) duration = time.time() - start_time print(f"Duration: {duration:.3f}s")

I've been eagerly awaiting access to the Playground app since its launch day. Is anyone else still stuck on the waitlist?

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)?

Once available, I immediately installed the MacOS 15.2 beta and configured MacOS and Siri enable Apple Intelligence. I joined the waiting list, and soon after the downloading process started. I have since then been stuck in Pending. I have recently (yesterday) installed 15.2 Beta 2 (Public) - and there is no difference. I have restarted multiple times; I have left my mac on - connected to WiFi - over night multiple times; I have changed language - no only of Siri but also on my Mac (requiring a restart) - multiple time. I am frustrated that I cannot see what is causing the Pending status- pending on what? I am frustrated that I cannot just start Apple Intelligence enrolment from scratch - no restart button. Any help and advice would be greatly welcome.

the specific context is that i would like to build an agent that monitors my phone call (with a customer support for example), and simiply identify whether or not im still put on hold, and notify me when im not. currently after reading the doc, i dont think its possible yet, but im so annoyed by the customer support calls that im willing to go the distance and see if theres any way.

I have an app that uses a couple of mlmodels (word tagger and gazetteer) and I’m trying to encrypt them before publishing. The models are part of a package. I understand that Xcode can’t automatically handle the encryption for a model in a package the way it can within a traditional app structure. Given that, I’ve generated the Apple MLModel encryption key from Xcode and am encrypting via the command line with: xcrun coremlcompiler compile Gazetteer.mlmodel GazetteerENC.mlmodelc --encrypt Gazetteerkey.mlmodelkey In the package manifest, I’ve listed the encrypted models as .copy resources for my target and have verified the URL to that file is good. When I try to load the encrypted .mlmodelc file (on a physical device) with the line:
 gazetteer = try NLGazetteer(contentsOf: gazetteerURL!) I get the error: Failed to open file: /…/Scanner.bundle/GazetteerENC.mlmodelc/coremldata.bin. It is not a valid .mlmodelc file. So my questions are: Does the NLGazetteer class support encrypted MLModel files? Given that my models are in a package, do I have the right general approach? Thanks for any help or thoughts.

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?

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 } } }

I have seen a lot of tutorials on pytorchvision models being able to be converted to coreml models but I have not been able to google or find any tutorials for torchaudio models. Is converting to a torchaudio to coreml model even possible? Does anybody have links that show how to do it?

Hi, I am new to developing on Apple’s platform yet I want to familiarize myself with Core ML and Core ML Tools. I was watching the WWDC24: Bring your machine learning and AI models to Apple Silicon video and was trying to follow along. After multiple attempts and much reading up on documentation, I am still unable to get a coherent script running that will convert the Mistral model that the host used and convert it to a valid Core ML model. here is a pastebin to what i have currently: https://pastebin.com/04cVjF1v if you require the output as well please let me know

I’m keep looking around documentation and some sample codes but still haven’t found example of how was used this type of Network Regressor . Does it take some special parameters to perform on ANE , what size,format of DataFrame ?

Hi Ty for playing

Hi, while trying to diagnose why some of my Core ML models are running slower when their configuration is set with compute units .CPU_AND_GPU compared to running with .CPU_ONLY I've been attempting to create Core ML model performance reports in Xcode to identify the operations that are not compatible with the GPU. However, when selecting an iPhone as the connected device and compute unit of 'All', 'CPU and GPU' or 'CPU and Neural Engine' Xcode displays one of the following two error messages: “There was an error creating the performance report. The performance report has crashed on device” "There was an error creating the performance report. Unable to compute the prediction using ML Program. It can be an invalid input data or broken/unsupported model." The performance reports are successfully generated when selecting the connected device as iPhone with compute unit 'CPU only' or Mac with any combination of compute units. Some of the models I have found the issue to occur with are stateful, some are not. I have tried to replicate the issue with some example models from the CoreML tools stateful model guide/video Bring your machine learning and AI models to Apple silicon. Running the performance report on a model generated from the Simple Accumulator example code the performance report is created successfully when trying all compute unit options, but using models from the toy attention and toy attention with kvcache examples it is only successful with compute units as 'CPU only' when choosing iPhone as the device. Versions I'm currently working with: Xcode Version 16.0 MacOS Sequoia 15.0.1 Core ML Tools 8.0 iPhone 16 Pro iOS 18.0.1 Is there a way to avoid these errors? Or is there another way to identify which operations within a CoreML model are supported to run on iPhone GPU/Neural engine?

I have been on the Bata 18.2 since it launched and I can not seem to get the playground keeps saying I’ve requested. Why! Please help

Hiya, I have downloaded the update and requested early access for playground etc yet almost a week in and I’m still waiting for early access, anyone else having this problem?

I Got Access and Then I opened the App and it said still waiting I was very sad

Woke up to a notification saying playground, Genmoji…etc was ready. but every time I try to use it says early access was requested. Anyone else had this issue? if so how did you fix it?

I'm trying to build llama.cpp, a popular tool for running LLMs locally on macos15.1.1 (24B91) Sonoma using cmake but am encountering errors. Here is the stack overflow post regarding the issue: https://stackoverflow.com/questions/79304015/cmake-unable-to-find-foundation-framework-on-macos-15-1-1-24b91?noredirect=1#comment139853319_79304015