Issue type: Bug TensorFlow metal version: 1.1.1 TensorFlow version: 2.18 OS platform and distribution: MacOS 15.2 Python version: 3.11.11 GPU model and memory: Apple M2 Max GPU 38-cores Standalone code to reproduce the issue: import tensorflow as tf if __name__ == '__main__': gpus = tf.config.experimental.list_physical_devices('GPU') print(gpus) Current behavior Apple silicone GPU with tensorflow-metal==1.1.0 and python 3.11 works fine with tensorboard==2.17.0 This is normal output: /Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/bin/python /Users/mspanchenko/VSCode/cryptoNN/ml/core_second_window/test_tensorflow_gpus.py [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] Process finished with exit code 0 But if I upgrade tensorflow to 2.18 I'll have error: /Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/bin/python /Users/mspanchenko/VSCode/cryptoNN/ml/core_second_window/test_tensorflow_gpus.py Traceback (most recent call last): File "/Users/mspanchenko/VSCode/cryptoNN/ml/core_second_window/test_tensorflow_gpus.py", line 1, in <module> import tensorflow as tf File "/Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/lib/python3.11/site-packages/tensorflow/__init__.py", line 437, in <module> _ll.load_library(_plugin_dir) File "/Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/lib/python3.11/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/lib/python3.11/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Symbol not found: __ZN3tsl8internal10LogMessageC1EPKcii Referenced from: <D2EF42E3-3A7F-39DD-9982-FB6BCDC2853C> /Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/lib/python3.11/site-packages/tensorflow-plugins/libmetal_plugin.dylib Expected in: <2814A58E-D752-317B-8040-131217E2F9AA> /Users/mspanchenko/anaconda3/envs/cryptoNN_ml_core/lib/python3.11/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so Process finished with exit code 1

Hi, I want to develop an app which makes use of Image Playground. However, I am located in Europe which makes it impossible for me as Image Playground is not available for me. Even if I would like to distribute the app in the US. Nor the simulator, nor a physical device will always return that support for ImagePlayground is not supported (@Environment(.supportsImagePlayground) private var supportsImagePlayground) How to set my environment such that I can test the feature in my iOS application

@property (assign,nonatomic) long long experimentalMLE5EngineUsage; //@synthesize experimentalMLE5EngineUsage=_experimentalMLE5EngineUsage - In the implementation block What is it, and why would disabling it fix NMS for a MLProgram? Is there anyway to ****** this flag from model metadata? Is there anyway to ****** or disable from a global, system-level scope? It's extremely easy to reproduce, but do not know how to investigate the drastic regression between toggling this flag let config = MLModelConfiguration() config.setValue(1, forKey: "experimentalMLE5EngineUsage")

Hello, I have a question regarding hybrid execution for deep learning models on Apple's Neural Engine and CPU. I am aware that setting the precision of some layers to 32-bit allows hybrid execution across both the Neural Engine and the CPU. However, I would like to know if it is possible to achieve the same with 16-bit precision. Is there any specific configuration or workaround to enable hybrid execution in this case? Any guidance or documentation references would be greatly appreciated. Thank you!

I am currently training a Tabular Classification model in CreatML. The dataset comprises 30 features, including 1,000,000 training data points and 1,000,000 verification data points. Could you please estimate the approximate training time for an M4Max MacBook Pro? During the training process, CreatML has been displaying the “Processing” status, but there is no progress bar. I would like to ascertain whether the training is still ongoing, as I have often suspected that it has ceased.

I live in EU, Ireland, And I don’t have access to apple intelligence. I have ios18 running on iPhone XR, but please make apple intelligence available on EU

Hi everyone, I'm working on a SwiftUI app and need help building a view that integrates the device's camera and uses a pre-trained Core ML model for real-time object recognition. Here's what I want to achieve: Open the device's camera from a SwiftUI view. Capture frames from the camera feed and analyze them using a Create ML-trained Core ML model. If a specific figure/object is recognized, automatically close the camera view and navigate to another screen in my app. I'm looking for guidance on: Setting up live camera capture in SwiftUI. Using Core ML and Vision frameworks for real-time object recognition in this context. Managing navigation between views when the recognition condition is met. Any advice, code snippets, or examples would be greatly appreciated! Thanks in advance!

I have images, and I annotated with polygon, actually simple trapezoid, so 4 points. I have been trying and trying but can't get Create ML to work. I am trying Object Detection. I am not a real programmer so really would greatly appreciate some guidance to help to get this model created. I think I made a Detectron2 model, and tried to get that converted into a mlmodel I need for xcode but had troubles there also. thank you. { "annotation": "IMG_1803.JPG", "annotations": [ { "label": "court", "coordinates": { "x": [ 187, 3710, 2780, 929 ], "y": [ 1689, 1770, 478, 508 ] } } ] },

I am attempting to install Tensorflow on my M1 and I seem to be unable to find the correct matching versions of jax, jaxlib and numpy to make it all work. I am in Bash, because the default shell gave me issues. I downgraded to python 3.10, because with 3.13, I could not do anything right. Current actions: bash-3.2$ python3.10 -m venv ~/venv-metal bash-3.2$ python --version Python 3.10.16 python3.10 -m venv ~/venv-metal source ~/venv-metal/bin/activate python -m pip install -U pip python -m pip install tensorflow-macos And here, I keep running tnto errors like: (venv-metal):~$ pip install tensorflow-macos tensorflow-metal ERROR: Could not find a version that satisfies the requirement tensorflow-macos (from versions: none) ERROR: No matching distribution found for tensorflow-macos What is wrong here? How can I fix that? It seems like the system wants to use the x86 version of python ... which can't be right.

Hi everyone, I've been struggling for a few weeks to integrate my Core ML Image Classifier model into my .swiftpm project, and I’m hoping someone can help. Here’s what I’ve done so far: I converted my .mlmodel file to .mlmodelc manually via the terminal. In my Package.swift file, I tried both "copy" and "process" options for the resource. The issues I’m facing: When using "process", Xcode gives me the error: "multiple resources named 'coremldata.bin' in target 'AppModule'." When using "copy", the app runs, but the model doesn’t work, and the terminal shows: "A valid manifest does not exist at path: .../Manifest.json." I even tried creating a Manifest.json manually to test, but this led to more errors, such as: "File format version must be in the form of major.minor.patch." "Failed to look up root model." To check if the problem was specific to my model, I tested other Core ML models in the same setup, but none of them worked either. I feel stuck and unsure of how to resolve these issues. Any guidance or suggestions would be greatly appreciated. Thanks in advance! :)

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).

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

The Core ML developer guide recommends saving reusable compiled Core ML models to a permanent location to avoid unnecessary rebuilds when creating a Core ML model instance. However, there is no location that remains consistent across app updates, since each update changes the UUID associated with the app’s resources path /var/mobile/Containers/Data/Application/<UUID>/Library/Application Support/ As a result, Core ML rebuilds models even if they are unchanged and located in the same relative directory within the app’s file structure.

I'm using Numbers to build a spreadsheet that I'm exporting as a CSV. I then import this file into Create ML to train a word tagger model. Everything has been working fine for all the models I've trained so far, but now I'm coming across a use case that has been breaking the import process: commas within the training data. This is a case that none of Apple's examples show. My project takes Navajo text that has been tokenized by syllables and labels the parts-of-speech. Case that works... Raw text: Naaltsoos yídéeshtah. Tokens column: Naal,tsoos, ,yí,déesh,tah,. Labels column: NObj,NObj,Space,Verb,Verb,VStem,Punct Case that breaks... Raw text: óola, béésh łigaii, tłʼoh naadą́ą́ʼ, wáin, akʼah, dóó á,shįįh Tokens column with tokenized text (commas quoted): óo,la,",", ,béésh, ,łi,gaii,",", ,tłʼoh, ,naa,dą́ą́ʼ,",", ,wáin,",", ,a,kʼah,",", ,dóó, ,á,shįįh (Create ML reports mismatched columns) Tokens column with tokenized text (commas escaped): óo,la,\,, ,béésh, ,łi,gaii,\,, ,tłʼoh, ,naa,dą́ą́ʼ,\,, ,wáin,\,, ,a,kʼah,\,, ,dóó, ,á,shįįh (Create ML reports mismatched columns) Tokens column with tokenized text (commas escape-quoted): óo,la,\",\", ,béésh, ,łi,gaii,\",\", ,tłʼoh, ,naa,dą́ą́ʼ,\",\", ,wáin,\",\", ,a,kʼah,\",\", ,dóó, ,á,shįįh (record not detected by Create ML) Tokens column with tokenized text (commas escape-quoted): óo,la,"","", ,béésh, ,łi,gaii,"","", ,tłʼoh, ,naa,dą́ą́ʼ,"","", ,wáin,"","", ,a,kʼah,"","", ,dóó, ,á,shįįh (Create ML reports mismatched columns) Labels column: NSub,NSub,Punct,Space,NSub,Space,NSub,NSub,Punct,Space,NSub,Space,NSub,NSub,Punct,Space,NSub,Punct,Space,NSub,NSub,Punct,Space,Conj,Space,NSub,NSub Sample From Spreadsheet Solution Needed It's simple enough to escape commas within CSV files, but the format needed by Create ML essentially combines entire CSV records into single columns, so I'm ending up needing a CSV record that contains a mixture of commas to use for parsing and ones to use as character literals. That's where this gets complicated. For this particular use case (which seems like it would frequently arise when training a word tagger model), how should I properly escape a comma literal?

iOS 18.2 includes a new feature called Visual Intelligence. If I hold down the Camera Control on my iPhone, I can take a photo of an object and use Google to look up items similar to what I've photographed. Is there a way to programmatically open this interface within my app? If so, can I see which result the user selects?

I am trying to create a Pipeline with 3 sub-models: a Feature Vectorizer -> a NN regressor converted from PyTorch -> a Feature Extractor (to convert the output tensor to a Double value). The pipeline works fine when I use just a Vectorizer and an Extractor, this is the code: vectorizer = models.feature_vectorizer.create_feature_vectorizer( input_features=["windSpeed", "theoreticalPowerCurve", "windDirection"], # Multiple input features output_feature_name="input" ) preProc_spec = vectorizer[0] ct.utils.convert_double_to_float_multiarray_type(preProc_spec) extractor = models.array_feature_extractor.create_array_feature_extractor( input_features=[("input",datatypes.Array(3,))], # Multiple input features output_name="output", extract_indices = 1 ) ct.utils.convert_double_to_float_multiarray_type(extractor) pipeline_network = pipeline.PipelineRegressor ( input_features = ["windSpeed", "theoreticalPowerCurve", "windDirection"], output_features=["output"] ) pipeline_network.add_model(preProc_spec) pipeline_network.add_model(extractor) ct.utils.convert_double_to_float_multiarray_type(pipeline_network.spec) ct.utils.save_spec(pipeline_network.spec,"Final.mlpackage") This model works ok. I created a regression NN using PyTorch and converted to Core ML either import torch import torch.nn as nn class TurbinePowerModel(nn.Module): def __init__(self): super().__init__() self.linear1 = nn.Linear(3, 4) self.activation1 = nn.ReLU() #self.linear2 = nn.Linear(5, 4) #self.activation2 = nn.ReLU() self.output = nn.Linear(4, 1) def forward(self, x): #x = F.normalize(x, dim = 0) x = self.linear1(x) x = self.activation1(x) # x = self.linear2(x) # x = self.activation2(x) x = self.output(x) return x def forward_inference(self, windSpeed,theoreticalPowerCurve,windDirection): input_tensor = torch.tensor([windSpeed, theoreticalPowerCurve, windDirection], dtype=torch.float32) return self.forward(input_tensor) model = torch.load('TurbinePowerRegression-1layer.pt', weights_only=False) import coremltools as ct print(ct.__version__) import pandas as pd from sklearn.preprocessing import StandardScaler df = pd.read_csv('T1_clean.csv',delimiter=';') X = df[['WindSpeed','TheoreticalPowerCurve','WindDirection']] y = df[['ActivePower']] scaler = StandardScaler() X = scaler.fit_transform(X) y = scaler.fit_transform(y) X_tensor = torch.tensor(X, dtype=torch.float32) y_tensor = torch.tensor(y, dtype=torch.float32) traced_model = torch.jit.trace(model, X_tensor[0]) mlmodel = ct.convert( traced_model, inputs=[ct.TensorType(name="input", shape=X_tensor[0].shape)], classifier_config=None # Optional, for classification tasks ) mlmodel.save("TurbineBase.mlpackage") This model has a Multiarray(Float 32 3) as input and a Multiarray(Float32 1) as output. When I try to include it in the middle of the pipeline (Adjusting the output and input types of the other models accordingly), the process runs ok, but I have the following error when opening the generated model on Xcode: What's is missing on the models. How can I set or adjust this metadata properly? Thanks!!!

Hi everyone, I’m currently using macOS Version 15.3 Beta (24D5034f), and I’m encountering an issue with Apple Intelligence. The image generation tools seem to work fine, but everything else shows a message saying that it’s “not available at this time.” I’ve tried restarting my Mac and double-checked my settings, but the problem persists. Is anyone else experiencing this issue on the beta version? Are there any fixes or settings I might be overlooking? Any help or insights would be greatly appreciated! Thanks in advance!

there was a beta version. after the update it worked just like regular Siri. this message has been there for two days now but there is no loading.

Hey I have a macbook pro M1 and I don't understand why but the download of apple intelligence since macOS 15.2 is remaining block at 100% with the same message telling me to be plug and connect to a network

I've implemented the imagePlaygroundSheet modifier in my app. It eventually all works but I've consistently noticed that the first time I present it, the sheet is totally blank. I then have to pull down to dismiss it (it doesn't even have a cancel button) and present it a second time and it loads content. Just me? This is on 18.2 final, iPhone 16 Pro Max.