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:

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

Hello, I need help I desire to select/filter the contours on an image. Not sure best way to do that. Idea select/filter for bottom left most contour? see image attached please. also will need end points or court corners. and need contour to be fine line, smooth, ie accurate of the court end line and side lines only is desired. thank you :) or also glad for other ideas or api to determine the lines/corners I need. glad to email to discuss if that is better/easier actually prefer that. thanks.

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?

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!!!

Submited as : FB16052050 I am looking to adopt Machine Learning in a more granular manner, going beyond just using pre-built Metal, Core ML, or Create ML approaches. Specifically, I want to train models using Open Python PyTorch libraries, as these offer greater flexibility compared to Apple's native tools. However, these PyTorch APIs are primarily optimised for NVIDIA GPUs (or TPUs), not Apple's M3 or Apple Neural Engine (ANE). My goal is to train the models locally without resorting to cloud-based solutions for training or inference, and to then convert the models into Core ML format for deployment on Apple hardware. This would allow me to leverage Apple's hardware acceleration (via ANE, Metal, and MPS) while maintaining control over the training process in PyTorch. I want to know: What are my options for training models in PyTorch on local hardware (Apple M3 or equivalent), and how can I ensure that the PyTorch model can eventually be converted to Core ML without losing flexibility in model training and customisation? How can I perform training in PyTorch and avoid being restricted to inference-only workflows as Core ML typically allows? Is it possible to use the training capabilities of PyTorch and still get the performance benefits of Apple's hardware for both training and inference? What are the best practices or tools to ensure that my training pipeline in PyTorch is compatible with Apple's hardware constraints and optimised for local execution? I'm seeking a practical, cloud-free approach on Apple Hardware only that allows me to train models in PyTorch (keeping control over the training process) while ensuring that they can be deployed efficiently using Core ML on Apple hardware.

In the 2019 WWDC session Training Object Detection Models in Create ML a JSON file named: annotations_832_newdice_copy.json was show alongside with the images folder named: Dice Training Images Two Sets. Are these resources made available for devs ? I am looking to understand whether the 6000 annotations were needed to be done manually ? Meaning, they have annotated around 1000 images making 6 labels on each manually to achieve this source ? Video shows around 1000 images. Can someone please clarify.

Hi everyone, I'm working on integrating object recognition from live video feeds into my existing app by following Apple's sample code. My original project captures video and records it successfully. However, after integrating the Vision-based object detection components (VNCoreMLRequest), no detections occur, and the callback for the request is never triggered. To debug this issue, I’ve added the following functionality: Set up AVCaptureVideoDataOutput for processing video frames. Created a VNCoreMLRequest using my Core ML model. The video recording functionality works as expected, but no object detection happens. I’d like to know: How to debug this further? Which key debug points or logs could help identify where the issue lies? Have I missed any key configurations? Below is a diff of the modifications I’ve made to my project for the new feature. Diff of Changes: (Attach the diff provided above) Specific Observations: The captureOutput method is invoked correctly, but there is no output or error from the Vision request callback. Print statements in my setup function setForVideoClassify() show that the setup executes without errors. Questions: Could this be due to issues with my Core ML model compatibility or configuration? Is the VNCoreMLRequest setup incorrect, or do I need to ensure specific image formats for processing? Platform: Xcode 16.1, iOS 18.1, Swift 5, SwiftUI, iPhone 11, Darwin MacBook-Pro.local 24.1.0 Darwin Kernel Version 24.1.0: Thu Oct 10 21:02:27 PDT 2024; root:xnu-11215.41.3~2/RELEASE_X86_64 x86_64 Any guidance or advice is appreciated! Thanks in advance.

Hello, I am exploring real-time object detection, and its replacement/overlay with another shape, on live video streams for an iOS app using Core ML and Vision frameworks. My target is to achieve high-speed, real-time detection without noticeable latency, similar to what’s possible with PageFault handling and Associative Caching in OS, but applied to video processing. Given that this requires consistent, real-time model inference, I’m curious about how well the Neural Engine or GPU can handle such tasks on A-series chips in iPhones versus M-series chips (specifically M1 Pro and possibly M4) in MacBooks. Here are a few specific points I’d like insight on: Hardware Suitability: How feasible is it to perform real-time object detection with Core ML on the Neural Engine (i.e., can it maintain low latency)? Would the M-series chips (e.g., M1 Pro or newer) offer a tangible benefit for this type of task compared to the A-series in mobile devices? Which A- and M- chips would be minimum feasible recommendation for such task. Performance Expectations: For continuous, live video object detection, what would be the expected frame rate or latency using an optimized Core ML model? Has anyone benchmarked such applications, and is the M-series required to achieve smooth, real-time processing? Differences Across Apple Hardware: How does performance scale between the A-series Neural Engine and M-series GPU and Neural Engine? Is the M-series vastly superior for real-time Core ML tasks like object detection on live video feeds? If anyone has attempted live object detection on these chips, any insights on real-time performance, limitations, or optimizations would be highly appreciated. Please refer: Apple APIs Thank you in advance for your help!

Hello All, I'm developing a machine learning model for image classification, which requires managing an exceptionally large dataset comprising over 18,000 classes. I've encountered several hurdles while using Create ML, and I would appreciate any insights or advice from those who have faced similar challenges. Current Issues: Create ML Failures with Large Datasets: When using Create ML, the process often fails with errors such as "Failed to create CVPixelBufferPool." This issue appears when handling particularly large volumes of data. Custom Implementation Struggles: To bypass some of the limitations of Create ML, I've developed a custom solution leveraging the MLImageClassifier within the CreateML framework in my own SwiftUI MacOS app. Initially I had similar errors as I did in Create ML, but I discovered I could move beyond the "extracting features" stage without crashing by employing a workaround: using a timer to cancel and restart the job every 30 seconds. This method is the only way I've been able to finish the extraction phase, even with large datasets, but it causes many errors in the console if I allow it to run too long. Lack of Progress Reporting: Using MLJob<MLImageClassifier>, I've noticed that progress reporting stalls after the feature extraction phase. Although system resources indicate activity, there is no programmatic feedback on what is occurring. Things I've Tried: Data Validation: Ensured that all images in the dataset are valid and non-corrupted, which helps prevent unnecessary issues from faulty data. Custom Implementation with CreateML Framework: Developed a custom solution using the MLImageClassifier within the CreateML framework to gain more control over the training process. Timer-Based Workaround: Employed a workaround using a timer to cancel and restart the job every 30 seconds to move past the "extracting features" phase, allowing progress even with larger datasets. Monitoring System Resources: Observed ongoing system resource usage when process feedback stalled, confirming background processing activity despite the lack of progress reporting. Subset Testing: Successfully created and tested a model on a subset of the data, which validated the approach worked for smaller datasets and could produce a functioning model. Router Model Concept: Considered training multiple models for different subsets of data and implementing a "router" model to decide which specialized model to utilize based on input characteristics. What I Need Help With: Handling Large Datasets: I'm seeking strategies or best practices for effectively utilizing Create ML with large datasets. Any guidance on memory management or alternative methodologies would be immensely helpful. Improving Progress Reporting: I'm looking for ways to obtain more consistent and programmatic progress updates during the training and testing phases. I'm working on a Mac M1 Pro w/ 32GB RAM, with Apple Silicon and am fully integrated within the Apple ecosystem. I am very grateful for any advice or experiences you could share to help overcome these challenges. Thank you! I've pasted the relevant code below: func go() { if self.trainingSession == nil { self.trainingSession = createTrainingSession() } if self.startTime == nil { self.startTime = Date() } job = try! MLImageClassifier.resume(self.trainingSession) job.phase .receive(on: RunLoop.main) .sink { phase in self.phase = phase } .store(in: &cancellables) job.checkpoints .receive(on: RunLoop.main) .sink { checkpoint in self.state = "\(checkpoint)\n\(self.job.progress)" self.progress = self.job.progress.fractionCompleted + 0.2 self.updateTimeEstimates() } .store(in: &cancellables) job.result .receive(on: DispatchQueue.main) .sink(receiveCompletion: { completion in switch completion { case .failure(let error): print("Training Failed: \(error.localizedDescription)") case .finished: print("🎉🎉🎉🎉 TRAINING SESSION FINISHED!!!!") self.trainingFinished = true } }, receiveValue: { classifier in Task { await self.saveModel(classifier) } }) .store(in: &cancellables) } private func createTrainingSession() -> MLTrainingSession<MLImageClassifier> { do { print("Initializing training Data...") let trainingData: MLImageClassifier.DataSource = .labeledDirectories(at: trainingDataURL) let modelParameters = MLImageClassifier.ModelParameters( validation: .split(strategy: .automatic), augmentation: self.augmentations, algorithm: .transferLearning( featureExtractor: .scenePrint(revision: 2), classifier: .logisticRegressor ) ) let sessionParameters = MLTrainingSessionParameters( sessionDirectory: self.sessionDirectoryURL, reportInterval: 1, checkpointInterval: 100, iterations: self.numberOfIterations ) print("Initializing training session...") let trainingSession: MLTrainingSession<MLImageClassifier> if FileManager.default.fileExists(atPath: self.sessionDirectoryURL.path) && isSessionCreated(atPath: self.sessionDirectoryURL.path()) { do { trainingSession = try MLImageClassifier.restoreTrainingSession(sessionParameters: sessionParameters) } catch { print("error resuming, exiting.... \(error.localizedDescription)") fatalError() } } else { trainingSession = try MLImageClassifier.makeTrainingSession( trainingData: trainingData, parameters: modelParameters, sessionParameters: sessionParameters ) } return trainingSession } catch { print("Failed to initialize training session: \(error.localizedDescription)") fatalError() } }

Hi Apple Developer Community, I’m exploring ways to fine-tune the SNSoundClassifier to allow users of my iOS app to personalize the model by adding custom sounds or adjusting predictions. While Apple’s WWDC session on sound classification explains how to train from scratch, I’m specifically interested in using SNSoundClassifier as the base model and building/fine-tuning on top of it. Here are a few questions I have: 1. Fine-Tuning on SNSoundClassifier: Is there a way to fine-tune this model programmatically through APIs? The manual approach using macOS, as shown in this documentation is clear, but how can it be done dynamically - within the app for users or in a cloud backend (AWS/iCloud)? Are there APIs or classes that support such on-device/cloud-based fine-tuning or incremental learning? If not directly, can the classifier’s embeddings be used to train a lightweight custom layer? Training is likely computationally intensive and drains too much on battery, doing it on cloud can be right way but need the right apis to get this done. A sample code will do good. 2. Recommended Approach for In-App Model Customization: If SNSoundClassifier doesn’t support fine-tuning, would transfer learning on models like MobileNetV2, YAMNet, OpenL3, or FastViT be more suitable? Given these models (SNSoundClassifier, MobileNetV2, YAMNet, OpenL3, FastViT), which one would be best for accuracy and performance/efficiency on iOS? I aim to maintain real-time performance without sacrificing battery life. Also it is important to see architecture retention and accuracy after conversion to CoreML model. 3. Cost-Effective Backend Setup for Training: Mac EC2 instances on AWS have a 24-hour minimum billing, which can become expensive for limited user requests. Are there better alternatives for deploying and training models on user request when s/he uploads files (training data)? 4. TensorFlow vs PyTorch: Between TensorFlow and PyTorch, which framework would you recommend for iOS Core ML integration? TensorFlow Lite offers mobile-optimized models, but I’m also curious about PyTorch’s performance when converted to Core ML. 5. Metrics: Metrics I have in mind while picking the model are these: Publisher, Accuracy, Fine-Tuning capability, Real-Time/Live use, Suitability of iPhone 16, Architectural retention after coreML conversion, Reasons for unsuitability, Recommended use case. Any insights or recommended approaches would be greatly appreciated. Thanks in advance!

I'm trying to use the Spatial model to perform Object Tracking on a .usdz file that I create. After loading the file, which I can view correctly in the console, I start the training. Initially, I notice that the disk usage on my PC increases. After several GB, the usage stops, but the training progress remains for hours at 0.00% with the message "About 8hr." How can I understand what the issue is? Has anyone else experienced the same problem? Thanks Diego

I’m working on real-time object detection using YOLOv8, but I only need to detect objects in approximately 40% of the screen area. Is it possible to limit the captureOut method to focus solely on that specific region of the screen? If this isn’t feasible, I’m considering an approach where the full-screen pixel buffer is captured and then cropped to the target area before running detection. However, I’m concerned about how this might affect real-time performance. I’d appreciate any insights on how to maintain real-time performance or suggestions for better alternatives. Thank you!

Hi everyone, I've been working with the autoAdjustmentFilters provided by Core Image, which includes filters like CIHighlightShadowAdjust, CIVibrance, and CIToneCurve. However, I’ve noticed that the results differ significantly from the "Auto" enhancement feature in the Photos app. In the Photos app, the Auto function seems to adjust multiple parameters such as contrast, exposure, white balance, highlights, and shadows in a more advanced manner. Is there an API or a framework available that can replicate the more sophisticated "Auto" adjustments as seen in the Photos app? Or would I need to manually combine filters (like CIExposureAdjust, CIWhitePointAdjust, etc.) to approximate this functionality? Any insights or recommendations on how to achieve this would be greatly appreciated. Thank you!

I am developing an iOS app that uses YOLOv8 for object detection and aims to detect objects at 60 FPS using the UltraWide camera. My goal is to process every frame within captureOutput and utilize the detected data (such as coordinates) for each one. I have a question regarding how background thread processing behaves in this scenario. Does the size of the YOLO model (n, s, m, etc.) or the weight of the operations inside captureOutput affect the number of frames that can be successfully processed? Specifically, I would like to know if all frames will be processed sequentially with a delay due to heavy processing in the background, or if some frames will be dropped and not processed at all. Any insights on how to handle this would be greatly appreciated. Thank you!

I was working on my project and when I tried to train a model the kernel crashed, so I restarted the kernel and tried the same and still I got the same crashing issue. Then I read one of the thread having the same issue where the apple support was saying to install tensorflow-macos and tensorflow-metal and read the guide from this site: https://developer.apple.com/metal/tensorflow-plugin/ and I did so, I tried every single thing and when I tried the test code provided in the site, I got the same error, here's the code and the output. Code: import tensorflow as tf cifar = tf.keras.datasets.cifar100 (x_train, y_train), (x_test, y_test) = cifar.load_data() model = tf.keras.applications.ResNet50( include_top=True, weights=None, input_shape=(32, 32, 3), classes=100,) loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"]) model.fit(x_train, y_train, epochs=5, batch_size=64) and here's the output: Epoch 1/5 The Kernel crashed while executing code in the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details. And here's the half of log file as it was not fully coming: metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1 2024-10-06 23:30:49.894405: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 8.00 GB 2024-10-06 23:30:49.894420: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 2.67 GB 2024-10-06 23:30:49.894444: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2024-10-06 23:30:49.894460: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: ) 2024-10-06 23:30:56.701461: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled. [libprotobuf FATAL google/protobuf/message_lite.cc:353] CHECK failed: target + size == res: libc++abi: terminating due to uncaught exception of type google::protobuf::FatalException: CHECK failed: target + size == res: Please respond to this post as soon as possible as I am working on my project now and getting this error again n again. Device: Apple MacBook Air M1.

When I use VNGenerateForegroundInstanceMaskRequest to generate the mask in the simulator by SwiftUI, there is an error "Could not create inference context". Then I add the code to make the vision by CPU: let request = VNGenerateForegroundInstanceMaskRequest() let handler = VNImageRequestHandler(ciImage: inputImage) #if targetEnvironment(simulator) if #available(iOS 18.0, *) { let allDevices = MLComputeDevice.allComputeDevices for device in allDevices { if(device.description.contains("MLCPUComputeDevice")){ request.setComputeDevice(.some(device), for: .main) break } } } else { // Fallback on earlier versions request.usesCPUOnly = true } #endif do { try handler.perform([request]) if let result = request.results?.first { let mask = try result.generateScaledMaskForImage(forInstances: result.allInstances, from: handler) return CIImage(cvPixelBuffer: mask) } } catch { print(error) } Even I force the simulator to run the code by CPU, but it still have the error: "Could not create inference context"

Where does the processing power to enact certain AI capabilities come from? Is it hosted on the originating device? Or does the device send contents of originating information to Apple assets to process and give product to end user? e.g. If I ask AI to summarize an email will it send the contents of the email to an Apple AI asset to process it and give the summary to the originating device.

getting this error again and again even if I tried reinstalling. Traceback (most recent call last): File "", line 1, in File "/Users/aman/LLM/env/lib/python3.8/site-packages/tensorflow/init.py", line 439, in _ll.load_library(_plugin_dir) File "/Users/aman/LLM/env/lib/python3.8/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/aman/LLM/env/lib/python3.8/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Symbol not found: OBJC_CLASS$_MPSGraphRandomOpDescriptor Referenced from: /Users/aman/LLM/env/lib/python3.8/site-packages/tensorflow-plugins/libmetal_plugin.dylib Expected in: /System/Library/Frameworks/MetalPerformanceShadersGraph.framework/Versions/A/MetalPerformanceShadersGraph

All errors in TranslationError return the same error code, making it difficult to differentiate between them. How can this issue be resolved?