Where can I find a comprehensive list of all the classes that the built in Sound Classifier model supports?

Hi everyone, I'm a Machine Learning Engineer, and I'm planning to buy the MacBook Pro M2 Max with a 38-core GPU variant. I'm uncertain about whether to choose the 32GB RAM or 64GB RAM option. Based on my research and use case, it seems that 32GB should be sufficient for most tasks, including the 4K video rendering I occasionally do. However, I'm concerned about the longevity of the device, as I'd like to keep the MacBook up-to-date for at least five years. Additionally, considering the 38-core GPU, I wonder if 32GB of unified memory might be insufficient, particularly when I need to train Machine Learning models or run docker or even kubernetes cluster. I don't have any budget constraints, as the additional $400 cost isn't an issue, but I want to make a wise decision. I would appreciate any advice on this matter. Thanks in advance!

I'm trying to use the randomTensor function from MPS graph to initialize the weights of a fully connected layer. I can create the graph and run inference using the randomly initialized values, but when I try to train and update these randomly initialized weights, I'm hitting a crash: Assertion failed: (isa<To>(Val) && "cast<Ty>() argument of incompatible type!"), function cast, file Casting.h, line 578. I can train the graph if I instead initialize the weights myself on the CPU, but I thought using the randomTensor functions would be faster/allow initialization to occur on the GPU. Here's my code for building the graph including both methods of weight initialization: func buildGraph(variables: inout [MPSGraphTensor]) -> (MPSGraphTensor, MPSGraphTensor, MPSGraphTensor, MPSGraphTensor) { let inputPlaceholder = graph.placeholder(shape: [2], dataType: .float32, name: nil) let labelPlaceholder = graph.placeholder(shape: [1], name: nil) // This works for inference but not training let descriptor = MPSGraphRandomOpDescriptor(distribution: .uniform, dataType: .float32)! let weightTensor = graph.randomTensor(withShape: [2, 1], descriptor: descriptor, seed: 2, name: nil) // This works for inference and training // let weights = [Float](repeating: 1, count: 2) // let weightTensor = graph.variable(with: Data(bytes: weights, count: 2 * MemoryLayout<Float32>.size), shape: [2, 1], dataType: .float32, name: nil) variables += [weightTensor] let output = graph.matrixMultiplication(primary: inputPlaceholder, secondary: weightTensor, name: nil) let loss = graph.softMaxCrossEntropy(output, labels: labelPlaceholder, axis: -1, reuctionType: .sum, name: nil) return (inputPlaceholder, labelPlaceholder, output, loss) } And to run the graph I have the following in my sample view controller: override func viewDidLoad() { super.viewDidLoad() var variables: [MPSGraphTensor] = [] let (inputPlaceholder, labelPlaceholder, output, loss) = buildGraph(variables: &variables) let gradients = graph.gradients(of: loss, with: variables, name: nil) let learningRate = graph.constant(0.001, dataType: .float32) var updateOps: [MPSGraphOperation] = [] for (key, value) in gradients { let updates = graph.stochasticGradientDescent(learningRate: learningRate, values: key, gradient: value, name: nil) let assign = graph.assign(key, tensor: updates, name: nil) updateOps += [assign] } let commandBuffer = MPSCommandBuffer(commandBuffer: Self.commandQueue.makeCommandBuffer()!) let executionDesc = MPSGraphExecutionDescriptor() executionDesc.completionHandler = { (resultsDictionary, nil) in for (key, value) in resultsDictionary { var output: [Float] = [0] value.mpsndarray().readBytes(&output, strideBytes: nil) print(output) } } let inputDesc = MPSNDArrayDescriptor(dataType: .float32, shape: [2]) let input = MPSNDArray(device: Self.device, descriptor: inputDesc) var inputArray: [Float] = [1, 2] input.writeBytes(&inputArray, strideBytes: nil) let source = MPSGraphTensorData(input) let labelMPSArray = MPSNDArray(device: Self.device, descriptor: MPSNDArrayDescriptor(dataType: .float32, shape: [1])) var labelArray: [Float] = [1] labelMPSArray.writeBytes(&labelArray, strideBytes: nil) let label = MPSGraphTensorData(labelMPSArray) // This runs inference and works // graph.encode(to: commandBuffer, feeds: [inputPlaceholder: source], targetTensors: [output], targetOperations: [], executionDescriptor: executionDesc) // // commandBuffer.commit() // commandBuffer.waitUntilCompleted() // This trains but does not work graph.encode( to: commandBuffer, feeds: [inputPlaceholder: source, labelPlaceholder: label], targetTensors: [], targetOperations: updateOps, executionDescriptor: executionDesc) commandBuffer.commit() commandBuffer.waitUntilCompleted() } And a few other relevant variables are created at the class scope: let graph = MPSGraph() static let device = MTLCreateSystemDefaultDevice()! static let commandQueue = device.makeCommandQueue()! How can I use these randomTensor functions on MPSGraph to randomly initialize weights for training?

I'm referring to this talk: https://developer.apple.com/videos/play/wwdc2021/10152 I was wondering if the code for the "Image composition" project he demonstrates at the end of the talk (around 24:00) is available somewhere? Would much appreciate any help.

I want to know how the preview function is implemented. I have a mlmodel for object detection. I found that when I open the model in xcode, xcode provides a preview function. I put a photo into it and get the target prediction box. I would like to know how this visualization function is implemented. At present, I can only get the three data items of Label, Confidence, and BoundingBox in the playground, and the drawing of the prediction box still requires me to write code for processing. import Vision func performObjectDetection() { do { let model = try VNCoreMLModel(for: court().model) let request = VNCoreMLRequest(model: model) { (request, error) in if let error = error { print("Failed to perform request: \(error)") return } guard let results = request.results as? [VNRecognizedObjectObservation] else { print("No results found") return } for result in results { print("Label: \(result.labels.first?.identifier ?? "No label")") print("Confidence: \(result.labels.first?.confidence ?? 0.0)") print("BoundingBox: \(result.boundingBox)") } } guard let image = UIImage(named: "nbaPics.jpeg"), let ciImage = CIImage(image: image) else { print("Failed to load image") return } let handler = VNImageRequestHandler(ciImage: ciImage, orientation: .up, options: [:]) try handler.perform([request]) } catch { print("Failed to load model: \(error)") } } performObjectDetection() These are my codes and results

We have CoreML models in our app, each encrypted with a separate key generated in XCode. After app update we are receiving following error ` `[coreml] Could not create persistent key blob for EFD428E8-CDE7-4E0A-B379-FC169E50DE4D : error=Error Domain=com.apple.CoreML Code=8 "Fetching decryption key from server failed." UserInfo={NSLocalizedDescription=Fetching decryption key from server failed., NSUnderlyingError=0x281d80ab0 {Error Domain=CKErrorDomain Code=6 "CKInternalErrorDomain: 2022" UserInfo={NSDebugDescription=CKInternalErrorDomain: 2022, RequestUUID=D5CF13CF-6A10-436B-AB93-4C5C04859FFE, NSLocalizedDescription=Request failed with http status code 503, CKErrorDescription=Request failed with http status code 503, CKRetryAfter=35, NSUnderlyingError=0x281d80000 {Error Domain=CKInternalErrorDomain Code=2022 "Request failed with http status code 503" UserInfo={CKRetryAfter=35, CKHTTPStatus=503, CKErrorDescription=Request failed with http status code 503, RequestUUID=D5CF13CF-6A10-436B-AB93-4C5C04859FFE, NSLocalizedDescription=Request failed with http status code 503}}, CKHTTPStatus=503}}}` Tried deleting app, restarting device but nothing works. This was released on Appstore earlier and was working fine. It stopped working after update. Any help is appreciated.

When I run the performance test on a CoreML model, it shows predictions are 834% faster running on the Neural Engine as it is on the GPU. It also shows, that 100% of the model can run on the Neural Engine: GPU only: But when I set the compute units to all: let config = MLModelConfiguration() config.computeUnits = .all and profile, it shows that the neural engine isn’t used at all. Well, other than loading the model which takes 25 seconds when allowed to use the neural engine versus less than a second when not allowing the neural engine: The difference in speed is the difference between the app being too slow to even release versus quite reasonable performance. I have a lot of work invested in this, so I am really hoping that I can get it to run on the Neural Engine. Why isn't it actually running on the Neural Engine when it shows that it is supported and I have the compute unit set to run on the Neural Engine?

Hey guys, I converted a T5-base (encoder/decoder) model to a CoreML model using https://github.com/huggingface/exporters (which are using coremltools under the hood). When creating a performance report for the decoder model within XCode it shows that all compute units are mapped to the CPU. This is also the experience I have when profiling the model (GPU and ANE are not used). I was under the impression that CoreML would divide up the layers and run those that can run on the GPU / ANE, but maybe I misunderstood. Is there anything I can do to get this to not run on the CPU exclusively?

Im on the recent version of MacOs and I recently trained a Style Transfer model using CreateML. I used the preview tab of CreateML to preview my model with a video (as well as an image), however when I press the button to export or share the result from the neural network none are exported. The modal window appears but doesnt save after the progress bar shows up for the conversion I tried converting the CoreML model file into a CoreML package, however when I tried exporting the preview it crashed and switched tabs to the package information section. I've been having this issue with all three export buttons on the model preview section of both the CreateML application and Xcode. Is this happening to anyone else? Ive also tried using the coremltools package for Python to extract a preview, however documentation for Style Transfer networks doesnt exist for loading videos with that package. The style transfer network only takes an input of images, so its unclear where a video file can be loaded.

failed assertion `Completed handler provided after commit call'. how to clear this error any. when i run with cpu i am getting storage error so i tried with GPU. partial code #PositionalEncoding class PositionalEncoding(nn.Module): def init(self, d_model, max_len, dropout_prob=0.1): super(PositionalEncoding, self).init() self.dropout = nn.Dropout(p=dropout_prob) # Create positional encoding matrix pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) # Pad div_term with zeros if necessary div_term_padded = torch.zeros(d_model) div_term_padded[:div_term.size(0)] = div_term pe[:, 0::2] = torch.sin(position * div_term_padded[0::2]) pe[:, 1::2] = torch.cos(position * div_term_padded[1::2]) pe = pe.unsqueeze(0).transpose(0, 1) self.register_buffer('pe', pe) def forward(self, x): x = x + self.pe[:x.size(0), :] return self.dropout(x) #transformermodel class class TransformerModel(nn.Module): def init(self, input_size, hidden_size, num_layers, d_model, num_heads, dropout_prob, output_size, device, max_len): super(TransformerModel, self).init() self.device = device self.hidden_size = hidden_size self.d_model = d_model self.num_heads = num_heads #self.embedding = nn.Embedding(input_size, d_model).to(device) self.embedding = nn.Linear(input_size, d_model).to(device) self.pos_encoder = PositionalEncoding(d_model, max_len, dropout_prob).to(device) self.transformer_encoder_layer = nn.TransformerEncoderLayer(d_model, num_heads, hidden_size, dropout_prob).to(device) self.transformer_encoder = nn.TransformerEncoder(self.transformer_encoder_layer, num_layers).to(device) self.decoder = nn.Linear(d_model, output_size).to(device) self.to(device) # Ensure the model is on the correct device def forward(self, x): #x = x.long() x = x.transpose(0, 1) # Transpose the input tensor to match the expected shape for the transformer x = x.squeeze() # Remove the extra dimension from the input tensor x = self.embedding(x) # Apply the input embedding x = self.pos_encoder(x) # Add positional encoding x = self.transformer_encoder(x) # Apply the transformer encoder x = self.decoder(x[:, -1, :]) # Decode the last time step's output to get the final prediction return x #train transformer model class def train_transformer_model(train_X_scaled, train_y, input_size, d_model, hidden_size, num_layers, output_size, learning_rate, num_epochs, num_heads, dropout_prob, device, n_accumulation_steps=32): train_X_tensor = torch.from_numpy(train_X_scaled).float().to(device) train_y_tensor = torch.from_numpy(train_y).float().unsqueeze(1).to(device) # Create the dataset and DataLoader train_data = TensorDataset(train_X_tensor, train_y_tensor) train_loader = DataLoader(train_data, batch_size=8, shuffle=True) # Compute the maximum length of the input sequences max_len = train_X_tensor.size(1) # Create the model model = TransformerModel(input_size, hidden_size, num_layers, d_model, num_heads, dropout_prob, output_size, device, max_len).to(device) q = 0.5 criterion = lambda y_pred, y_true: quantile_loss(q, y_true, y_pred) optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) for epoch in range(1, num_epochs + 1): model.train() print(f"Transformer inputs shape: {train_X_tensor.shape}, targets shape: {train_y_tensor.shape}") for epoch in range(1, num_epochs + 1): model.train() print(f"transformer Epoch {epoch}/{num_epochs}") for i, (batch_X, batch_y) in enumerate(train_loader): batch_X = batch_X.to(device) print("transformer batch_X shape:", batch_X.shape) batch_y = batch_y.to(device) print("transformer batch_Y shape:", batch_y.shape) optimizer.zero_grad() batch_X = batch_X.transpose(0, 1) train_pred = model(batch_X.squeeze(0)).to(device) print("train_pred=",train_pred) loss = criterion(train_pred, batch_y).to(device) loss.backward() # Gradient accumulation if (i + 1) % n_accumulation_steps == 0: optimizer.step() optimizer.zero_grad() print(f"transformer Epoch {epoch}/{num_epochs}, Step {i+1}/{len(train_loader)}, Loss: {loss.item():.6f}") return model

This issue has already been raised a few times in the coremltools repo (here, here, and here). I'm reposting here because this may be an issue in CoreML itself. In short, converting Huggingface's Bert implementation from PyTorch to CoreML results in significantly different model outputs. This test was originally posted in one of the linked issues: import numpy as np import torch from transformers import AutoTokenizer, AutoModel import coremltools as ct MODEL_NAME = "bert-base-uncased" sentences = ["This is a test."] tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModel.from_pretrained(MODEL_NAME, torchscript=True).eval() encoded_input = tokenizer(sentences, return_tensors='pt') traced_model = torch.jit.trace(model, tuple(encoded_input.values())) scripted_model = torch.jit.script(traced_model) model = ct.convert(scripted_model, source="pytorch", inputs=[ct.TensorType(name="input_ids", shape=(ct.RangeDim(), ct.RangeDim()), dtype=np.int32), ct.TensorType(name="token_type_ids", shape=(ct.RangeDim(), ct.RangeDim()), dtype=np.int32), ct.TensorType(name="attention_mask", shape=(ct.RangeDim(), ct.RangeDim()), dtype=np.int32)], convert_to="mlprogram", compute_units=ct.ComputeUnit.CPU_ONLY) with torch.no_grad(): pt_out = scripted_model(**encoded_input) cml_inputs = {k: v.to(torch.int32).numpy() for k, v in encoded_input.items()} pred_coreml = model.predict(cml_inputs) np.testing.assert_allclose(pt_out[0].detach().numpy(), pred_coreml["hidden_states"], atol=1e-5, rtol=1e-4) Running this shows that the model outputs are highly divergent: Max absolute difference: 7.901174 Max relative difference: 3424.6594 By contrast, running the same test with Huggingface's Distilbert implementation (distilbert-base-uncased) shows a much smaller difference in output: Max absolute difference: 0.00523943 Max relative difference: 45.603153 Again, I'm not totally sure that this is an issue in CoreML, but it would be great to be able to run Bert based models with CoreML!

We are developing an app that requires background location tracking at intervals... we are, somewhat obviously in designing the app, finding the correct balance between benefits to the user of more accurate tracking, given our user case, against the costs of battery usage. We have I believe, as have a few other Apps we have seen which are already live, found a satisfactory compromise... IN STEPS APPLE'S MACHINE LEARNING!!! The app will work for a couple of weeks, and then machine learning will step in for a few days and throttle the frequency of background checks... now get this... There seems to be A STRONG CORRELATION between these periods and battery draining attributed to the app via the phone analytics... increasing A LOT.... ie 10x Apple's machine learning is I presume designed to protect the users from too much battery drainage... from background tasks... So what we have when this occurs is this machine learning function apparently draining the phone of much more battery than the original function it is seeking to improve... and in the process the performance of the original function is greatly reduced. Without Machine Learning interfering... battery draining appears to be sustainable and insignificant. Has anyone else encountered this. Apple do you have any comment?

Hello Apple Developer Community, I'm experiencing an issue when using PyTorch in combination with Metal Performance Shaders (MPS) on an A14 device. During the execution of the backward() function, I encounter the following error message: /AppleInternal/Library/BuildRoots/9941690d-bcf7-11ed-a645-863efbbaf80d/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShaders/MPSNDArray/Kernels/MPSNDArrayConvolutionA14.mm:4332: failed assertion `destination datatype must be fp32' I have already verified that both the input tensors and gradient tensors are of float32 datatype before the backward() function is called. However, the error seems to be originating from the MPS code, specifically within the MPSNDArrayConvolutionA14.mm file. Could you provide any guidance or recommendations on how to resolve this issue? Is there any specific constraint or requirement that I should be aware of when using MPS with PyTorch on A14 devices? I would greatly appreciate any help or suggestions. Thank you in advance for your support. Best regards, kiyotaka86

Hello, Is there an API available for "Visual Look Up"? https://support.apple.com/en-gb/guide/iphone/iph21c29a1cf/ios

I have MacBook with M1 Pro and I'm trying to use tensorflow_decision_forests. I followed instruction and created new conda env with tenserflow-metal (https://developer.apple.com/metal/tensorflow-plugin/), but it doesn't work right away. I had to downgrade library versions to tenserflow-macos==2.9.0 and tensorflow-metal==0.5.0. nNow i am trying to install tensorflow_decision_forests using pip, however this library requires tenserflow-macos~=2.10.0. Has anyone successfully installed this library?

Hey guys, First post so I'm sorry for making any newbie mistakes. Anyways, I've been trying to get into ML and I wanted to follow a course on it but it requires Tensorflow and I've been trying to get that working on my system. I have the 2021 14" 16GB Macbook Pro with the M1 Pro Chip and I am running Ventura 13.1. I have been following this article as well as digging around about getting Tensorflow working on M1 but to no avail. I managed to get tensorflow-macos installed in my environment as well as tensorflow-metal but when I try to run some sample code in Juyter, I'm getting an error that I do not understand. In Jupyter, when I run: import tensorflow as tf print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU'))) I get Num GPUs Available: 1 but when I try to run the rest of the sample code %%time import tensorflow as tf import tensorflow_datasets as tfds print("TensorFlow version:", tf.__version__) print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU'))) tf.config.list_physical_devices('GPU') (ds_train, ds_test), ds_info = tfds.load( 'mnist', split=['train', 'test'], shuffle_files=True, as_supervised=True, with_info=True, ) def normalize_img(image, label): """Normalizes images: `uint8` -> `float32`.""" return tf.cast(image, tf.float32) / 255., label batch_size = 128 ds_train = ds_train.map( normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE) ds_train = ds_train.cache() ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples) ds_train = ds_train.batch(batch_size) ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE) ds_test = ds_test.map( normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE) ds_test = ds_test.batch(batch_size) ds_test = ds_test.cache() ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE) model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu'), tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), # tf.keras.layers.Dropout(0.25), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation='relu'), # tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile( loss='sparse_categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(0.001), metrics=['accuracy'], ) model.fit( ds_train, epochs=12, validation_data=ds_test, ) I get TensorFlow version: 2.11.0 Num GPUs Available: 1 Metal device set to: Apple M1 Pro WARNING:tensorflow:AutoGraph could not transform <function normalize_img at 0x14a4cec10> and will run it as-is. Cause: Unable to locate the source code of <function normalize_img at 0x14a4cec10>. Note that functions defined in certain environments, like the interactive Python shell, do not expose their source code. If that is the case, you should define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.experimental.do_not_convert. Original error: could not get source code To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert 2022-12-13 13:54:33.658225: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-12-13 13:54:33.658309: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] 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: <undefined>) WARNING:tensorflow:AutoGraph could not transform <function normalize_img at 0x14a4cec10> and will run it as-is. Cause: Unable to locate the source code of <function normalize_img at 0x14a4cec10>. Note that functions defined in certain environments, like the interactive Python shell, do not expose their source code. If that is the case, you should define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.experimental.do_not_convert. Original error: could not get source code To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert WARNING: AutoGraph could not transform <function normalize_img at 0x14a4cec10> and will run it as-is. Cause: Unable to locate the source code of <function normalize_img at 0x14a4cec10>. Note that functions defined in certain environments, like the interactive Python shell, do not expose their source code. If that is the case, you should define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.experimental.do_not_convert. Original error: could not get source code To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert Epoch 1/12 2022-12-13 13:54:34.162300: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-12-13 13:54:34.163015: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled. 2022-12-13 13:54:35.383325: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660 2022-12-13 13:54:35.383350: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660 2022-12-13 13:54:35.389028: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660 2022-12-13 13:54:35.389049: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660 2022-12-13 13:54:35.401250: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660 2022-12-13 13:54:35.401274: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660 2022-12-13 13:54:35.405004: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660 2022-12-13 13:54:35.405025: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660 in addition to the traceback. Can someone help me decipher what's going wrong here?

Hello, I have created a data model with Text Classification which contains many garments with the label clothing, technology equipment with the label technology, etc.... . I have inserted it so that when entering for example "T-shirt" the label clothing is output. But I want it so that when I create an equipment category with the clothing, that it suggests me clothing items, which I add. How do I implement this. In the function you can only use"preditction.label". I program with SwiftUI. func textml() {         do {             let model = try DataModel_Symbols_Consilia(configuration: .init())             let prediction = try model.prediction(text: addProjectVM.title)             addProjectVM.symbol = prediction.label         } catch {             modelOutput = "Something went wrong"         }     } Greetings Janik

Hello Everyone, I am trying to train a hand pose classifier. But I stumble upon the problem where if I give training data above certain number (i.e. ~4000 images). Is it a limit imposed by Apple? because I even tried to reduce the size of images and even dimensions but it always gives me error while training whenever I give training data above certain number. Has anyone else stumbled upon this problem?if so, is there any work around for any number of data can be given to CreateML? Thanks

Hello. I would like to compile the YOLOv3.mlmodel for object detection as a framework and call it from applescript to use it, but I do not know how. The flow I am imagining is as follows Import "YOLOv3.mlmodel" on xcode and create a framework. Load the created framework with applescript's use statement and execute it. Please let me know if you know anything about this.

Greetings! I have a MacBook Pro (2018) running macOS Monterey and was wondering if it could be repurposed for machine learning work. I have a Razer Core X, which would enable me to use AMD Radeon 6900 XT as a eGPU supported configuration. Before doing any shopping, my question is whether the usage of TensorFlow is feasible with this setup. I am aware of tensorflow-metal, but if I understand correctly, only natively supported Mac GPUs can be used with it (not eGPUs)? PlaidML is also an option, but it still not clear which external GPUs are supported. Any guidance on this topic will be appreciated.