On accessing the CoreML Model Deployment dashboard with my developer account, the page gives a bad request error saying "Your request was invalid". Also, when I try to create a Model Collection it gives an error saying "One of the fields was invalid".

It's a little bit unclear to me whether I get a list of available or installed voices on the device. If an app requests a voice which is available but not installed, what happened? Is it upon the app to install the missing voice or is this done by iOS automatically? For some reasons, a male and female gender is not offered for each language. What's the reason for?

System information Script can be found below MacBook Pro M1 (Mac OS Big Sir (11.5.1)) TensorFlow installed from (source) TensorFlow version (2.5 version) with Metal Support Python version: 3.9 GPU model and memory: MacBook Pro M1 and 16 GB Steps needed for installing Tensorflow with metal support. https://developer.apple.com/metal/tensorflow-plugin/ I am trying to train a model on Macbook Pro M1, but the performance is so bad and the train doesn't work properly. It takes a ridiculously long time just for a single epoch. Code needed for reproducing this behavior. import tensorflow as tf from tensorflow.keras.datasets import imdb from tensorflow.keras.layers import Embedding, Dense, LSTM from tensorflow.keras.losses import BinaryCrossentropy from tensorflow.keras.models import Sequential from tensorflow.keras.optimizers import Adam from tensorflow.keras.preprocessing.sequence import pad_sequences # Model configuration additional_metrics = ['accuracy'] batch_size = 128 embedding_output_dims = 15 loss_function = BinaryCrossentropy() max_sequence_length = 300 num_distinct_words = 5000 number_of_epochs = 5 optimizer = Adam() validation_split = 0.20 verbosity_mode = 1 # Disable eager execution tf.compat.v1.disable_eager_execution() # Load dataset (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=num_distinct_words) print(x_train.shape) print(x_test.shape) # Pad all sequences padded_inputs = pad_sequences(x_train, maxlen=max_sequence_length, value = 0.0) # 0.0 because it corresponds with <PAD> padded_inputs_test = pad_sequences(x_test, maxlen=max_sequence_length, value = 0.0) # 0.0 because it corresponds with <PAD> # Define the Keras model model = Sequential() model.add(Embedding(num_distinct_words, embedding_output_dims, input_length=max_sequence_length)) model.add(LSTM(10)) model.add(Dense(1, activation='sigmoid')) # Compile the model model.compile(optimizer=optimizer, loss=loss_function, metrics=additional_metrics) # Give a summary model.summary() # Train the model history = model.fit(padded_inputs, y_train, batch_size=batch_size, epochs=number_of_epochs, verbose=verbosity_mode, validation_split=validation_split) # Test the model after training test_results = model.evaluate(padded_inputs_test, y_test, verbose=False) print(f'Test results - Loss: {test_results[0]} - Accuracy: {100*test_results[1]}%') I have noticed this same problem with LSTM layers Also, this issue is been reported in Keras and they can't debug. Keras issue https://github.com/keras-team/keras/issues/15003

Following the instructions at https://developer.apple.com/metal/tensorflow-plugin/ I got as far as python -m pip install tensorflow-macos and it responded "ERROR: Could not find a version that satisfies the requirement tensorflow-macos (from versions: none) ERROR: No matching distribution found for tensorflow-macos" I'd be grateful for any suggestions

Device: MacBook Pro 16 M1 Max, 64GB running MacOS 12.0.1. I tried setting up GPU Accelerated TensorFlow on my Mac using the following steps: Setup: XCode CLI / Homebrew/ Miniforge Conda Env: Python 3.9.5 conda install -c apple tensorflow-deps python -m pip install tensorflow-macos python -m pip install tensorflow-metal brew install libjpeg conda install -y matplotlib jupyterlab In Jupyter Lab, I try to execute this code: from tensorflow.keras import layers from tensorflow.keras import models model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.Flatten()) model.add(layers.Dense(64, activation='relu')) model.add(layers.Dense(10, activation='softmax')) model.summary() The code executes, but I get this warning, indicating no GPU Acceleration can be used as it defaults to a 0MB GPU. Error: Metal device set to: Apple M1 Max 2021-10-27 08:23:32.872480: 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. 2021-10-27 08:23:32.872707: 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: <undefined>) Anyone has any idea how to fix this? I came across a bunch of posts around here related to the same issue but with no solid fix. I created a new question as I found the other questions less descriptive of the issue, and wanted to comprehensively depict it. Any fix would be of much help.

Hi, I installed skearn successfully and ran the MINIST toy example successfully. then I started to run my project. The finning thing everything seems good at the start point (at least no ImportError occurs). but when I made some changes of my code and try to run all cells (I use jupyter lab) again, ImportError occurs..... ImportError: dlopen(/Users/a/miniforge3/lib/python3.9/site-packages/scipy/spatial/qhull.cpython-39-darwin.so, 0x0002): Library not loaded: @rpath/liblapack.3.dylib   Referenced from: /Users/a/miniforge3/lib/python3.9/site-packages/scipy/spatial/qhull.cpython-39-darwin.so   Reason: tried: '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/python3.9/site-packages/scipy/spatial/../../../../liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/python3.9/site-packages/scipy/spatial/../../../../liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/bin/../lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/lib/liblapack.3.dylib' (no such file), '/Users/a/miniforge3/bin/../lib/liblapack.3.dylib' (no such file), '/usr/local/lib/liblapack.3.dylib' (no such file), '/usr/lib/liblapack.3.dylib' (no such file) then I have to uninstall scipy, sklearn, etc and reinstall all of them. and my code can be ran again..... Magically I hate to say, anyone knows how to permanently solve this problem? make skearn more stable?

Hi everyone, I found that the performance of GPU is not good as I expected (as slow as a turtle), I wanna switch from GPU to CPU. but mlcompute module cannot be found, so wired. The same code ran on colab and my computer (jupyter lab) take 156s vs 40 minutes per epoch, respectively. I only used a small dataset (a few thousands of data points), and each epoch only have 20 baches. I am so disappointing and it seems like the "powerful" GPU is a joke. I am using 12.0.1 macOS and the version of tensorflow-macos is 2.6.0 Can anyone tell me why this happens?

I am using NLTagger to tag lexical classes of words, but it suddenly just stopped working. I boiled my code down to the most basic version, but it's never executing the closure of the enumerateTags() function. What do I have to change or what should I try? for e in sentenceArray { let cupcake = "I like you, have a cupcake" tagger.string = cupcake tagger.enumerateTags(in: cupcake.startIndex..<cupcake.endIndex, unit: .word, scheme: .nameTypeOrLexicalClass) { tag, range in print("TAG") return true }

Hi all, I'm using my new M1 Pro with the latest Mac OS 12.1 and I'm experiencing issues with installing tensorflow. I've created an environment and have it activated. I tried conda install -c apple tensorflow-deps but returned with Collecting package metadata (current_repodata.json): done Solving environment: failed with initial frozen solve. Retrying with flexible solve. Collecting package metadata (repodata.json): done Solving environment: failed with initial frozen solve. Retrying with flexible solve. PackagesNotFoundError: The following packages are not available from current channels: tensorflow-deps Current channels: https://conda.anaconda.org/apple/osx-64 https://conda.anaconda.org/apple/noarch https://repo.anaconda.com/pkgs/main/osx-64 https://repo.anaconda.com/pkgs/main/noarch https://repo.anaconda.com/pkgs/r/osx-64 https://repo.anaconda.com/pkgs/r/noarch To search for alternate channels that may provide the conda package you're looking for, navigate to https://anaconda.org and use the search bar at the top of the page. Note: you may need to restart the kernel to use updated packages. Did anyone have the same issue and any advice to address this? Thanks, Andrew

I'm running example from TF site and getting different results from CPU and GPU. Results from GPU are obviously wrong (second image). Why? If I'm executing code with with tf.device('/cpu:0') then the code works as expected, but slower. It's sufficient to execute this lines on CPU to fix the issue: with tf.device('/cpu:0'): real_output = discriminator(images, training=True) fake_output = discriminator(generated_images, training=True) Source code: https://www.tensorflow.org/tutorials/generative/dcgan My complete results: https://disk.yandex.ru/d/E-hU5dpffOmkLg

I am comparing my M1 MBA with my 2019 16" Intel MBP. The M1 MBA has tensorflow-metal, while the Intel MBP has TF directly from Google. Generally, the same programs runs 2-5 times FASTER on the Intel MBP, which presumably has no GPU acceleration. Is there anything I could have done wrong on the M1? Here is the start of the metal run: Metal device set to: Apple M1 systemMemory: 16.00 GB maxCacheSize: 5.33 GB 2022-01-19 04:43:50.975025: 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. 2022-01-19 04:43:50.975291: 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: ) 2022-01-19 04:43:51.216306: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz Epoch 1/10 2022-01-19 04:43:51.298428: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.

Im using my 2020 Mac mini with M1 chip and this is the first time try to use it on convolutional neural network training. So the problem is I install the python(ver 3.8.12) using miniforge3 and Tensorflow following this instruction. But still facing the GPU problem when training a 3D Unet. Here's part of my code and hoping to receive some suggestion to fix this. import tensorflow as tf from tensorflow import keras import json import numpy as np import pandas as pd import nibabel as nib import matplotlib.pyplot as plt from tensorflow.keras import backend as K #check available devices def get_available_devices(): local_device_protos = device_lib.list_local_devices() return [x.name for x in local_device_protos] print(get_available_devices()) Metal device set to: Apple M1 ['/device:CPU:0', '/device:GPU:0'] 2022-02-09 11:52:55.468198: 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. 2022-02-09 11:52:55.468885: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: ) X_norm_with_batch_dimension = np.expand_dims(X_norm, axis=0) #tf.device('/device:GPU:0') #Have tried this line doesn't work #tf.debugging.set_log_device_placement(True) #Have tried this line doesn't work patch_pred = model.predict(X_norm_with_batch_dimension) InvalidArgumentError: 2 root error(s) found. (0) INVALID_ARGUMENT: CPU implementation of Conv3D currently only supports the NHWC tensor format. [[node model/conv3d/Conv3D (defined at /Users/mwshay/miniforge3/envs/tensor/lib/python3.8/site-packages/keras/layers/convolutional.py:231) ]] [[model/conv3d/Conv3D/_4]] (1) INVALID_ARGUMENT: CPU implementation of Conv3D currently only supports the NHWC tensor format. [[node model/conv3d/Conv3D (defined at /Users/mwshay/miniforge3/envs/tensor/lib/python3.8/site-packages/keras/layers/convolutional.py:231) ]] 0 successful operations. 0 derived errors ignored. The code is executable on Google Colab but can't run on Mac mini locally with Jupyter notebook. The NHWC tensor format problem might indicate that Im using my CPU to execute the code instead of GPU. Is there anyway to optimise GPU to train the network in Tensorflow?

I am aware this question has been asked before, but resolutions have worked for me. When I try to import TensorFlow on my python 3.9 environment I get the following error: uwewinter@Uwes-MBP % python3 Python 3.9.10 (main, Jan 15 2022, 11:40:53)  [Clang 13.0.0 (clang-1300.0.29.3)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow 2022-02-09 21:30:01.701794: F tensorflow/c/experimental/stream_executor/stream_executor.cc:808] Non-OK-status: stream_executor::MultiPlatformManager::RegisterPlatform( std::move(cplatform)) status: INTERNAL: platform is already registered with name: "METAL" zsh: abort      python3 I have the newest versions of TensorFlow-macos and TensorFlow-metal installed: uwewinter@Uwes-MBP % pip3 list | grep tensorflow         tensorflow-estimator           2.7.0 tensorflow-macos               2.7.0 tensorflow-metal               0.3.0 OSX is latest: uwewinter@Uwes-MBP % sw_vers  ProductName: macOS ProductVersion: 12.2 BuildVersion: 21D49 Mac is a 2021 MBP uwewinter@Uwes-MBP % sysctl hw.model hw.model: MacBookPro18,3

I m using macbook air 13" and pursuing Artificial Intelligence course I m facing huge problem with Jupyter notebook post installing tensorflow as the kernel keeps dying and I have literally tried solution in every article/resource on Google Nothing seems to be fixing the issue. It began only when I started to run code for Convolutional Neural Network Please help me fix this issue and understand why its not getting fixed At the moment, I can only think of trading Macbook for Windows Laptop but It will be very hard as I have not had hands-on Windows laptop Hope to hear back soon Thanks Keshav Lal Seth

I know you guys already support tensorflow by two packages: tensorflow-macos and tensorflow-metal. However to work with NLP, users also need tensorflow-text package. Could you devlopers build a dedicated version tensorflow-text-macos to support this demands. Best Regards,

We run into an issue that a more complex model fails to converge on M1 Max GPU while it converges on its CPU and on Non-M1 based models. the performance is the same for CPU and GPU for models with single RNN but once we use two RNNs GPU fails to converge. That said, the below example is based on non-sensical data for the model architecture used. but we can observe here the same behavior as the one we observe in our production models (which for obvious reasons we cannot share here). Mainly: the loss goes down to the bottom of the e-06 precision in all cases but when we use two RNNs on GPU. during training we often test e-07 precision level for double RNN with GPU condition, the results do not go that low sometimes reaching also e-05 value level. for our production data we see that double RNN with GPU results in loss of 1.0 and basically stays the same from the first epoch; but for the other conditions it often reaches 0.2 level with clear learning curve. in production model increasing the LSTM_Cell number made the divergence more visible (in this syntactic date it does not happen) the more complex the model is (after the RNN layers) the more visible the issue. Suspected issues: different precision used in CPU and GPU training - we had to decrease the data values a lot to make the effect visible ( if you work with raw data all approaches seem to produce the comparable results) somehow the vanishing gradient problem is more pronounced on GPU as indicated by worse performance as the complexity of the model increases. please let me know if you need any further details Software Stack: Mac OS 12.1 tf 2.7 metal 0.3 also tested on tf. 2.8 Sample Syntax: TEST CONDITIONS: #conditions with issue: 1,2 gpu = 1 # 0 CPU, 1 GPU model_size = 2 # 1 single RNN, 2 double RNN #PARAMETERS LSTM_Cells = 64 epochs = 300 batch = 128 import numpy as np import pandas as pd import sys from sklearn import preprocessing #""" if 'tensorflow' in sys.modules: print("tensorflow uploaded") del sys.modules["tensorflow"] #del tf import tensorflow as tf else: print("tensorflow not uploaded") import tensorflow as tf if gpu == 1: pass else: tf.config.set_visible_devices([], 'GPU') #print("GPUs:", tf.config.list_physical_devices('GPU')) print("GPUs:", tf.config.list_logical_devices('GPU')) #print("CPUs:", tf.config.list_physical_devices('CPU')) print("CPUs:", tf.config.list_logical_devices('CPU')) #""" from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data' column_names = ['MPG', 'Displacement', 'Horsepower', 'Weight'] dataset = pd.read_csv(url, names=column_names, na_values='?', comment='\t', sep=' ', skipinitialspace=True).dropna() scaler = preprocessing.StandardScaler().fit(dataset) X_scaled = scaler.transform(dataset) X_scaled = X_scaled * 0.001 Large Values #x_train = np.array(dataset[['Horsepower', 'Weight']]).reshape(-1,2,2) #y_train = np.array(dataset[['MPG','Displacement']]).reshape(-1,2,2) Small Values x_train = np.array(X_scaled[:,2:]).reshape(-1,2,2) y_train = np.array(X_scaled[:,:2]).reshape(-1,2,2) #print(dataset) print(x_train.shape) print(y_train.shape) print(weight.shape) train_data = tf.data.Dataset.from_tensor_slices((x_train[:,:,:8], y_train)).cache().shuffle(x_train.shape[0]).batch(batch).repeat().prefetch(tf.data.experimental.AUTOTUNE) if model_size == 2: #""" # MINIMAL NOT WORKING encoder_inputs = tf.keras.Input(shape=(x_train.shape[1],x_train.shape[2])) encoder_l1 = tf.keras.layers.LSTM(LSTM_Cells,return_sequences = True, return_state=True) encoder_l1_outputs = encoder_l1(encoder_inputs) encoder_l2 = tf.keras.layers.LSTM(LSTM_Cells, return_state=True) encoder_l2_outputs = encoder_l2(encoder_l1_outputs[0]) dense_1 = tf.keras.layers.Dense(128, activation='relu')(encoder_l2_outputs[0]) dense_2 = tf.keras.layers.Dense(64, activation='relu')(dense_1) dense_3 = tf.keras.layers.Dense(32, activation='relu')(dense_2) dense_4 = tf.keras.layers.Dense(16, activation='relu')(dense_3) flat = tf.keras.layers.Flatten()(dense_2) dense_5 = tf.keras.layers.Dense(22)(flat) reshape_output = tf.keras.layers.Reshape([2,2])(dense_5) model = tf.keras.models.Model(encoder_inputs, reshape_output) #""" else: #""" # WORKING encoder_inputs = tf.keras.Input(shape=(x_train.shape[1],x_train.shape[2])) encoder_l1 = tf.keras.layers.LSTM(LSTM_Cells,return_sequences = True, return_state=True) encoder_l1_outputs = encoder_l1(encoder_inputs) dense_1 = tf.keras.layers.Dense(128, activation='relu')(encoder_l1_outputs[0]) dense_2 = tf.keras.layers.Dense(64, activation='relu')(dense_1) dense_3 = tf.keras.layers.Dense(32, activation='relu')(dense_2) dense_4 = tf.keras.layers.Dense(16, activation='relu')(dense_3) flat = tf.keras.layers.Flatten()(dense_2) dense_5 = tf.keras.layers.Dense(22)(flat) reshape_output = tf.keras.layers.Reshape([2,2])(dense_5) model = tf.keras.models.Model(encoder_inputs, reshape_output) #""" print(model.summary()) loss_tf = tf.keras.losses.MeanSquaredError() model.compile(optimizer='adam', loss=loss_tf, run_eagerly=True) model.fit(train_data, epochs = epochs, steps_per_epoch = 3)

I have updated to macOS Monterrey and my code for SFSPeechRecognizer just broke. I get this error if I try to configure an offline speech recognizer for macOS Error Domain=kLSRErrorDomain Code=102 "Failed to access assets" UserInfo={NSLocalizedDescription=Failed to access assets, NSUnderlyingError=0x6000003c5710 {Error Domain=kLSRErrorDomain Code=102 "No asset installed for language=es-ES" UserInfo={NSLocalizedDescription=No asset installed for language=es-ES}}} Here is a code snippet from a demo project: private func process(url: URL) throws {     speech = SFSpeechRecognizer.init(locale: Locale(identifier: "es-ES"))     speech.supportsOnDeviceRecognition = true     let request = SFSpeechURLRecognitionRequest(url: url)     request.requiresOnDeviceRecognition = true     request.shouldReportPartialResults = false     speech.recognitionTask(with: request) { result, error in       guard let result = result else {         if let error = error {           print(error)           return         }         return       }       if let error = error {         print(error)         return       }       if result.isFinal {         print(result.bestTranscription.formattedString)       }     }   } I have tried with different languages (es-ES, en-US) and it says the same error each time. Any idea on how to install these assets or how to fix this?

I'd like to control whether the network training happens on CPU or GPU when using tensorflow-metal. How to do this? Thanks!

Hi, I have a custom object detection CoreML model and I notice something strange when using the model with the Vision framework. I have tried two different approaches as to how to process an image and do inference on the CoreML model. The first one is using the CoreML "raw": initialising the model, getting the input image ready and using the model's .prediction() function to get the models output. The second one is using Vision to wrap the CoreML model in a VNCoreMLModel, creating a VNCoreMLRequest and using the VNImageRequestHandler to actually perform the model inference. The result of the VNCoreMLRequest is of type VNRecognizedObjectObservation. The issue I now face is in the difference in the output of both methods. The first method gives back the raw output of the CoreML model: confidence and coordinates. The confidence is an array with size equal to the number of classes in my model (3 in my case). The second method gives back the boundingBox, confidence and labels. However here the confidence is only the confidence for the most likely class (so size is equal to 1). But the confidence I get from the second approach is quite different from the confidence I get during the first approach. I can use either one of the approaches in my application. However, I really want to find out what is going on and understand how this difference occurred. Thanks!

I'm trying to create a list that users can pick for the voice they want to use in my app. The code below works for US but if I change my locale settings to any other country, it fails to load any available voices even though I have downloaded other for other countries.. func voices() -> [String] {          AVSpeechSynthesisVoice.speechVoices().filter { $0.language == NSLocale.current.voiceLanguage }.map { $0.name }                    // AVSpeechSynthesisVoice.speechVoices().map { $0.name }     } If I list all voices available, I can select the voices for other countries that are loaded.