Hi folks, I'm working on converting a GPT2 model to coreml with KV caching enabled. I have a GPT2 model runinng on GPU with static input shape It seems once I enable flexible shape (i.e. either range shape or enumerated shape), the model will be run on CPU according to the performance report. I can see new operators being added ( get_shape and general_slice ) and it is not supported by GPU / ANE Wondering if there's any way to get around this to get the model running on GPU / ANE? How does the machine decide whether to run the model on GPU / Neural Engine? Thanks!

Hi, I am developing a fitness app that detects technique mistakes during workout. Can we use 3D data from VNDetectHumanBodyPose3DRequest with ML model?

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?

It doesn't matter if I install miniforge or mamba, directly or through brew, when I try to fit the sample model from https://developer.apple.com/metal/tensorflow-plugin/, even with a simple sequential model, I always get this error. Is there any workaround on this? I'll appreciate any help, thanks! 2022-12-10 11:18:19.941623: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2022-12-10 11:18:20.427283: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled. 2022-12-10 11:18:21.222950: 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: 0x28edf1f90 2022-12-10 11:18:21.223003: 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: 0x28edf1f90 2022-12-10 11:18:21.363366: 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: 0x28edf1f90 2022-12-10 11:18:21.364757: 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: 0x28edf1f90 2022-12-10 11:18:21.388739: 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: 0x28edf1f90 2022-12-10 11:18:21.388757: 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: 0x28edf1f90 NotFoundError Traceback (most recent call last) Cell In[25], line 2 1 model = create_model() ----> 2 history = model.fit(Xf_train, yf_train, epochs=3, batch_size=64); File /opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback..error_handler(*args, **kwargs) 67 filtered_tb = _process_traceback_frames(e.traceback) 68 # To get the full stack trace, call: 69 # tf.debugging.disable_traceback_filtering() ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb File /opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:52, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 50 try: 51 ctx.ensure_initialized() ---> 52 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, 53 inputs, attrs, num_outputs) 54 except core._NotOkStatusException as e: 55 if name is not None: NotFoundError: Graph execution error: Detected at node 'StatefulPartitionedCall_4' defined at (most recent call last): File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/ipykernel_launcher.py", line 17, in app.launch_new_instance() File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/traitlets/config/application.py", line 992, in launch_instance app.start() File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/ipykernel/kernelapp.py", line 711, in start self.io_loop.start() File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/tornado/platform/asyncio.py", line 215, in start self.asyncio_loop.run_forever() File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/asyncio/base_events.py", line 603, in run_forever self._run_once() File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/asyncio/base_events.py", line 1899, in _run_once handle._run() ... File "/var/folders/f9/bp40pn0d401d974fy48dxm8h0000gn/T/ipykernel_63636/3393788193.py", line 2, in <module> history = model.fit(Xf_train, yf_train, epochs=3, batch_size=64); File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1650, in fit tmp_logs = self.train_function(iterator) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1249, in train_function return step_function(self, iterator) ...... File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1222, in run_step outputs = model.train_step(data) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/engine/training.py", line 1027, in train_step self.optimizer.minimize(loss, self.trainable_variables, tape=tape) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 527, in minimize self.apply_gradients(grads_and_vars) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1140, in apply_gradients return super().apply_gradients(grads_and_vars, name=name) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 634, in apply_gradients iteration = self._internal_apply_gradients(grads_and_vars) File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1166, in _internal_apply_gradients return tf.__internal__.distribute.interim.maybe_merge_call( File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1216, in _distributed_apply_gradients_fn distribution.extended.update( File "/opt/homebrew/Caskroom/miniforge/base/envs/tf/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1211, in apply_grad_to_update_var return self._update_step_xla(grad, var, id(self._var_key(var))) Node: 'StatefulPartitionedCall_4' could not find registered platform with id: 0x28edf1f90 [[{{node StatefulPartitionedCall_4}}]] [Op:__inference_train_function_1241]

I am looking for an experienced LiDAR programmer for our room surveying app, any recommendations? Thanks

I am just starting to learn neural networks. If I run my code and try to fit a simple trigonometric function, the model builds a good-looking function. If I pip install tensorflow-metal and run, I get a straight line not resembling the non-linear function at all. if I uninstall metal, everything works again. Which suggests there is something wrong with metal. Any help would be appreciated. I would use the metal acceleration for the next steps in my project. Thank you

I initially raised this issue in the tensorflow forum, and they directed me back here since this is a tf-macos specific problem [see https://github.com/tensorflow/tensorflow/issues/60673]. When calling Model.compile() with the AdamW optimizer, a warning is thrown saying that v2.11+ optimizers have a known slowdown on M1/M2 devices, and so the backend attempts to fallback to a legacy version. However, no legacy version of the AdamW optimizer exists. In a previous tf-macos version 2.12, this lead to an error during Model.compile() [see issue https://github.com//issues/60652 and https://developer.apple.com/forums/thread/729732]. In the current nightly, this error is not thrown - however, after calling model.compile(), the attribute model.optimizer is set to string 'adamw' instead of an optimizer object. Later, when we call model.fit(), this leads to an AttributeError, because model.optimizer.minimize() does not exist when model.optimizer is a string. Expected behaviour: correctly compile the model with either a v2.11+ optimiser without slowdown, or a legacy-compatible implementation of the AdamW optimizer. Then the model will train correctly with a valid AdamW optimizer when calling model.fit(). Note: a warning message suggests using the optimizer located at tf.keras.optimizers.legacy.AdamW, but this does not exist It would be nice to be able to either use modern optimizers, or have a legacy-compatible version of AdamW, since weight-decay is an important tool in modern ML research, and currently cannot be used on mac. Standalone code to reproduce the issue ##===========## ## Imports ## ##===========## import sys import tensorflow as tf import numpy as np from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Dense from tensorflow.keras.optimizers import AdamW ##===================## ## Report versions ## ##===================## # # Expected outputs: # Python version is: 3.10.11 | packaged by conda-forge | (main, May 10 2023, 19:01:19) [Clang 14.0.6 ] # TF version is: 2.14.0-dev20230523 # Numpy version is: 1.23.2 # print(f"Python version is: {sys.version}") print(f"TF version is: {tf.__version__}") print(f"Numpy version is: {np.__version__}") ##==============================## ## Create a very simple model ## ##==============================## # # Expected outputs: # Model: "model_1" # _________________________________________________________________ # Layer (type) Output Shape Param # # ================================================================= # Layer_in (InputLayer) [(None, 2)] 0 # # Layer_hidden (Dense) (None, 10) 30 # # Layer_out (Dense) (None, 2) 22 # # ================================================================= # Total params: 52 (208.00 Byte) # Trainable params: 52 (208.00 Byte) # Non-trainable params: 0 (0.00 Byte) # _________________________________________________________________ # x_in = Input(2 , dtype=tf.float32, name="Layer_in" ) x = x_in x = Dense(10, dtype=tf.float32, name="Layer_hidden", activation="relu" )(x) x = Dense(2 , dtype=tf.float32, name="Layer_out" , activation="linear")(x) model = Model(x_in, x) model.summary() ##===================================================## ## Compile model with MSE loss and AdamW optimizer ## ##===================================================## # # Expected outputs: # WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.AdamW` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.AdamW`. # WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.AdamW`. # model.compile( loss = "mse", optimizer = AdamW(learning_rate=1e-3, weight_decay=1e-2) ) ##===========================## ## Generate some fake data ## ##===========================## # # Expected outputs: # X shape is (100, 2), Y shape is (100, 2) # dataset_size = 100 X = np.random.normal(size=(dataset_size, 2)) X = tf.constant(X, dtype=tf.float32) Y = np.random.normal(size=(dataset_size, 2)) Y = tf.constant(Y, dtype=tf.float32) print(f"X shape is {X.shape}, Y shape is {Y.shape}") ##===================================## ## Fit model to data for one epoch ## ##===================================## # # Expected outputs: # --------------------------------------------------------------------------- # AttributeError Traceback (most recent call last) # Cell In[9], line 51 # 1 ##===================================## # 2 ## Fit model to data for one epoch ## # 3 ##===================================## # (...) # 48 # • mask=None # 49 # # ---> 51 model.fit(X, Y, epochs=1) # File ~/miniforge3/envs/tf_macos_nightly_230523/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs) # 67 filtered_tb = _process_traceback_frames(e.__traceback__) # 68 # To get the full stack trace, call: # 69 # `tf.debugging.disable_traceback_filtering()` # ---> 70 raise e.with_traceback(filtered_tb) from None # 71 finally: # 72 del filtered_tb # File /var/folders/6_/gprzxt797d5098h8dtk22nch0000gn/T/__autograph_generated_filezzqv9k36.py:15, in outer_factory.<locals>.inner_factory.<locals>.tf__train_function(iterator) # 13 try: # 14 do_return = True # ---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope) # 16 except: # 17 do_return = False # AttributeError: in user code: # File "/Users/Ste/miniforge3/envs/tf_macos_nightly_230523/lib/python3.10/site-packages/keras/src/engine/training.py", line 1338, in train_function * # return step_function(self, iterator) # File "/Users/Ste/miniforge3/envs/tf_macos_nightly_230523/lib/python3.10/site-packages/keras/src/engine/training.py", line 1322, in step_function ** # outputs = model.distribute_strategy.run(run_step, args=(data,)) # File "/Users/Ste/miniforge3/envs/tf_macos_nightly_230523/lib/python3.10/site-packages/keras/src/engine/training.py", line 1303, in run_step ** # outputs = model.train_step(data) # File "/Users/Ste/miniforge3/envs/tf_macos_nightly_230523/lib/python3.10/site-packages/keras/src/engine/training.py", line 1084, in train_step # self.optimizer.minimize(loss, self.trainable_variables, tape=tape) # AttributeError: 'str' object has no attribute 'minimize' model.fit(X, Y, epochs=1)

Hello, I am trying to use gpu for machine learning task from apple using "mps" as device for GPU but it is not working. I am using PyTorch Stable version. How can I use MacBook GPU for machine learning tasks?

hey if i wanted to create an app that takes screenshots from an apple device (and any app within) to give context to an ai so the ai can then respond. Then the app parses the response then executes commands on behalf of the ai/user, how would I do so with the rule that "screenshots/captures are not allowed within other apps"? Want to stay within bounds of the rules in place. Possibilities: Ai assistant, Ai pals, passive automation

I have export a quantization model with ct.convert whose "minimum_deployment_target=ct.target.iOS17,", can I run it without a iphone ?

I am in the process of developing a matrix-vector multiplication kernel. While conducting performance evaluations, I've noticed that on M1/M1 Pro/M1 Max, the kernel demonstrates an impressive memory bandwidth utilization of around 90%. However, when executed on the M1 Ultra/M2 Ultra, this figure drops to approximately 65%. My suspicion is that this discrepancy is attributed to the dual-die architecture of the M1 Ultra/M2 Ultra. It's plausible that the necessary data might be stored within the L2 cache of the alternate die. Could you kindly provide any insights or recommendations for mitigating the occurrence of on-die L2 cache misses on the Ultra chips? Additionally, I would greatly appreciate any general advice aimed at enhancing memory load speeds on these particular chips.

I've been working on an app that combines CoreML and ARKit/SceneKit to detect and measure some objects, with success. Now I need to make it available to a React Native app, and I'm trying this approach here: https://github.com/riteshakya037/react-native-native-module where I can navigate and instantiate the view controller. The problem occurs when my view gets called. I have errors at the sceneView, not being loaded. Is there a way to use it without the Storyboard? For now it seems the incompatibility.

Hello fellow developers, I am currently developing an application involving machine learning models, specifically CoreML models, and I have encountered an intriguing issue that I am hoping to get some insights on. In my current scenario, I'm planning to create a simple application with minimal UI, possibly using PyQT or similar tools. Therefore, I'm seeking a way to utilize NeuralEngine and GPU for CoreML model inference in Python. I discovered the 'predict' API in CoreMLTools which allows for model inference, but I'm unsure if its performance is on par with that of a properly built MacOS application using Swift and Neural Engine. Can anyone provide insights into whether there's a considerable difference in inference performance between these two methods? Is the performance of CoreMLTools 'predict' API comparable to that of a full-fledged Swift MacOS application leveraging the Neural Engine? Any clarification or guidance on this matter would be greatly appreciated. Thanks!

Hi! I've just got a MacBook Air with M2 chip. I'm doing some research on ML and I was wondering if it is possible to use Neural Engine as an accelerator for training. If it is possible, where can I find ressources on how to enable it and how to check it? Thank you!

Build and installed Jax and Jax-metal following instructions on a M2Pro Mac-mini from here - https://developer.apple.com/metal/jax/ However, the following check seems to suggest XLA using CPU and not GPU. >>> from jax.lib import xla_bridge >>> print(xla_bridge.get_backend().platform) cpu Has anyone got it working to dump GPU? 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?

Hello, I have my largely iOS app running using Mac Catalyst, but I need to limit what Macs will be able to install it from the Mac App Store based on the GPU Family like MTLGPUFamily.mac2. Is that possible? Or I could limit it to Apple Silicon using the Designed for iPad target, but I would prefer to use Mac Catalyst instead of Designed for iPad. Is it possible to limit Mac Catalyst installs to Apple Silicon Macs? Side question: what capabilities are supported by MTLGPUFamily.mac2? I can't find it. My main interest is in CoreML inference acceleration. Thank you.

I am performing a grid search over a parameter grid and train the model with different combinations of hyperparameters. I am receiving the following Warning: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz Why is that and what can I do to fix it? Thank you very much. Here is the code: def grid_search(model_name): ... elif model_name == 'LSTM': def build_model(units, activation, dropout, layers): model = Sequential() model.add(LSTM(units=units, kernel_initializer="normal", activation=activation, return_sequences=True, input_shape=(2, 1152), recurrent_dropout=0)) model.add(Dropout(dropout)) for i in range(layers): if i != layers-1: model.add(LSTM(units=units, kernel_initializer="normal", activation=activation, return_sequences=True,recurrent_dropout=0)) model.add(Dropout(dropout)) elif i == (layers-1): model.add(LSTM(units=units, kernel_initializer="normal", activation=activation, recurrent_dropout=0)) model.add(Dropout(dropout)) model.add(Dense(units=6, kernel_initializer="normal", activation=activation)) model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]) return model param_grid = {'units': [200, 300, 400], 'activation': ['tanh'], 'dropout': [0, 0.2, 0.4, 0.6], 'layers': [0, 5]} group_kfold = GroupKFold(n_splits=len(np.unique(groups_train))) model = KerasClassifier(model=build_model, units=param_grid['units'], activation=param_grid['activation'], dropout=param_grid['dropout'], layers=param_grid['layers']) grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=group_kfold) X_test, X_train, y_test, y_train = raw_dataassigner(model_name) (X_train, y_train) = shuffle(X_train, y_train) with tf.device('/cpu:0'): grid_result = grid_search.fit(X_train, y_train, groups=groups_train) print(f'Best score ({grid_search.best_score_}) for {model_name} model achieved with parameters: ', grid_search.best_params_) means = grid_result.cv_results_['mean_test_score'] stds = grid_result.cv_results_['std_test_score'] params = grid_result.cv_results_['params'] for mean, stdev, param in zip(means, stds, params): print("%f (%f) with: %r" % (mean, stdev, param)) grid_search('LSTM')

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?

Hi Guys, I am using a MacBook pro 2019 Intel core i7, 16inch 2019 model device. When i am using an external monitor my CPU spikes and Kernel_task is using more than 700% CPU. I am using the apple manufactured HDMI extender to connect to the monitor. As soon as i disconnect the monitor, everything works fine. I am running Ventura 13.3.1.