Project is based on python3.8 and 3.9, containing some C and C++ source How can I do parallel computing on CPU and GPU of M1max In deed, I buy Mac m1max for the strong GPU to do quantitative finance, for which the speed is extremely important. Unfortunately, cuda is not compatible with Mac. Show me how to do it, thx. Are Accelerate(for CPU) and Metal(for GPU) can speed up any source by building like this: Step 1: download source from github Step 2: create a file named "site.cfg"in this souce file, and add content: [accelerate] libraries=Metal, Acelerate, vecLib Step 3: Terminal: NPY_LAPACK_Order=accelerate python3 setup.py build Step 4: pip3 install . or python3 setup.py install ? (I am not sure which method to apply) 2、how is the compatibility of such method? I need speed up numpy, pandas and even a open souce project, such as https://github.com/microsoft/qlib 3、just show me the code 4、when compiling C++, C source, a lot of errors were reported, which gcc and g++ to choose? the default gcc installed by brew is 4.2.1, which cannot work. and I even tried to download gcc from the offical website of ARM， still cannot work. give me a hint. thx so much urgent

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 working on the neural network classifier provided on the coremltools.readme.io in the updatable->neural network section(https://coremltools.readme.io/docs/updatable-neural-network-classifier-on-mnist-dataset). I am using the same code but I get an error saying that the coremltools.converters.keras.convert does not exist. But this I know can be coreml version issue. Right know I am using coremltools version 6.2. I converted this model to mlmodel with .convert only. It got converted successfully. But I face an error in the make_updatable function saying the loss layer must be softmax output. Even the coremlt package API reference there I found its because the layer name is softmaxND but it should be softmax. Now the problem is when I convert the model from Keras sequential model to coreml model. the layer name and type change. And the softmax changes to softmaxND. Does anyone faced this issue? if I execute this builder.inspect_layers(last=4) I get this output [Id: 32], Name: sequential/dense_1/Softmax (Type: softmaxND) Updatable: False Input blobs: ['sequential/dense_1/MatMul'] Output blobs: ['Identity'] [Id: 31], Name: sequential/dense_1/MatMul (Type: batchedMatmul) Updatable: False Input blobs: ['sequential/dense/Relu'] Output blobs: ['sequential/dense_1/MatMul'] [Id: 30], Name: sequential/dense/Relu (Type: activation) Updatable: False Input blobs: ['sequential/dense/MatMul'] Output blobs: ['sequential/dense/Relu'] In the make_updatable function when I execute builder.set_categorical_cross_entropy_loss(name='lossLayer', input='Identity') I get this error ValueError: Categorical Cross Entropy loss layer input (Identity) must be a softmax layer output.

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.

Hi, someone can tell me if it's possible and eventually how to see the Confusion Matrix about a CreateML Model? Thank you!

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 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')

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 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)

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!

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?

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!

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

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