First of all, as I understand that this is a problem related with tensorflow addons, I've been in contact with tfa developers (https://github.com/tensorflow/addons/issues/2578), and this issue only happens in M1, so they think it has to do with Apple tensorflow-metal. I've been getting spurious errors while doing model.fit with the Lookahead optimizer (I'm doing fine-tuning with big datasets, and my code just breaks while fitting to different files, and in a not-reproducible way, i.e. each time I run it it breaks on a different file, and on different operations). I can see that these errors are undoubtedly related to the Lookahead optimizer. Let me try to explain this new info in a clear manner. I've tried with 2 different versions of tf+tfaddons (conda environments), but I got the same type of errors, probably more frequent with the pylast conda environment: pylast:tensorflow-macos 2.9.0, tensorflow-metal 0.5.0, tensorflow-addons 0.17.0 py39deps26-source: tensorflow-macos 2.6.0, tensorflow-metal 0.2.0, tensorflow-addons 0.15.0.dev0 The base code is always the same, I use tf.config.set_soft_device_placement(True) and also with tf.device('/cpu:0'): in every call to tensorflow, otherwise I get errors. As explained before, in my code, I just load a model, and fine-tune it to each file of a dataset. Here are a pair of example error outputs (obtained with the pylast conda environment): File "/Users/machine/Projects/finetune-asp/src/finetune_IMR2020.py", line 138, in finetune_dataset_db history = model.fit(ft, steps_per_epoch=len(ft), epochs=ft_cfg["num_epochs"], shuffle=True, File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler raise e.with_traceback(filtered_tb) from None File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/tensorflow/python/eager/execute.py", line 54, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error: Detected at node 'Lookahead/Lookahead/update_64/mul_11' defined at (most recent call last): File "/Users/machine/Projects/finetune-asp/src/finetune_IMR2020.py", line 138, in finetune_dataset_db history = model.fit(ft, steps_per_epoch=len(ft), epochs=ft_cfg["num_epochs"], shuffle=True, File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler return fn(*args, **kwargs) File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/keras/engine/training.py", line 1409, in fit tmp_logs = self.train_function(iterator) File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/keras/engine/training.py", line 1051, in train_function return step_function(self, iterator) File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/keras/engine/training.py", line 1040, in step_function outputs = model.distribute_strategy.run(run_step, args=(data,)) File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/keras/engine/training.py", line 1030, in run_step outputs = model.train_step(data) File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/keras/engine/training.py", line 893, in train_step self.optimizer.minimize(loss, self.trainable_variables, tape=tape) File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 539, in minimize return self.apply_gradients(grads_and_vars, name=name) File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/tensorflow_addons/optimizers/lookahead.py", line 104, in apply_gradients return super().apply_gradients(grads_and_vars, name, **kwargs) File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 678, in apply_gradients return tf.__internal__.distribute.interim.maybe_merge_call( File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 723, in _distributed_apply update_op = distribution.extended.update( File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/keras/optimizers/optimizer_v2/optimizer_v2.py", line 706, in apply_grad_to_update_var update_op = self._resource_apply_dense(grad, var, **apply_kwargs) File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/tensorflow_addons/optimizers/lookahead.py", line 130, in _resource_apply_dense train_op = self._optimizer._resource_apply_dense( File "/Users/machine/miniforge3/envs/pylast/lib/python3.9/site-packages/tensorflow_addons/optimizers/rectified_adam.py", line 249, in _resource_apply_dense coef["r_t"] * m_corr_t / (v_corr_t + coef["epsilon_t"]), Node: 'Lookahead/Lookahead/update_64/mul_11' Incompatible shapes: [0] vs. [5,40,20] [[{{node Lookahead/Lookahead/update_64/mul_11}}]] [Op:__inference_train_function_30821] and Another error output

error message I ran the following notebook with tensorflow-metal--0.5.0 import tensorflow as tf from tensorflow import keras import numpy as np (X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data() X_train, X_valid = X_train_full[:-5000], X_train_full[-5000:] y_train, y_valid = y_train_full[:-5000], y_train_full[-5000:] X_mean = X_train.mean(axis=0, keepdims=True) X_std = X_train.std(axis=0, keepdims=True) + 1e-7 X_train = (X_train - X_mean) / X_std X_valid = (X_valid - X_mean) / X_std X_test = (X_test - X_mean) / X_std X_train = X_train[..., np.newaxis] X_valid = X_valid[..., np.newaxis] X_test = X_test[..., np.newaxis] from functools import partial DefaultConv2D = partial(keras.layers.Conv2D, kernel_size=3, activation='relu', padding="SAME") input_ = keras.layers.Input(shape=[28, 28, 1]) conv0 = DefaultConv2D(filters=64, kernel_size=7)(input_) pool1 = keras.layers.MaxPooling2D(pool_size=2)(conv0) conv1 = DefaultConv2D(filters=128)(pool1) conv2 = DefaultConv2D(filters=128)(conv1) pool2 = keras.layers.MaxPooling2D(pool_size=2)(conv2) conv3 = DefaultConv2D(filters=256)(pool2) conv4 = DefaultConv2D(filters=256)(conv3) pool3 = keras.layers.MaxPooling2D(pool_size=2)(conv4) flatten = keras.layers.Flatten()(conv4) hidden1 = keras.layers.Dense(units=128, activation='relu')(flatten) dropout1 = keras.layers.Dropout(0.5)(hidden1) hidden2 = keras.layers.Dense(units=64, activation='relu')(dropout1) dropout2 = keras.layers.Dropout(0.5)(hidden2) output = keras.layers.Dense(units=10, activation='softmax')(dropout2) model = keras.Model(inputs=[input_], outputs=[output]) model.compile(loss="sparse_categorical_crossentropy", optimizer="nadam", metrics=["accuracy"]) model.fit(X_train, y_train, epochs=20, validation_data=(X_valid, y_valid)) However I got the error message the kernel appears to have died. it will restart automatically. in the 3rd cell. I also ran this python script from terminal one line at a time, and I got the error message I attached above when I tried to run the code conv0 = DefaultConv2D(filters=64, kernel_size=7)(input_) With tensorflow-metal uninstalled, this code runs without any error messages.

I am noticing huge memory usage with TensorFlow. The memory usage will keep on increasing up to 36GB of memory usage only after one epoch. The following is the dataset preprocessing process: with tf.device('CPU: 0'): data_augmentation = keras.Sequential([ keras.layers.experimental.preprocessing.RandomFlip("horizontal"), keras.layers.experimental.preprocessing.RandomRotation(0.2), keras.layers.experimental.preprocessing.RandomHeight(0.2), keras.layers.experimental.preprocessing.RandomWidth(0.2), keras.layers.experimental.preprocessing.RandomZoom(0.2), ], name="data_augmentation") train_data = train_data.map(map_func=lambda x, y: (data_augmentation(x), y), num_parallel_calls=tf.data.AUTOTUNE).prefetch(buffer_size=tf.data.AUTOTUNE) test_data = test_data.prefetch(buffer_size=tf.data.AUTOTUNE) And the following is the model I used base_model = keras.applications.EfficientNetB0(include_top=False) base_model.trainable = False inputs = keras.layers.Input(shape=(224, 224, 3), name='input_layer') x = base_model(inputs, training=False) x = keras.layers.GlobalAveragePooling2D(name='global_average_pooling')(x) outputs = keras.layers.Dense(101, activation='softmax', name='output_layer')(x) model = keras.Model(inputs, outputs) # Compile model.compile(loss="categorical_crossentropy", optimizer=tf.keras.optimizers.Adam(), # use Adam with default settings metrics=["accuracy"]) from tqdm.keras import TqdmCallback tqdm_callback = TqdmCallback() # Fit history_all_classes_10_percent = model.fit(train_data, verbose=0, epochs=5, validation_data=test_data, validation_steps=int(0.15 * len(test_data)), callbacks=[checkpoint_callback, tqdm_callback]) # save best model weights to file

Not only Upgrading tensorflow-macos and tensorflow-metal breaks Conv2d with groups arg , it also makes training unable to finish. Today, after upgrading the tensorflow-macos to 2.9.0 and tensorflow-metal to 0.5.0, my notebook can no longer make progress after training around 16 minutes. I tested 4 times. It could happily run around 17 to 18 epochs, each epoch around 55 seconds. After that, it just stopped making progress. I checked the activity monitor, both cpu and gpu usage were 0 at that point. I accidentally found that there are a lot of kernel faults in the Console app. The last one before I force-killed the process: IOReturn IOGPUDevice::new_resource(IOGPUNewResourceArgs *, struct IOGPUNewResourceReturnData *, IOByteCount, uint32_t *): PID 68905 likely leaking IOGPUResource (count=200000) The PID 68905 is in fact the training process. I have always observed this kind of issue for several months. But it's not as frequent and I can restart my notebook train successfully. No luck today. Hope Apple engineers can found the cause and fix it.

Today I upgraded tensorflow-macos to 2.9.0 and tensorflow-metal to 0.5.0, and found my old notebook failed to run. It ran well with tensorflow-macos 2.8.0 and tensorflow-metal 0.4.0. Specifically, I found that the groups arg of Conv2d layer was the cause. Here is a demo: import tensorflow as tf from tensorflow import keras as tfk # tf.config.set_visible_devices([], 'GPU') Xs = tf.random.normal((32, 64, 48, 4)) ys = tf.random.normal((32,)) tf.random.set_seed(0) model = tfk.Sequential([ tfk.layers.Conv2D( filters=16, kernel_size=(4, 3), groups=4, # groups arg activation='relu', ), tfk.layers.Flatten(), tfk.layers.Dense(1, activation='sigmoid'), ]) model.compile( loss=tfk.losses.BinaryCrossentropy(), metrics=[ tfk.metrics.BinaryAccuracy(), ], ) model.fit(Xs, ys, epochs=2, verbose=1) The error is: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at xla_ops.cc:296 : UNIMPLEMENTED: Could not find compiler for platform METAL: NOT_FOUND: could not find registered compiler for platform METAL -- check target linkage Removing groups arg would make the code run again. Training on CPU, by uncommenting line 4, gives different error: 'apple-m1' is not a recognized processor for this target (ignoring processor) LLVM ERROR: 64-bit code requested on a subtarget that doesn't support it! And removing groups arg also would make training on CPU work. However I didn't test training on CPU before the upgrade. My device is a MacBook Pro 14' running macOS 12.4.

I'm trying to get TensorFlow with Metal support running on my iMac (2017, Radeon 580 Pro) following these instructions. However, simply importing tensorflow ( import tensorflow ) results in the following error with the Python console crashing: 2022-05-27 11:46:12.419950: 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" Abort trap: 6 Versions: macOS 12.3, Python 3.8.13, tensorflow-macos 2.9.0, tensorflow-metal 0.5.0

I completed till the step of installing tensor flow dependencies which itself posed so many errors, but when I try to run python -m pip install tensorflow-metal or python -m pip install tensorflow-macos I get the following error, ERROR: Could not find a version that satisfies the requirement tensorflow-metal (from versions: none) ERROR: No matching distribution found for tensorflow-metal ERROR: Could not find a version that satisfies the requirement tensorflow-macos (from versions: none) ERROR: No matching distribution found for tensorflow-macos What am I supposed to do now to install tensor flow directly?

Hi, OS: macOS 12.4 CPU: Apple M1 I cannot import the new TensorFlow 2.9.0 on Apple M1. I got an error: --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /Users/martin/Documents/Projects/Solar-Transformer/Testing.ipynb Cell 3' in <cell line: 1>() ----> 1 from tensorflow.keras.layers import Add, Dense, Dropout, Layer, LayerNormalization, MultiHeadAttention, Normalization 2 from tensorflow.keras.models import Model 3 from tensorflow.keras.initializers import TruncatedNormal File ~/miniforge3/lib/python3.9/site-packages/tensorflow/__init__.py:37, in <module> 34 import sys as _sys 35 import typing as _typing ---> 37 from tensorflow.python.tools import module_util as _module_util 38 from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader 40 # Make sure code inside the TensorFlow codebase can use tf2.enabled() at import. File ~/miniforge3/lib/python3.9/site-packages/tensorflow/python/__init__.py:42, in <module> 37 from tensorflow.python.eager import context 39 # pylint: enable=wildcard-import 40 41 # Bring in subpackages. ---> 42 from tensorflow.python import data 43 from tensorflow.python import distribute 44 # from tensorflow.python import keras File ~/miniforge3/lib/python3.9/site-packages/tensorflow/python/data/__init__.py:21, in <module> 15 """`tf.data.Dataset` API for input pipelines. 16 17 See [Importing Data](https://tensorflow.org/guide/data) for an overview. 18 """ 20 # pylint: disable=unused-import ---> 21 from tensorflow.python.data import experimental 22 from tensorflow.python.data.ops.dataset_ops import AUTOTUNE 23 from tensorflow.python.data.ops.dataset_ops import Dataset File ~/miniforge3/lib/python3.9/site-packages/tensorflow/python/data/util/structure.py:22, in <module> 19 import six 20 import wrapt ---> 22 from tensorflow.python.data.util import nest 23 from tensorflow.python.framework import composite_tensor 24 from tensorflow.python.framework import ops File ~/miniforge3/lib/python3.9/site-packages/tensorflow/python/data/util/nest.py:36, in <module> 16 """## Functions for working with arbitrarily nested sequences of elements. 17 18 NOTE(mrry): This fork of the `tensorflow.python.util.nest` module (...) 31 arrays. 32 """ 34 import six as _six ---> 36 from tensorflow.python.framework import sparse_tensor as _sparse_tensor 37 from tensorflow.python.util import _pywrap_utils 38 from tensorflow.python.util import nest File ~/miniforge3/lib/python3.9/site-packages/tensorflow/python/framework/sparse_tensor.py:24, in <module> 22 from tensorflow.python import tf2 23 from tensorflow.python.framework import composite_tensor ---> 24 from tensorflow.python.framework import constant_op 25 from tensorflow.python.framework import dtypes 26 from tensorflow.python.framework import ops File ~/miniforge3/lib/python3.9/site-packages/tensorflow/python/framework/constant_op.py:25, in <module> 23 from tensorflow.core.framework import types_pb2 24 from tensorflow.python.eager import context ---> 25 from tensorflow.python.eager import execute 26 from tensorflow.python.framework import dtypes 27 from tensorflow.python.framework import op_callbacks File ~/miniforge3/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:23, in <module> 21 from tensorflow.python import pywrap_tfe 22 from tensorflow.python.eager import core ---> 23 from tensorflow.python.framework import dtypes 24 from tensorflow.python.framework import ops 25 from tensorflow.python.framework import tensor_shape File ~/miniforge3/lib/python3.9/site-packages/tensorflow/python/framework/dtypes.py:29, in <module> 26 from tensorflow.python.lib.core import _pywrap_bfloat16 27 from tensorflow.python.util.tf_export import tf_export ---> 29 _np_bfloat16 = _pywrap_bfloat16.TF_bfloat16_type() 32 @tf_export("dtypes.DType", "DType") 33 class DType(_dtypes.DType): 34 """Represents the type of the elements in a `Tensor`. 35 36 `DType`'s are used to specify the output data type for operations which (...) 46 See `tf.dtypes` for a complete list of `DType`'s defined. 47 """ Example code: from tensorflow.keras.layers import Add, Dense, Dropout, Layer, LayerNormalization, MultiHeadAttention, Normalization from tensorflow.keras.models import Model from tensorflow.keras.initializers import TruncatedNormal from tensorflow.keras.utils import timeseries_dataset_from_array import tensorflow as tf import tensorflow_probability as tfp import numpy as np

So I'm trying to install Tensorflow on my M1 MacBook Air using the guidelines here https://developer.apple.com/metal/tensorflow-plugin/ I'm able to download and install the Conda env (at least I believe), but when I try to install the TensorFlow dependencies, I get the error PackagesNotFoundError: The following packages are not available from current channels:   - tensorflow-deps Would really appreciate any help.

Hii, would u help me to install tensorflow o my m1

I have followed all the instructions to install tensorflow for my M1 mac from "https://developer.apple.com/metal/tensorflow-plugin/". Despite of showing a successful installation, there is an error when I am trying to import the tensorflow library. --------------------------------------------------------------------------- OSError Traceback (most recent call last) Input In [1], in <cell line: 1>() ----> 1 import tensorflow as tf 2 tf.__version__ File ~/.local/lib/python3.9/site-packages/tensorflow/__init__.py:37, in <module> 34 import sys as _sys 35 import typing as _typing ---> 37 from tensorflow.python.tools import module_util as _module_util 38 from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader 40 # Make sure code inside the TensorFlow codebase can use tf2.enabled() at import. File ~/.local/lib/python3.9/site-packages/tensorflow/python/__init__.py:36, in <module> 27 import traceback 29 # We aim to keep this file minimal and ideally remove completely. 30 # If you are adding a new file with @tf_export decorators, 31 # import it in modules_with_exports.py instead. 32 33 # go/tf-wildcard-import 34 # pylint: disable=wildcard-import,g-bad-import-order,g-import-not-at-top ---> 36 from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow 37 from tensorflow.python.eager import context 39 # pylint: enable=wildcard-import 40 41 # Bring in subpackages. File ~/.local/lib/python3.9/site-packages/tensorflow/python/pywrap_tensorflow.py:24, in <module> 21 from tensorflow.python.platform import self_check 23 # Perform pre-load sanity checks in order to produce a more actionable error. ---> 24 self_check.preload_check() 26 # pylint: disable=wildcard-import,g-import-not-at-top,unused-import,line-too-long 28 try: 29 # This import is expected to fail if there is an explicit shared object 30 # dependency (with_framework_lib=true), since we do not need RTLD_GLOBAL. File ~/.local/lib/python3.9/site-packages/tensorflow/python/platform/self_check.py:65, in preload_check() 58 else: 59 # Load a library that performs CPU feature guard checking as a part of its 60 # static initialization. Doing this here as a preload check makes it more 61 # likely that we detect any CPU feature incompatibilities before we trigger 62 # them (which would typically result in SIGILL). 63 cpu_feature_guard_library = os.path.join( 64 os.path.dirname(__file__), "../../core/platform/_cpu_feature_guard.so") ---> 65 ctypes.CDLL(cpu_feature_guard_library) File ~/miniforge3/envs/tfm1/lib/python3.9/ctypes/__init__.py:374, in CDLL.__init__(self, name, mode, handle, use_errno, use_last_error, winmode) 371 self._FuncPtr = _FuncPtr 373 if handle is None: --> 374 self._handle = _dlopen(self._name, mode) 375 else: 376 self._handle = handle OSError: dlopen(/Users/k_krishna/.local/lib/python3.9/site-packages/tensorflow/python/platform/../../core/platform/_cpu_feature_guard.so, 0x0006): tried: '/Users/k_krishna/.local/lib/python3.9/site-packages/tensorflow/python/platform/../../core/platform/_cpu_feature_guard.so' (mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64e')), '/Users/k_krishna/.local/lib/python3.9/site-packages/tensorflow/core/platform/_cpu_feature_guard.so' (mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64e')) I have tried stack-overflow but non of the solutions worked. Please help me resolve the issue.

We are developing a simple GAN an when training the solution, the behavior of the convergence of the discriminator is different if we use GPU than using only CPU or even executing in Collab. We've read a lot, but this is the only one post that seems to talk about similar behavior. Unfortunately, after updating to 0.4 version problem persists. My Hardware/Software: MacBook Pro. model: MacBookPro18,2. Chip: Apple M1 Max. Cores: 10 (8 de rendimiento y 2 de eficiencia). Memory: 64 GB. firmware: 7459.101.3. OS: Monterey 12.3.1. OS Version: 7459.101.3. Python version 3.8 and libraries (the most related) using !pip freeze keras==2.8.0 Keras-Preprocessing==1.1.2 .... tensorboard==2.8.0 tensorboard-data-server==0.6.1 tensorboard-plugin-wit==1.8.1 tensorflow-datasets==4.5.2 tensorflow-docs @ git+https://github.com/tensorflow/docs@7d5ea2e986a4eae7573be3face00b3cccd4b8b8b tensorflow-macos==2.8.0 tensorflow-metadata==1.7.0 tensorflow-metal==0.4.0 #####. CODE TO REPRODUCE. ####### Code does not fit in the max space in this message... I've shared a Google Collab Notebook at: https://colab.research.google.com/drive/1oDS8EV0eP6kToUYJuxHf5WCZlRL0Ypgn?usp=sharing You can easily see that loss goes to 0 after 1 or 2 epochs when GPU is enabled, buy if GPU is disabled everything is OK

class RankingModel(tf.keras.Model): def init(self): super().init() embedding_dimension = 32 # Compute embeddings for users. self.user_embeddings = tf.keras.Sequential([ tf.keras.layers.StringLookup( vocabulary=unique_user_ids, mask_token=None), tf.keras.layers.Embedding(len(unique_user_ids) + 1, embedding_dimension) ]) # Compute embeddings for movies. self.movie_embeddings = tf.keras.Sequential([ tf.keras.layers.StringLookup( vocabulary=unique_movie_titles, mask_token=None), tf.keras.layers.Embedding(len(unique_movie_titles) + 1, embedding_dimension) ]) # Compute predictions. self.ratings = tf.keras.Sequential([ # Learn multiple dense layers. tf.keras.layers.Dense(256, activation="relu"), tf.keras.layers.Dense(64, activation="relu"), # Make rating predictions in the final layer. tf.keras.layers.Dense(1) ]) def call(self, inputs): user_id, movie_title = inputs user_embedding = self.user_embeddings(user_id) movie_embedding = self.movie_embeddings(movie_title) return self.ratings(tf.concat([user_embedding, movie_embedding], axis=1)) task = tfrs.tasks.Ranking( loss = tf.keras.losses.MeanSquaredError(), metrics=[tf.keras.metrics.RootMeanSquaredError()] ) class MovielensModel(tfrs.models.Model): def init(self): super().init() self.ranking_model: tf.keras.Model = RankingModel() self.task: tf.keras.layers.Layer = tfrs.tasks.Ranking( loss = tf.keras.losses.MeanSquaredError(), metrics=[tf.keras.metrics.RootMeanSquaredError()] ) def call(self, features: Dict[str, tf.Tensor]) -> tf.Tensor: return self.ranking_model( (features["user_id"], features["movie_title"])) def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor: labels = features.pop("user_rating") rating_predictions = self(features) # The task computes the loss and the metrics. return self.task(labels=labels, predictions=rating_predictions) model = MovielensModel() model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1)) cached_train = train.shuffle(100_000).batch(8192).cache() cached_test = test.batch(4096).cache() model.fit(cached_train, epochs=3) InvalidArgumentError Traceback (most recent call last) Input In [40], in <cell line: 5>() 1 #physical_devices = tf.config.list_physical_devices('GPU') 2 #tf.config.set_visible_devices(physical_devices[0], 'GPU') 3 #with tf.device("GPU"): 4 #model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1)) ----> 5 model.fit(cached_train, epochs=3) File /opt/homebrew/Caskroom/miniforge/base/envs/mlp/lib/python3.8/site-packages/keras/utils/traceback_utils.py:67, in filter_traceback..error_handler(*args, **kwargs) 65 except Exception as e: # pylint: disable=broad-except 66 filtered_tb = _process_traceback_frames(e.traceback) ---> 67 raise e.with_traceback(filtered_tb) from None 68 finally: 69 del filtered_tb File /opt/homebrew/Caskroom/miniforge/base/envs/mlp/lib/python3.8/site-packages/tensorflow/python/eager/execute.py:54, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 52 try: 53 ctx.ensure_initialized() ---> 54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, 55 inputs, attrs, num_outputs) 56 except core._NotOkStatusException as e: 57 if name is not None: InvalidArgumentError: Cannot assign a device for operation movielens_model_1/ranking_model_3/sequential_9/embedding_6/embedding_lookup: Could not satisfy explicit device specification '' because the node {{colocation_node movielens_model_1/ranking_model_3/sequential_9/embedding_6/embedding_lookup}} was colocated with a group of nodes that required incompatible device '/job:localhost/replica:0/task:0/device:GPU:0'. All available devices [/job:localhost/replica:0/task:0/device:CPU:0, /job:localhost/replica:0/task:0/device:GPU:0]. Colocation Debug Info: Colocation group had the following types and supported devices: Root Member(assigned_device_name_index_=2 requested_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' assigned_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' resource_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' supported_device_types_=[CPU] possible_devices_=[] ResourceSparseApplyAdagradV2: CPU UnsortedSegmentSum: GPU CPU StridedSlice: GPU CPU Const: GPU CPU Shape: GPU CPU _Arg: GPU CPU Unique: GPU CPU Identity: GPU CPU ResourceGather: GPU CPU Colocation members, user-requested devices, and framework assigned devices, if any: movielens_model_1_ranking_model_3_sequential_9_embedding_6_embedding_lookup_4370 (_Arg) framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0 adagrad_adagrad_update_resourcesparseapplyadagradv2_accum (_Arg) framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0 movielens_model_1/ranking_model_3/sequential_9/embedding_6/embedding_lookup (ResourceGather) movielens_model_1/ranking_model_3/sequential_9/embedding_6/embedding_lookup/Identity (Identity) Adagrad/Adagrad/update/Unique (Unique) /job:localhost/replica:0/task:0/device:GPU:0 Adagrad/Adagrad/update/Shape (Shape) /job:localhost/replica:0/task:0/device:GPU:0 Adagrad/Adagrad/update/strided_slice/stack (Const) /job:localhost/replica:0/task:0/device:GPU:0 Adagrad/Adagrad/update/strided_slice/stack_1 (Const) /job:localhost/replica:0/task:0/device:GPU:0 Adagrad/Adagrad/update/strided_slice/stack_2 (Const) /job:localhost/replica:0/task:0/device:GPU:0 Adagrad/Adagrad/update/strided_slice (StridedSlice) /job:localhost/replica:0/task:0/device:GPU:0 Adagrad/Adagrad/update/UnsortedSegmentSum (UnsortedSegmentSum) /job:localhost/replica:0/task:0/device:GPU:0 Adagrad/Adagrad/update/ResourceSparseApplyAdagradV2 (ResourceSparseApplyAdagradV2) /job:localhost/replica:0/task:0/device:GPU:0 [[{{node movielens_model_1/ranking_model_3/sequential_9/embedding_6/embedding_lookup}}]] [Op:__inference_train_function_4593]

Hey I can't install Tensorflow on my M1 Mac here is attached some screenshot.

I installed the tensorflow-deps package on my MacBook Pro, and noted that it install python 3.10. Unfortunately there is no whl for python 3.10 for tensorflow-macos on pypi. I had to downgrade to python 3.9 before I could install tensorflow and tensorflow-metal.

I tried to install dependencies with PDM for a project and found the inconvenience that TensorFlow does not detect the GPU of the M1 Pro. When I create a virtual environment with poetry I do not have the same problem. Any hint on how to solve this inconvenience?