This issue might be related to https://developer.apple.com/forums/thread/684263

Hello, I got the same problem and only got limited power on Tensorflow because of non access to GPU on Macbook pro M1 Max. Is this coming from the apple version of Tensorflow??

What do you think about my results? Now it looks M1 max GPU works without mlcompute. my test program is as follows ------------------------------ import tensorflow as tf from tensorflow.keras import datasets, layers, models (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() train_images, test_images = train_images / 255.0, test_images / 255.0 ONLY ON THE MAC #from tensorflow.python.compiler.mlcompute import mlcompute #mlcompute.set_mlc_device(device_name='gpu') model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) 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)) model.compile(   optimizer='adam',   loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),   metrics=['accuracy'] ) history = model.fit(   train_images,    train_labels,    epochs=10,    validation_data=(test_images, test_labels) ) the results from the code is as follows --------------------------------------- Metal device set to: Apple M1 Max 2021-11-30 13:05:12.085180: 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. 2021-11-30 13:05:12.085560: 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: ) 2021-11-30 13:05:13.115918: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2) 2021-11-30 13:05:13.118560: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz 2021-11-30 13:05:13.256877: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. Epoch 1/10 1563/1563 [==============================] - ETA: 0s - loss: 1.4715 - accuracy: 0.4650 2021-11-30 13:05:27.622014: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 1563/1563 [==============================] - 16s 9ms/step - loss: 1.4715 - accuracy: 0.4650 - val_loss: 1.2344 - val_accuracy: 0.5623 Epoch 2/10 1563/1563 [==============================] - 14s 9ms/step - loss: 1.1120 - accuracy: 0.6048 - val_loss: 1.0613 - val_accuracy: 0.6261 Epoch 3/10 1563/1563 [==============================] - 14s 9ms/step - loss: 0.9570 - accuracy: 0.6646 - val_loss: 0.9228 - val_accuracy: 0.6778 Epoch 4/10 1563/1563 [==============================] - 14s 9ms/step - loss: 0.8554 - accuracy: 0.6993 - val_loss: 0.9274 - val_accuracy: 0.6766 Epoch 5/10 1563/1563 [==============================] - 14s 9ms/step - loss: 0.7799 - accuracy: 0.7273 - val_loss: 0.8858 - val_accuracy: 0.6945 Epoch 6/10 1563/1563 [==============================] - 14s 9ms/step - loss: 0.7126 - accuracy: 0.7508 - val_loss: 0.8594 - val_accuracy: 0.7021 Epoch 7/10 1563/1563 [==============================] - 14s 9ms/step - loss: 0.6618 - accuracy: 0.7685 - val_loss: 0.8287 - val_accuracy: 0.7161 Epoch 8/10 1563/1563 [==============================] - 14s 9ms/step - loss: 0.6097 - accuracy: 0.7863 - val_loss: 0.8918 - val_accuracy: 0.7053 Epoch 9/10 1563/1563 [==============================] - 14s 9ms/step - loss: 0.5659 - accuracy: 0.7997 - val_loss: 0.8921 - val_accuracy: 0.7072 Epoch 10/10 1563/1563 [==============================] - 14s 9ms/step - loss: 0.5211 - accuracy: 0.8160 - val_loss: 0.9404 - val_accuracy: 0.7065 In [ ]: ​