Hi yuliya808, Thank you very much for your support! It went all well following your guidance. I did not noticed the OS Requirements on Getting Started with tensorflow-metal PluggableDevice. Actually I am running an optimization problem using the Simulated Annealing algorithm. It seems to me that the Python code is handled more efficiently by the OS since I am just using one core and getting the same speed. Best regards, Roberto

Hello yuliya808, Thank you for your feedback. My code breaks the kernel as soon as a CPU intensive function is called. Please, try the following test code that is also killing the kernel: %%time import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds tf.enable_v2_behavior() from tensorflow.python.framework.ops import disable_eager_execution disable_eager_execution() (ds_train, ds_test), ds_info = tfds.load( 'mnist', split=['train', 'test'], shuffle_files=True, as_supervised=True, with_info=True, ) def normalize_img(image, label): """Normalizes images: uint8 -> float32.""" return tf.cast(image, tf.float32) / 255., label batch_size = 128 ds_train = ds_train.map( normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE) ds_train = ds_train.cache() ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples) ds_train = ds_train.batch(batch_size) ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE) ds_test = ds_test.map( normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE) ds_test = ds_test.batch(batch_size) ds_test = ds_test.cache() ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE) model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu'), tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), tf.keras.layers.Dropout(0.25), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile( loss='sparse_categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(0.001), metrics=['accuracy'], ) model.fit( ds_train, epochs=12, validation_data=ds_test, )

Please, I need help to run M1 native Python again! I'd been successfully running M1 native Python code on a MacBook Pro (13-inch, M1, 2020) using Jupyter Notebook, but since 10/13/2021 the notebook kernel dies as soon as the M1 CPU is used intensively. The .py version of the same code aborts too. I have formatted the MacBook several times, followed the instructions on https://developer.apple.com/metal/tensorflow-plugin/ and the problem persists. I am running macOS Big Sur 11.6. As a remedy I am now running the same code on Anaconda (Rosetta) and it is taking 50% more time. We have more than 50 data scientists in our company and I am leading a research on CoreML and the adoption of the new MacBook Pro as a standard platform to our developers. I would appreciate very much any help from Apple support or the developers community. The test code is as follows: import tensorflow as tf print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU'))) %%time import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds tf.enable_v2_behavior() from tensorflow.python.framework.ops import disable_eager_execution disable_eager_execution() (ds_train, ds_test), ds_info = tfds.load( 'mnist', split=['train', 'test'], shuffle_files=True, as_supervised=True, with_info=True, ) def normalize_img(image, label): """Normalizes images: uint8 -> float32.""" return tf.cast(image, tf.float32) / 255., label batch_size = 128 ds_train = ds_train.map( normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE) ds_train = ds_train.cache() ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples) ds_train = ds_train.batch(batch_size) ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE) ds_test = ds_test.map( normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE) ds_test = ds_test.batch(batch_size) ds_test = ds_test.cache() ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE) model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu'), tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation='relu'), tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), tf.keras.layers.Dropout(0.25), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile( loss='sparse_categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(0.001), metrics=['accuracy'], ) model.fit( ds_train, epochs=12, validation_data=ds_test, )