Hi everyone, to round back on this, I was able to run TF 2.18 with Metal 1.2.0 successfully on M3.

Try following the instructions here - https://github.com/ianlokh/TensorFlow-macos

Try to install as per instructions here - https://github.com/ianlokh/TensorFlow-macos

Hi I tried to upload the sample script but somehow wasn't able to. I am copying and pasting the sample script here import os import tensorflow as tf import tensorflow_datasets as tfds BATCH_SIZE = 256 EPOCHS = 100 AUTOTUNE = tf.data.AUTOTUNE def set_gpu(gpu_ids_list): gpus = tf.config.list_physical_devices('GPU') if gpus: try: gpus_used = [gpus[i] for i in gpu_ids_list] tf.config.set_visible_devices(gpus_used, 'GPU') for gpu in gpus_used: tf.config.experimental.set_memory_growth(gpu, True) logical_gpus = tf.config.experimental.list_logical_devices('GPU') print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU") except RuntimeError as e: # Visible devices must be set before GPUs have been initialized print(e) set_gpu([0]) # load MNIST print('\ndownloading mnist') (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): """Normalize images: 'unit8' -> 'float32'.""" return tf.cast(image, tf.float32) / 255., label ds_train = ds_train.map(normalize_img, num_parallel_calls=AUTOTUNE) ds_train = ds_train.cache() ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples) ds_train = ds_train.batch(BATCH_SIZE, num_parallel_calls=AUTOTUNE) ds_train = ds_train.prefetch(AUTOTUNE) ds_test = ds_test.map(normalize_img, num_parallel_calls=AUTOTUNE) ds_test = ds_test.cache() ds_test = ds_test.batch(BATCH_SIZE, num_parallel_calls=AUTOTUNE) print('\ncreate and compile model') model = tf.keras.Sequential([ tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10) ]) model.compile( optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.001), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'] ) model.fit(ds_train, epochs=EPOCHS, validation_data=ds_test, verbose=1)

Hi, Thanks for the reply. I am using Ventura 13.1. Please find the code snippet here. model.compile( optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.001), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'] )