Unfortunately, it still does not work on Monterey 12.2.

For me it still occurs on Monterey 12.3.1 with newest versions: tensorflow-metal==0.5.0 tensorflow-macos=2.9.2 For example this code will still always print the same values: import tensorflow as tf class CustomLayer(tf.keras.layers.Layer): def __init__(self, **kwargs): super().__init__(**kwargs) def call(self, x, training): a = tf.random.uniform([]) tf.print(a) return x mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test, y_train, y_test = x_train[:10], x_test[:10], y_train[:10], y_test[:10] model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), CustomLayer(), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10) ]) loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy']) model.fit(x_train, y_train, epochs=1, batch_size=1)