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.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
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,
)
