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,
)
I am encountering the same problem with every approach I take. Why can't you all just work in the beta and try to give us an ETA? All you have done is made the repository questions read only. This might just make me switch over to a Windows machine...
See also https://stackoverflow.com/questions/56604348/how-to-install-tensorflow-2-0-on-mac-or-linux.
This covers the efforts of several developers to use several methods to get ML working on a Mac - all have run into dead-ends.