M1 tensorflow Test Preview

$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/apple/tensorflow_macos/master/scripts/download_and_install.sh)"
Installation script for pre-release tensorflow_macos 0.1alpha1.  Please visit https://github.com/apple/tensorflow_macos 
for instructions and license information.

This script will download tensorflow_macos 0.1alpha1 and needed binary dependencies, then install them into a new 
or existing Python 3.8 virtual enviornoment.
Continue [y/N]? 

Path to new or existing virtual environment [default: /Users/your_id/tensorflow_macos_venv/]:

./install_venv.sh will perform the following operations:
-> Create new python 3.8 virtual environment at /Users/evan/tensorflow_macos_venv.
-> Install tensorflow_macos 0.1a1 into created virtual environment /Users/evan/tensorflow_macos_venv.
-> Install bundled binary wheels for numpy-1.18.5, grpcio-1.33.2, h5py-2.10.0, scipy-1.5.4, tensorflow_addons-0.11.2+mlcompute into /Users/evan/tensorflow_macos_venv.

Confirm[y/N]? 

TensorFlow and TensorFlow Addons with ML Compute for macOS 11.0 successfully installed.
To begin, activate the virtual environment:
   . "/Users/your_id/tensorflow_macos_venv/bin/activate" 

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.
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Successfully installed tensorflow-macos-0.1a1
WARNING: You are using pip version 20.2.4; however, version 20.3.3 is available.
You should consider upgrading via the '/Users/your_id/tensorflow_macos_venv/bin/python3 -m pip install --upgrade pip' command.
.
.
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$ /Users/your_id/tensorflow_macos_venv/bin/python3 -m pip install --upgrade pip

$ . /Users/your_id/tensorflow_macos_venv/bin/activate
(tensorflow_macos_venv) your_name-MacBook-Air-2:blog your_id$ 

import tensorflow as tf
print(tf.__version__)

from tensorflow.python.framework.ops import disable_eager_execution
disable_eager_execution()

from tensorflow.python.compiler.mlcompute import mlcompute
mlcompute.set_mlc_device(device_name='gpu')

import 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()

from tensorflow.python.compiler.mlcompute import mlcompute
mlcompute.set_mlc_device(device_name='gpu')

(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'],
)

start_time = time.time()

model.fit(
    ds_train,
    epochs=12,
    validation_data=ds_test,
)

elapsed_time = time.time() - start_time

print(f"Model Training Time: {elapsed_time}")

# Import mlcompute module to use the optional set_mlc_device API for device selection with ML Compute.
from tensorflow.python.compiler.mlcompute import mlcompute

# Select CPU device.
mlcompute.set_mlc_device(device_name=‘gpu’) # Available options are 'cpu', 'gpu', and ‘any'.

$ python3 m1_tensorflow.py