Apple silicon m 1にテンソルストリームを取り付けGPUで加速

ある日、アップルの天書流公式傘下に入り、アーカイブされていることに気づいた.
ここに案内されます.
現在apple siliconのTensorflowではAlphaバージョンではなくTensorflov 2がサポートされています.5から本格的な支援を始める.

https://developer.apple.com/metal/tensorflow-plugin/
公式サイトに入ると、最初からちょっとでたらめです.
なんとmacos 12ということで、今はベストバージョンのオペレーティングシステムをインストールさせていただきます.
念のためにやってみましょう.
番組は公式ホームページで行います.
Download and install Conda env :

chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh
sh ~/Downloads/Miniforge3-MacOSX-arm64.sh
source ~/miniforge3/bin/activate

Install the Tensorflow dependencies:

conda install -c apple tensorflow-deps

Install base tensorflow:

python -m pip install tensorflow-macos

Install metal plugin:

python -m pip install tensorflow-metal

環境を個別に作成してテストします.

conda create --clone base -n tensorflow-test

作成した環境をアクティブにするには、次の手順に従います.

conda activate tensorflow-test

この機能は、データム用のコードを実行するためにインストールされています.

pip install tensorflow_datasets

vscodeまたはコードエディタをテストします.pyを作成し、次のコードを挿入します.
ここは、基準のためのコードをもたらす.

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.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'],
)

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

実行:

python test.py

実行中にアクティブなステータスを表示します.

GPU加速をうまく利用しているのが見えます.
本人は以下のような結果になり、他のシステムと比較しても面白いと思います.

Init Plugin
Init Graph Optimizer
Init Kernel
Metal device set to: Apple M1
2021-07-20 17:32:24.807252: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-07-20 17:32:24.807441: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
2021-07-20 17:32:24.859240: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-07-20 17:32:24.859261: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
2021-07-20 17:32:24.865391: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
2021-07-20 17:32:24.956177: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-07-20 17:32:24.965837: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-07-20 17:32:25.012885: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-07-20 17:32:25.026958: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-07-20 17:32:25.090473: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-07-20 17:32:25.104164: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-07-20 17:32:25.125363: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-07-20 17:32:25.142313: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-07-20 17:32:25.159820: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
Train on 469 steps, validate on 79 steps
2021-07-20 17:32:25.175285: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
Epoch 1/12
2021-07-20 17:32:25.186795: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
468/469 [============================>.] - ETA: 0s - batch: 233.5000 - size: 1.0000 - loss: 0.1561 - accuracy: 0.9540/Users/qone/miniforge3/envs/tensorflow-test/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py:2426: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
  warnings.warn('`Model.state_updates` will be removed in a future version. '
2021-07-20 17:32:35.514217: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.1559 - accuracy: 0.9541 - val_loss: 0.0478 - val_accuracy: 0.9848
Epoch 2/12
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.0437 - accuracy: 0.9866 - val_loss: 0.0416 - val_accuracy: 0.9861
Epoch 3/12
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.0269 - accuracy: 0.9917 - val_loss: 0.0353 - val_accuracy: 0.9878
Epoch 4/12
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.0187 - accuracy: 0.9941 - val_loss: 0.0306 - val_accuracy: 0.9898
Epoch 5/12
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.0133 - accuracy: 0.9958 - val_loss: 0.0389 - val_accuracy: 0.9885
Epoch 6/12
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.0097 - accuracy: 0.9968 - val_loss: 0.0431 - val_accuracy: 0.9876
Epoch 7/12
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.0104 - accuracy: 0.9966 - val_loss: 0.0334 - val_accuracy: 0.9899
Epoch 8/12
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.0047 - accuracy: 0.9984 - val_loss: 0.0359 - val_accuracy: 0.9897
Epoch 9/12
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.0060 - accuracy: 0.9981 - val_loss: 0.0414 - val_accuracy: 0.9890
Epoch 10/12
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.0053 - accuracy: 0.9983 - val_loss: 0.0366 - val_accuracy: 0.9906
Epoch 11/12
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.0042 - accuracy: 0.9987 - val_loss: 0.0415 - val_accuracy: 0.9899
Epoch 12/12
469/469 [==============================] - 11s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.0050 - accuracy: 0.9984 - val_loss: 0.0462 - val_accuracy: 0.9890