어느날 애플의 텐서플로 공식 깃헙에 들어가보니 archived 되어있었고
이곳 으로 안내된다.
이제 apple silicon용 텐서플로가 알파 버전으로 지원되는것이 아니라 텐서플로 v2.5부터 공식 지원된다.
https://developer.apple.com/metal/tensorflow-plugin/
공식 페이지에 들어가보면 초장부터 조금 황당한 소리를 하고 있다.
macOS 12 라니 지금 현재는 베타버전인 OS를 설치하라고 한다.
그래도 혹시 모르니 한번 해보도록 한다.
프로시져는 공식 페이지의 것을 그대로 따라하도록 한다.
Download and install Conda env:
chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh
sh ~/Downloads/Miniforge3-MacOSX-arm64.sh
source ~/miniforge3/bin/activateInstall the Tensorflow dependencies:
conda install -c apple tensorflow-depsInstall base tensorflow:
python -m pip install tensorflow-macosInstall metal plugin:
python -m pip install tensorflow-metal이제 따로 환경을 만들어서 테스트 해본다:
conda create --clone base -n tensorflow-test만든 환경을 활성화 하고:
conda activate tensorflow-test벤치마크용 코드를 실행하기 위해 이것도 설치한다:
pip install tensorflow_datasetsvscode던 아무 코드에디터로 test.py를 만들고 다음 코드를 넣는다:
이곳 에서 벤치마크용으로 쓰이던 코드를 가져왔다.
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,
)실행한다:
python test.py실행되는 동안 활성상태를 보면
GPU가속을 잘 활용하는것을 볼 수 있다.
본인은 다음과 같이 결과가 나왔는데, 다른 시스템하고 비교해보는것도 재밌을 것이다.
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
8개의 댓글
2021년 10월 9일
MAC OS 12.0+ 로 설치하신건가요?
11.6에서는 libc++abi: terminating with uncaught exception of type NSException 에러가 발생하고, 타 커뮤니티에서 12.0+ 로 설치하면 해결된다고는 하는데, 선뜻 12.0+로 올리기가 쉽지는 않네요.
1개의 답글
2021년 10월 19일
재밌는글 잘 읽었습니다. 같은 코드로, i5-8400(6코어), GTX10060 6GB 로 tf-gpu로 실행시, GPU 이용률이 74%, 8ms/step(469steps), 에폭당 4s (8ms*469 = 3752ms) 나오네요. M1 GPU의 딥러닝 성능은 1060의 약 36%(8ms/22ms)로 계산되네요. 오늘 발표한 M1 프로랑 M1 맥스에서도 어떨지 궁금합니다.! 참고로 1060 6GB에서는 batch_size 8192 까지 되네요.(마지노인듯..) M1은 통합 램을 쓴다는데, 어디까지 되는지 확인부탁드려도 될까요?
1개의 답글
저이거 오류가 뜨는데 혹시 하는 방법 좀 알 수 있을까요?
conda install -c apple tensorflow-deps 를 터미널에 설치를 하면
Command errored out with exit status 1: /Users/woonbaecho/miniforge3/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/private/var/folders/rg/4hcnk0k95_l1nbjnhbnb3mlr0000gn/T/pip-install-o7nf6vnr/grpcio_3fa5571b205244afb1a79fc92867d4fc/setup.py'"'"'; file='"'"'/private/var/folders/rg/4hcnk0k95_l1nbjnhbnb3mlr0000gn/T/pip-install-o7nf6vnr/grpcio_3fa5571b205244afb1a79fc92867d4fc/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(file) if os.path.exists(file) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' install --record /private/var/folders/rg/4hcnk0k95_l1nbjnhbnb3mlr0000gn/T/pip-record-__2uc4v9/install-record.txt --single-version-externally-managed --compile --install-headers /Users/woonbaecho/miniforge3/include/python3.9/grpcio Check the logs for full command output.
이라는 오류가 발생합니다.