keras - 无论如何要在带有 AMD GPU 的 Mac 中使用 Keras？

Question

我有一台配备 AMD 处理器的 MacBook Pro，我想在这个 GPU 中运行 Keras(Tensorflow 后端)。我开始知道 Keras 只适用于 NVIDIA GPU。解决方法是什么(如果可能)？

Answer 1

您可以 OpenCL 库来克服这一点。我已经对其进行了测试，它对我来说工作正常。

注意:我有 python 3.7 版，我将使用 pip3 进行软件包安装。

步骤:

使用以下命令安装 OpenCL 包

pip3 install pyopencl

安装 PlaidML使用以下命令的库

pip3 install pip install plaidml-keras

运行 的安装程序PlaidML .设置时，您可能会收到选择 GPU 的提示。如果设置正确，您将在最后收到一条成功消息。

plaidml-setup

安装 格子凳在您的 GPU 上测试 plaidml。

pip3 install plaidbench

测试一下。如果一切顺利，您将获得基准分数。

plaidbench keras mobilenet

现在我们必须设置一个环境路径。把它放在你的代码的顶部。

import os
os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"

os.environ["RUNFILES_DIR"] = "/Library/Frameworks/Python.framework/Versions/3.7/share/plaidml"
# plaidml might exist in different location. Look for "/usr/local/share/plaidml" and replace in above path

os.environ["PLAIDML_NATIVE_PATH"] = "/Library/Frameworks/Python.framework/Versions/3.7/lib/libplaidml.dylib"
# libplaidml.dylib might exist in different location. Look for "/usr/local/lib/libplaidml.dylib" and replace in above path

在实际代码中测试 .使用 keras而不是 tensorflow.keras在您的代码中并运行以下命令。 (keras 安装在在 GPU 中运行的第 2 步)

import os

# IMPORTANT: PATH MIGHT BE DIFFERENT. SEE STEP 6
os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
os.environ["RUNFILES_DIR"] = "/Library/Frameworks/Python.framework/Versions/3.7/share/plaidml"
os.environ["PLAIDML_NATIVE_PATH"] = "/Library/Frameworks/Python.framework/Versions/3.7/lib/libplaidml.dylib"

# Don't use tensorflow.keras anywhere, instead use keras
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
batch_size = 128
num_classes = 10
epochs = 12
# input image dimensions
img_rows, img_cols = 28, 28
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

当你运行这个你会得到

Using plaidml.keras.backend backend.
INFO:plaidml:Opening device "metal_intel(r)_iris(tm)_graphics_6100.0"
# or whatever GPU you selected in step 3

这确认您正在 GPU 中运行它。

引用: https://towardsdatascience.com/gpu-accelerated-machine-learning-on-macos-48d53ef1b545