使用multi_gpu_model即可。观察了一下GPU的利用率，非常的低，大部分时候都是0，估计在相互等待，同步更新模型；
当然了，使用多GPU最明显的好处是可以使用更大的batch size
import tensorflow as tf from keras.applications import Xception from keras.utils import multi_gpu_model import numpy as np num_samples = 1000 height = 224 width = 224 num_classes = 1000
# Instantiate the base model # (here, we do it on CPU, which is optional).
with tf.device('/cpu:0'): model = Xception(weights=None, input_shape=(height, width, 3), classes=num_classes) # Replicates the model on 8 GPUs. # This assumes that your machine has 8 available GPUs.
parallel_model = multi_gpu_model(model, gpus=8) parallel_model.compile(loss='categorical_crossentropy', optimizer='rmsprop') # Generate dummy data.
x = np.random.random((num_samples, height, width, 3)) y = np.random.random((num_samples, num_classes)) # This `fit` call will be distributed on 8 GPUs. # Since the batch size is 256, each GPU will process 32 samples.
parallel_model.fit(x, y, epochs=20, batch_size=256)
https://www.jianshu.com/p/d57595dac5a9
https://keras.io/utils/#multi_gpu_model