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