使用tensorflow model库里的cifar10 多gpu训练时，最后测试发现时间并没有减少，反而更慢
It seems that CPU-side data-preprocessing can be one of the reason that greatly slow down the multi-GPU training, do you try disabling some pre-processing options such as data-augmentation and then see any boost?
Besides, the current version of
multi_gpu_model seems to benefit large NN-models only, such as
Xception, since weights synchronization is not the bottleneck. When it is wrapped to simple model such as
cifar_cnn, weights synchronization is pretty frequent and makes the whole time much slower.