This section of the chapter will introduce some extremely aggressive deep CNN architecture, associated challenges for these networks, and the need of much larger distributed computing to overcome this. This section will explain how Hadoop and its YARN can provide a sufficient solution for this problem.
CNNs have shown stunning results in image recognition in recent years. However, unfortunately, they are extremely expensive to train. In the case of a sequential training process, the convolution operation takes around 95% of the total running time. With big datasets, even with low-scale distributed training, the training process takes many days to complete. The award winning CNN, AlexNet with ImageNet in 2012, took nearly an entire week to train on with two GTX 580 3 GB GPUs. The following table displays few of the most popular distributed deep CNNs with their configuration and corresponding time taken...