Distributed computing
As we have seen in figure 5.1, the performance of the neural network improves with an increasing volume of training data. With more and more devices generating data that can potentially be used for training and model generation, the models are getting better at generalizing the stochastic environment and handling complex tasks. However, with more data and more complex structures for the deep neural networks, the computational requirements increase.
Even though we have started leveraging GPUs for deep neural network training, the vertical scaling of the compute infrastructure has its own limitations and cost implications. Leaving the cost implications aside, the time it takes to train a significantly large deep neural network on a large set of training data is not reasonable. However, due to the nature and network topology of the neural networks, it is possible to distribute the computation on multiple machines at the same time and merge the results back with a centralized...
