Distributed Machine Learning with Python

Chapter 1: Splitting Input Data

Over the recent years, data has grown drastically in size. For instance, if you take the computer vision domain as an example, datasets such as MNIST and CIFAR-10/100 consist of only 50k training images each, whereas recent datasets such as ImageNet-1k contain over 1 million training images. However, having a larger input data size leads to a much longer model training time on a single GPU/node. In the example mentioned previously, the total training time of a useable state-of-the-art single GPU training model on a CIFAR-10/100 dataset only takes a couple of hours. However, when it comes to the ImageNet-1K dataset, the training time for a GPU model will take days or even weeks.

The standard practice for speeding ...