Why and How to Use Multiple GPUs for Distributed Training

Data Scientists or Machine Learning enthusiasts training AI models at scale will inevitably reach a cap. When the datasets size increases, the processing time can increase from minutes to hours to days to weeks! Data scientists turn to the inclusion of multiple GPUs along with distributed training for machine learning models to accelerate and develop complete AI models in a fraction of the time.
We will discuss the usefulness of GPUs versus CPUs for machine learning, why distributed training with multiple GPUs is optimal for larger datasets, and how to get started training machine learning models using the best practices.

Why Are GPUs Good for Training Neural Networks?

The training phase is the most resource-intensive part of building a neural network or machine learning model. A neural network requires data inputs during the training phase. The model outputs a relevant prediction based on processed data in layers based on changes made between datasets. The first round of input data essentially forms a baseline for the machine learning model to understand; subsequent datasets calculate weights and parameters to train machine prediction accuracy.