A Data-Centric Approach for Analyzing Large-Scale Deep Learning Applications

Over the past two decades, deep learning techniques have emerged as an immensely powerful technology, with break-through in computer vision, speech to text technologies, natural language processing and many more such fields. As neural networks grow in size and capacity, they require higher compute power and larger datasets to converge to a model with higher accuracy. In this poster, we present a data-centric approach to studying the system level requirements (GPU utilization, CPU utilization, I/O) of deep learning training workloads, and uncover a few insights that help us understand the nature of deep learning training workloads. We analyse three datasets found in industry, academia and national laboratories to understand the requirements and properties of deep learning training workloads.