Exploring the Cost-benefit of AWS EC2 GPU Instances for Deep Learning Applications

Deep Learning is a subfield of machine learning methods based on artificial neural networks. Thanks to the increased data availability and computational power, such as Graphic Process Units (GPU), training deep networks - a time-consuming process - became possible. Cloud computing is an excellent option to acquire the computational power to train these models since it provides elastic products with a pay-per-use model. Amazon Web Services (AWS), for instance, has GPU-based virtual machine instances in its catalog, which differentiates themselves by the GPU type, number of GPUs, and price per hour. The challenge consists in determining which instance is better for a specific deep learning problem. This paper presents the implications, in terms of runtime and cost, of running two different deep learning problems on AWS GPU-based instances, and it proposes a methodology, based on the previous study cases, that analyzes instances for deep learning algorithms by using the information provided by the Keras framework. Our experimental results indicate that, despite having a higher price per hour, the instances that contain the NVIDIA V100 GPUs (p3) are faster and usually less expensive to use than the instances that contain the NVIDIA K80 GPUs (p2) for the problems we analyzed. Also, the results indicate that the performance of both applications did not scale well with the number of GPUs and that increasing the batch size to improve scalability may affect the final model accuracy. Finally, the proposed methodology provides accurate cost and estimated runtime for the tested applications on different AWS instances with a small cost.