One common approach to significantly speed up training times and efficiently scale model inference workloads is to deploy GPU-accelerated deep learning microservices to the cloud, enabling flexible, on-demand compute for training and inference tasks. 

This article provides a comprehensive guide covering the setup and optimization of such a microservice architecture. We’ll explore installing CUDA, choosing the right Amazon EC2 instances, and architecting a scalable, GPU-enabled deep learning platform on AWS.