DL Inference and Training Optimization Towards Speed and Scale

Abstract

The application of deep learning models presents significant improvement to many services and products in Microsoft. However, it is challenging to provide efficient computation and memory capabilities for both DNN workload inference and training given that the model size and complexities keep increasing. From the serving aspect, many DL models suffer from long inference latency and high cost, preventing their deployment in production. On the training side, large-scale model training often requires complex refactoring of models and access to prohibitively expensive GPU clusters, which are not always accessible to many practitioners. We want to deliver solid solutions and systems while exploring the cutting-edge techniques to address these issues. In this talk, I will introduce our experience and lessons from designing and implementing optimizations for both DNN serving and training at large scale with remarkable compute and memory efficiency improvement and infrastructure cost reduction.