  In Machine Learning (ML) system research, efficient resource scheduling and
utilization have always been an important topic given the compute-intensive
nature of ML applications. In this paper, we introduce the design of TACC, a
full-stack cloud infrastructure that efficiently manages and executes
large-scale machine learning applications in compute clusters. TACC implements
a 4-layer application workflow abstraction through which system optimization
techniques can be dynamically combined and applied to various types of ML
applications. TACC also tailors to the lifecycle of ML applications with an
efficient process of managing, deploying, and scaling ML tasks. TACC's design
simplifies the process of integrating the latest ML system research work into
cloud infrastructures, which we hope will benefit more ML researchers and
promote ML system researches.
