Target-based Resource Allocation for Deep Learning Applications in a Multi-tenancy System

The neural-network based deep learning is the key technology that enables many powerful applications, which include self-driving vehicles, computer vision, and natural language processing. Although various algorithms focus on different directions, generally, they mainly employ an iteration by iteration training and evaluating the process. Each iteration aims to find a parameter set, which minimizes a loss function defined by the learning model. When completing the training process, the global minimum is achieved with a set of optimized parameters. At this stage, deep learning applications can be shipped with a trained model to provide services. While deep learning applications are reshaping our daily life, obtaining a good learning model is an expensive task. Training deep learning models is, usually, time-consuming and requires lots of resources, e.g. CPU and GPU. In a multi-tenancy system, however, limited resources are shared by multiple clients that lead to severe resource contention. Therefore, a carefully designed resource management scheme is required to improve the overall performance. In this project, we propose a target based scheduling scheme named TRADL. In TRADL, developers have options to specify a two-tier target. If the accuracy of the model reaches a target, it can be delivered to clients while the training is still going on to continue improving the quality. The experiments show that TRADL is able to significantly reduce the time cost, as much as 48.2%, for reaching the target.