An Improved Kubernetes Scheduling Algorithm for Deep Learning Platform
Shi Huaxin, Xiaofeng Gu, Ping Kuang, Hongyu Huang · 2020
Most existing deep learning platforms only focus on helping users to start task training quickly, but they tend to ignore the application scenario of multi-team collaboration using one resource pool. In this paper, we propose an improved scheduling algorithm oriented to a multi-tenant model, in which team users are modeled as virtual clusters and cluster load will be monitored regularly. We apply the optimized Kubernetes scheduling algorithm to the Docker-based deep learning platform, our method can ensure the load balance and meet the needs of users.