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https://hdl.handle.net/2142/110290
Description
Title
Managing GPU with Kubernetes
Author(s)
Chen, Leihao
Contributor(s)
Xiong, Jinjun
Issue Date
2021-05
Keyword(s)
Cloud
Docker
Kubernetes
Abstract
Machine learning is increasingly being used to solve problems in many domains. This results
in a surge in the need for GPU computation as a common method for training and inferencing
acceleration. Containerization technologies like Docker have made it possible to manage resource
isolation and utilization in a GPU cloud compute server. However, containerization cannot
handle scaling and failover of the running application. Kubernetes is an open-source platform for
automating deployment, scaling, and managing containerized applications. This platform helps us
to run distributed systems more resiliently. This thesis reports on an exploration of the application
of Kubernetes in managing GPU resources for accelerating AI workloads. The study started with
setting up a Kubernetes cluster to manage computation jobs. In application, the cluster is integrated
into RAI, a project-submission system designed as a configurable programming environment for
parallel programming courses.
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