GPU as a Service Over K8s: Drive Productivity and Increase Utilization
Building machine learning applications is hard. Surprisingly enough, its not the data science thats hard, but all the operations around it. GPUs accelerate performance, but pose challenges such as G …
| Talk Title | GPU as a Service Over K8s: Drive Productivity and Increase Utilization |
| Speakers | Yaron Haviv (CTO, Iguazio) |
| Conference | KubeCon + CloudNativeCon North America |
| Conf Tag | |
| Location | San Diego, CA, USA |
| Date | Nov 15-21, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Building machine learning applications is hard. Surprisingly enough, it’s not the data science that’s hard, but all the operations around it. GPUs accelerate performance, but pose challenges such as GPU resource sharing, software dependencies and data bottlenecks. In a cloud-native era, data scientists are looking for a GPU-powered machine learning PaaS like AWS Sagemaker or Google AI, only based on open source technologies, without vendor lock-ins and/or on-premises. Yaron Haviv will demonstrate how to integrate Kubernetes, KubeFlow, high-speed data layers and GPU-powered servers to build self-service machine learning platforms. He will show how GPU resources can be pooled to maximize utilization and increase scalability, how to use RAPIDS for 10x faster data processing and how to integrate GPUs with the rest of the machine learning stack.
