Kubeflow: Multi-Tenant, Self-Serve, Accelerated Platform for Practitioners
The kubeflow platform provides a self-serve multi-tenant platform on k8s for ML developers. Users can train their models using accelerated hardware in an isolated environment. Jobs can be configured a …
| Talk Title | Kubeflow: Multi-Tenant, Self-Serve, Accelerated Platform for Practitioners |
| Speakers | Kunming Qu (Software Engineer, Google), Kam Kasravi (Senior Software Engineer, Intel) |
| Conference | KubeCon + CloudNativeCon North America |
| Conf Tag | |
| Location | San Diego, CA, USA |
| Date | Nov 15-21, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
The kubeflow platform provides a self-serve multi-tenant platform on k8s for ML developers. Users can train their models using accelerated hardware in an isolated environment. Jobs can be configured and triggered from a notebook with no devops involvement. We leverage optimized libraries such as Intel® DAAL, Intel® MKL-DNN now included in tensorflow 1.14.+. Models can be monitored using Application CR deployed with kubeflow. All attendees can join the demo, create their own workspace and try out features. Attendees will walk away understanding how to run multi-tenancy on Kubernetes with kubeflow.Highlights:Self-serve multi-tenant workplaceWorkspace owners can share / revoke accessSystem admin can reset access policy & resource quota per workspaceMulti-tenancy service is transparent to other apps.A UI is available to simplify user experience.
