Deploying JupyterHub with Kubernetes
summary
Shows how a production JupyterHub is built on Kubernetes with concrete choices: GKE + Helm chart for quick deploy, alternatives on Azure and DIY on AWS, and a live demo cluster running on Google Cloud. Gives costs and scale: €80/month for three small nodes example, cites Data 8: 1000+ users and a case of ~100k users using multiple Hubs and Postgres for state. Explains hub proxy single-user servers, resource isolation with containers, and autoscaling tradeoffs.
AI-written summary · about
chapters (9)
- 0:00JupyterHub and cloud deployment
- 4:13What JupyterHub is and challenges
- 8:55JupyterHub production challenges
- 10:19Containers for deployment issues
- 13:29Kubernetes for JupyterHub
- 16:36Deploying JupyterHub on cloud
- 20:50Scaling JupyterHub for users
- 22:57Summary
- 23:19Q&A
related talks
37mJupyterHub from the Ground Up with Kubernetes
39mHow We Hosted 200+ Data Scientists in a K8S Cluster
35mLarge Scale Teaching Infrastructure with Kubernetes
26mTerraforming Jupyter and Dask: How to Get Your Own Cloud Data Science Platform on the Cheap
31mBuilding an interactive training environment using JupyterHub
19mOptimizing Resilience and Availability by Migrating from JupyterHub to the...
20mKeynote: Kubernetes at GitHub
32mDemocratizing Distributed Computing with Dask and JupyterHub
