This project, Multi-Tenant GPU Cluster, provides a detailed guide on setting up a Kubernetes-based GPU cluster with JupyterHub, aimed at facilitating efficient resource sharing across multiple users.
- Kubernetes Cluster Setup: Step-by-step setup of Kubernetes on multi-GPU servers.
- JupyterHub Integration: Configures JupyterHub to enable multi-user access with dedicated resource allocation for each user.
- GPU Resource Management: Uses Kubernetes and Helm configurations to ensure effective multi-tenant utilization of GPUs.
- Networking & Security: Ensures network configurations and secure access via Kubernetes Dashboard and JupyterHub authentication.
- Prerequisites: Linux system (Ubuntu 20.04+), Docker, kubectl, kubeadm, and Helm installations.
- Deployment Steps: Follow the guide to initialize the Kubernetes cluster, install network plugins, and deploy JupyterHub.
- Includes solutions for common deployment issues, such as pod scheduling and networking, with commands to diagnose and resolve them.
- Kubernetes Dashboard: Access via port-forwarding for cluster management.
- JupyterHub: Configure and launch JupyterHub instances with specified resource limits for each user.
For full details on each step, refer to the documentation. This setup is tailored to efficiently manage and scale GPU resources for multi-user environments, supporting research, data processing, and other intensive computational tasks.