This guide details how to deploy JupyterHub on Google Kubernetes Engine (GKE) using a provided Terraform template, including options for persistent storage and Identity-Aware Proxy (IAP) for secure access. It covers the necessary prerequisites, configuration steps, and installation process, emphasizing the use of Terraform for automation and IAP for authentication. The guide also provides instructions for accessing JupyterHub, setting up user access, and running an example notebook.
This guide explains how to deploy NVIDIA NIM inference microservices on a Google Kubernetes Engine (GKE) cluster, requiring an NVIDIA AI Enterprise License for access to the models. It details the process of setting up a GKE cluster with GPU-enabled nodes, configuring access to the NVIDIA NGC registry, and deploying a NIM using a Helm chart with persistent storage. Finally, it demonstrates how to test the deployed NIM service by sending a sample prompt and verifying the response, ensuring the inference microservice is functioning correctly.
This guide provides instructions and examples for deploying and managing Ray clusters on Google Kubernetes Engine (GKE) using KubeRay and Terraform. It covers setting up a GKE cluster, deploying a Ray cluster, submitting Ray jobs, and using the Ray Client for interactive sessions. The guide also points to various resources, including tutorials, best practices, and examples for running different types of Ray applications on GKE, such as serving LLMs, using TPUs, and integrating with GCS.