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.
NVIDIA BioNeMo
Deploying and managing servers dedicated to performing inference tasks for machine learning models.
NVIDIA NeMo
NVIDIA NeMo™ is an end-to-end platform for development of custom generative AI models anywhere. NVIDIA NeMo framework is designed for enterprise development, it utilizes NVIDIA’s state-of-the-art technology to facilitate a complete workflow from automated distributed data processing to training of large-scale bespoke models using sophisticated 3D parallelism techniques, and finally, deployment using retrieval-augmented generation for large-scale inference on an infrastructure of your choice, be it on-premises or in the cloud.
NVIDIA NIMs
These guides explains how to deploy NVIDIA NIM inference microservices on a Google Kubernetes Engine (GKE) cluster
RAG on GKE
This tutorial demonstrates how to deploy a Retrieval Augmented Generation (RAG) application on Google Kubernetes Engine (GKE), integrating a Hugging Face TGI inference server, a Cloud SQL pgvector database, and a Ray cluster for generating vector embeddings. It walks you through setting up the infrastructure with Terraform, populating the vector database with embeddings from a sample dataset using a Jupyter notebook, and launching a frontend chat interface. The guide also covers optional configurations like using your own cluster or VPC, enabling authenticated access via IAP, and troubleshooting common issues.