Blueprints

Jupyter on GKE

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.

NIM on GKE

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.

Ray on GKE

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.

Slurm on GKE

This guide shows you how to deploy Slurm on a Google Kubernetes Engine (GKE) cluster.

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MLFlow

In this tutorial we will fine-tune gemma-2-9b using LoRA as an experiment in MLFlow. We will deploy MLFlow on a GKE cluster and set up MLFlow to store artifacts inside a GCS bucket. In the end, we will deploy a fine-tuned model using KServe.

E2E GenAI

This guide demonstrates deploying an end-to-end Generative AI application on Google Kubernetes Engine (GKE). It utilizes a Hugging Face model with Langchain for prompt engineering, Ray Serve for model inference, a Flask API for the backend, and a React frontend for user interaction. The setup includes infrastructure provisioning with Terraform, model experimentation in Jupyter Notebook, and containerized deployment of the backend and frontend services to GKE, all managed through kubectl.

Orchestration

Streamline your AI/ML workflows with GKE's powerful orchestration capabilities. Manage complex pipelines, schedule jobs, and automate resource allocation.

Cross region capacity chasing with SkyPilot

In this tutorial, we will demonstrate how to leverage the open-source software [SkyPilot](https://skypilot.readthedocs.io/en/latest/docs/index.html) to help GKE customers efficiently obtain accelerators across regions, ensuring workload continuity and optimized resource utilization.

KServe

This tutorial will guide you step by step through the process of installing KServe in a GKE Autopilot cluster.

Ray on GKE

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.