GKE AI Labs

Use Cases

Blueprints

Tutorials & Notebooks

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Resource Management with SkyPilot

This tutorial expands on the [SkyPilot Tutorial](https://github.com/GoogleCloudPlatform/ai-on-gke/tree/main/tutorials-and-examples/skypilot) by leveraging [Dynamic Workload Scheduler](https://cloud.google.com/blog/products/compute/introducing-dynamic-workload-scheduler) with the help of an open-source project called [Kueue](https://kueue.sigs.k8s.io/)

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.

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.

LangChain Chatbot

In this tutorial, you will learn how to deploy a chatbot application using [LangChain](https://python.langchain.com/) and [Streamlit](https://streamlit.io/) on Google Cloud Platform (GCP).

Evaluation

Ensure the quality and performance of your AI/ML models with GKE's robust evaluation infrastructure. Deploy evaluation services and dashboards to monitor key metrics and track model performance.

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.