GKE AI Labs

Agents

Blueprints

Tutorials & Notebooks

Benchmarking

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ADK VertexAI Example

This tutorial guides you through deploying a containerized agent built with the [Google Agent Development Kit (ADK)](https://google.github.io/adk-docs/) to [Google Kubernetes Engine (GKE)](https://cloud.google.com/kubernetes-engine/docs/concepts/kubernetes-engine-overview). The agent uses [VertexAI](https://cloud.google.com/vertex-ai/docs) to access LLMs. GKE provides a managed environment for deploying, managing, and scaling your containerized applications using Google infrastructure.

N8n with Agent and Tool example

This tutorial will provide instructions on how to deploy and use [n8n](httpshttps://n8n.io/) on GKE (Google Kubernetes Engine) to build and operate AI applications using a low-code/no-code approach.

Flyte

This guide illustrates the deployment of Flyte on Google Kubernetes Engine (GKE) using Helm, utilizing Google Cloud Storage for scalable data storage and Cloud SQL PostgreSQL for a reliable metadata store. By the end of this tutorial, you will have a fully functional Flyte instance on GKE, offering businesses seamless integration with the GCP ecosystem, improved resource efficiency, and cost-effectiveness.

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).

Time slicing of GPUs with DRA

This tutorial guides you through how to do device sharing of NVIDIA GPUs with Dynamic Resource Allocation on Google Kubernetes Engine (GKE) with the time slicing mode

Llamaindex

This tutorial will guide you through creating a robust Retrieval-Augmented Generation (RAG) system using LlamaIndex and deploying it on Google Kubernetes Engine (GKE).