Important
🚀 Dynamic Landscape 🚀: The field of AI training is experiencing continuous, rapid evolution. This document is regularly updated to reflect the latest products, features, and architectural patterns, ensuring it remains current with the advancements in AI, Google Cloud and Google Kubernetes Engine.
Last Update: 2025-12-11 (YYYY-MM-DD)
This document outlines the reference architecture for deploying and managing training workloads, particularly on Google Kubernetes Engine (GKE). It serves as a foundational guide for building robust and scalable training solutions. This implementation is an extension of the GKE Base Platform tailored for training workloads.
Refer to the Getting Started section below for instructions on setting up the infrastructure described in this document.
A practical guide to setting up the infrastructure as described can be found in the Training reference implementation
This reference architecture is designed to support various training patterns. Some example patterns provided are:
Further use cases and patterns can be built upon this foundational architecture.