Get started with Gemma on Ray on Vertex AI- Google Developers Blog

This post introduces the integration of Gemma, a family of open models, with Ray on Vertex AI for supervised fine-tuning and serving. It details the process of setting up a Ray cluster on Vertex AI using custom Docker images, preparing datasets (XSum), configuring cloud resources (Cloud Storage, Artifact Registry, Vertex AI TensorBoard), and submitting fine-tuning jobs using the Ray Jobs API. The post highlights the use of Ray Train for distributed HuggingFace Transformers training with PyTorch and demonstrates how to monitor training artifacts and progress.