Using SkyPilot and Kubernetes for multi-node fine-tuning of Llama 3.1

This tutorial provides a comprehensive guide on setting up distributed multi-node fine-tuning of Large Language Models (LLMs) using Managed Kubernetes and SkyPilot, focusing on deploying a Kubernetes cluster optimized for AI training, setting up distributed fine-tuning, and monitoring the training process. It highlights the benefits of using Managed Kubernetes, which simplifies the deployment and scaling of containerized applications, thereby allowing machine learning teams to concentrate on core tasks. SkyPilot, an open-source framework, complements this by abstracting infrastructure complexities and facilitating seamless distributed training across multiple nodes, both on cloud and on-premises clusters. The tutorial details the steps for deploying a Kubernetes cluster using the Nebius Solution Library, configuring it for AI workloads, and setting up the training environment with tools like Torchtune for efficient fine-tuning. It also covers monitoring the training process using Grafana dashboards and Weights & Biases, and optionally transferring the fine-tuned model to Object Storage. The tutorial concludes with optional steps for cleaning up resources and serving the fine-tuned model on Nebius AI Studio for scalable inference.

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