Large Language Models (LLMs) are revolutionizing natural language processing, but their size and complexity can make them challenging to deploy and manage. This talk will provide a beginner-friendly introduction to using Kubernetes for training and serving LLMs.
We'll cover:
The Basics of Kubernetes: This is a quick overview of core Kubernetes concepts (pods, containers, deployments, services) essential for understanding LLM deployment.
LLMs and Resource Demands: This section discusses LLMs' unique computational resource requirements and how Kubernetes helps manage them effectively.
Training LLMs on Kubernetes: Practical guidance on setting up training pipelines, addressing data distribution, and model optimization within a Kubernetes environment.
Serving LLMs for Inference: Walkthroughs of strategies for deploying LLMs as services, load balancing, and scaling to handle real-world traffic.
If you're interested in harnessing the power of LLMs for your projects, this talk will provide a solid foundation for utilizing Kubernetes to streamline your workflow