Training Large Language Models on Kubernetes

Explore the challenges and best practices of training Large Language Models (LLMs) on Kubernetes in this informative conference talk. Discover how to optimize networking, manage distributed resources, schedule effectively, and manipulate code for LLM training on K8s. Learn about pre-made configurations, data pre-processing workflows, and training setups based on NVIDIA's Megatron Transformer framework to quickly start LLM training on Kubernetes. Compare training throughput between bare metal and K8s-based environments for models like GPT, T5, and BERT across various GPU node configurations. Gain insights into the massive computational requirements of LLMs and how Kubernetes can be leveraged for their training, as opposed to traditional bare metal servers with high-performance computing workload schedulers like Slurm.