Self-healing ML Training Infrastructure

This post contributes to the field of ML infrastructure by detailing a robust self-healing system for distributed training. It addresses the inevitability of hardware failures in large-scale GPU clusters by integrating Kubernetes for dynamic resource management and observability with Slurm for familiar researcher workflows. The system automates the remediation of hardware failures, enabling seamless resumption of training from checkpoints with minimal human intervention, thus improving uptime and resource utilization.