Running Stateful ML Training Jobs with Mounted Lustre Volumes

With Amazon FSx for Lustre now successfully integrated into your EKS cluster as a PersistentVolume, the real power of this architecture begins to shine. In this part of the tutorial, we’ll run actual machine learning training jobs that use this file system to handle high-throughput data ingestion, persistent checkpoints, and intermediate artifacts. We’ll explore best practices for configuring ML frameworks like TensorFlow and PyTorch to use shared Lustre volumes and cover both single-node and distributed training approaches.