Question

A team is building a 16-node GPU cluster for distributed deep learning. Each node must read the same multi-terabyte training dataset and write shared checkpoints during training. The infrastructure architect wants every node to see identical files under a single mount point without manually copying data between nodes. Which storage approach best meets this requirement?

Accepted Answer

A shared parallel/network file system that presents a single global namespace mounted on all 16 nodes

Answer

Independent local NVMe drives on each node with a script that rsyncs the dataset before each job

Answer

A single USB-attached external drive moved between nodes as needed

Answer

Per-node RAM disks populated at boot from each node's boot image