Storage along the training timeline
Budget for input reads, checkpoint writes, and restart traffic.
help for the full list, or solutions for reference procedures. Simulated outputs do not validate your environment.Storage demand changes during a run. A system sized only for steady-state input reads can stall when several ranks save state or when a replacement allocation restores checkpoints.
Measure three workloads
| Phase | Measure | Frequent constraint |
|---|---|---|
| Input | Read bytes/s, metadata latency, batch wait | Small files, decode cost, cold cache |
| Save | Durable bytes, save duration, exposed pause | Shared write capacity, serialization |
| Restore | Time until useful steps resume | Parallel reads, metadata, initialization |
Document the client path too: local cache, filesystem client, network, and storage service. Aggregate backend bandwidth is useful only if clients can reach it efficiently.
Calculate headroom with compatible measurements
Assume a shared service sustains 40 GB/s for the tested workload mix. Input reads need 14 GB/s and concurrent saves demand 32 GB/s. The combined 46 GB/s demand exceeds that measured budget by 6 GB/s.
This simplified budget is useful only when the read and write measurements refer to the same shared bottleneck. Real storage systems can have different read and write limits, metadata constraints, and full-duplex network paths.
Make the layout part of the test
Compare representative file sizes, worker counts, and access patterns. A large sequential benchmark does not predict the behavior of millions of small samples. Record cache state and dataset locality so repeated tests remain comparable.
Choose storage by the workload and recovery requirements. Check consistency, durability, client support, metadata behavior, and operational constraints instead of assuming one product wins every training pattern.
See checkpointing, Weka architecture, and NFS for GPU workloads.