Advanced
Best Practices
Guidelines for selecting, deploying, monitoring, and optimizing distributed file systems for production AI infrastructure.
File System Selection Guide
| Scenario | Recommended | Why |
|---|---|---|
| Large-scale LLM training | Lustre or BeeGFS | Maximum aggregate throughput for multi-node training |
| Enterprise multi-tenant AI | Spectrum Scale | Quotas, ACLs, policy tiering, multi-protocol |
| Cloud-native ML on K8s | Managed NFS or FSx for Lustre | Zero ops, CSI driver integration, auto-scaling |
| On-prem GPU cluster | BeeGFS | Easy to deploy, co-locate with compute, cost-effective |
| Burst training in cloud | FSx for Lustre + S3 | Ephemeral high-performance with durable backing store |
I/O Optimization Checklist
- Shard training data: Split datasets into many files (100+) to enable parallel reads across storage targets.
- Match stripe count to OSTs: Set stripe count equal to number of OSTs for maximum bandwidth on large files.
- Separate metadata workloads: Put logging and small file operations on a different file system or directory than large training data.
- Stagger checkpoint writes: Offset checkpoint timing across processes to avoid write storms that overwhelm storage.
- Use async I/O: Enable asynchronous writes for checkpoints so training can resume before all data is flushed to disk.
- Pre-stage data: Copy training data to the parallel file system before starting the training job to avoid cold-start delays.
Monitoring and Alerting
Throughput Metrics
Monitor aggregate read/write throughput per storage target. Alert when throughput drops below expected baseline during training.
Latency Metrics
Track metadata operation latency (open, stat, readdir). High metadata latency causes training jobs to stall between epochs.
Capacity Planning
Track storage utilization trends. AI datasets and checkpoints grow rapidly. Plan capacity 6-12 months ahead based on project roadmaps.
Congratulations! You have completed the Distributed File Systems for AI course. Continue your learning with the Serverless AI Inference course to explore running AI models without managing infrastructure.