Treat recovery as a new worker generation, then compare spare replacement with a legal shrink and its batch consequences.
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Design a training system that survives losing a node without a human in the loop. What does elasticity cost you?
Treat recovery as a new worker generation, then compare spare replacement with a legal shrink and its batch consequences.
Updated Oct 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.
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Advanced
Elastic and Fault-Tolerant TrainingRecover the intended training run after a failure: fence old workers, restore a supported layout, reject stale checkpoint uploads and measure progress that survives. Includes a generation-check example and replay budget.🕸️ Distributed Training🔒 Premium
Advanced
Incident Response for GPU FleetsGPU incident response restores useful service while preserving enough evidence to diagnose the failure. Mitigation, repair and return-to-service checks have different purposes.🩺 Fleet Reliability & Observability🔒 Premium
Foundational
Data Parallelism and DDPReplicas combine gradients from different data. Follow a two-worker update, fix unequal-token weighting, and see why queued gradient buckets can leave more than one reduction exposed.🕸️ Distributed Training
Core
ZeRO and FSDPZeRO and FSDP reduce replicated training state by distributing it across workers. The memory saving comes with communication and temporary-buffer costs.🕸️ Distributed TrainingSign in
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FEDITOR'S NOTE
Explain how a new worker generation takes ownership after failure. Compare replacement with a supported smaller layout and recompute the effective batch.
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