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Zero-I/O Fault Recovery for Sharded Deep Learning via Dynamic Framework Dependency Rebinding
Distributed model training at scale is frequently interrupted by transient network failures, conventionally forcing cluster managers to abort all processes and roll back to the latest checkpoint. While periodic checkpointing provides durability, frequent snapshotting introduces severe storage backpr
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