Large-Scale Distributed ML Checkpointing System
Design a highly performant and reliable checkpointing system for a distributed machine learning cluster consisting of 10,000+ GPUs. The system must minimize the time training is paused (blocking time) while ensuring that multi-terabyte model states are durably stored. Address the challenges of massive synchronized I/O, network congestion, and high failure rates in large clusters, and explain how you would handle recovery and model resharding.
NVMeS3etcdgRPCCRC32CmTLSCopy-on-Write
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