What is checkpointing strategy in large-scale ML training?

Checkpointing saves model state periodically to recover from failures and enable resumption.

Checkpointing in distributed training involves saving model weights, optimizer state, and training metadata. Efficient checkpointing minimizes I/O overhead and ensures fault tolerance. Advanced strategies include incremental checkpointing and asynchronous saving to distributed storage.