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Large deep learning models have recently garnered substantial attention from both academia and industry. Nonetheless, frequent failures are observed during large model training due to large-scale resources involved and extended training time. Existing solutions have significant failure recovery costs due to the severe restriction imposed by the bandwidth of remote storage in which they store checkpoints.

This paper presents Gemini, a distributed training system that enables fast failure recovery for large model training by checkpointing to CPU memory of the host machines with much larger aggregated bandwidth. However, two challenges prevent naïvely checkpointing to CPU memory. First, the availability of checkpoints in CPU memory cannot be guaranteed when failures occur. Second, since the communication traffic for training and checkpointing share the same network, checkpoint traffic can interfere with …