As training jobs become larger, the likelihood of failures such as preemptions, crashes, or infrastructure instability rises. This can lead to significant inefficiencies in training and delays in time-to-market. At these large scales, efficient distributed checkpointing is crucial to mitigate the negative impact of failures and to optimize overall training efficiency (training goodput).
