  The paper proposes and optimizes a partial recovery training system, CPR, for
recommendation models. CPR relaxes the consistency requirement by enabling
non-failed nodes to proceed without loading checkpoints when a node fails
during training, improving failure-related overheads. The paper is the first to
the extent of our knowledge to perform a data-driven, in-depth analysis of
applying partial recovery to recommendation models and identified a trade-off
between accuracy and performance. Motivated by the analysis, we present CPR, a
partial recovery training system that can reduce the training time and maintain
the desired level of model accuracy by (1) estimating the benefit of partial
recovery, (2) selecting an appropriate checkpoint saving interval, and (3)
prioritizing to save updates of more frequently accessed parameters. Two
variants of CPR, CPR-MFU and CPR-SSU, reduce the checkpoint-related overhead
from 8.2-8.5% to 0.53-0.68% compared to full recovery, on a configuration
emulating the failure pattern and overhead of a production-scale cluster. While
reducing overhead significantly, CPR achieves model quality on par with the
more expensive full recovery scheme, training the state-of-the-art
recommendation model using Criteo's Ads CTR dataset. Our preliminary results
also suggest that CPR can speed up training on a real production-scale cluster,
without notably degrading the accuracy.
