  Deep-learning-based recommendation models (DLRMs) are widely deployed to
serve personalized content to users. DLRMs are large in size due to their use
of large embedding tables, and are trained by distributing the model across the
memory of tens or hundreds of servers. Server failures are common in such large
distributed systems and must be mitigated to enable training to progress.
Checkpointing is the primary approach used for fault tolerance in these
systems, but incurs significant training-time overhead both during normal
operation and when recovering from failures. As these overheads increase with
DLRM size, checkpointing is slated to become an even larger overhead for future
DLRMs, which are expected to grow in size. This calls for rethinking fault
tolerance in DLRM training.
  We present ECRM, a DLRM training system that achieves efficient fault
tolerance using erasure coding. ECRM chooses which DLRM parameters to encode,
correctly and efficiently updates parities, and enables training to proceed
without any pauses, while maintaining consistency of the recovered parameters.
We implement ECRM atop XDL, an open-source, industrial-scale DLRM training
system. Compared to checkpointing, ECRM reduces training-time overhead for
large DLRMs by up to 88%, recovers from failures up to 10.3$\times$ faster, and
allows training to proceed during recovery. These results show the promise of
erasure coding in imparting efficient fault tolerance to training current and
future DLRMs.
