Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding

Abstract

Deep-learning-based recommendation models (DLRMs) are widely deployed to serve personalized content. In addition to using neural networks, DLRMs have large, sparsely-accessed embedding tables, which map categorical features to a learned dense representation. Due to the large sizes of embedding tables, DLRM training is typically distributed across the memory of tens or hundreds of nodes. Node failures are common in such large systems and must be mitigated to enable training to complete within production deadlines. Checkpointing is the primary approach used for fault tolerance in these systems, but incurs significant 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. This calls for rethinking fault tolerance in DLRM training. We present ECRec, a DLRM training system that achieves efficient fault tolerance by coupling erasure coding with the unique characteristics of DLRM training. ECRec takes a hybrid approach between erasure coding and replicating different DLRM parameters, correctly and efficiently updates redundant parameters, and enables training to proceed without pauses, while maintaining the consistency of the recovered parameters. We implement ECRec atop XDL, an open-source, industrial-scale DLRM training system. Compared to checkpointing, ECRec reduces training-time overhead on large DLRMs by up to 66%, recovers from failure up to 9.8× faster, and continues training during recovery with only a 7--13% drop in throughput (whereas checkpointing must pause).