  Training large-scale language models is increasingly critical in various
domains, but it is hindered by frequent failures, leading to significant time
and economic costs. Current failure recovery methods in cloud-based settings
inadequately address the diverse and complex scenarios that arise, focusing
narrowly on erasing downtime for individual tasks without considering the
overall cost impact on a cluster. We introduce Unicron, a workload manager
designed for efficient self-healing in large-scale language model training.
Unicron optimizes the training process by minimizing failure-related costs
across multiple concurrent tasks within a cluster. Its key features include
in-band error detection for real-time error identification without extra
overhead, a dynamic cost-aware plan generation mechanism for optimal
reconfiguration, and an efficient transition strategy to reduce downtime during
state changes. Deployed on a 128-GPU distributed cluster, Unicron demonstrates
up to a 1.9x improvement in training efficiency over state-of-the-art methods,
significantly reducing failure recovery costs and enhancing the reliability of
large-scale language model training.
