  Many distributed training techniques like Parameter Server and AllReduce have
been proposed to take advantage of the increasingly large data and rich
features. However, stragglers frequently occur in distributed training due to
resource contention and hardware heterogeneity, which significantly hampers the
training efficiency. Previous works only address part of the stragglers and
could not adaptively solve various stragglers in practice. Additionally, it is
challenging to use a systematic framework to address all stragglers because
different stragglers require diverse data allocation and fault-tolerance
mechanisms. Therefore, this paper proposes a unified distributed training
framework called AntDT (Ant Distributed Training Framework) to adaptively solve
the straggler problems. Firstly, the framework consists of four components,
including the Stateful Dynamic Data Sharding service, Monitor, Controller, and
Agent. These components work collaboratively to efficiently distribute
workloads and provide a range of pre-defined straggler mitigation methods with
fault tolerance, thereby hiding messy details of data allocation and fault
handling. Secondly, the framework provides a high degree of flexibility,
allowing for the customization of straggler mitigation solutions based on the
specific circumstances of the cluster. Leveraging this flexibility, we
introduce two straggler mitigation solutions, namely AntDT-ND for non-dedicated
clusters and AntDT-DD for dedicated clusters, as practical examples to resolve
various types of stragglers at Ant Group. Justified by our comprehensive
experiments and industrial deployment statistics, AntDT outperforms other SOTA
methods more than 3x in terms of training efficiency. Additionally, in Alipay's
homepage recommendation scenario, using AntDT reduces the training duration of
the ranking model from 27.8 hours to just 5.4 hours.
