Основні поняття
The AntDT framework provides a unified and self-adaptive approach to efficiently address various types of stragglers in distributed deep learning training, including deterministic, transient, and persistent stragglers, while ensuring data integrity and scalability.
Анотація
The paper proposes the AntDT (Ant Distributed Training) framework, a unified and self-adaptive distributed training framework, to effectively address the straggler problem in industrial-scale distributed deep learning training.
Key highlights:
- The AntDT framework comprises four main components: Stateful Dynamic Data Sharding Service, Monitor, Controller, and Agent. These components work collaboratively to efficiently distribute workloads, provide a range of pre-defined straggler mitigation methods, and handle faults.
- The Stateful Dynamic Data Sharding Service enables agile data allocation and ensures data integrity under various straggler mitigation actions, addressing the challenges of incompatible data allocation strategies in existing approaches.
- The framework provides a high degree of flexibility, allowing users to customize straggler mitigation solutions based on the specific circumstances of the cluster. The paper presents two example solutions, AntDT-ND for non-dedicated clusters and AntDT-DD for dedicated clusters, to handle different types of stragglers.
- Comprehensive experiments and industrial deployment statistics demonstrate the significant efficiency of AntDT, outperforming other state-of-the-art methods by more than 3x in terms of training speed. In a real-world scenario at Ant Group, AntDT reduced the training duration of a ranking model from 27.8 hours to just 5.4 hours.
Статистика
The paper does not provide specific numerical data points to support the key claims. However, it presents the following high-level statistics:
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.
AntDT outperforms other state-of-the-art methods by more than 3x in terms of training efficiency.
Цитати
The paper does not contain any direct quotes that support the key claims.