Ferret让在线持续学习在不同内存下都高效准确。
Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints
- 用细粒度流水线并行+梯度补偿，降低延迟，解决并行训练梯度滞后问题。
- 在20个基准上，相同准确率下内存开销比现有方法低3.7倍。
- 自动模型分片与流水线规划，适应各种内存限制，适合实时系统部署。
在高频数据流场景中，实现在变化内存约束下的实时学习至关重要。本文提出Ferret，一个全面的框架，旨在提升在线持续学习（OCL）算法的在线精度，并动态适应不同的内存预算。Ferret采用细粒度流水线并行策略结合迭代梯度补偿算法，确保对高频数据的低延迟处理，有效缓解并行训练中的梯度过时问题。为适应不同内存预算，其自动模型分片与流水线规划机制可在各类内存限制下优化性能。在20个基准和5种集成OCL算法上的大量实验表明，Ferret表现出卓越效率：达到相同在线准确率时，内存开销最高降低3.7倍。此外，Ferret在多种内存预算下均优于对比方法，展现出更强的适应性。这些成果使其成为实时环境中高效自适应OCL框架的优选方案。
原文摘要 · Abstract (English)
In the realm of high-frequency data streams, achieving real-time learning within varying memory constraints is paramount. This paper presents Ferret, a comprehensive framework designed to enhance online accuracy of Online Continual Learning (OCL) algorithms while dynamically adapting to varying memory budgets. Ferret employs a fine-grained pipeline parallelism strategy combined with an iterative gradient compensation algorithm, ensuring seamless handling of high-frequency data with minimal latency, and effectively counteracting the challenge of stale gradients in parallel training. To adapt to varying memory budgets, its automated model partitioning and pipeline planning optimizes performance regardless of memory limitations. Extensive experiments across 20 benchmarks and 5 integrated OCL algorithms show Ferret's remarkable efficiency, achieving up to 3.7$\times$ lower memory overhead to reach the same online accuracy compared to competing methods. Furthermore, Ferret consistently outperforms these methods across diverse memory budgets, underscoring its superior adaptability. These findings position Ferret as a premier solution for efficient and adaptive OCL framework in real-time environments.
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