MLink:将来自多个领域的黑盒模型连接起来,以进行协作推理
概括
本研究介绍了模型链接,以在预算限制下有效运行多个机器学习 (ML) 模型. 通过链接黑盒模型,MLink算法提高了推断准确度并减少了计算,节省了66.7%的计算,同时保持了94%的准确度.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 具有成本效益的模型推断对于现实世界的机器学习 (ML) 应用至关重要,特别是对于延迟敏感的任务和资源有限的设备.
- 为复杂的服务部署多个ML模型往往超过预算限制,例如GPU内存,从而造成了重大挑战.
- 现有的方法在严格的资源预算下努力有效地管理多个ML模型.
研究的目的:
- 提出一个新的学习任务,模型链接,在不同的黑子ML模型之间建立知识桥梁.
- 开发一个调度算法,MLink,使协作多模型推理.
- 在预算限制下提高推断准确度和计算效率.
主要方法:
- 引入了"模型链接",以学习异质黑盒ML模型的输出空间之间的映射.
- 开发了适应和聚合方法,以解决模型链接中的分布差异.
- 设计了MLink调度算法,利用模型链接进行协作推理.
主要成果:
- 在多模式和视频分析数据集中证明了各种黑子ML模型之间的模型链接的有效性.
- MLink实现了显著的成本节约,推断计算减少了66.7%,同时在GPU内存预算下保持了94%的推断准确性.
- MLink的表现优于基线方法,包括多任务学习,基于深度强化学习的调度和过.
结论:
- 模型链接提供了一种有效的方法来连接各种黑子ML模型.
- MLink算法成功地利用模型链接来提高多模型推理场景的成本效益和准确性.
- 这项工作为在资源限制范围内部署复杂的ML应用程序提供了实际解决方案.
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