一个端到端的决策森林推断管道的比较
Hong Guan1, Saif Masood1, Mahidhar Dwarampudi1
1Arizona State University.
概括
本研究介绍了决策森林的数据库内推断设计,其性能优于现有的脱和数据库内方法. 这种以关系为中心的方法在大型机器学习模型中表现出卓越的性能.
科学领域:
- 机器学习
- 数据库系统
- 数据管理
背景情况:
- 决策森林 (RandomForest,XGBoost,LightGBM) 在表格数据方面表现出色.
- 现有的推断框架 (ONNX,TreeLite,TF-DF,HummingBird,Nvidia FIL,lleaves) 与数据库脱而出,导致性能开销.
研究的目的:
- 开发和评估决策森林的数据库推断系统.
- 使用DICT模型分析脱和数据库内推断之间的性能差距.
- 在数据库中比较以UDF为中心和以关系为中心的表示.
主要方法:
- 开发了一个DICT模型来量化性能差异.
- 实施和优化了以UDF为中心和以关系为中心的数据库内推断方法.
- 与分离框架和现有的数据库解决方案 (SparkSQL,PostgresML) 进行了全面的基准测试.
主要成果:
- DICT模型准确地反映了业绩差异.
- 在数据库中以关系为中心的表示方法显著优于以UDF为中心的大型模型.
- 拟议的数据库内推断设计在所有评估的基线上都显示出卓越的性能.
结论:
- 对决策森林的数据库内推断提供了显著的性能优势.
- 在数据库中优化大模型推理的关键是以关系为中心的表示.
- 这项工作弥合了机器学习推断和高效数据管理之间的差距.
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