エンドツーエンドの森林推論パイプラインの比較
Hong Guan1, Saif Masood1, Mahidhar Dwarampudi1
1Arizona State University.
まとめ
この研究は,既存のデコップされた方法とデータベース内の方法よりも優れた意思決定森林のためのデータベース内推論設計を導入します. 大規模な機械学習モデルでは,関係中心のアプローチは優れたパフォーマンスを示しています.
科学分野:
- 機械学習
- データベースシステム
- データ管理
背景:
- 決定森林 (RandomForest,XGBoost,LightGBM) は表型データで優れている.
- 既存の推論フレームワーク (ONNX,TreeLite,TF-DF,HummingBird,Nvidia FIL,lleaves) はデータベースから切り離され,パフォーマンスオーバーヘッドが発生します.
研究 の 目的:
- 意思決定森林のデータベース内推論システムを開発し,評価する.
- DICTモデルを使用して,デコップされたとデータベース内の推論の間のパフォーマンスのギャップを分析する.
- UDF中心と関係中心のデータベース内表現を比較する.
主な方法:
- 性能の違いを定量化するためのDICTモデルを開発しました.
- UDF中心と関係中心のデータベース内推論アプローチの両方を実装し,最適化しました.
- 分離されたフレームワークと既存のデータベース内ソリューション (SparkSQL,PostgresML) に対して包括的なベンチテストを実施しました.
主要な成果:
- DICTモデルは,パフォーマンスの格差を正確に反映しています.
- 大型モデルでは,関係中心のデータベース内表示が,UDF中心のアプローチを大幅に上回ります.
- 提案されたデータベース内推論設計は,評価されたすべてのベースラインよりも優れたパフォーマンスを示しています.
結論:
- 意思決定フォレストのデータベース内推論は,重要なパフォーマンスの利点を提供します.
- データベース内の大規模なモデル推論を最適化するために,関係中心の表現は鍵となる.
- この研究は,機械学習の推論と効率的なデータ管理の間のギャップを埋めます.
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