lensacom/sparkit-learn

[RFC] Integrate with numba / parakeet

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#26 建立於 2015年6月11日

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 (0 留言) (0 反應) (0 負責人)Python (1,144 star) (255 fork)batch import
enhancementhelp wantedrfc

描述

Execution time could benefit from an optimizer like numba.

From numba's documentation: "Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack."

貢獻者指南

技術棧
pythonscikit learnsparknumpy
領域
performancedatamachine learning
議題類型
feature
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
4
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
over 1 week
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
stale
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
unclear
前置要求
numba basicsPython and numpyPySparkscikit learn
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
10
研究方向
Investigate the sparkit learn codebase to identify performance bottlenecks, particularly in core computation functions like parallel predict or fit. Review numba documentation and examples for integrating with numpy and scikit learn. Propose specific functions to accelerate with numba and provide benchmark comparisons to demonstrate improvements.