FluxML/FastAI.jl

Benchmark image pipelines against ffcv

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#236 创建于 2022年6月10日

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 (0 评论) (1 反应) (0 负责人)Julia (592 star) (49 fork)batch import
help wantedperformance

描述

[https://github.com/libffcv/ffcv] seems to be the state-of-the-art when it comes to fast image pipelines for training deep learning models on computer vision. Would be interesting to benchmark against this and see how we compare.

Results could motivate work on #235.

贡献者指南

技术栈
无
领域
machine learningperformance
议题类型
performance
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
1-2 days
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
stale
清晰度议题是否清楚说明期望改动、验收标准和下一步。
needs investigation
前置要求
Julia programmingFlux.jl familiarityBenchmarking concepts
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
30
研究方向
The issue proposes benchmarking FastAI.jl image pipelines against ffcv. First, understand the current image pipeline implementation in FastAI.jl (likely in src/). Then, replicate ffcv's optimized pipeline in Julia or compare using similar datasets. The results could inform improvements mentioned in issue #235. Look at ffcv's documentation and performance benchmarks to design a fair comparison.