Github项目推荐 | 机器学习系统研究相关资源大列表

Awesome-System-for-Machine-Learning

by HuaizhengZhang

这是在机器学习系统研究的时候整理的列表。如果有代码的话会添加链接。有些比较有趣的论文我也将其进行了整理。

我已经将它们进行了归类。欢迎你提出请求!

Github项目地址:

https://github.com/HuaizhengZhang/Awesome-System-for-Machine-Learning

注:本项目持续更新,更新内容请直接访问本项目查看。

书籍

  • Computer Architecture: A Quantitative Approach [Must read]
  • Streaming Systems [Book]
  • Kubernetes in Action (start to read) [Book]

视频

  • A New Golden Age for Computer Architecture History, Challenges, and Opportunities. David Patterson [YouTube]
  • How to Have a Bad Career. David Patterson (I am a big fan) [YouTube]
  • SysML 18: Perspectives and Challenges. Michael Jordan [YouTube]
  • SysML 18: Systems and Machine Learning Symbiosis. Jeff Dean [YouTube]

课程

  • CS294: AI For Systems and Systems For AI. [UC Berkeley] (Strong Recommendation)
  • CSE 599W: System for ML. [Chen Tianqi] [University of Washington]
  • CSE 291F: Advanced Data Analytics and ML Systems. [UCSD]
  • CSci 8980: Machine Learning in Computer Systems [University of Minnesota, Twin Cities]

调查

  • Hidden technical debt in machine learning systems [Paper]
    • Sculley, David, et al. (NIPS 2015)
    • Summary:
  • End-to-end arguments in system design [Paper]
    • Saltzer, Jerome H., David P. Reed, and David D. Clark.
  • System Design for Large Scale Machine Learning [Thesis]
  • Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications [Paper]
    • Park, Jongsoo, Maxim Naumov, Protonu Basu et al. arXiv 2018
    • Summary: This paper presents a characterizations of DL models and then shows the new design principle of DL hardware.

有用的工具

  • Intel® VTune™ Amplifier [Website]
    • Stop guessing why software is slow. Advanced sampling and profiling techniques quickly analyze your code, isolate issues, and deliver insights for optimizing performance on modern processors
  • NVIDIA DALI [GitHub]
    • A library containing both highly optimized building blocks and an execution engine for data pre-processing in deep learning applications
  • gpushare-scheduler-extender [GitHub]
    • Some of these tasks can be run on the same Nvidia GPU device to increase GPU utilization
  • TensorRT [NVIDIA]
    • It is designed to work in a complementary fashion with training frameworks such as TensorFlow, Caffe, PyTorch, MXNet, etc. It focuses specifically on running an already trained network quickly and efficiently on a GPU for the purpose of generating a result

本文分享自微信公众号 - AI研习社(okweiwu)

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原始发表时间:2019-03-31

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