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