AI-Infra-from-Zero-to-Hero
HuaizhengZhang/AI-Infra-from-Zero-to-Hero
🚀 Awesome System for Machine Learning ⚡️ AI System Papers and Industry Practice
Overview
A comprehensive collection of resources, papers, and industry practices related to machine learning systems, large language models (LLMs), and generative AI. The repository is organized into categories such as ML/DL infrastructure, LLM infrastructure, and domain-specific infrastructures, including video tutorials and links to recent conference proceedings.
Categories
Tags
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Install
git clone https://github.com/HuaizhengZhang/AI-Infra-from-Zero-to-HeroREADME
AI System School
💫💫💫 System for Machine Learning, LLM (Large Language Model), GenAI (Generative AI)
Updates:
- Video Tutorials [YouTube] [bilibili] [小红书]
- We are preparing a new website [Lets Go AI] for this repo!!!
Path to System for AI [Whitepaper You Must Read]
A curated list of research in machine learning systems. Link to the code if available is also present. Now we have a team to maintain this project. You are very welcome to pull request by using our template.
System for AI (Ordered by Category)
ML / DL Infra
- Data Processing
- Training System
- Inference System
- Machine Learning Infrastructure
LLM Infra
- LLM Training
- LLM Serving
Domain-Specific Infra
- Video System
- AutoML System
- Edge AI
- GNN System
- Federated Learning System
- Deep Reinforcement Learning System
System for ML/LLM Conference
Conference
- OSDI
- SOSP
- SIGCOMM
- NSDI
- MLSys
- ATC
- Eurosys
- Middleware
- SoCC
- TinyML
General Resources
- Survey
- Book
- Video
- Course
- Blog
Survey
- Toward Highly Available, Intelligent Cloud and ML Systems [Slide]
- A curated list of awesome System Designing articles, videos and resources for distributed computing, AKA Big Data. [GitHub]
- awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning [GitHub]
- Opportunities and Challenges Of Machine Learning Accelerators In Production [Paper]
- Ananthanarayanan, Rajagopal, et al. "
- 2019 {USENIX} Conference on Operational Machine Learning (OpML 19). 2019.
- How (and How Not) to Write a Good Systems Paper [Advice]
- Applied machine learning at Facebook: a datacenter infrastructure perspective [Paper]
- Hazelwood, Kim, et al. (HPCA 2018)
- Infrastructure for Usable Machine Learning: The Stanford DAWN Project
- Bailis, Peter, Kunle Olukotun, Christopher Ré, and Matei Zaharia. (preprint 2017)
- Hidden technical debt in machine learning systems [Paper]
- Sculley, David, et al. (NIPS 2015)
- 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.
- A Berkeley View of Systems Challenges for AI [[Paper]](https://arxiv.org/pdf/