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f974d3e841
* docs: split the book into English and Chinese builds * feat: update english version framework * fix: fix ci
129 lines
5.2 KiB
Markdown
129 lines
5.2 KiB
Markdown
<p align="center">
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<img src="static/logo-with-text.png" alt="OpenMLSys Logo" width="400"/>
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</p>
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<p align="center">
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<a href="https://github.com/openmlsys/openmlsys-zh/actions/workflows/main.yml">
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<img src="https://github.com/openmlsys/openmlsys-zh/actions/workflows/main.yml/badge.svg" alt="CI"/>
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</a>
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<a href="https://openmlsys.github.io/">
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<img src="https://img.shields.io/badge/book-online-blue" alt="Book Online"/>
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</a>
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<a href="https://github.com/openmlsys/openmlsys-zh/blob/main/LICENSE">
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<img src="https://img.shields.io/github/license/openmlsys/openmlsys-zh" alt="License"/>
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</a>
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<a href="https://github.com/openmlsys/openmlsys-zh/stargazers">
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<img src="https://img.shields.io/github/stars/openmlsys/openmlsys-zh?style=social" alt="GitHub Stars"/>
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</a>
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</p>
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<p align="center">
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<a href="README.md">中文</a> | <b>English</b>
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</p>
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---
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# Machine Learning Systems: Design and Implementation
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An open-source book explaining the design principles and implementation experience of modern machine learning systems, covering the complete technology stack from programming interfaces and computational graphs to compilers and distributed training.
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**Read Online:** [openmlsys.github.io](https://openmlsys.github.io/)
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## Table of Contents
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- [Target Audience](#target-audience)
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- [Content Overview](#content-overview)
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- [Build Guide](#build-guide)
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- [Contributing](#contributing)
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- [Community](#community)
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- [License](#license)
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## Target Audience
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- **Students**: Those who have mastered machine learning fundamentals and want to deeply understand the design and implementation of modern ML systems.
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- **Researchers**: Those who need to develop custom operators or leverage distributed execution for large model development.
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- **Engineers**: Those responsible for building ML infrastructure and need to tune system performance or customize ML systems for business needs.
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## Content Overview
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The book is organized into three parts: Fundamentals, Advanced Topics, and Extensions.
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### Part I: Fundamentals
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| Chapter | Content |
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|---------|---------|
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| [Programming Interface](chapter_programming_interface/) | Framework API design, ML workflows, deep learning model definition, C/C++ framework development |
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| [Computational Graph](chapter_computational_graph/) | Graph components, generation methods, scheduling strategies, automatic differentiation |
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### Part II: Advanced Topics
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| Chapter | Content |
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|---------|---------|
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| [Compiler Frontend & IR](chapter_frontend_and_ir/) | Type inference, intermediate representation (IR), automatic differentiation, common optimization passes |
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| [Compiler Backend & Runtime](chapter_backend_and_runtime/) | Graph optimization, operator selection, memory allocation, compute scheduling and execution |
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| [Hardware Accelerators](chapter_accelerator/) | GPU/Ascend architecture, high-performance programming interfaces (CUDA/CANN) |
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| [Data Processing](chapter_data_processing/) | Usability, efficiency, order preservation, distributed data processing |
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| [Model Deployment](chapter_model_deployment/) | Model conversion, compression, inference, and security |
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| [Distributed Training](chapter_distributed_training/) | Data parallelism, model parallelism, pipeline parallelism, collective communication, parameter servers |
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### Part III: Extensions
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| Chapter | Content |
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|---------|---------|
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| [Recommender Systems](chapter_recommender_system/) | Recommendation principles, large-scale industrial architecture |
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| [Federated Learning](chapter_federated_learning/) | Federated learning methods, privacy protection, system implementation |
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| [Reinforcement Learning Systems](chapter_reinforcement_learning/) | Single-agent and multi-agent RL systems |
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| [Explainable AI Systems](chapter_explainable_AI/) | XAI methods and production practices |
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| [Robot Learning Systems](chapter_rl_sys/) | Robot perception, planning, control, and system safety |
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## Build Guide
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### Prerequisites
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- Python >= 3.10
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- pandoc >= 2.19
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### Installation
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```bash
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# Clone the repository
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git clone https://github.com/openmlsys/openmlsys-zh.git
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cd openmlsys-zh
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# Install d2lbook
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git clone https://github.com/openmlsys/d2l-book.git
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cd d2l-book && pip install . && cd ..
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# Install Python dependencies
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pip install -r requirements.txt
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```
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### Build HTML
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```bash
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sh build_html.sh
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# Output is in _build/html/
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```
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For more details, see the [Build Guide](info/info.md).
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## Contributing
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We welcome all forms of contributions, including:
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- **Errata**: If you find text or figure errors, please open an Issue and @ the [chapter editors](info/editors.md), or submit a PR directly.
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- **Content updates**: Submit PRs to update or add Markdown files.
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- **New chapters**: We welcome community contributions on topics such as meta-learning systems, automatic parallelism, cluster scheduling, green AI, and graph learning.
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Before contributing, please read:
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- [Writing Style Guide](info/style.md)
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- [Terminology Guide](info/terminology.md)
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## Community
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Join our WeChat group by scanning the QR code in [info/mlsys_group.png](info/mlsys_group.png).
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## License
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This project is licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
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