中文 | English

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Machine Learning Systems: Design and Implementation

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.

English version 1 (stable): openmlsys.github.io/html-en/

English version 2: Under reconstruction.

Table of Contents

• Target Audience
• Content Overview
• Build Guide
• Contributing
• Community
• License

Target Audience

• Students: Those who have mastered machine learning fundamentals and want to deeply understand the design and implementation of modern ML systems.
• Researchers: Those who need to develop custom operators or leverage distributed execution for large model development.
• Engineers: Those responsible for building ML infrastructure and need to tune system performance or customize ML systems for business needs.

Content Overview

The book is organized into three parts: Fundamentals, Advanced Topics, and Extensions.

Part I: Fundamentals

Chapter	Content
Programming Interface	Framework API design, ML workflows, deep learning model definition, C/C++ framework development
Computational Graph	Graph components, generation methods, scheduling strategies, automatic differentiation

Part II: Advanced Topics

Chapter	Content
Compiler Frontend & IR	Type inference, intermediate representation (IR), automatic differentiation, common optimization passes
Compiler Backend & Runtime	Graph optimization, operator selection, memory allocation, compute scheduling and execution
Hardware Accelerators	GPU/Ascend architecture, high-performance programming interfaces (CUDA/CANN)
Data Processing	Usability, efficiency, order preservation, distributed data processing
Model Deployment	Model conversion, compression, inference, and security
Distributed Training	Data parallelism, model parallelism, pipeline parallelism, collective communication, parameter servers

Part III: Extensions

Chapter	Content
Recommender Systems	Recommendation principles, large-scale industrial architecture
Federated Learning	Federated learning methods, privacy protection, system implementation
Reinforcement Learning Systems	Single-agent and multi-agent RL systems
Explainable AI Systems	XAI methods and production practices
Robot Learning Systems	Robot perception, planning, control, and system safety

Changelog

Date	Event
2022-01	Project initialized; Chinese content writing begins
2022-05	Extension chapters released (Federated Learning, RL Systems, Explainable AI)
2023-05	Codebase adapted to MindSpore 2.0
2026-03	Bilingual (CN/EN) build architecture refactored; English version launched

Build Guide

Prerequisites

• curl
• git

Installation

# Clone the repository
git clone https://github.com/openmlsys/openmlsys-zh.git
cd openmlsys-zh

# Install Rust toolchain (Linux/macOS)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Install mdbook
cargo install mdbook

Build HTML

sh build_mdbook.sh
# Output is in .mdbook/book

For more details, see the Build Guide.

Contributing

We welcome all forms of contributions. For the full workflow, see the Contributing Guide.

Before contributing, please read:

• Writing Style Guide
• Terminology Guide

Community

Join our WeChat group by scanning the QR code in info/mlsys_group.png.

Citation

If this book has been helpful to your research or work, please cite it as:

Plain text:

OpenMLSys Team. Machine Learning Systems: Design and Implementation. 2022. https://openmlsys.github.io/

BibTeX:

@book{openmlsys2022,
  title     = {Machine Learning Systems: Design and Implementation},
  author    = {OpenMLSys Team},
  year      = {2022},
  url       = {https://openmlsys.github.io/},
  note      = {Open-source textbook, \url{https://github.com/openmlsys/openmlsys-zh}}
}

License

This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.