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Table Of Contents
1. Preface
2. Introduction
2.1. Machine Learning Applications
2.2. Design Objectives of Machine Learning Frameworks
2.3. Machine Learning Framework Architecture
2.4. Application Scenarios of Machine Learning Systems
2.5. Book Organization and Intended Audience
3. Part I Framework Design
4. Programming Model
4.1. Overview
4.2. Machine Learning Workflow
4.3. Neural Network Programming
4.4. Functional Programming
4.5. Bridging Python and C/C++ Functions
4.6. Chapter Summary
5. AI Compiler Frontend
5.1. Overview of AI Compilers
5.2. Overview of AI Compiler Frontends
5.3. Intermediate Representation
5.4. Automatic Differentiation
5.5. Type Systems and Static Analysis
5.6. Frontend Compilation Optimization
5.7. Chapter Summary
5.8. Further Reading
6. AI Compiler Backend
6.1. Overview
6.2. Graph Optimization
6.3. Operator Selection
6.4. Memory Allocation
6.5. Operator Compiler
6.6. Chapter Summary
6.7. Further Reading
7. Hardware Accelerator
7.1. Overview
7.2. Components of Hardware Accelerators
7.3. Programming Methods
7.4. Performance Optimization Methods
7.5. Chapter Summary
8. Distributed Training
8.1. Overview
8.2. Parallelism Methods
8.3. Pipeline Parallelism with Micro-Batching
8.4. Architecture of Machine Learning Clusters
8.5. Collective Communication
8.6. Parameter Server
8.7. Federated Learning
8.8. Training Large Language Models
8.9. Chapter Summary
8.10. Further Reading
9. Model Deployment
9.1. Overview
9.2. Conversion to Inference Model and Model Optimization
9.3. Model Compression
9.4. Advanced Efficient Techniques
9.5. Model Inference
9.6. Security Protection of Models
9.7. Chapter Summary
9.8. Further Reading
10. Part II Application Scenarios
11. Recommender System
11.1. Overview
11.2. System Components
11.3. Recommendation Pipeline
11.4. Model Update
11.5. Supporting Real-time Machine Learning
11.6. Chapter Summary
11.7. Further Reading
12. Reinforcement Learning System
12.1. Overview
12.2. Introduction to Reinforcement Learning
12.3. Single-Node Reinforcement Learning System
12.4. Distributed Reinforcement Learning System
12.5. Multi-agent Reinforcement Learning
12.6. Multi-agent Reinforcement Learning System
12.7. Chapter Summary
13. Robotic System
13.1. Overview of Robotic Systems
13.2. Robot Operating System
13.3. Case Study: Using ROS
13.4. Modern Robot Learning
13.5. Chapter Summary
Table Of Contents
1. Preface
2. Introduction
2.1. Machine Learning Applications
2.2. Design Objectives of Machine Learning Frameworks
2.3. Machine Learning Framework Architecture
2.4. Application Scenarios of Machine Learning Systems
2.5. Book Organization and Intended Audience
3. Part I Framework Design
4. Programming Model
4.1. Overview
4.2. Machine Learning Workflow
4.3. Neural Network Programming
4.4. Functional Programming
4.5. Bridging Python and C/C++ Functions
4.6. Chapter Summary
5. AI Compiler Frontend
5.1. Overview of AI Compilers
5.2. Overview of AI Compiler Frontends
5.3. Intermediate Representation
5.4. Automatic Differentiation
5.5. Type Systems and Static Analysis
5.6. Frontend Compilation Optimization
5.7. Chapter Summary
5.8. Further Reading
6. AI Compiler Backend
6.1. Overview
6.2. Graph Optimization
6.3. Operator Selection
6.4. Memory Allocation
6.5. Operator Compiler
6.6. Chapter Summary
6.7. Further Reading
7. Hardware Accelerator
7.1. Overview
7.2. Components of Hardware Accelerators
7.3. Programming Methods
7.4. Performance Optimization Methods
7.5. Chapter Summary
8. Distributed Training
8.1. Overview
8.2. Parallelism Methods
8.3. Pipeline Parallelism with Micro-Batching
8.4. Architecture of Machine Learning Clusters
8.5. Collective Communication
8.6. Parameter Server
8.7. Federated Learning
8.8. Training Large Language Models
8.9. Chapter Summary
8.10. Further Reading
9. Model Deployment
9.1. Overview
9.2. Conversion to Inference Model and Model Optimization
9.3. Model Compression
9.4. Advanced Efficient Techniques
9.5. Model Inference
9.6. Security Protection of Models
9.7. Chapter Summary
9.8. Further Reading
10. Part II Application Scenarios
11. Recommender System
11.1. Overview
11.2. System Components
11.3. Recommendation Pipeline
11.4. Model Update
11.5. Supporting Real-time Machine Learning
11.6. Chapter Summary
11.7. Further Reading
12. Reinforcement Learning System
12.1. Overview
12.2. Introduction to Reinforcement Learning
12.3. Single-Node Reinforcement Learning System
12.4. Distributed Reinforcement Learning System
12.5. Multi-agent Reinforcement Learning
12.6. Multi-agent Reinforcement Learning System
12.7. Chapter Summary
13. Robotic System
13.1. Overview of Robotic Systems
13.2. Robot Operating System
13.3. Case Study: Using ROS
13.4. Modern Robot Learning
13.5. Chapter Summary
Index
