Here's a revised course design in English, with a focus on conceptual flow and process guidance (excluding technical commands):
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### **Course Title**
**Local AI Development Environment Setup & Implementation**
*AI Toolkit Integration with Ollama & Open WebUI*
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### **Learning Objectives**
1. Master local AI environment setup on Mac Studio or dual RTX 3090 workstations
2. Deploy Ollama and Open WebUI for model interaction
3. Implement OpenAI-compatible API integration
4. Configure VS Code for local LLM-based code generation
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### **Course Duration**
**2 Weeks (10 Class Hours)**
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### **Curriculum Outline**
#### **Module 1: Local AI Environment Setup (2 Days)**
- **Hardware Requirements**
- Mac Studio: macOS 10.14+, 64G RAM, 1TB SSD [2]
- Dual RTX 3090: NVIDIA GPU drivers with CUDA support
- **Software Foundation**
- Install essential development tools (Xcode, Docker)
- Configure GPU acceleration for AI workloads
- **Security Considerations**
- Local-only processing for codebase privacy [1]
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#### **Module 2: Ollama Deployment (1 Day)**
- **Model Hosting**
- Containerized deployment via Docker
- GPU optimization for inference performance
- **Model Lifecycle Management**
- Model pulling, running, and monitoring
- Resource allocation for multi-model environments
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#### **Module 3: Open WebUI Integration (1 Day)**
- **Web Interface Deployment**
- GPU-enabled Docker configuration for interactive model access [1]
- CPU fallback mode for compatibility scenarios [1]
- **User Interface Customization**
- Dashboard configuration for model selection and parameter tuning
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#### **Module 4: OpenAI API Compatibility (1 Day)**
- **API Gateway Implementation**
- FastAPI-based wrapper for Ollama endpoints
- RESTful API design for third-party integration
- **Security & Authentication**
- Local API key management
- Rate limiting and request validation
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#### **Module 5: VS Code Integration (1 Day)**
- **IDE Configuration**
- Local LLM plugin installation (e.g., Roo Code)
- API endpoint configuration for code generation
- **Workflow Optimization**
- Context-aware code suggestions
- Error detection and real-time feedback
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### **Delivery Format**
1. **Documentation**
- Markdown-based technical guides
- YouTube video tutorials
2. **Hands-on Projects**
- End-to-end environment setup tasks
- Code generation challenge exercises
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### **Key Features**
- Cost-effective local AI development [1]
- No cloud dependency for data privacy [1]
- Free open-source toolchain [2]
---
### **References**
- [1] Open WebUI and Ollama deployment architecture
- [2] Mac Studio AI development environment configuration
This design maintains technical depth while focusing on conceptual understanding, with citations included where context sources explicitly reference implementation details.
- Teacher: Admin User