Architecture.md

LlamaHome Architecture

Table of Contents

• System Overview
• Core Components
• Training Pipeline
• Model Management System
• Cache System Architecture
• Resource Management
• System Integration
• Development Workflow
• Security Architecture
• Performance Optimization
• Directory Structure
• Configuration Management
• Testing Strategy
• Future Extensibility

Overview

LlamaHome is a comprehensive training and inference pipeline for LLM models with efficient resource management and monitoring. The system is designed with modularity and extensibility in mind, focusing on efficient training, smart caching, and robust monitoring.

System Overview

LlamaHome is designed as a modular, extensible system for training and deploying large language models. The architecture follows clean code principles with clear separation of concerns, dependency injection, and comprehensive configuration management.

graph TB
    CLI[CLI Interface] --> Core[Core System]
    GUI[GUI Interface] --> Core
    Core --> Training[Training Pipeline]
    Core --> ModelMgmt[Model Management]
    Core --> Cache[Cache System]
    Core --> Config[Config Management]
    
    subgraph Training Pipeline
        Training --> DataMgmt[Data Management]
        Training --> ResourceMgmt[Resource Management]
        Training --> Monitor[Monitoring]
        Training --> Optimize[Optimization]
    end
    
    subgraph Model Management
        ModelMgmt --> Download[Download Manager]
        ModelMgmt --> Version[Version Control]
        ModelMgmt --> Storage[Storage Manager]
    end
    
    subgraph Cache System
        Cache --> MemCache[Memory Cache]
        Cache --> DiskCache[Disk Cache]
        Cache --> Invalidation[Cache Invalidation]
    end
    
    subgraph Config Management
        Config --> YAMLConfig[YAML Configs]
        Config --> EnvConfig[Environment]
        Config --> RuntimeConfig[Runtime Params]
    end

Loading

Core Components

1. Training Pipeline

The training pipeline consists of the following key components:

graph TB
    Data[Data Management] --> Training[Training Pipeline]
    Training --> Monitor[Monitoring]
    Training --> Cache[Cache System]
    Training --> Checkpoint[Checkpoint Management]
    
    subgraph Data Management
        DataProcessor[DatasetProcessor]
        DataCache[DatasetCache]
        BatchGen[BatchGenerator]
        Augment[DataAugmenter]
    end
    
    subgraph Training Pipeline
        Forward[Forward Pass]
        Backward[Backward Pass]
        Optimize[Optimization]
        Resource[Resource Management]
    end
    
    subgraph Monitoring
        Metrics[Metrics Collection]
        Logger[Logging System]
        Visual[Visualization]
    end

Loading

2. Data Management

The data management system includes:

• DatasetProcessor: Handles data preprocessing and validation
• DatasetCache: Manages efficient data caching and retrieval
• BatchGenerator: Implements dynamic batch generation and padding
• DataAugmenter: Provides data augmentation capabilities

Directory Structure:

src/
├── training/
│   ├── data.py          # Dataset processing
│   ├── cache.py         # Caching system
│   ├── batch.py         # Batch generation
│   ├── augmentation.py  # Data augmentation
│   └── pipeline.py      # Training pipeline

3. Cache System

The caching system implements:

graph LR
    Cache[CacheManager] --> Policy[Cache Policy]
    Policy --> Store[Cache Store]
    Store --> Memory[Memory Cache]
    Store --> Disk[Disk Cache]
    
    subgraph Cache Policies
        LRU[LRU Policy]
        Size[Size Policy]
    end

Loading

Key features:

• Configurable cache policies
• Memory and disk backends
• Automatic cache invalidation
• Resource-aware caching

4. Checkpoint Management

The checkpoint system provides:

graph TB
    Checkpoint[CheckpointManager] --> Save[Save Checkpoint]
    Checkpoint --> Load[Load Checkpoint]
    Checkpoint --> Track[Track Best]
    
    subgraph Checkpoint Features
        Model[Model State]
        Optimizer[Optimizer State]
        Scheduler[Scheduler State]
        Metrics[Training Metrics]
    end

Loading

Features:

• Configurable save intervals
• Best checkpoint tracking
• Automatic cleanup
• Safe file operations

Configuration Management

Configuration is handled through:

1. Environment variables (.env)
2. YAML/TOML configuration files
3. Command-line arguments

Example configuration structure:

[training]
batch_size = 32
learning_rate = 1e-4
gradient_accumulation_steps = 4

[cache]
memory_size = "4GB"
disk_size = "100GB"
policy = "lru"

[checkpoint]
save_steps = 1000
keep_last_n = 5
save_best = true

Testing Strategy

The testing suite includes:

1. Unit Tests

   • Component-level testing
   • Mocked dependencies
   • Fast execution

2. Integration Tests

   • End-to-end workflows
   • Real data processing
   • Resource management

3. Performance Tests

   • Memory usage
   • Training speed
   • Cache efficiency

Directory Structure:

tests/
├── unit/
│   ├── training/
│   ├── data/
│   └── cache/
├── integration/
└── performance/

Security Considerations

1. Data Protection

   • Secure data handling
   • Token management
   • Access control

2. Resource Protection

   • Memory limits
   • Disk usage limits
   • Process isolation

Future Extensibility

The system is designed for easy extension of:

1. Training Features

   • New optimizers
   • Custom schedulers
   • Advanced monitoring

2. Data Processing

   • Custom augmentations
   • New batch strategies
   • Additional cache backends

3. Model Support

   • New architectures
   • Custom attention mechanisms
   • Specialized optimizations