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Development Guide

This guide provides information for developers who want to contribute to or extend Transfer Learning.

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Project Structure

The Transfer Learning project follows a modular architecture with clear separation of concerns: 
transfer-learning/
├── data/               # Data storage directory
├── docs/               # Documentation
├── logs/               # Log files
├── metrics/            # Metrics collection
├── src/                # Source code
│   └── transfer_learning/
│       ├── core/       # Core processing modules
│       │   ├── video_processor.py
│       │   ├── audio_transcriber.py
│       │   ├── content_analyzer.py
│       │   └── frame_extractor.py
│       ├── guide/      # Guide generation
│       │   └── generator.py
│       ├── models/     # AI model interfaces
│       │   ├── openai.py
│       │   └── whisper.py
│       ├── monitoring/ # Monitoring and metrics
│       │   ├── logger.py
│       │   └── metrics.py
│       ├── utils/      # Utility functions
│       │   ├── downloader.py
│       │   ├── validation.py
│       │   └── path.py
│       ├── cli.py      # CLI implementation
│       └── config.py   # Configuration
├── tests/              # Test suite
├── .env                # Environment variables
├── pyproject.toml      # Project configuration
└── setup.py            # Package setup

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Development Setup

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Installation Scripts

Transfer Learning provides automated installation scripts in the scripts directory:
  • install.sh - For Unix-based systems (Linux, macOS)
  • install.bat - For Windows systems
These scripts handle the complete setup process: 
# On Unix-based systems
chmod +x scripts/install.sh
./scripts/install.sh

# On Windows
scripts\install.bat
The installation scripts perform the following tasks:
  • Verify Python version compatibility (3.9+)
  • Create and activate a virtual environment
  • Install UV package manager
  • Install project dependencies
  • Set up required directories (data, logs, metrics, cache)
  • Create a default .env configuration file
  • Install development dependencies (optional)
To modify or extend the installation process:
  1. Edit scripts/install.sh or scripts/install.bat
  2. Update the directory creation in the create_directories function
  3. Modify the default configuration in the create_env_file function
  4. Add new dependencies or setup steps as needed
1

Clone the Repository

git clone https://github.com/Wearer-Lab/transfer-learning.git
cd transfer-learning
2

Set Up Virtual Environment

python -m venv .venv
source .venv/bin/activate  # On Windows, use `.venv\Scripts\activate`
3

Install Development Dependencies

pip install uv
uv pip install -e ".[dev]"
Transfer Learning uses UV as the package manager for faster, more reliable dependency management. UV provides significant performance improvements over traditional pip, especially for large dependency trees.
4

Set Up Environment Variables

Create a .env file in the project root directory:
OPENAI_API_KEY=your-api-key-here
ENABLE_MONITORING=true
LOG_LEVEL=DEBUG

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Development Workflow

Run the test suite to ensure your changes don’t break existing functionality:
pytest
Run tests with coverage report:
pytest --cov=transfer_learning
Check code style with Ruff:
ruff check .
Format code with Black:
black src tests
Run the CLI in development mode:
python -m transfer_learning.cli process-video path/to/video.mp4
Or use the installed entry point:
transfer-learning process-video path/to/video.mp4
You can also use the convenient tl command alias:
tl process-video path/to/video.mp4
The tl command is available through shell scripts in the scripts directory:
  • scripts/tl for Unix-like systems (macOS/Linux)
  • scripts/tl.bat for Windows

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Extending Transfer Learning

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Adding a New Command

To add a new command to the CLI:
  1. Open src/transfer_learning/cli.py
  2. Add a new function with the @app.command() decorator:
@app.command()
def my_new_command(
    arg1: str = typer.Argument(..., help="Description of arg1"),
    option1: str = typer.Option("default", help="Description of option1"),
):
    """Description of my new command."""
    # Command implementation
    console.print(f"Running my new command with {arg1} and {option1}")

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Adding a New Module

To add a new module:
  1. Create a new Python file in the appropriate directory
  2. Implement your module functionality
  3. Import and use it in the CLI or other modules
Example of a new utility module: 
# src/transfer_learning/utils/my_utility.py
"""My utility module."""

def my_utility_function(param1: str, param2: int = 10) -> str:
    """
    Description of my utility function.
    
    Args:
        param1: Description of param1
        param2: Description of param2
        
    Returns:
        Description of return value
    """
    return f"{param1} processed with {param2}"

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Enhancing Monitoring

To add new metrics to the monitoring system:
  1. Open src/transfer_learning/monitoring/metrics.py
  2. Add new fields to the ProcessingMetrics dataclass
  3. Update the to_dict method to include the new fields
  4. Use the new metrics in your code

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Documentation

Transfer Learning uses Mintlify for documentation. To update the documentation:
  1. Edit the MDX files in the docs directory
  2. Preview the documentation locally:
npx mintlify dev
  1. Submit a pull request with your documentation changes

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Contribution Guidelines

Code Style

  • Follow PEP 8 guidelines
  • Use type hints for all functions
  • Write docstrings for all modules, classes, and functions
  • Keep functions small and focused

Testing

  • Write tests for all new functionality
  • Ensure all tests pass before submitting a PR
  • Aim for high test coverage
  • Include both unit and integration tests

Documentation

  • Update documentation for all new features
  • Include examples in docstrings
  • Keep the README and docs in sync
  • Document configuration options

Pull Requests

  • Create a feature branch for your changes
  • Keep PRs focused on a single feature or fix
  • Include a clear description of changes
  • Reference any related issues

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Development Checklist

Use this checklist when developing new features:
  • Implement the feature
  • Write tests
  • Update documentation
  • Check code style
  • Run the test suite
  • Update the README if necessary
  • Submit a pull request

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Monitoring and Logging

Transfer Learning includes a comprehensive monitoring and logging system:
The MetricsTracker class in src/transfer_learning/monitoring/metrics.py provides metrics collection:
from transfer_learning.monitoring.metrics import MetricsTracker, Timer

# Initialize metrics tracker
metrics = MetricsTracker()

# Start tracking metrics for a video
metrics.start_processing("video_id")

# Use timer context manager to track duration
with Timer(metrics, "frame_analysis_duration"):
    # Process frames
    analyze_frames(frames)

# Update metrics manually
metrics.update_metrics(
    frame_count=100,
    processed_frames=100,
    error_count=0
)

# End tracking and save metrics
metrics.end_processing()

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Performance Optimization

When developing performance-critical components:
  1. Use asynchronous processing for I/O-bound operations
  2. Implement batching for API calls
  3. Use caching to avoid redundant processing
  4. Monitor memory usage and optimize as needed
Example of asynchronous processing: 
import asyncio
from typing import List, Dict, Any

async def process_batch(batch: List[str]) -> List[Dict[str, Any]]:
    """Process a batch of items asynchronously."""
    tasks = [process_item(item) for item in batch]
    return await asyncio.gather(*tasks)

async def process_item(item: str) -> Dict[str, Any]:
    """Process a single item."""
    # Implement processing logic
    return {"item": item, "result": "processed"}

async def main():
    """Main processing function."""
    batches = [["item1", "item2"], ["item3", "item4"]]
    results = []
    
    for batch in batches:
        batch_results = await process_batch(batch)
        results.extend(batch_results)
    
    return results