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Contributing to max-learning

Thank you for your interest in contributing! This project is part of DataBooth's exploration of high-performance AI infrastructure with Modular's MAX framework.

Development Setup

  1. Install Pixi (if not already installed):

    curl -fsSL https://pixi.sh/install.sh | bash

  2. Clone the repository:

    git clone https://github.com/DataBooth/max-learning.git
cd max-learning

  3. Install dependencies:

    pixi install

  4. Run the application:

    pixi run inference

Development Workflow

Running Tests

pixi run test              # Run all tests
pixi run test-config       # Run config tests only
pixi run test-inference    # Run inference tests only

Code Formatting

pixi run format            # Format code
pixi run check             # Check code quality

Building

pixi run build             # Build standalone binary

Project Structure

  • src/ - Source code
    • main.mojo - Entry point
    • config.mojo - Configuration management
    • inference.mojo - Model inference logic
  • tests/ - Test suite
  • examples/ - Example usage
  • benchmarks/ - Performance benchmarks
  • models/ - Model files (gitignored)

Coding Guidelines

  1. Follow Mojo best practices - Use idiomatic Mojo patterns
  2. Document thoroughly - Include docstrings for all public functions/structs
  3. Write tests - Add tests for new functionality
  4. Performance matters - This is a performance-focused project
  5. Use Australian English - For documentation and comments

Pull Request Process

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

Commit Message Format

<type>: <subject>

<body>

Co-Authored-By: Warp <agent@warp.dev>

Types: feat, fix, docs, test, refactor, perf, chore

Questions?

Open an issue or reach out via DataBooth.