Query-based All-in-one Research Kit (QUARK)

The Query-based All-in-one Research Kit (QUARK) is a comprehensive toolkit designed to revolutionize how researchers, data scientists, and developers manage their experimental workflows. With a focus on PyTorch-based research projects, QUARK provides a unified platform that handles everything from experiment configuration to result summarization, ensuring reproducibility and efficiency throughout the research lifecycle.

🌟 Key Features

📊 Experiment Management

• Query-based Configuration: Intuitive interfaces for setting up experiments
• Automated Environment Setup: Seamless configuration of dependencies and resources
• Experiment Versioning: Track and manage different versions of your experiments
• Result Summarization: Generate comprehensive reports of experimental outcomes

🔧 Technical Capabilities

• Environment Isolation: Built-in support for virtual environments and Docker containers
• Plugin System: Extensible architecture for custom functionality
• Task Automation: Powered by Invoke for efficient task management
• Native Code Integration: CMake support for C++/native code components

🔍 Research Tools

• Data Processing: Efficient data handling with numpy and msgpack
• Validation: Robust data validation using Pydantic
• Documentation: Integrated MkDocs with Material theme for beautiful documentation
• Testing: Comprehensive testing infrastructure with PyTest

🚀 Getting Started

Prerequisites

1. Python Environment:

   # Ensure Python 3.9 or higher is installed
   python --version

2. System Requirements:

   • Python 3.9+
   • invoke (for task automation)
   • venv (for environment management)

Installation

1. Using Poetry (Recommended):

   # Install dependencies and set up the project
   poetry install

2. Verify Installation:

   quark --help

📖 Project Structure

quark/
├── quark/              # Core package
│   ├── coordinator/    # Coordination logic
│   └── tasks.py       # Task definitions
├── plugins/           # Plugin system
├── utility/           # Utility functions
├── tools/            # Development tools
├── experiments/      # Experiment configurations
├── environments/     # Environment settings
├── tests/           # Test suite
├── data/            # Data storage
└── runtime/         # Runtime configurations

🛠️ Usage

Core Commands

# View all available commands
quark --help

# Bootstrap the environment (sets up both PyTorch and TensorFlow)
quark bootstrap

# Install dependencies
quark install

# Build the project
quark build

# Run tests
quark test                    # Run all tests
quark unittest               # Run unit tests for all frameworks
quark unittest-torch         # Run PyTorch specific tests
quark unittest-tf           # Run TensorFlow specific tests

# Format code (Python with black/isort, C++ with clang-format)
quark format

# Clean up virtual environments
quark clean

Benchmark and Experiments

# Run benchmarks
quark bench <task-name> --task-dir=<directory>

# Run the QUARK engine tests
quark quark-engine-test

Plugin Management

# Get available plugins
quark get-plugins

# Pull plugins from repositories
quark pull-plugins

# Build plugins
quark build-plugins

# Run plugin tests
quark catz-smoke-test            # Test Catzilla plugins
quark serialisation-smoke-test   # Test serialization plugins

Development Workflow

1. Initial Setup:

   # Bootstrap the environment
   quark bootstrap
   
   # Install dependencies
   quark install

2. Development Cycle:

   # Format your code
   quark format
   
   # Run tests
   quark test
   
   # Build the project
   quark build

3. Plugin Development:

   # Build and test plugins
   quark build-plugins
   quark catz-smoke-test

All commands are integrated into the quark CLI - there's no need to manually run pytest, poetry, or other tools directly. The QUARK command-line interface handles all necessary tool interactions for you.

🤝 Contributing

We welcome contributions! Please follow these steps:

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

📚 Documentation

Comprehensive documentation is available through MkDocs:

# Build documentation
mkdocs build

# Serve documentation locally
mkdocs serve

🔧 Development

Setting Up Development Environment

# Install development dependencies
poetry install --with dev

# Run tests
pytest tests/

# Build documentation
mkdocs build

Code Style

• Follow PEP 8 guidelines
• Use type hints
• Write comprehensive docstrings
• Include unit tests for new features

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with Poetry
• Documentation powered by MkDocs
• Testing framework: PyTest

📞 Contact

Albert Shi - [email protected]

Project Link: https://github.com/codes1gn/quark

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Made with ❤️ by the QUARK team

🔄 Comparison with Other Tools

QUARK differentiates itself from other experiment tracking and MLOps tools in several key ways:

📊 Compared to Traditional Experiment Trackers

Feature	QUARK	MLflow	Weights & Biases	DVC
Query-based Configuration	✅	❌	❌	❌
Automated Environment Management	✅	Limited	Limited	Limited
Integrated Data Version Control	✅	❌	Limited	✅
Framework Agnostic	✅	✅	✅	✅
Built-in Reproducibility	✅	Limited	Limited	✅

🌟 QUARK's Unique Advantages

1. Query-First Approach

   • Intuitive query-based interface for experiment configuration
   • Natural language-like syntax for defining experiments
   • Reduced cognitive load compared to traditional configuration files

2. Unified Research Environment

   • Seamless integration of experiment tracking, data versioning, and model management
   • Consistent workflow across different ML frameworks
   • Built-in support for both PyTorch and TensorFlow ecosystems

3. Advanced Reproducibility

   • Automatic environment snapshots
   • Complete experiment lineage tracking
   • Deterministic experiment reproduction

4. Scalable Architecture

   • Designed for large-scale research projects
   • Efficient handling of distributed training
   • Cloud-native architecture with Kubernetes support

5. Research-Oriented Features

   • First-class support for academic research workflows
   • Built-in citation and paper tracking
   • Easy experiment sharing and collaboration

🔍 When to Choose QUARK

QUARK is particularly well-suited for:

• Research Teams who need robust experiment tracking with academic workflow support
• Production ML Teams requiring seamless transition from research to deployment
• Organizations looking for a unified platform that scales with their ML initiatives
• Projects that require extensive experimentation and rigorous reproducibility
• Teams working across multiple ML frameworks and environments