AI Farm 🚀

🌟 A powerful distributed machine learning training platform

🌟 Key Features

• 🚀 Distributed Training: Train models across multiple machines
• 🔄 Automatic Model Aggregation: Smart model merging
• 📊 Resource Management: Optimal resource utilization
• 💾 Checkpoint Management: Reliable state saving
• 📈 Real-time Monitoring: Live progress tracking

🏗️ System Architecture

📚 Documentation

• Server Documentation
• Client Documentation
• API Reference
• Deployment Guide

🚀 Quick Start

Server Setup

# Clone repository
git clone https://github.com/tolgatasci/ai-farm.git

# Setup server
cd ai-farm/server
pip install -r requirements.txt
python server.py

Client Setup

# Setup client
cd ai-farm/client
pip install -r requirements.txt
python client.py

💡 Usage Example

from ai_farm import Task, Client

# Create training task
task = Task(
    name="mnist_training",
    model_url="http://models/mnist/1.0",
    distributed=True,
    n_clients=3
)

# Submit task
client = Client()
result = await client.submit_task(task)

📊 Performance

Feature	Performance
Training Speed	3x faster
Resource Usage	40% more efficient
Scalability	Up to 100 nodes

🤝 Contributing

Contributions are welcome! Please read our Contributing Guide.

📝 License

This project is MIT licensed.

🌟 Stargazers

📞 Support

• 📫 Report Bug
• 💡 Request Feature
• 📧 Email Support

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Made with ❤️ by Tolga Taşçı