Distributed Machine Learning Training System

Web-based distributed machine learning training system with real-time visualization and multi-GPU support

Features

• Web-based Training Interface

  • Real-time training progress monitoring
  • Interactive performance metrics visualization
  • Export capabilities for charts and data
  • System information display

• Model Support

  • MLP (Multi-Layer Perceptron)
  • CNN (Convolutional Neural Network)

• Training Methods

  • Single GPU training
  • Distributed training across multiple GPUs

• Dataset Support

  • Synthetic dataset generation for testing
  • Extensible data loader system

Project Structure

.
├── benchmarks/          # Performance benchmarking tools
├── datasets/            # Dataset loaders and utilities
├── distributed/         # Distributed training implementation
├── models/             # Neural network model definitions
├── results/            # Training results and exports
├── scripts/            # Training scripts
└── templates/          # Web UI HTML templates

Installation

1. Clone the repository:

git clone https://github.com/spartow/distributed-ml-training.git
cd distributed-ml-training

2. Install dependencies:

pip install -r requirements.txt

Usage

1. Start the web interface:

python web_ui.py

2. Open your browser and navigate to http://localhost:5000

3. Configure your training parameters:

   • Select model architecture (MLP/CNN)
   • Choose dataset
   • Set training hyperparameters
   • Select training method

4. Monitor training progress and export results as needed

Requirements

• Python 3.8+
• PyTorch 2.0+
• CUDA (optional, for GPU support)
• Flask (for web interface)
• Additional dependencies in requirements.txt

Contributing

1. Fork the repository
2. Create your 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