README_HYDRA.md

Hydra Configuration System - Quick Start

🚀 Installation

# Install pip and create virtual environment
sudo apt install python3-pip python3.12-venv -y
python3 -m venv venv
source venv/bin/activate

# Install minimal requirements for Hydra
pip install -r requirements-minimal.txt

📋 Usage Examples

Basic Configuration

# Use default configuration
python test_config.py

# Override parameters
python test_config.py experiments.training.lr=0.001 experiments.model.hidden_dims=[128,64]

Pre-configured Experiments

# Debug experiment (small model, 10 epochs)
python test_config.py --config-name experiments/debug

# Baseline experiment (standard settings)
python test_config.py --config-name experiments/baseline

Command-line Overrides

# Learning rate override
python test_config.py experiments.training.lr=0.001

# Model architecture override
python test_config.py experiments.model.hidden_dims=[32]

# Multiple overrides
python test_config.py experiments.training.lr=0.001 experiments.model.hidden_dims=[128,64] experiments.training.epochs=300

📁 Configuration Structure

configs/
├── config.yaml                    # Main configuration
├── model/gcn.yaml                 # GCN model configs
├── training/default.yaml          # Training configs
├── data/cora.yaml                 # Dataset configs
└── experiments/
    ├── debug.yaml                 # Debug experiment
    ├── baseline.yaml              # Baseline experiment
    └── hyperparameter_search.yaml # Parameter sweep

🎯 Key Features

• Easy Parameter Override: experiments.training.lr=0.001
• Pre-configured Experiments: Debug, baseline, hyperparameter search
• Automatic Output Organization: Results saved to timestamped directories
• Configuration Tracking: Full config saved with results

📊 Test Results

The system successfully demonstrates:

• ✅ Configuration loading and merging
• ✅ Command-line parameter overrides
• ✅ Pre-configured experiment selection
• ✅ Automatic output organization
• ✅ Configuration tracking and logging

🔄 Next Steps

1. Install full PyTorch dependencies when needed:

   pip install -r requirements.txt  # May take time due to large downloads

2. Use the full training script:

   python train.py experiments.training.lr=0.001

3. Run hyperparameter sweeps:

   python train.py --multirun experiments.training.lr=0.001,0.01,0.1

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The Hydra configuration system is ready for easy experiment management! 🚀