feat: Add Hydra configuration system for ML experiments#95
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gelluisaac merged 4 commits intoTraqora:mainfrom Mar 24, 2026
Merged
feat: Add Hydra configuration system for ML experiments#95gelluisaac merged 4 commits intoTraqora:mainfrom
gelluisaac merged 4 commits intoTraqora:mainfrom
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- Implement comprehensive Hydra configuration system for easy experiment management - Create YAML configs for model, training, and data parameters - Add flexible train.py script supporting command-line overrides - Include pre-configured experiments (debug, baseline, hyperparameter search) - Enable hyperparameter sweeps with --multirun functionality - Add comprehensive documentation and usage examples Features: - Easy parameter override: python train.py model.lr=0.001 - Hyperparameter sweeps: python train.py --multirun training.lr=0.001,0.01,0.1 - Pre-configured experiments for common use cases - Automatic output organization and logging - Model checkpointing with early stopping - Support for multiple datasets and model architectures - Comprehensive documentation in docs/experiment-configs.md Configuration Structure: - configs/config.yaml: Main configuration with defaults - configs/model/gcn.yaml: GCN model configurations - configs/training/default.yaml: Training hyperparameters - configs/data/cora.yaml: Dataset configurations - configs/experiments/: Pre-configured experiment setups This enables users to easily run experiments with simple command-line overrides and systematic hyperparameter searches.
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- Fix experiment configuration paths and structure - Add test_config.py for testing Hydra without PyTorch dependencies - Create requirements-minimal.txt for quick setup - Add requirements-cpu.txt for CPU-only PyTorch installation - Update experiment configs with proper defaults and _self_ - Add README_HYDRA.md with quick start guide - Successfully test command-line overrides and pre-configured experiments Features: - Working parameter overrides: python test_config.py experiments.training.lr=0.001 - Pre-configured experiments: debug, baseline, hyperparameter search - Virtual environment setup with minimal dependencies - Automatic output organization and configuration tracking Tested and verified: - Configuration loading and merging - Command-line parameter overrides - Experiment selection with --config-name - Combined overrides working correctly - Output directory structure and logging
- Add docs/conf.py with comprehensive Sphinx configuration - Create docs/index.md as main documentation homepage - Set up Sphinx RTD theme with modern extensions - Enable MyST parser for Markdown support - Configure autodoc, napoleon, and type hints - Add intersphinx links to Python, NumPy, Pandas, PyTorch - Successfully build HTML documentation in docs/_build/ - Include all required static assets and themes Features: - Modern responsive theme (Read the Docs) - Markdown support with MyST parser - Automatic API documentation generation - Code syntax highlighting and copy buttons - Search functionality and navigation - Cross-references to popular Python libraries The documentation system is now fully functional and ready for content expansion.
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Features:
Configuration Structure:
This enables users to easily run experiments with simple command-line overrides and systematic hyperparameter searches.
closes #79