Neural-LAM#
Neural-LAM is a PyTorch and PyTorch Lightning framework for high-resolution weather prediction using Graph Neural Networks. It provides a modular approach to Limited Area Modeling, supporting multiple graph-based architectures such as GraphLAM, HiLAM, and HiLAMParallel to process and predict meteorological data efficiently.
🚀 Getting Started: Installation guide and quickstart tutorial to get you up and running.
📚 API Reference: Auto-generated reference for all modules, classes, and functions.
Key Features#
Modular design: Swap datastores, models, and graph structures independently
Multiple model architectures: GraphLAM (flat), HiLAM (hierarchical), HiLAMParallel (parallel hierarchical)
Flexible data handling: Abstract datastore interface supporting zarr, numpy, and custom formats via mllam-data-prep
Production-ready: PyTorch Lightning for training, W&B/MLflow logging, checkpoint management