#Makani

Developed by NVIDIA and NERSC, Makani is a PyTorch-based library optimized for parallel training of ML-driven weather and climate models across multiple GPUs. By integrating advanced features like automatic mixed precision and spatial model parallelism, Makani boosts the efficiency of training large models, such as the FourCastNet with Spherical Fourier Neural Operators, while reducing memory overhead. It offers extensive configuration options and supports large dataset inference, positioning itself as a vital resource for contemporary weather and climate research.