NERSC/sc25-dl-tutorial
Deep Learning at Scale @ SC25
This project helps researchers and scientists train advanced deep learning models for weather forecasting, similar to those used in FourCastNet or Pangu-Weather. It takes atmospheric data, like the ERA5 reanalysis dataset, and outputs a trained vision transformer model capable of predicting future atmospheric states. This tool is designed for computational scientists and climate researchers who need to develop and optimize high-performance weather prediction models.
Use this if you are a researcher or scientist at NERSC looking to train large-scale deep learning models for weather forecasting on the Perlmutter supercomputer.
Not ideal if you are a practitioner looking for an out-of-the-box weather prediction application rather than a tutorial on training large-scale deep learning models.
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Python
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Last pushed
Nov 16, 2025
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