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aifs-modal: Serverless Weather Forecasts
Run AI weather forecasts on rented GPUs with a serverless Python library. This talk demonstrates reproducible forecasts and reforecasts from any historical date, managed in a versioned Zarr store.
The aifs-modal library is a thin serverless wrapper to easily run AIFS inference (for forecasts or reforecasts) on a rented GPU in one Python call, storing the outputs in a versioned Zarr store using Icechunk.
The demo shows how (claude and) I wired together anemoi-inference (ECMWF’s model runtime), Modal (serverless GPU), and Icechunk (git-like array storage) to get reproducible forecasts or reforecasts from any historical date, all driven from a local Jupyter notebook with no cluster, no Colab, no infrastructure to manage. Initial conditions can be ingested from ECMWF operational analysis or ERA5 reanalysis out of the box. Once the GPU job finishes, the output is managed in a versioned Icechunk store and opens instantly in your local notebook as an xarray Dataset for postprocessing. Ensemble forecasts are also supported: each member runs on a separate GPU in parallel, with members writing concurrently into the same Icechunk store.