ai-models-gencast is an ai-models plugin to run Google Deepmind's GenCast.
GenCast: Diffusion-based ensemble forecasting for medium-range weather, arXiv preprint: 2312.15796, 2024. https://arxiv.org/abs/2312.15796
GenCast was created by Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Tom R. Andersson, Andrew El-Kadi, Dominic Masters, Timo Ewalds, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Remi Lam, Matthew Willson
The model weights are made available for use under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). You may obtain a copy of the License at: https://creativecommons.org/licenses/by-nc-sa/4.0/.
To install the package, run:
pip install ai-models-gencastThis will install the package and most of its dependencies.
Then to install gencast dependencies (and Jax on GPU):
Caution
GenCast requires significant GPU & Memory Resources. See here
GenCast depends on Jax, which needs special installation instructions for your specific hardware.
Please see the installation guide to follow the correct instructions.
We have prepared two requirements.txt you can use. A CPU and a GPU version:
For the preferred GPU usage:
pip install -r requirements-gpu.txt -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
For the slower CPU usage:
pip install -r requirements.txt
There are three ways to control the ensemble members and behaviour of the GenCast ai-model.
| Description | Args | Result |
|---|---|---|
type=fc, single member |
--num-ensemble-members 0 |
Will create a grib file of type=fc |
N members per process with ID = range(num-ensemble-members) |
--num-ensemble-members $N>1 |
N ensemble members created all in same process, with id from the range |
| N members per process with controlled ID | --num-ensemble-members $N>1 --member-number 1,2...N |
N ensemble members created all in same process, with id controlled from member-number |
With these approaches it is possible to create either a single forecast, many ensembles in a single process, or many ensembles over many processes.