ai-models-gencast 0.0.5

An ai-models plugin to run Deepmind's gencast model

ai-models-gencast

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/.

Installation

To install the package, run:

pip install ai-models-gencast

This 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 and Jax

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

Specifying ensemble numbers

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.