frameworks: JAX
language:
- en
license: apache-2.0
tags:
- OneScience
- Earth Science
- Weather Forecasting
- Ensemble Forecasting
- ERA5
tasks: []
datasets:
- OneScience/ERA5
GenCast
Model Overview
GenCast is a probabilistic global weather forecasting model developed by Google DeepMind. Its paper appeared as the cover article of the leading scientific journal Nature on December 4, 2024.
Paper: GenCast: Diffusion-Based Ensemble Forecasting for Medium-Range Weather
https://arxiv.org/abs/2312.15796
Model Description
GenCast is an ensemble forecasting model built with graph neural networks and diffusion models. Across a comprehensive set of evaluations, it outperformed ENS, the European Centre for Medium-Range Weather Forecasts' (ECMWF) leading ensemble forecasting system.
Use Cases
| Use Case | Description |
|---|---|
| Weather forecasting training | Train the model on ERA5 data in HDF5 format that conforms to the GenCast data protocol. |
| Quick local validation | Use synthetic data to validate data loading, model training and inference, and visualization of inference results. |
| ModelScope/OneCode execution | Download the standalone model package, install its dependencies, and run the included scripts directly. |
| Multi-GPU training | Use JAX pmap for data-parallel training across multiple GPUs or accelerators on a single host. |
Usage
1. Using OneCode
Use the OneCode online environment for an intelligent, one-click AI4S development experience:
Try one-click AI4S development with OneCode
2. Manual Setup
Hardware Requirements
- A GPU or DCU is recommended.
- A CPU can be used for import checks and connectivity validation with a minimal configuration, but full training and inference will be slow.
- DCU users must install DTK in advance. DTK 25.04.2 or later is recommended; alternatively, use the OneScience-recommended version compatible with your cluster.
Download the Model Package
hf download --model OneScience-Group/GenCast --local-dir ./GenCast
cd GenCast
Set Up the Runtime Environment
DCU Environment
# Activate DTK and conda first.
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# Installation with uv is also supported.
pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
GPU Environment
# Activate conda first.
conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
# Installation with uv is also supported.
pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
Training Data
The OneScience community provides ERA5 data for training. Because of file-size constraints, the repository currently contains a self-contained data slice. Download the data with the following command and ensure that the data path in conf/config.yaml is configured correctly:
hf download --dataset OneScience-Group/ERA5 --local-dir ./data
Training
Single GPU:
# If real data is unavailable, first run `python scripts/fake_data.py` to generate synthetic data.
python scripts/train.py
Multiple GPUs:
CUDA_VISIBLE_DEVICES=0,1 python scripts/train.py --config conf/config.yaml --parallel-mode pmap --num-devices 2 --global-batch-size 2
# CUDA_VISIBLE_DEVICES specifies the GPU indices to expose.
# --num-devices specifies the number of GPUs to use.
# --global-batch-size specifies the batch size and must be divisible by the number of GPUs.
After training, the weights are saved to data/checkpoints/model_bak.npz.
Pre-trained Weights
This repository will provide weights trained on ERA5 reanalysis data in the weights/ directory. The weight files are being prepared and will be uploaded soon.
Inference
By default, inference loads data/checkpoints/model_bak.npz:
python scripts/inference.py
Evaluation and Visualization
python scripts/result.py
Official OneScience Resources
| Platform | OneScience Main Repository | Skills Repository |
|---|---|---|
| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
Citation and License
- This repository is a reproduction of the original GenCast paper.