datasets:
- OneScience/ERA5
frameworks:
- ''
language:
- en
license: mit
tags:
- OneScience
- Earth Science
- ERA5
- Medium-Range Weather Forecasting
- ViT
tasks: []
Stormer
Model Overview
Stormer was jointly developed by researchers at Argonne National Laboratory and the University of California, Los Angeles (UCLA). Its core paper was published at NeurIPS 2024, a leading conference in artificial intelligence.
Paper: Scaling Transformer Neural Networks for Skillful and Reliable Medium-Range Weather Forecasting
https://arxiv.org/abs/2312.03876
Model Description
Stormer uses a standard Vision Transformer architecture and provides a streamlined deep learning model for medium-range weather forecasting.
Use Cases
| Use Case | Description |
|---|---|
| Weather forecasting training | Train Stormer on ERA5 data in HDF5 format. |
| 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 | Launch multi-process training with torchrun. |
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/Stormer --local-dir ./Stormer
cd Stormer
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:
python scripts/train.py
Multiple GPUs:
torchrun --nproc_per_node=8 scripts/train.py
Training saves the model_bak.pth checkpoint under data/checkpoints/.
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
python scripts/inference.py
By default, inference loads data/checkpoints/model_bak.pth, and results are saved to result/output/.
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 Stormer paper.