frameworks: PyTorch
language:
- en
- zh
license: apache-2.0
tags:
- OneScience
- Earth Science
- Weather Forecast
- Short-to-Medium-Range Weather Forecast
- ERA5
tasks: []
datasets:
- OneScience/ERA5
FuXi
Model Introduction
FuXi is a global weather forecast foundation model jointly developed by Fudan University and multiple institutions. It is the first end-to-end machine learning framework capable of independently performing data assimilation (DA) and cyclic forecasting.
Paper: FuXi: A cascade machine learning forecasting system for 15-day global weather forecast
https://arxiv.org/abs/2306.12873
Model Description
The FuXi model is trained through a three-stage cascaded approach: short → medium → long. Its training input primarily consists of ERA5 reanalysis data.
Use Cases
| Scenario | Description |
|---|---|
| Weather Forecast Training | Train FuXi (short/medium/long three stages) using ERA5 HDF5 data |
| Local Quick Validation | Use synthetic data to verify data loading, model training & inference, and inference result visualization. |
| ModelScope / OneCode Execution | Download as a standalone model package, install dependencies, and run scripts directly. |
| Multi-GPU Training | Launch multi-process training via torchrun. |
Usage Guide
1. OneCode Usage
Experience intelligent one-click AI4S programming through the OneCode online environment:
Click to Experience Intelligent One-Click AI4S Programming
2. Manual Installation and Usage
Hardware Requirements
- A GPU or DCU is recommended.
- CPU can be used for import and small-scale connectivity verification; full training and inference will be slow.
- DCU users must install DTK in advance. DTK 25.04.2 or above, or the OneScience recommended version matching your cluster, is recommended.
Download the Model Package
modelscope download --model OneScience/FuXi --local_dir ./FuXi
cd FuXi
Install the Runtime Environment
DCU Environment
# Please activate DTK and CONDA first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# uv installation is supported
pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
GPU Environment
# Please 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
# uv installation is supported
pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
Training Data Introduction
The OneScience community provides ERA5 data for training (due to file size limits, the current repository contains a slice of the full dataset). Users can download it with the command below and confirm that the data path in conf/config.yaml is set correctly:
modelscope download --dataset OneScience/ERA5 --local_dir ./data
Training
FuXi consists of 3 stages and must be executed in order. The inference result of each stage serves as the input for the next stage:
short (train) → short (inference) → medium (train) → medium (inference) → long (train) → long (inference)
1) Train the short model (train from scratch, as the starting entry point)
Single GPU:
python scripts/train_short.py
Multi-GPU:
torchrun --nproc_per_node=8 --nnodes=1 --rdzv_id=1000 --rdzv_backend=c10d --max_restarts=0 --master_addr="localhost" --master_port=29500 scripts/train_short.py
2) Short inference (generate input data for medium)
python scripts/inference.py short
3) Train the medium model (requires short weights + short inference results)
python scripts/train_medium.py
4) Medium inference (generate input data for long)
python scripts/inference.py medium
5) Train the long model (requires medium weights + medium inference results)
python scripts/train_long.py
Training Weights
This repository provides weights trained on 39 years of ERA5 reanalysis data in the weights/ folder. The weight files will be uploaded soon and are expected to be available in the near future.
Inference
Each stage can perform inference independently:
python scripts/inference.py short
python scripts/inference.py medium
python scripts/inference.py long
Inference results will be saved to result/output/<stage>/.
Evaluation and Visualization
python scripts/result.py short
python scripts/result.py medium
python scripts/result.py long
OneScience Official Information
| 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 & License
- This repository is a reproduction of the original FuXi paper.