README.md

---
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
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">FuXi</span>
  </strong>
</p>

# 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](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 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

```bash
modelscope download --model OneScience/FuXi --local_dir ./FuXi
cd FuXi
```

### Install the Runtime Environment

**DCU Environment**

```bash
# 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**
```bash
# 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:

```bash
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:

```bash
python scripts/train_short.py
```

Multi-GPU:

```bash
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)**

```bash
python scripts/inference.py short
```

**3) Train the medium model (requires short weights + short inference results)**

```bash
python scripts/train_medium.py
```

**4) Medium inference (generate input data for long)**

```bash
python scripts/inference.py medium
```

**5) Train the long model (requires medium weights + medium inference results)**

```bash
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:

```bash
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

```bash
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.