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](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/