| --- |
| frameworks: ONNX Runtime |
| language: |
| - en |
| license: cc-by-nc-nd-4.0 |
| tags: |
| - OneScience |
| - Earth Science |
| - Weather Forecast |
| - Subseasonal Forecast |
| - Ensemble Forecasting |
| - ERA5 |
| - ONNX |
| tasks: [] |
| datasets: |
| - OneScience/ERA5 |
| --- |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">FuXi-S2S</span> |
| </strong> |
| </p> |
| |
| # Model Introduction |
|
|
| FuXi-S2S is a global subseasonal forecasting model proposed by researchers from Fudan University and collaborating institutions. |
|
|
| Paper: A machine learning model that outperforms conventional global subseasonal forecast models |
|
|
| https://doi.org/10.1038/s41467-024-50714-1 |
|
|
| # Model Description |
|
|
| FuXi-S2S takes two consecutive daily mean atmospheric states as input and targets the two-week to two-month forecast range, where conventional numerical models remain challenging to use effectively. This model package exposes the official ONNX inference graph through a small ONNX Runtime adapter. |
|
|
| # Use Cases |
|
|
| | Scenario | Description | |
| | :---: | :--- | |
| | Global subseasonal forecasting | Run the official FuXi-S2S ONNX weights with ERA5 inputs following the fixed 76-channel order. | |
| | Local quick validation | Use synthetic HDF5 data to check data loading, ONNX Runtime execution, and visualization. | |
| | ModelScope / OneCode execution | Download the standalone model package, configure an ONNX Runtime provider, and run the scripts directly. | |
|
|
| # 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 for practical inference. CPU can be used for import and small-scale connectivity checks, but full-resolution inference will be slow. |
| - Install the ONNX Runtime build that provides the execution provider required by your hardware. |
| - 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 |
| hf download OneScience-Group/FuXi-S2S --local-dir ./FuXi-S2S |
| cd FuXi-S2S |
| ``` |
|
|
| ### 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 |
| ``` |
|
|
| Install or select an ONNX Runtime provider matching the target hardware, then update `model.providers` in `conf/config.yaml` if necessary. The default configuration targets a DCU-compatible provider list. |
|
|
| ### Training Data Introduction |
|
|
| The official model uses daily mean ERA5 states with a fixed 76-channel order. The OneScience community provides an ERA5 data slice: |
|
|
| ```bash |
| hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data |
| ``` |
|
|
| The adapter expects yearly files under `data/data/` and normalization arrays under `data/stats/`. Confirm the variable order in `conf/config.yaml` before inference. |
|
|
| ### Generate Synthetic Data |
|
|
| When real ERA5 data is unavailable, generate native-grid HDF5 files for interface checks: |
|
|
| ```bash |
| python scripts/fake_data.py |
| ``` |
|
|
| For a smaller smoke fixture, pass `--height 32 --width 64`; synthetic data does not reproduce the official forecast quality. |
|
|
| ### Pre-trained Weights |
|
|
| The official ONNX graph requires both files below: |
|
|
| ```text |
| weight/fuxi_s2s.onnx |
| weight/fuxi_s2s |
| ``` |
|
|
| The large weight files are not bundled in this working copy and must be supplied from the authorized release. The `weight/` directory is reserved for these files. |
|
|
| ### Inference |
|
|
| Inference reads `weight/fuxi_s2s.onnx` and its external data file by default. It converts ERA5 fields to the model's `121x240` grid and writes ONNX outputs to `result/output/`: |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| Use `--device cpu`, `--device cuda`, or `--device dcu` and configure `model.providers` for the selected runtime. |
|
|
| ### Evaluation and Visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
|
| The result script reads the newest NPY output and writes multi-variable forecast figures to `result/visualization/`. |
|
|
| # 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 model package contains an adapter for the official FuXi-S2S ONNX release. |
| - The official ONNX graph, external data file, and related data are subject to the CC BY-NC-ND 4.0 terms stated by the authorized release. |
|
|
