license: apache-2.0
language:
- en
- zh
tags:
- OneScience
- Earth Science
- Ocean Forecast
- Subsurface Forecast
- Sea Temperature and Salinity Forecast
- CMEMS
frameworks: PyTorch
datasets:
- OneScience/CMEMS
XiHe
Model Introduction
XiHe is the world's first data-driven, eddy-resolving global ocean forecast model, jointly proposed by the College of Meteorology and Oceanography at the National University of Defense Technology and multiple universities and research institutions.
Paper: XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving Forecasting
https://arxiv.org/abs/2402.02995
Model Description
XiHe is a Transformer model designed for high-resolution global ocean forecasting, suitable for global ocean eddy-resolving forecast research.
Use Cases
| Scenario | Description |
|---|---|
| Global Ocean Forecast Research | Train an XiHe-style ocean forecast model using annual CMEMS HDF5 data. |
| Local Quick Validation | Use synthetic data to verify data loading, training entry points, inference, and result scripts. |
| 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/XiHe --local_dir ./XiHe
cd XiHe
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 CMEMS 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 config/config.yaml is set correctly:
modelscope download --dataset OneScience/CMEMS --local_dir ./data
Training
Single GPU:
python scripts/train.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.py
Training outputs:
data/checkpoints/model_bak.pth
data/checkpoints/trloss.npy
data/checkpoints/valoss.npy
Training Weights
This repository provides weights trained on GLORYS12 global ocean reanalysis data in the weights/ folder. The weight files will be uploaded soon and are expected to be available in the near future.
Inference
Inference reads data/checkpoints/model_bak.pth by default:
python scripts/inference.py
Prediction results are output to:
result/output/
Evaluation and Visualization
python scripts/result.py
Output contents include:
result/rmse.npyresult/acc.npyresult/loss.png- Forecast comparison plots for specified dates and variables
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 XiHe paper.