OneScience-Group/GLONET

GLONET

Model Introduction

GLONET (Global Ocean Neural Network) is a global ocean neural-network forecasting system developed by Mercator Ocean International, a leading European ocean forecasting center.

Model Description

GLONET forecasts global ocean states. It takes two consecutive daily states as input and outputs the 34-channel ocean state for the next day.

Use Cases

Scenario	Description
Global ocean forecast research	Train a dual-branch FNO/CNN ocean forecast model with GLORYS12-compatible data.
Local quick validation	Use synthetic ocean fields to check data loading, pretraining, fine-tuning, inference, and visualization.
ModelScope / OneCode execution	Download the standalone model package, install dependencies, and run the scripts directly.
Multi-GPU training	Run multi-GPU training with 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

hf download OneScience-Group/GLONET --local-dir ./GLONET
cd GLONET

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 original work uses GLORYS12 reanalysis data. Real data must first be converted to the channel order and grid specified in conf/config.yaml; the raw GLORYS12 data is not included in this package. The default synthetic data is only for interface checks:

python scripts/fake_data.py

Training

Single GPU:

python scripts/train.py

Multi-GPU:

torchrun --nproc_per_node=8 scripts/train.py

Checkpoints are saved to data/checkpoints/ by default.

Training Weights

This repository provides weights trained on GLORYS12 data in the weight/ folder. The weight files will be uploaded soon and are expected to be available in the near future.

Inference

python scripts/inference.py

The prediction tensor is written to result/glonet/data/prediction.pt by default.

Evaluation and Visualization

python scripts/result.py

The default output is result/glonet/prediction.png. Meaningful errors are computed only when a real reference field is provided.

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 repository is a reproduction of the original GLONET paper.