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OneScience-Group/
GLONET

Global Ocean Forecasting

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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

bash

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

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 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.