---
frameworks: PyTorch
language:
- en
license: apache-2.0
tags:
- OneScience
- Earth Science
- Ocean Forecasting
- Global Ocean Forecasting
- GLORYS12
- FNO
tasks: []
datasets:
- GLORYS12
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">GLONET</span>
  </strong>
</p>

# 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](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
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:

```bash
python scripts/fake_data.py
```

### Training

Single GPU:

```bash
python scripts/train.py
```

Multi-GPU:

```bash
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

```bash
python scripts/inference.py
```

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

### Evaluation and Visualization

```bash
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.