README.md

---
frameworks: PyTorch
language:
- en
- zh
license: apache-2.0
tags:
- OneScience
- Earth Science
- Weather Forecast
- Short-to-Medium-Range Weather Forecast
- ERA5
tasks: []
datasets:
  - OneScience/ERA5
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">GraphCast</span>
  </strong>
</p>

# Model Introduction

GraphCast is a global medium-range weather forecast model developed by the Google DeepMind team, with its core paper published in the top-tier international journal *Science*.

Paper: GraphCast: Learning skillful medium-range global weather forecasting

https://arxiv.org/abs/2212.12794

# Model Description

GraphCast is a global medium-range weather forecast model built on a Graph Neural Network (GNN). It is trained on the ERA5 global atmospheric reanalysis dataset (1979–2017) provided by ECMWF.

# Use Cases

| Scenario | Description |
| :---: | :--- |
| Global Weather Forecast Research | Train a GraphCast-style GNN forecast model using annual ERA5 HDF5 data. |
| Local Quick Validation | Use synthetic data to verify data loading, auxiliary file generation, training entry points, 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](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
modelscope download --model OneScience/GraphCast --local_dir ./GraphCast
cd GraphCast
```

### 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 OneScience community provides ERA5 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 `conf/config.yaml` is set correctly:

```bash
modelscope download --dataset OneScience/ERA5 --local_dir ./data
```

### Generate Auxiliary Files

```bash
python scripts/get_data_json.py
python scripts/compute_time_diff_std.py
```

Generated files:

- `data.json`
- `time_diff_std.npy`

### Training

Single GPU:

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

Multi-GPU:

```bash
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:

```text
data/checkpoints/model_bak.pth
data/checkpoints/trloss.npy
```

### Training Weights

This repository provides weights trained on ERA5 data from 1979 to 2017 in the `weights/` folder. The weight files will be uploaded soon and are expected to be available in the near future.

### Fine-tuning

Before fine-tuning, you must first complete training and generate `data/checkpoints/model_bak.pth`.

```bash
python scripts/finetune.py
```

Fine-tuning outputs:

```text
data/checkpoints/model_finetune_bak.pth
data/checkpoints/ft_trloss.npy
```

### Inference

Inference reads `data/checkpoints/model_finetune_bak.pth` by default:

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

Prediction results are output to:

```text
result/output/
```

### Evaluation and Visualization

```bash
python scripts/result.py
```

Output contents include:

- `result/rmse.npy`
- `result/acc.npy`
- `result/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

- Apache License 2.0. The code is open source, permitting both commercial and non-commercial use.
- The weights are permitted for non-commercial use only.