OneScience-Group/GraphCast

<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

Usage Guide

1. OneCode Usage

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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/GraphCast --local-dir ./GraphCast
cd GraphCast

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 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:

hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data

Generate Auxiliary Files

python scripts/get_data_json.py
python scripts/compute_time_diff_std.py

Generated files:

• —data.json
• —time_diff_std.npy

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

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.

python scripts/finetune.py

Fine-tuning outputs:

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

Inference

Inference reads data/checkpoints/model_finetune_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.npy
• —result/acc.npy
• —result/loss.png
• —Forecast comparison plots for specified dates and variables

OneScience Official Information

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.