| --- |
| 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 |
| hf download OneScience-Group/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 |
| hf download --repo-type dataset OneScience-Group/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. |
|
|
