FourCastNet_v2
# Model Introduction FourCastNet v2 is a global weather forecast model based on the Spherical Fourier Neural Operator (SFNO), proposed by NVIDIA and its collaborators. Paper: Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere https://arxiv.org/abs/2306.03838 # Model Description The key architectural change from v1 is replacing the Adaptive Fourier Neural Operator (AFNO) with the Spherical Fourier Neural Operator (SFNO). # Use Cases | Scenario | Description | | :---: | :--- | | Global weather forecast training | Train an SFNO-style FourCastNet v2 model with 73-channel ERA5 HDF5 data. | | Local quick validation | Use synthetic ERA5 files to check the training, inference, and result-visualization pipeline. | | ModelScope / OneCode execution | Download the standalone model package, install dependencies, and run the scripts directly. | | Multi-GPU training | Launch PyTorch DDP 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/FourCastNet_v2 --local-dir ./FourCastNet_v2 cd FourCastNet_v2 ``` ### 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 an ERA5 data slice that can be downloaded as follows: ```bash hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data ``` Real HDF5 annual files must contain `fields`, variable attributes, `time_step`, `global_means`, and `global_stds`. When real data is unavailable, first generate synthetic files for pipeline validation: ```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 ``` The default checkpoint is saved to `data/checkpoint/one_step/model_bak.pt`. ### Fine-tuning Single GPU: ```bash python scripts/train.py --stage finetune ``` Multi-GPU: ```bash torchrun --nproc_per_node=8 scripts/train.py --stage finetune ``` The checkpoint is saved to `data/checkpoint/