ClimODE
# Model Introduction ClimODE is a weather forecasting model proposed in 2024 by researchers from Aalto University and collaborating institutions. Paper: ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs https://arxiv.org/abs/2404.10024 # Model Description ClimODE is a physics-informed neural ordinary differential equation model for global, monthly-scale, and regional climate and weather forecasting. It represents atmospheric evolution as a continuous-time dynamical system and incorporates a transport-based physical inductive bias into the neural ODE. # Use Cases | Scenario | Description | | :---: | :--- | | Global weather forecasting | Train or evaluate ClimODE on ERA5 data following this project's five-variable protocol. | | Local quick validation | Use synthetic ERA5 HDF5 data to check data loading, training, inference, evaluation, and visualization. | | ModelScope / OneCode execution | Download the standalone model package, install dependencies, and run the scripts directly. | | Multi-GPU training | Launch PyTorch DistributedDataParallel 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** - Training and inference require a GPU or DCU recognized by PyTorch. CPU can be used to generate synthetic data and inspect configuration, but cannot run the current training and inference scripts. - Multi-GPU training uses the NCCL backend. Ensure that the device driver, communication libraries, and PyTorch version are compatible. - 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/ClimODE --local-dir ./ClimODE cd ClimODE ``` ### 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 for training. Download it and confirm that the paths in `conf/config.yaml` point to the downloaded data: ```bash hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data ``` ClimODE reads the five variables `z`, `t`, `t2m`, `u10`, and `v10`, regridding the source `721x1440` fields to the model's `32x64` grid. The source variable mapping is defined in `conf/config.yaml`. ### Generate Synthetic Data When real ERA5 data is unavailable, generate a full-resolution synthetic fixture for pipeline validation: ```bash python scripts/fake_data.py ``` Synthetic data does not represent ERA5 and cannot reproduce the paper's metrics. ### 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/checkpoints/model_bak.pth`. ### Fine-tuning To fine-tune from an existing checkpoint, pass an explicit checkpoint and mode: ```bash python scripts/train.py --mode finetune --checkpoint data/checkpoints/model_bak.pth ``` An official pretrained checkpoint can be selected explicitly with `--use-pretrained --pretrained-checkpoint