| --- |
| frameworks: PyTorch |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - OneScience |
| - Earth Science |
| - Ocean Simulation |
| - Global Ocean Forecasting |
| - OM4 |
| - ConvNeXt |
| tasks: [] |
| datasets: |
| - M2LInES/Samudra-OM4 |
| --- |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">Samudra</span> |
| </strong> |
| </p> |
| |
| # Model Introduction |
|
|
| Samudra is a global ocean emulator developed by the M2LInES team. |
|
|
| Paper: Samudra: An AI Global Ocean Emulator for Climate |
|
|
| https://doi.org/10.1029/2024GL114318 |
|
|
| # Model Description |
|
|
| Samudra predicts global ocean states on an approximately one-degree grid with a five-day time step. It is designed to emulate the evolution of the OM4 ocean circulation model with a deep neural network. |
|
|
| # Use Cases |
|
|
| | Scenario | Description | |
| | :---: | :--- | |
| | Global ocean simulation | Train the model on OM4 data following the 77-state-channel and 4-forcing-channel Samudra protocol. | |
| | Local quick validation | Use synthetic NPZ data to check training, inference, and ocean-field 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** |
|
|
| - 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/Samudra --local-dir ./Samudra |
| cd Samudra |
| ``` |
|
|
| ### 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 official training data is generated by NOAA/GFDL OM4. The M2LInES project provides the dataset and documentation: |
|
|
| https://huggingface.co/datasets/M2LInES/Samudra-OM4 |
|
|
| The dataset must be converted to the native NPZ layout expected by this repository. When real OM4 data is unavailable, generate a synthetic fixture for pipeline validation: |
|
|
| ```bash |
| python scripts/fake_data.py |
| ``` |
|
|
| The synthetic fixture contains 77 prognostic state channels and 4 boundary-forcing channels and is not suitable for scientific evaluation. |
|
|
| ### 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`. |
|
|
| ### Training Weights |
|
|
| This repository provides a `weight/` directory for Samudra checkpoints. The weight files will be uploaded soon and are expected to be available in the near future. |
|
|
| ### Inference |
|
|
| Inference performs an autoregressive rollout from `data/test.npz` and reads `data/checkpoints/model_bak.pth` by default: |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| Predictions are written to `result/output/prediction.npz`. |
|
|
| ### Evaluation and Visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
|
| The default outputs are `result/forecast_maps.png` and `result/temperature_profile.png`. |
|
|
| # Official OneScience Resources |
|
|
| | 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 and License |
|
|
| - Paper: https://doi.org/10.1029/2024GL114318 |
| - This repository is an independent reproduction of the original Samudra paper. |
|
|
