An open source AI model and interface for Earth.
Launch into a JupyterLab environment on
| Binder | SageMaker Studio Lab |
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To help out with development, start by cloning this repo-url
git clone <repo-url>
cd model
Then we recommend using mamba to install the dependencies. A virtual environment will also be created with Python and JupyterLab installed.
mamba env create --file environment.yml
Note
The command above will only work for Linux devices with CUDA GPUs. For installation on macOS devices (either Intel or ARM chips), follow the 'Advanced' section in https://clay-foundation.github.io/model/getting-started/installation.html#advanced
Activate the virtual environment first.
mamba activate claymodel
Finally, double-check that the libraries have been installed.
mamba list
mamba activate claymodel
python -m ipykernel install --user --name claymodel # to install virtual env properly
jupyter kernelspec list --json # see if kernel is installed
jupyter lab &
The neural network model can be ran via LightningCLI v2. To check out the different options available, and look at the hyperparameter configurations, run:
python trainer.py --help
To quickly test the model on one batch in the validation set:
python trainer.py fit --model ClayMAEModule --data ClayDataModule --config configs/config.yaml --trainer.fast_dev_run=True
To train the model:
python trainer.py fit --model ClayMAEModule --data ClayDataModule --config configs/config.yaml
More options can be found using python trainer.py fit --help, or at the
LightningCLI docs.
Our Documentation uses Jupyter Book.
Install it with:
pip install -U jupyter-bookThen build it with:
jupyter-book build docs/You can preview the site locally with:
python -m http.server --directory _build/htmlThere is a GitHub Action on ./github/workflows/deploy-docs.yml that builds the site and pushes it to GitHub Pages.