Awesome
Clay Foundation Model
An open source AI model and interface for Earth.
Quickstart
Launch into a JupyterLab environment on
Binder | SageMaker Studio Lab |
---|---|
Installation
Basic
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
Usage
Running jupyter lab
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 &
Running the model
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.
Contributing
Writing documentation
Our Documentation uses Jupyter Book.
Install it with:
pip install -U jupyter-book
Then build it with:
jupyter-book build docs/
You can preview the site locally with:
python -m http.server --directory _build/html
There is a GitHub Action on ./github/workflows/deploy-docs.yml
that builds the site and pushes it to GitHub Pages.