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This applies to the source code as well as the trained model weights.\n\nThe Documentation is licensed under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.\n\n## Quickstart\n\nLaunch into a [JupyterLab](https://jupyterlab.readthedocs.io) environment on\n\n| [Binder](https://mybinder.readthedocs.io/en/latest) | [SageMaker Studio Lab](https://studiolab.sagemaker.aws) |\n|:--:|:--:|\n| [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Clay-foundation/model/main) | [![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/Clay-foundation/model/blob/main/docs/tutorials/wall-to-wall.ipynb) |\n\n## Installation\n\n### Pip Installation (Recommended)\n\nThe easiest way to install Clay Foundation Model is via pip:\n\n    pip install git+https://github.com/Clay-foundation/model.git\n\nThis will install the `claymodel` package and all its dependencies. You can then import and use it in your Python code:\n\n```python\nfrom claymodel.datamodule import ClayDataModule\nfrom claymodel.module import ClayMAEModule\n```\n\n### Development Installation\n\nFor development or advanced usage, you can set up the full development environment:\n\nTo help out with development, start by cloning this [repo-url](/../../)\n\n    git clone \u003crepo-url\u003e\n    cd model\n\nThen we recommend [using mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html)\nto install the dependencies. A virtual environment will also be created with Python and\n[JupyterLab](https://github.com/jupyterlab/jupyterlab) installed.\n\n    mamba env create --file environment.yml\n\n\u003e [!NOTE]\n\u003e The command above has been tested on Linux devices with CUDA GPUs.\n\nActivate the virtual environment first.\n\n    mamba activate claymodel\n\nFinally, double-check that the libraries have been installed.\n\n    mamba list\n\n\n## Usage\n\n### Running jupyter lab\n\n    mamba activate claymodel\n    python -m ipykernel install --user --name claymodel  # to install virtual env properly\n    jupyter kernelspec list --json                       # see if kernel is installed\n    jupyter lab \u0026\n\n\n### Running the model\n\nThe neural network model can be ran via\n[LightningCLI v2](https://pytorch-lightning.medium.com/introducing-lightningcli-v2supercharge-your-training-c070d43c7dd6).\n\n\u003e [!NOTE]\n\u003e If you installed via pip, you'll need to clone the repository to access the trainer script and config files.\n\nTo check out the different options available, and look at the hyperparameter\nconfigurations, run:\n\n    python trainer.py --help\n\nTo quickly test the model on one batch in the validation set:\n\n    python trainer.py fit --model ClayMAEModule --data ClayDataModule --config configs/config.yaml --trainer.fast_dev_run=True\n\nTo train the model:\n\n    python trainer.py fit --model ClayMAEModule --data ClayDataModule --config configs/config.yaml\n\nMore options can be found using `python trainer.py fit --help`, or at the\n[LightningCLI docs](https://lightning.ai/docs/pytorch/2.1.0/cli/lightning_cli.html).\n\n## Contributing\n\n### Writing documentation\n\nOur Documentation uses [Jupyter Book](https://jupyterbook.org/intro.html).\n\nInstall it with:\n```bash\npip install -U jupyter-book\n```\n\nThen build it with:\n```bash\njupyter-book build docs/\n```\n\nYou can preview the site locally with:\n```bash\npython -m http.server --directory _build/html\n```\n\nThere is a GitHub Action on `./github/workflows/deploy-docs.yml` that builds the site and pushes it to GitHub Pages.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FClay-foundation%2Fmodel","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FClay-foundation%2Fmodel","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FClay-foundation%2Fmodel/lists"}