GPyTorchWrapper
===============

**GPyTorchWrapper** is a lightweight Python package designed to streamline the training of Gaussian Process (GP) models using `GPyTorch <https://gpytorch.ai/>`_. While it was developed for modeling potential energy surfaces (PES) in small molecular systems using custom permutationally invariant kernels (based on Bartók & Csányi [Bartok2015]_), it is general enough for other regression tasks.

Features
--------

- Modular YAML-based configuration
- Fully differentiable, permutationally invariant kernel functions
- Script for exporting models as TorchScript for deployment

Installation
------------

Set up the environment locally:

.. code-block:: bash

  conda env create -f environment.yml

.. code-block:: bash

  conda activate gpytorchwrapper

.. code-block:: bash

  pip install -e . --use-pep517

For an Intel-optimized HPC environment, use `environment_hpc.yml`.

HPC Support
-----------

A SLURM-based submission script is available in `bash/sub_gp_training.sh`. Use `gp_training.sh` to configure and launch training jobs with CLI options.

Docker Example
--------------

Run the example in Docker:

.. code-block:: bash

   docker build -t gpytorchwrapper . && ./run-example-in-docker.sh

After training, `3d_plot.png` will appear in the working directory.


Documentation
-------------

.. toctree::
   :maxdepth: 1

   training
   deployment
   custom_models
   permutationally_invariant_kernels
   api

Reference
---------

.. [Bartok2015] Bartók, A. P.; Csányi, G. *Gaussian Approximation Potentials: A Brief Tutorial Introduction*. Int. J. Quantum Chem. 2015, 115 (16), 1051–1057. https://doi.org/10.1002/qua.24927