HyperNOs Documentation

Introduction

HyperNOs is a Python project focused on the implementation of completely automatic, distributed and parallel neural operators hyperparameter optimization. The project aims to provide a framework for training neural operator models using Pytorch and Ray Tune for hyperparameter tuning.

Visualization website

The project also includes a visualization website: (https://hypernos.streamlit.app) that allows users to visualize the results obtained with HyperNOs library.

Installation

To set up the HyperNOs project, follow these steps:

1. Clone the repository:

   git clone --depth=1 https://github.com/MaxGhi8/HyperNOs.git
   cd HyperNOs

2. Install the required dependencies. It is recommended to create a virtual environment before installing the dependencies; I personally use pyenv (but others, like uv, are fine) and Python version 3.12.7 for this purpose:

   pyenv install 3.12.7
   pyenv virtualenv 3.12.7 hypernos
   pyenv activate hypernos

   Then, install the dependencies using pip:

   pip install -r requirements.txt

Warning

For pytorch can be need more attention in the installation, if you do not want the default installation please follow the documentation.

3. Download the dataset using the download_data.sh script:

   ./download_data.sh

Warning

Only for Windows I recommend to install WSL. Then open the WSL terminal and navigate where you have installed the HyperNOs library

cd /mnt/c/Users/<your_user>/<your_path_to_HyperNOs>

and then try to run the program with ./download_data.sh if you get an error like /bin/bash^M: bad interpreter. No such file or directory this can be due to CR and LF in Windows. In this case try to run the following line and then rerun the program.

sed -i -e 's/\r$//' download_data.sh
./download_data.sh

4. If you want to download our trained model this have to be done in two steps. First of all clone the following github repository:

    git clone --depth=1 https://github.com/MaxGhi8/tests

   The previous repository contains the Tensorboard support for every model, the information about the training and the architecture's hyperparameters chosen. Then you can download running the following script and select the model that you want to download:

   ./download_trained_model.sh

Warning

As before, for Windows, if you are on WSL and get the error /bin/bash^M: bad interpreter. No such file or directory try to run sed -i -e 's/\r$//' download_trained_model.sh and then rerun the script ./download_trained_model.sh.

Citation

If you use our library please consider citing our paper:

@misc{ghiotto2025hypernosautomatedparallellibrary,
      title={HyperNOs: Automated and Parallel Library for Neural Operators Research}, 
      author={Massimiliano Ghiotto},
      year={2025},
      eprint={2503.18087},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2503.18087}, 
}