rapidFlow

This is a project, that tries to accelerate micro research projects by providing a richer functionality for the already known hpyerparameter optimization library optuna. The code of optuna is not modified, it is incorporated into rapidFlow to provide richer evaluation and easy parallel processing.

Getting Started

Prerequisites

• Python >= 3.7
• PyTorch

Install

rapidFlow is build upon Pytorch, so make sure you have PyTorch installed.

1. From Pip Install package with:
   pip install rapidflow

2. With cloned repository Install package with:
   pip install -e /src

TODO:

• move experiment library to another repo
• experiments in docker container with gpu? (or singularity)
• test on multiple gpus
• testing and propper doku
• significance testing

Acknowledgments

Feel free to contribute. If you use this repository please cite with:

    @misc{rapidFlow_geb,
    author = {Gebauer, Michael},
    title = {rapidFlow},
    year = {2022},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/gebauerm/model_storage}},
    }

Author

elysias