QuickTuneTool: A Framework for Efficient Model Selection and Hyperparameter Optimization

QuickTune is a tool designed to address the challenge of selecting the optimal pretrained model and its finetuning hyperparameters for new datasets. QuickTune aims to streamline this process by using a Combined Algorithm Selection and Hyperparameter Optimization (CASH) technique within a Bayesian optimization framework.

The approach is based on three key components:

1. Gray-Box Hyperparameter Optimization (HPO): We explore learning curves partially by training models for a few epochs initially and investing more time into the most promising candidates.
2. Meta-Learning: We utilize information from previous evaluations on related tasks to guide the search process more effectively.
3. Cost-Awareness: We balance the trade-off between time and performance during the search for optimal models and hyperparameters.

Find more information in the paper Quick-Tune: Quickly Learning Which Pre Trained Model to Fine Tune and How ICLR2024

At the moment only Image Classification is implemented.

Getting Started

Installation

Create environment:

conda create -n qtt python=3.10
conda activate qtt

Install from source

git clone https://github.com/automl/QTT
pip install -e QTT

Basic Usage

We provide a simple to use script.

from qtt import QuickCVCLSTuner
tuner = QuickCVCLSTuner("path/to/dataset")
tuner.run(fevals=100, time_budget=3600)

For more code examples take a look into the notebooks folder.

Advanced Usage

Download the QuickTune Meta-Album-Dataset:

wget https://nextcloud.tf.uni-freiburg.de/index.php/s/fQmPmB84EmwxddJ/download/mtlbm.zip
unzip mtlbm.zip

The Meta-Dataset consists of learning curves generated with different vision datasets of the Meta Album. They are divided into three different groups: micro, mini and extended.

If you want to train your own predictors, take a look at the examples folder and modify the script to your needs.

References

The concepts and methodologies of this project are discussed in the following workshop paper:

Title: Quick-Tune-Tool: A Practical Tool and its User Guide for Automatically Finetuning Pretrained Models

Authors: Ivo Rapant, Lennart Purucker, Fabio Ferreira, Sebastian Pineda Arango, Arlind Kadra, Josif Grabocka, Frank Hutter

Conference: AutoML 2024 Workshop

You can access the full paper and additional details on OpenReview here.

Further References / Citations

This project is based on the following paper. Please also consider citing this paper:

@inproceedings{
arango2024quicktune,
title={Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How},
author={Sebastian Pineda Arango and Fabio Ferreira and Arlind Kadra and Frank Hutter and Josif Grabocka},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=tqh1zdXIra}
}