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Quick-Tune-Tool#

A Practical Tool and User Guide for Automatically Finetuning Pretrained Models

Quick-Tune-Tool is an automated solution designed to streamline the process of selecting and finetuning pretrained models across various machine learning domains. Built upon the Quick-Tune algorithm, this tool abstracts complex research-level code into a user-friendly framework, making model finetuning accessible and efficient for practitioners.

Installation#

pip install quicktunetool
# or
git clone https://github.com/automl/quicktunetool
pip install -e quicktunetool  # Use -e for editable mode

Usage#

A simple example for using Quick-Tune-Tool with a pretrained optimizer for image classification:

from qtt import QuickTuner, get_pretrained_optimizer
from qtt.finetune.cv.classification import finetune_script

# Load task information and meta-features
task_info, metafeat = extract_task_info_metafeat("path/to/dataset")

# Initialize the optimizer
optimizer = get_pretrained_optimizer("mtlbm/micro")
optimizer.setup(128, metafeat)

# Create QuickTuner instance and run
qt = QuickTuner(optimizer, finetune_script)
qt.run(task_info, time_budget=3600)

This code snippet demonstrates how to run QTT on an image dataset in just a few lines of code.

Contributing#

Contributions are welcome! Please follow these steps:

  1. Fork the repository
  2. Create a new branch (git checkout -b feature/YourFeature)
  3. Commit your changes (git commit -m 'Add your feature')
  4. Push to the branch (git push origin feature/YourFeature)
  5. Open a pull request

For any questions or suggestions, please contact the maintainers.

Project Status#

  • ✅ Active development

Support#

License#

This project is licensed under the BSD License - see the LICENSE file for details.

Made with ❤️ by @automl