| Name | quicktunetool JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | None |
| Summary | A Framework for Efficient Model Selection and Hyperparameter Optimization |
| upload_time | 2024-12-19 09:26:57 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | BSD 3-Clause License Copyright (c) 2024, AutoML-Freiburg-Hannover Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
machine learning
automl
hpo
fine-tuning
meta-learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
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|
# Quick-Tune-Tool
[](https://pypi.python.org/pypi/quicktunetool)
[](https://pypi.python.org/pypi/quicktunetool)
[](https://github.com/astral-sh/ruff)
[](CONTRIBUTING.md)
**A Practical Tool and User Guide for Automatically Finetuning Pretrained Models**
> Quick-Tune-Tool is an automated solution for selecting and finetuning pretrained models across various machine learning domains. Built upon the Quick-Tune algorithm, this tool bridges the gap between research-code and practical applications, making model finetuning accessible and efficient for practitioners.
## Installation
```bash
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:
```python
from qtt import QuickTuner, get_pretrained_optimizer
from qtt.finetune.image.classification import fn
# Load task information and meta-features
task_info, metafeat = extract_task_info_metafeat("path/to/dataset")
# Initialize the optimizer
optimizer = get_pretrained_optimizer("mtlbm/full")
optimizer.setup(128, metafeat)
# Create QuickTuner instance and run
qt = QuickTuner(optimizer, fn)
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
- 📝 [Documentation](https://automl.github.io/quicktunetool/)
- 🐛 [Issue Tracker](https://github.com/automl/quicktunetool/issues)
- 💬 [Discussions](https://github.com/automl/quicktunetool/discussions)
## License
This project is licensed under the BSD License - see the LICENSE file for details.
## References
The concepts and methodologies of QuickTuneTool are detailed in the following workshop paper:
```
@inproceedings{
rapant2024quicktunetool,
title={Quick-Tune-Tool: A Practical Tool and its User Guide for Automatically Finetuning Pretrained Models},
author={Ivo Rapant and Lennart Purucker and Fabio Ferreira and Sebastian Pineda Arango and Arlind Kadra and Josif Grabocka and Frank Hutter},
booktitle={AutoML Conference 2024 (Workshop Track)},
year={2024},
url={https://openreview.net/forum?id=d0Hapti3Uc}
}
```
If you use QuickTuneTool in your research, please also cite the following 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}
}
```
---
Made with ❤️ by https://github.com/automl
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"description": "# Quick-Tune-Tool\n\n[](https://pypi.python.org/pypi/quicktunetool)\n[](https://pypi.python.org/pypi/quicktunetool)\n[](https://github.com/astral-sh/ruff)\n[](CONTRIBUTING.md)\n\n**A Practical Tool and User Guide for Automatically Finetuning Pretrained Models**\n\n> Quick-Tune-Tool is an automated solution for selecting and finetuning pretrained models across various machine learning domains. Built upon the Quick-Tune algorithm, this tool bridges the gap between research-code and practical applications, making model finetuning accessible and efficient for practitioners.\n\n\n## Installation\n```bash\npip install quicktunetool\n# or\ngit clone https://github.com/automl/quicktunetool\npip install -e quicktunetool # Use -e for editable mode\n```\n\n\n## Usage\n\nA simple example for using Quick-Tune-Tool with a pretrained optimizer for image classification:\n\n```python\nfrom qtt import QuickTuner, get_pretrained_optimizer\nfrom qtt.finetune.image.classification import fn\n\n# Load task information and meta-features\ntask_info, metafeat = extract_task_info_metafeat(\"path/to/dataset\")\n\n# Initialize the optimizer\noptimizer = get_pretrained_optimizer(\"mtlbm/full\")\noptimizer.setup(128, metafeat)\n\n# Create QuickTuner instance and run\nqt = QuickTuner(optimizer, fn)\nqt.run(task_info, time_budget=3600)\n```\n\nThis code snippet demonstrates how to run QTT on an image dataset in just a few lines of code.\n\n## Contributing\n\nContributions are welcome! Please follow these steps:\n\n1. Fork the repository\n2. Create a new branch (`git checkout -b feature/YourFeature`)\n3. Commit your changes (`git commit -m 'Add your feature'`)\n4. Push to the branch (`git push origin feature/YourFeature`)\n5. Open a pull request\n\nFor any questions or suggestions, please contact the maintainers.\n\n## Project Status\n\n- \u2705 Active development\n\n## Support\n\n- \ud83d\udcdd [Documentation](https://automl.github.io/quicktunetool/)\n- \ud83d\udc1b [Issue Tracker](https://github.com/automl/quicktunetool/issues)\n- \ud83d\udcac [Discussions](https://github.com/automl/quicktunetool/discussions)\n\n## License\n\nThis project is licensed under the BSD License - see the LICENSE file for details.\n\n## References\n\nThe concepts and methodologies of QuickTuneTool are detailed in the following workshop paper:\n\n```\n@inproceedings{\nrapant2024quicktunetool,\ntitle={Quick-Tune-Tool: A Practical Tool and its User Guide for Automatically Finetuning Pretrained Models},\nauthor={Ivo Rapant and Lennart Purucker and Fabio Ferreira and Sebastian Pineda Arango and Arlind Kadra and Josif Grabocka and Frank Hutter},\nbooktitle={AutoML Conference 2024 (Workshop Track)},\nyear={2024},\nurl={https://openreview.net/forum?id=d0Hapti3Uc}\n}\n```\n\nIf you use QuickTuneTool in your research, please also cite the following paper:\n\n```\n@inproceedings{\narango2024quicktune,\ntitle={Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How},\nauthor={Sebastian Pineda Arango and Fabio Ferreira and Arlind Kadra and Frank Hutter and Josif Grabocka},\nbooktitle={The Twelfth International Conference on Learning Representations},\nyear={2024},\nurl={https://openreview.net/forum?id=tqh1zdXIra}\n}\n```\n\n---\n\nMade with \u2764\ufe0f by https://github.com/automl\n",
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