finetuning-scheduler 0.1.4

A PyTorch Lightning extension that enhances model experimentation with flexible finetuning schedules.

A PyTorch Lightning extension that enhances model experimentation with flexible finetuning schedules.

---

Docs • Setup • Examples • Community

---

FinetuningScheduler is simple to use yet powerful, offering a number of features that facilitate model research and exploration:

• easy specification of flexible finetuning schedules with explicit or regex-based parameter selection
  • implicit schedules for initial/naive model exploration
  • explicit schedules for performance tuning, fine-grained behavioral experimentation and computational efficiency
• automatic restoration of best per-phase checkpoints driven by iterative application of early-stopping criteria to each finetuning phase
• composition of early-stopping and manually-set epoch-driven finetuning phase transitions

---

Setup

Step 0: Install from PyPI

pip install finetuning-scheduler

Step 1: Import the FinetuningScheduler callback and start finetuning!

from pytorch_lightning import Trainer
from finetuning_scheduler import FinetuningScheduler

trainer = Trainer(callbacks=[FinetuningScheduler()])

Get started by following the Finetuning Scheduler introduction which includes a CLI-based example or by following the notebook-based Finetuning Scheduler tutorial.

---

Examples

Scheduled Finetuning For SuperGLUE

• Notebook-based Tutorial
• CLI-based Tutorial

---

Continuous Integration

Finetuning Scheduler is rigorously tested across multiple CPUs, GPUs and against major Python and PyTorch versions. Each Finetuning Scheduler minor release (major.minor.patch) is paired with a PyTorch Lightning minor release (e.g. Finetuning Scheduler 0.1 depends upon PyTorch Lightning 1.6).

To ensure maximum stability, the latest PyTorch Lightning patch release fully tested with Finetuning Scheduler is set as a maximum dependency in Finetuning Scheduler's requirements.txt (e.g. <= 1.6.1). If you'd like to test a specific PyTorch Lightning patch version greater than that currently in Finetuning Scheduler's requirements.txt, it will likely work but you should install Finetuning Scheduler from source and update the requirements.txt as desired.

Current build statuses for Finetuning Scheduler

System / PyTorch ver	1.8 (LTS, min. req.)	1.11 (latest)
Linux py3.9 [GPUs**]		-
Linux py3.{7,9}
OSX py3.{7,9}
Windows py3.{7,9}

• ** tests run on two RTX 2070s

Community

Finetuning Scheduler is developed and maintained by the community in close communication with the PyTorch Lightning team. Thanks to everyone in the community for their tireless effort building and improving the immensely useful core PyTorch Lightning project.

PR's welcome! Please see the contributing guidelines (which are essentially the same as PyTorch Lightning's).

---

Citing Finetuning Scheduler

Please cite:

@misc{Dan_Dale_2022_6463952,
    author       = {Dan Dale},
    title        = {{Finetuning Scheduler}},
    month        = Feb,
    year         = 2022,
    doi          = {10.5281/zenodo.6463952},
    publisher    = {Zenodo},
    url          = {https://zenodo.org/record/6463952}
    }

Feel free to star the repo as well if you find it useful or interesting. Thanks 😊!