Fine-Tuning Scheduler Release 0.2.0

[0.2.0] - 2022-08-06

Added

• support for pytorch-lightning 1.7.0
• switched to src-layout project structure
• increased flexibility of internal package management
• added a patch to examples to allow them to work with torch 1.12.0 despite issue #80809
• added sync for test log calls for multi-gpu testing

Fixed

• adjusted runif condition for examples tests
• minor type annotation stylistic correction to avoid jsonargparse issue fixed in
  #148

Changed

• streamlined MANIFEST.in directives
• updated docker image dependencies
• disable mypy unused ignore warnings due to variable behavior depending on ptl installation method
  (e.g. pytorch-lightning vs full lightning package)
• changed full ci testing on mac to use macOS-11 instead of macOS-10.15
• several type-hint mypy directive updates
• unpinned protobuf in requirements as no longer necessary
• updated cuda docker images to use pytorch-lightning 1.7.0, torch 1.12.0 and cuda-11.6
• refactored mock strategy test to use a different mock strategy
• updated pyproject.toml with jupytext metadata bypass configuration for nb test cleanup
• updated ptl external class references for ptl 1.7.0
• narrowed scope of runif test helper module to only used conditions
• updated nb tutorial links to point to stable branch of docs
• unpinned jsonargparse and bumped min version to 4.9.0
• moved core requirements.txt to requirements/base.txt and update load_requirements and setup to reference lightning
  meta package
• update azure pipelines ci to use torch 1.12.0
• renamed instantiate_registered_class meth to instantiate_class due to ptl 1.7 deprecation of cli registry
  functionality

Deprecated

• removed ddp2 support
• removed use of ptl cli registries in examples due to its deprecation

Fine-Tuning Scheduler Patch Release 0.1.8

[0.1.8] - 2022-07-13

Added

• enhanced support and testing for lr schedulers with lr_lambdas attributes
• accept and automatically convert schedules with non-integer phase keys (that are convertible to integers) to integers

Fixed

• pinned jsonargparse to be <= 4.10.1 due to regression with PTL cli with 4.10.2

Changed

• updated PL links for new lightning-ai github urls
• added a minimum hydra requirement for cli usage (due to omegaconf version incompatibility)
• separated cli requirements
• replace closed compound instances of finetuning with the hyphenated compound version fine-tuning in textual
  contexts. (The way language evolves, fine-tuning will eventually become finetuning but it seems like the research
  community prefers the hyphenated form for now.)
• update fine-tuning scheduler logo for hyphenation
• update strategy resolution in test helper module runif

Deprecated

Finetuning Scheduler Patch Release 0.1.7

[0.1.7] - 2022-06-10

Fixed

• bump omegaconf version requirement in examples reqs (in addition to extra reqs) due to omegaconf bug

Finetuning Scheduler Patch Release 0.1.6

[0.1.6] - 2022-06-10

Added

• Enable use of untested strategies with new flag and user warning
• Update various dependency minimum versions
• Minor example logging update

Fixed

• minor privacy policy link update
• bump omegaconf version requirement due to omegaconf bug

Finetuning Scheduler Patch Release 0.1.5

[0.1.5] - 2022-06-02

Added

• Bumped latest tested PL patch version to 1.6.4
• Added basic notebook-based example tests a new ipynb-specific extra
• Updated docker definitions
• Extended multi-gpu testing to include both oldest and latest supported PyTorch versions
• Enhanced requirements parsing functionality

Fixed

• cleaned up acknowledged warnings in multi-gpu example testing

Finetuning Scheduler Release 0.1.4

[0.1.4] - 2022-05-24

Added

• LR scheduler reinitialization functionality (#2)
• advanced usage documentation
• advanced scheduling examples
• notebook-based tutorial link
• enhanced cli-based example hparam logging among other code clarifications

Fixed

• addressed URI length limit for custom badge
• allow new deberta fast tokenizer conversion warning for transformers >= 4.19

Finetuning Scheduler Patch Release 0.1.3

[0.1.3] - 2022-05-04

Changed

• bumped latest tested PL patch version to 1.6.3

Finetuning Scheduler Patch Release 0.1.2

[0.1.2] - 2022-04-27

Added

• added multiple badges (docker, conda, zenodo)
• added build status matrix to readme

Changed

• bumped latest tested PL patch version to 1.6.2
• updated citation cff configuration to include all version metadata
• removed tag-based trigger for azure-pipelines multi-gpu job

Fixed

Deprecated

Finetuning Scheduler Patch Release 0.1.1

[0.1.1] - 2022-04-15

Added

• added conda-forge package (pending approval by conda-forge maintainers, should be available within a few days)
• added docker release and pypi workflows
• additional badges for readme, testing enhancements for oldest/newest pl patch versions

Changed

• bumped latest tested PL patch version to 1.6.1, CLI example depends on PL logger fix (#12609)

Fixed

• Addressed version prefix issue with readme transformation for pypi

Finetuning Scheduler Initial Release

Finetuning Scheduler is a PyTorch Lightning extension that accelerates and enhances model experimentation with flexible finetuning schedules.

It's 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