# Changelog

All notable changes to this project will be documented in this file.

The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

## [2.10.0] - 2025-XX-XX

## [2.9.0] - 2025-10-20

### [2.9.0] - Added

- Added YAML (multi)representer for ``PretrainedConfig`` object types
- Introduced a `log_dir` parameter to allow specifying a custom directory for artifacts, defaulting to `trainer.log_dir` or `trainer.default_root_dir` [#17](https://github.com/speediedan/finetuning-scheduler/pull/17/).
- Added trainer convenience reference to FTS for a cleaner interface and enable future improved encapsulation
- Improved testing infrastructure with centralized test warnings and coverage/environment build management scripts
- Added dynamic versioning system with CLI tool `toggle-lightning-mode` for switching between unified (`lightning.pytorch`) and standalone (`pytorch_lightning`) imports. Resolves [#10](https://github.com/speediedan/finetuning-scheduler/issues/10).
- Added support for Lightning CI commit pinning via `USE_CI_COMMIT_PIN` environment variable
- Modernized build system using pyproject.toml with setuptools PEP 639 support

### [2.9.0] - Fixed

- Addressed upstream issue [#20615](https://github.com/Lightning-AI/pytorch-lightning/issues/20615) where `trainer.log_dir` resolution logic does not accommodate artifact persistence via `trainer.log_dir` for loggers that do not have a `save_dir` set (using `trainer.default_root_dir`) by refactoring and enhancing FTS `log_dir` handling in [#17](https://github.com/speediedan/finetuning-scheduler/pull/17/).

### [2.9.0] - Changed

- Official versioning policy to align with PyTorch minor releases; see the new Versioning documentation and compatibility matrix
  (docs/versioning.rst) for details on supported PyTorch/Lightning ranges: [Versioning docs](https://finetuning-scheduler.readthedocs.io/en/stable/versioning.html)

- Updated documentation and improved type annotations
- einsum patch no longer required for PyTorch >= 2.6 for FTS to leverage 2D mesh parallelism
- Improved CI configuration with automatic Lightning commit pinning

### [2.9.0] - Deprecated

- removed support for PyTorch `2.2`, `2.3`, and `2.4`
- removed use of conda builds (aligning with upstream PyTorch)

## [2.5.3] - 2025-08-14

### [2.5.3] - Added

- Verified support for Lightning ``2.5.2`` and ``2.5.3``

### [2.5.3] - Fixed

- Updated explicit pytorch version mapping matrix to include recent PyTorch release
- Fixed newly failing test dependent on deprecated Lightning class attribute. Resolved [#19](https://github.com/speediedan/finetuning-scheduler/issues/19).

### [2.5.3] - Changed

- For the examples extra, updated minimum `datasets` version to `4.0.0` to ensure the new API (especially important removal of `trust_remote_code`) is used.

## [2.5.1] - 2025-03-27

### [2.5.1] - Added

- Support for Lightning ``2.5.1``
- Added (multi)representer for ``PretrainedConfig`` object types

## [2.5.0] - 2024-12-20

### [2.5.0] - Added

- Support for Lightning and PyTorch ``2.5.0``
- FTS support for PyTorch's composable distributed (e.g. ``fully_shard``, ``checkpoint``) and Tensor Parallelism (TP) APIs
- Support for Lightning's ``ModelParallelStrategy``
- Experimental 'Auto' FSDP2 Plan Configuration feature, allowing application of the ``fully_shard`` API using module
name/pattern-based configuration instead of manually inspecting modules and applying the API in ``LightningModule.configure_model``
- FSDP2 'Auto' Plan Convenience Aliases, simplifying use of both composable and non-composable activation checkpointing APIs
- Flexible orchestration of advanced profiling combining multiple complementary PyTorch profilers with FTS ``MemProfiler``

### [2.5.0] - Fixed

- Added logic to more robustly condition depth-aligned checkpoint metadata updates to address edge-cases where `current_score` precisely equaled the `best_model_score` at multiple different depths. Resolved [#15](https://github.com/speediedan/finetuning-scheduler/issues/15).

### [2.5.0] - Deprecated

- As upstream PyTorch [has deprecated](https://github.com/pytorch/pytorch/issues/138506) official Anaconda channel builds, `finetuning-scheduler` will no longer be releasing conda builds. Installation of FTS via pip (irrespective of the virtual environment used) is the recommended installation approach.
- removed support for PyTorch `2.1`

## [2.4.0] - 2024-08-15

### [2.4.0] - Added

- Support for Lightning and PyTorch ``2.4.0``
- Support for Python ``3.12``

### [2.4.0] - Changed

- Changed default value of the ``frozen_bn_track_running_stats`` option to the FTS callback constructor to ``True``.

### [2.4.0] - Deprecated

- removed support for PyTorch `2.0`
- removed support for Python `3.8`

## [2.3.3] - 2024-07-09

- Support for Lightning <= ``2.3.3`` (includes critical security fixes) and PyTorch <= ``2.3.1``

## [2.3.2] - 2024-07-08

- Support for Lightning <= ``2.3.2`` and PyTorch <= ``2.3.1``

## [2.3.0] - 2024-05-17

### [2.3.0] - Added

- Support for Lightning and PyTorch ``2.3.0``
- Introduced the ``frozen_bn_track_running_stats`` option to the FTS callback constructor, allowing the user to override the default Lightning behavior that disables ``track_running_stats`` when freezing BatchNorm layers. Resolves[#13](https://github.com/speediedan/finetuning-scheduler/issues/13).

### [2.3.0] - Deprecated

- removed support for PyTorch `1.13`

## [2.2.4] - 2024-05-04

### [2.2.4] - Added

- Support for Lightning ``2.2.4`` and PyTorch ``2.2.2``

## [2.2.1] - 2024-03-04

### [2.2.1] - Added

- Support for Lightning ``2.2.1``

## [2.2.0] - 2024-02-08

### [2.2.0] - Added

- Support for Lightning and PyTorch ``2.2.0``
- FTS now inspects any base `EarlyStopping` or `ModelCheckpoint` configuration passed in by the user and applies that configuration when instantiating the required FTS callback dependencies (i.e., `FTSEarlyStopping` or `FTSCheckpoint`). Part of the resolution to [#12](https://github.com/speediedan/finetuning-scheduler/issues/12).

### [2.2.0] - Changed

- updated reference to renamed `FSDPPrecision`
- increased `jsonargparse` minimum supported version to `4.26.1`

### [2.2.0] - Fixed

- Explicitly `rank_zero_only`-guarded `ScheduleImplMixin.save_schedule` and `ScheduleImplMixin.gen_ft_schedule`. Some codepaths were incorrectly invoking them from non-`rank_zero_only` guarded contexts. Resolved [#11](https://github.com/speediedan/finetuning-scheduler/issues/11).
- Added a note in the documentation indicating more clearly the behavior of FTS when no monitor metric configuration is provided. Part of the resolution to [#12](https://github.com/speediedan/finetuning-scheduler/issues/12).

### [2.2.0] - Deprecated

- removed support for PyTorch `1.12`
- removed legacy FTS examples


## [2.1.4] - 2024-02-02

### [2.1.4] - Added

- Support for Lightning ``2.1.4``

### [2.1.4] - Changed

- bumped `sphinx` requirement to `>5.0,<6.0`

### [2.1.4] - Deprecated

- removed deprecated lr `verbose` init param usage
- removed deprecated `tensorboard.dev` references


## [2.1.3] - 2023-12-21

### [2.1.3] - Added

- Support for Lightning ``2.1.3``


## [2.1.2] - 2023-12-20

### [2.1.2] - Added

- Support for Lightning ``2.1.2``

### [2.1.2] - Fixed

- Explicitly `rank_zero_only`-guarded `ScheduleImplMixin.save_schedule` and `ScheduleImplMixin.gen_ft_schedule`. Some codepaths were incorrectly invoking them from non-`rank_zero_only` guarded contexts. Resolves [#11](https://github.com/speediedan/finetuning-scheduler/issues/11).


## [2.1.1] - 2023-11-08

### [2.1.1] - Added

- Support for Lightning ``2.1.1``


## [2.1.0] - 2023-10-12

### [2.1.0] - Added

- Support for Lightning and PyTorch ``2.1.0``
- Support for Python ``3.11``
- Support for simplified scheduled FSDP training with PyTorch >= ``2.1.0`` and ``use_orig_params`` set to ``True``
- Unified different FSDP `use_orig_params` mode code-paths to support saving/restoring full, consolidated OSD (PyTorch versions >= ``2.0.0``)
- added support for FSDP `activation_checkpointing_policy` and updated FSDP profiling examples accordingly
- added support for `CustomPolicy` and new implementation of `ModuleWrapPolicy` with FSDP `2.1.0`

### [2.1.0] - Changed

- FSDP profiling examples now use a patched version of ``FSDPStrategy`` to avoid https://github.com/omni-us/jsonargparse/issues/337 with ``jsonargparse`` < ``4.23.1``

### [2.1.0] - Fixed

- updated ``validate_min_wrap_condition`` to avoid overly restrictive validation in some ``use_orig_params`` contexts
- for PyTorch versions < 2.0, when using the FSDP strategy, disabled optimizer state saving/restoration per https://github.com/Lightning-AI/lightning/pull/18296
- improved fsdp strategy adapter `no_decay` attribute handling

### [2.1.0] - Deprecated

- ``FSDPStrategyAdapter`` now uses the ``configure_model`` hook rather than the deprecated ``configure_sharded_model`` hook to apply the relevant model wrapping. See https://github.com/Lightning-AI/lightning/pull/18004 for more context regarding ``configure_sharded_model`` deprecation.
- Dropped support for PyTorch ``1.11.x``.

## [2.0.9] - 2023-10-02

- Support for Lightning 2.0.8 and 2.0.9

## [2.0.7] - 2023-08-16

- Support for Lightning 2.0.7

## [2.0.6] - 2023-08-15

- Support for Lightning 2.0.5 and 2.0.6

## [2.0.4] - 2023-06-22

- Support for PyTorch Lightning 2.0.3 and 2.0.4
- adjusted default example log name
- disabled fsdp 1.x mixed precision tests temporarily until https://github.com/Lightning-AI/lightning/pull/17807 is merged


## [2.0.2] - 2023-04-06

### [2.0.2] - Added

- Beta support for [optimizer reinitialization](https://finetuning-scheduler.readthedocs.io/en/stable/advanced/optimizer_reinitialization.html). Resolves [#6](https://github.com/speediedan/finetuning-scheduler/issues/6)
- Use structural typing for Fine-Tuning Scheduler supported optimizers with ``ParamGroupAddable``
- Support for ``jsonargparse`` version ``4.20.1``

### [2.0.2] - Changed

- During schedule phase transitions, the latest LR state will be restored before proceeding with the next phase configuration and execution (mostly relevant to lr scheduler and optimizer reinitialization but also improves configuration when restoring best checkpoints across multiple depths)

### [2.0.2] - Fixed

- Allow sharded optimizers ``ZeroRedundancyOptimizer`` to be properly reconfigured if necessary in the context of ``enforce_phase0_params`` set to ``True``.


## [2.0.1] - 2023-04-05

### [2.0.1] - Added

- Support for PyTorch Lightning 2.0.1
- Lightning support for ``use_orig_params`` via ([#16733](https://github.com/Lightning-AI/lightning/pull/16733))

## [2.0.0] - 2023-03-15

### [2.0.0] - Added

- Support for PyTorch and PyTorch Lightning 2.0.0!
- New ``enforce_phase0_params`` feature.  FTS ensures the optimizer configured in ``configure_optimizers`` will optimize the parameters (and only those parameters) scheduled to be optimized in phase ``0`` of the current fine-tuning schedule. ([#9](https://github.com/speediedan/finetuning-scheduler/pull/9))
- Support for ``torch.compile``
- Support for numerous new FSDP options including preview support for some FSDP options coming soon to Lightning (e.g. ``use_orig_params``)
- When using FTS with FSDP, support the use of ``_FSDPPolicy`` ``auto_wrap_policy`` wrappers (new in PyTorch 2.0.0)
- Extensive testing for FSDP in many newly supported 2.x contexts (including 1.x FSDP compatibility multi-gpu tests)
- Support for strategies that do not have a canonical `strategy_name` but use `_strategy_flag`

### [2.0.0] - Changed

- Now that the core Lightning package is `lightning` rather than `pytorch-lightning`, Fine-Tuning Scheduler (FTS) by default depends upon the `lightning` package rather than the standalone `pytorch-lightning`. If you would like to continue to use FTS with the standalone `pytorch-lightning` package instead, you can still do so (see [README](https://github.com/speediedan/finetuning-scheduler/blob/main/README.md)). Resolves ([#8](https://github.com/speediedan/finetuning-scheduler/issues/8)).
- Fine-Tuning Scheduler (FTS) major version numbers will align with the rest of the PyTorch ecosystem (e.g. FTS 2.x supports PyTorch and Lightning >= 2.0)
- Switched to use ``ruff`` instead of ``flake8`` for linting
- Replaced `fsdp_optim_view` with either `fsdp_optim_transform` or `fsdp_optim_inspect` depending on usage context because the transformation is now not always read-only
- Moved Lightning 1.x examples to `legacy` subfolder and created new FTS/Lightning 2.x examples in `stable` subfolder


### [2.0.0] - Removed

- Removed ``training_epoch_end`` and ``validation_epoch_end`` in accord with Lightning
- Removed `DP` strategy support in accord with Lightning
- Removed support for Python `3.7` and PyTorch `1.10` in accord with Lightning

### [2.0.0] - Fixed

- Adapted loop synchronization during training resume to upstream Lightning changes

## [0.4.1] - 2023-03-14

### [0.4.1] - Added

- Support for ``pytorch-lightning`` 1.9.4 (which may be the final Lightning 1.x release as PyTorch 2.0 will be released tomorrow)

## [0.4.0] - 2023-01-25

### [0.4.0] - Added

- **FSDP Scheduled Fine-Tuning** is now supported! [See the tutorial here.](https://finetuning-scheduler.readthedocs.io/en/stable/distributed/fsdp_scheduled_fine_tuning.html)
- Introduced [``StrategyAdapter``](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.strategy_adapters.html#finetuning_scheduler.strategy_adapters.StrategyAdapter)s. If you want to extend Fine-Tuning Scheduler (FTS) to use a custom, currently unsupported strategy or override current FTS behavior in the context of a given training strategy, subclassing ``StrategyAdapter`` is now a way to do so. See [``FSDPStrategyAdapter``](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.strategy_adapters.html#finetuning_scheduler.strategy_adapters.FSDPStrategyAdapter) for an example implementation.
- support for `pytorch-lightning` 1.9.0

### [0.4.0] - Changed

- decomposed ``add_optimizer_groups`` to accommodate the corner case where FTS is being used without an lr scheduler configuration, also cleanup unrequired example testing warning exceptions
- updated the fts repo issue template


### [0.4.0] - Fixed

- removed PATH adjustments that are no longer necessary due to https://github.com/Lightning-AI/lightning/pull/15485

### [0.4.0] - Removed

- removed references to the ``finetuning-scheduler`` conda-forge package (at least temporarily) due to the current unavailability of upstream dependencies (i.e. the [pytorch-lightning conda-forge package](https://anaconda.org/conda-forge/pytorch-lightning/files) ). Installation of FTS via pip within a conda env is the recommended installation approach (both in the interim and in general).


## [0.3.4] - 2023-01-24

### [0.3.4] - Added

- support for `pytorch-lightning` 1.8.6
- Notify the user when ``max_depth`` is reached and provide the current training session stopping conditions. Resolves [#7](https://github.com/speediedan/finetuning-scheduler/issues/7).


### [0.3.4] - Changed

- set package version ceilings for the examples requirements along with a note regarding their introduction for stability
- promoted PL CLI references to top-level package

### [0.3.4] - Fixed

- replaced deprecated ``Batch`` object reference with ``LazyDict``


## [0.3.3] - 2022-12-09

### [0.3.3] - Added

- support for `pytorch-lightning` 1.8.4

### [0.3.3] - Changed

- pinned `jsonargparse` dependency to <4.18.0 until [#205](https://github.com/omni-us/jsonargparse/issues/205) is fixed

## [0.3.2] - 2022-11-18

### [0.3.2] - Added

- support for `pytorch-lightning` 1.8.2

## [0.3.1] - 2022-11-10

### [0.3.1] - Added

- support for `pytorch-lightning` 1.8.1
- augmented `standalone_tests.sh` to be more robust to false negatives

### [0.3.1] - Changed

- added temporary expected `distutils` warning until fixed upstream in PL
- updated `depth` type hint to accommodate updated mypy default config
- bumped full test timeout to be more conservative given a dependent package that is currently slow to install in some contexts (i.e. `grpcio` on MacOS 11 with python `3.10`)

## [0.3.0] - 2022-11-04

### [0.3.0] - Added

- support for pytorch-lightning 1.8.0
- support for python 3.10
- support for PyTorch 1.13
- support for `ZeroRedundancyOptimizer`

### [0.3.0] - Fixed

- call to PL `BaseFinetuning.freeze` did not properly hand control of `BatchNorm` module thawing to FTS schedule. Resolves [#5](https://github.com/speediedan/finetuning-scheduler/issues/5).
- fixed codecov config for azure pipeline gpu-based coverage

### [0.3.0] - Changed

- Refactored unexpected and expected multi-warning checks to use a single test helper function
- Adjusted multiple FTS imports to adapt to reorganized PL/Lite imports
- Refactored fts-torch collect_env interface to allow for (slow) collect_env evolution on a per-torch version basis
- Bumped required jsonargparse version
- adapted to PL protection of `_distributed_available`
- made callback setup stage arg mandatory
- updated mypy config to align with PL `Trainer` handling
- updated dockerfile defs for PyTorch 1.13 and python 3.10
- updated github actions versions to current versions
- excluded python 3.10 from torch 1.9 testing due to incompatibility

### [0.3.0] - Deprecated

- removed use of deprecated `LightningCLI` `save_config_overwrite` in PL 1.8


## [0.2.3] - 2022-10-01

### [0.2.3] - Added

- support for pytorch-lightning 1.7.7
- add new temporary HF expected warning to examples
- added HF `evaluate` dependency for examples

### [0.2.3] - Changed

- Use HF `evaluate.load()` instead of `datasets.load_metric()`

## [0.2.2] - 2022-09-17

### [0.2.2] - Added

- support for pytorch-lightning 1.7.6
- added detection of multiple instances of a given callback dependency parent
- add new expected warning to examples

### [0.2.2] - Fixed

- import fts to workaround pl TypeError via sphinx import, switch to non-TLS pytorch inv object connection due to current certificate issues

### [0.2.2] - Changed

- bumped pytorch dependency in docker image to 1.12.1

## [0.2.1] - 2022-08-13

### [0.2.1] - Added

- support for pytorch-lightning 1.7.1
- added support for ReduceLROnPlateau lr schedulers
- improved user experience with additional lr scheduler configuration inspection (using an allowlist approach) and
  enhanced documentation. Expanded use of ``allow_untested`` to allow use of unsupported/untested lr schedulers
- added initial user-configured optimizer state inspection prior to phase ``0`` execution, issuing warnings to the user
  if appropriate. Added associated documentation [#4](https://github.com/speediedan/finetuning-scheduler/issues/4)

### [0.2.1] - Fixed

- pruned test_examples.py from wheel

### [0.2.1] - Changed

- removed a few unused internal conditions relating to lr scheduler reinitialization and parameter group addition

## [0.2.0] - 2022-08-06

### [0.2.0] - Added

- support for pytorch-lightning 1.7.0
- switched to [src-layout project structure](https://setuptools.pypa.io/en/latest/userguide/package_discovery.html)
- 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

### [0.2.0] - Fixed

- adjusted runif condition for examples tests
- minor type annotation stylistic correction to avoid jsonargparse issue fixed in
  [#148](https://github.com/omni-us/jsonargparse/pull/148)

### [0.2.0] - 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

### [0.2.0] - Deprecated

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

## [0.1.8] - 2022-07-13

### [0.1.8] - 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

### [0.1.8] - Fixed

- pinned jsonargparse to be <= 4.10.1 due to regression with PTL cli with 4.10.2
### [0.1.8] - 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

### [0.1.8] - Deprecated

## [0.1.7] - 2022-06-10
### [0.1.7] - Fixed

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

### [0.1.7] - Added

### [0.1.7] - Changed

### [0.1.7] - Deprecated

## [0.1.6] - 2022-06-10

### [0.1.6] - Added

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

### [0.1.6] - Fixed
- minor privacy policy link update
- bump omegaconf version requirement due to omegaconf bug

### [0.1.6] - Changed

### [0.1.6] - Deprecated

## [0.1.5] - 2022-06-02

### [0.1.5] - 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
### [0.1.5] - Fixed
- cleaned up acknowledged warnings in multi-gpu example testing
### [0.1.5] - Changed

### [0.1.5] - Deprecated

## [0.1.4] - 2022-05-24

### [0.1.4] - Added

- Added LR scheduler reinitialization functionality ([#2](https://github.com/speediedan/finetuning-scheduler/pull/2))
- Added advanced usage documentation
- Added advanced scheduling examples
- added notebook-based tutorial link
- enhanced cli-based example hparam logging among other code clarifications

### [0.1.4] - Changed

### [0.1.4] - Fixed

- addressed URI length limit for custom badge
- allow new deberta fast tokenizer conversion warning for transformers >= 4.19
### [0.1.4] - Deprecated

## [0.1.3] - 2022-05-04

### [0.1.3] - Added

-

### [0.1.3] - Changed

- bumped latest tested PL patch version to 1.6.3
### [0.1.3] - Fixed

-
### [0.1.3] - Deprecated

-

## [0.1.2] - 2022-04-27

### [0.1.2] - Added

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

### [0.1.2] - 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

### [0.1.2] - Fixed

-
### [0.1.2] - Deprecated

-

## [0.1.1] - 2022-04-15

### [0.1.1] - Added

- added conda-forge package
- added docker release and pypi workflows
- additional badges for readme, testing enhancements for oldest/newest pl patch versions

### [0.1.1] - Changed

- bumped latest tested PL patch version to 1.6.1, CLI example depends on PL logger fix ([#12609](https://github.com/Lightning-AI/lightning/pull/12609))

### [0.1.1] - Deprecated

-

### [0.1.1] - Fixed

- Addressed version prefix issue with readme transformation for pypi


## [0.1.0] - 2022-04-07

### [0.1.0] - Added

- None (initial release)

### [0.1.0] - Changed

- None (initial release)

### [0.1.0] - Deprecated

- None (initial release)

### [0.1.0] - Fixed

- None (initial release)