Finetuning-scheduler

Added

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

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`)

Added

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

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

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

Deprecated

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

Added

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

Changed

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

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

Fixed

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

Changed

- bumped pytorch dependency in docker image to 1.12.1

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)

Fixed

- pruned test_examples.py from wheel

Changed

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

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

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)

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