v1.2.0

List of changes:

• Update template for compatibility with lightning v1.5 and pytorch v1.10
• General documentation improvements
• Move LICENSE to README.md
• Add manual resetting of metrics at the end of every epoch, to make sure no one makes hard to spot calculation mistakes
• Add experiment mode to all experiment configs
• Improve logging paths for experiment mode
• Add MaxMetric to model, for computation of best so far validation accuracy
• Add RichProgressBar to default callbacks for the pretty formatted progress bar
• Get rid of the trick for preventing auto hparam logging, since lightning now supports it with self.save_hyperparameters(logger=False)
• Add self.save_hyperparameters() to datamodule since lightinng now supports it
• Deprecate Apex support since native pytorch mixed-precision is better
• Deprecate bash script for conda setup since installation commands change too often to maintain it
• Change trainer.terminate_on_nan debug option to trainer.detect_anomaly for compatibility with lightning v1.5
• Specify model and datamodule during trainer.test(), for compatibility with lightning v1.5
• Remove configs/trainer/all_params.yaml
• Make hyperparameter optimization compatible with lightning v1.5
• Specify that EarlyStopping patience is counted in validation epochs and not in training epochs.
• Add a new way for accessing datamodule attributes to the README.md
• Make debug mode automatically set the level of all command-line loggers to DEBUG
• Make debug mode automatically set the trainer config to debug.yaml
• Add generator seed to prevent test data leaking to train data in datamodule.setup() when seed is not set up
• Move Dockerfile to dockerfiles branch
• Modifiy configs/trainer/debug.yaml to enable some debug options
• Remove unused if config.get("debug"): in extras

Special thanks for PRs to: @CharlesGaydon, @eungbean, @gscriva

v1.1.0

• introduce different running modes: default, debug, experiment
• fix pytorch installation in setup_conda.sh
• fix incorrect calculation of precision, recall and f1 score in wandb callback
• add _self_ to config.yaml for compatibility with hydra1.1
• fix setting seed in train.py so it's skipped when seed=null
• add exception message when trying to use wandb callbacks with trainer.fast_dev_run=true
• change axis=-1 to dim=-1 in LogImagePredictions callback
• add 'Reproducibilty' section to README.md
• UploadCodeAsArtifact: now uploads all files that are not ignored by git instead of all *.py files.
• UploadCheckpointsAsArtifact: now uses experiment.log_artifact(ckpts) and uploads also on keyboard interrupt

v1.0.0

• update to Hydra 1.1
• add bash folder with scripts for conda setup and run scheduling
• add saving seed in log_hyperparameters() method
• redesign Dockerfile to make it weight less
• add test_after_training parameter to config
• add inheritance to trainer configs
• remove forcing ddp-friendly configuration
• refactor tests
• remove conda_env_gpu.yaml
• remove default langage version from pre-commit config
• add mnist datamodule unit test
• rename test folder from 'smoke' to 'shell'
• remove wandb import from utils.py
• change 'use_artifact' to 'log_artifact' in wandb callbacks
• add rank zero decorator to wandb callbacks
• add dumping rich tree config to file
• get rid of = character in ckpt names
• update requirements.txt
• update README.md

v0.9.0

Release/0.9 (#141)

* add flake8 and prettier to pre-commit-config
* add setup.cfg
* add workers=True to seed_everything()
* update lightning badge logo
* bump package versions
* update README.md
* add __init__.py files
* add more logger configs parameters
* add default Dockerfile
* change .env.template to .env.example
* move inference example to readme
* remove img_dataset.py
* simplify names of wandb callbacks
* remove wandb test marker
* format files with prettier