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