Safely retrieves value of the metric logged in LightningModule.

Initializes multi-GPU-friendly python command line logger.

Instantiates callbacks from config.

Instantiates loggers from config.

Controls which config parts are saved by lightning loggers. Additionally saves: - Number of model parameters

Prints content of DictConfig using Rich library and its tree structure. Args: cfg (DictConfig): Configuration composed by Hydra.

Optional decorator that controls the failure behavior when executing the task function. This wrapper can be used to: - make sure loggers are

Clean up after fit or test.

Test running all available experiment configs with fast_dev_run=True.

Test default hydra sweep.

Test default hydra sweep with ddp sim.

Test optuna sweep.

Test optuna sweep with wandb and ddp sim.

Simulate DDP (Distributed Data Parallel) on 2 CPU processes.

Train 1 epoch with validation loop twice per epoch.

Train 1 epoch on GPU with mixed-precision.

Train for 1 epoch with `train.py` and evaluate with `eval.py`

Run for 1 train, val and test step.

Run for 1 train, val and test step on GPU.

Run 1 epoch, finish, and resume for another epoch.