core

core.utils

get_logger

def get_logger(name=__name__, level=logging.INFO) -> logging.Logger

Initializes multi-GPU-friendly python logger.

extras

def extras(config: DictConfig) -> None

A couple of optional utilities, controlled by main config file: - disabling warnings - easier access to debug mode - forcing debug friendly configuration - forcing multi-gpu friendly configuration - Ensure correct number of timesteps/etc for all of them

Modifies DictConfig in place.

Arguments:

• config DictConfig - Configuration composed by Hydra.

print_config

@rank_zero_only
def print_config(config: DictConfig, fields: Sequence[str] = (
        "trainer",
        "model",
        "datamodule",
        "callbacks",
        "logger",
        "hparams_search"
        # "logger",
        # "seed",
    ), resolve: bool = True) -> None

Prints content of DictConfig using Rich library and its tree structure.

Arguments:

• config DictConfig - Configuration composed by Hydra.
• fields Sequence[str], optional - Determines which main fields from config will be printed and in what order.
• resolve bool, optional - Whether to resolve reference fields of DictConfig.

log_hyperparameters

@rank_zero_only
def log_hyperparameters(config: DictConfig, model: pl.LightningModule, trainer: pl.Trainer) -> None

This method controls which parameters from Hydra config are saved by Lightning loggers.

Additionaly saves: - number of trainable model parameters