lightning

LightningTrainerBaseSetup(log_every_n_steps=1)

Bases: Callback

Custom callback used to setup a lightning trainer with default options.

Parameters:

• log_every_n_steps (int, default: 1 ) –

  Default value for trainer.log_every_n_steps if the dataloader is too small.

Source code in quadra/callbacks/lightning.py

def __init__(self, log_every_n_steps: int = 1) -> None:
    self.log_every_n_steps = log_every_n_steps

on_fit_start(trainer, pl_module)

Called on every stage.

Source code in quadra/callbacks/lightning.py

@rank_zero_only
def on_fit_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
    """Called on every stage."""
    if not hasattr(trainer, "datamodule") or not hasattr(trainer, "log_every_n_steps"):
        raise ValueError("Trainer must have a datamodule and log_every_n_steps attribute.")

    len_train_dataloader = len(trainer.datamodule.train_dataloader())
    if len_train_dataloader <= trainer.log_every_n_steps:
        if len_train_dataloader > self.log_every_n_steps:
            trainer.log_every_n_steps = self.log_every_n_steps
            log.info("`trainer.log_every_n_steps` is too high, setting it to %d", self.log_every_n_steps)
        else:
            trainer.log_every_n_steps = 1
            log.warning(
                "The default log_every_n_steps %d is too high given the datamodule lenght %d, fallback to 1",
                self.log_every_n_steps,
                len_train_dataloader,
            )