def on_run_start(self, dataloader_iter: Iterator, **kwargs: Any) -> None:
# hook
self.trainer.logger_connector.on_epoch_start()
self.trainer.call_hook("on_epoch_start")
self.trainer.call_hook("on_train_epoch_start")
self.trainer.fit_loop.epoch_progress.increment_started()
self.dataloader_iter = _prepare_dataloader_iter(
dataloader_iter, self.batch_idx + 1)
def on_run_start(self, data_fetcher: AbstractDataFetcher,
dataloader_idx: int, dl_max_batches: int,
num_dataloaders: int) -> None:
"""Adds the passed arguments to the loop's state if necessary.
Args:
data_fetcher: the current data_fetcher wrapping the dataloader
dataloader_idx: index of the current dataloader
dl_max_batches: maximum number of batches the dataloader can produce
num_dataloaders: the total number of dataloaders
"""
void(dataloader_idx)
self._dl_max_batches = dl_max_batches
self._num_dataloaders = num_dataloaders
self.dataloader_iter = _prepare_dataloader_iter(
data_fetcher, self.batch_progress.current.ready)