TrainingEpochLoop

class pytorch_lightning.loops.epoch.TrainingEpochLoop(min_steps=0, max_steps=- 1)[source]

Runs over all batches in a dataloader (one epoch).

Parameters

min_steps (Optional[int]) – The minimum number of steps (batches) to process
max_steps (int) – The maximum number of steps (batches) to process

advance(*args, **kwargs)[source]

Runs a single training batch.

Parameters: dataloader_iter – the iterator over the dataloader producing the new batch
Raises: StopIteration – When the epoch is canceled by the user returning -1
Return type: None

connect(batch_loop=None, val_loop=None)[source]

Optionally connect a custom batch or validation loop to this training epoch loop.

on_advance_end()[source]

Runs validation and Checkpointing if necessary.

Raises: StopIteration – if done evaluates to True to finish this epoch

on_load_checkpoint(state_dict)[source]

Called when loading a model checkpoint, use to reload loop state.

on_run_end()[source]

Calls the on_epoch_end hook.

Return type: None
Returns: The output of each training step for each optimizer
Raises: MisconfigurationException – train_epoch_end does not return None

on_run_start(data_fetcher, **kwargs)[source]

Hook to be called as the first thing after entering run (except the state reset).

Accepts all arguments passed to run.

on_save_checkpoint()[source]

Called when saving a model checkpoint, use to persist loop state.

Resets the internal state of the loop for a new run.

teardown()[source]

Use to release memory etc.

update_lr_schedulers(interval, update_plateau_schedulers)[source]

updates the lr schedulers based on the given interval.

property batch_idx: int: Returns the current batch index (within this epoch)

property done: bool

Returns whether the training should be stopped.

The criteria are that the number of steps reached the max steps, the last batch is reached or the trainer signals to stop (e.g. by early stopping).

property total_batch_idx: int: Returns the current batch index (across epochs)