FitLoop

class pytorch_lightning.loops.FitLoop(min_epochs=1, max_epochs=1000)

Bases: abc.ABC, Generic[pytorch_lightning.loops.base.T]

This Loop iterates over the epochs to run the training.

Parameters

• min_epochs (Optional[int]) – The minimum number of epochs

• max_epochs (int) – The maximum number of epochs, can be set -1 to turn this limit off

advance()

Runs one whole epoch.

Return type

None

connect(epoch_loop)

Connects a training epoch loop to this fit loop.

on_advance_end()

Hook to be called each time after advance is called.

Return type

None

on_advance_start()

Prepares the dataloader for training and calls the hooks on_epoch_start and on_train_epoch_start

Return type

None

on_run_end()

Calls the on_train_end hook.

Return type

None

on_run_start()

Calls the on_train_start hook.

Return type

None

reset()

Resets the internal state of this loop.

Return type

None

teardown()

Use to release memory etc.

Return type

None

property batch_idx: int

Returns the current batch index (within this epoch)

property current_epoch: int

Return the current epoch.

property done: bool

Evaluates when to leave the loop.

Returns True if trainer.should_stop was set (e.g. by early stopping) or if the maximum number of steps or epochs is reached.

property global_step: int

Returns the global step.

property max_steps: int

Returns the maximum number of steps to run.

property min_steps: int

Returns the minimum numnber of steps to run.

property running_loss: pytorch_lightning.trainer.supporters.TensorRunningAccum

Returns the running loss.

property skip: bool

Whether we should skip the training and immediately return from the call to run().

property split_idx: int

Returns the index of the current batch split (within the current batch) for bptt.

property total_batch_idx: int

Returns the current batch index (across epochs)