OptimizerLoop

class pytorch_lightning.loops.optimization.OptimizerLoop

Bases: pytorch_lightning.loops.base.Loop[Dict[int, Dict[str, Any]]]

Runs over a sequence of optimizers.

This loop implements what is known in Lightning as Automatic Optimization.

output_result_cls

alias of pytorch_lightning.loops.optimization.optimizer_loop.ClosureResult

advance(batch, *args, **kwargs)

Performs a single step.

Accepts all arguments passed to run.

Example:

def advance(self, iterator):
    batch = next(iterator)
    loss = self.trainer.lightning_module.training_step(batch, batch_idx)
    ...

Return type

None

connect(**kwargs)

Optionally connect one or multiple loops to this one.

Linked loops should form a tree.

Return type

None

on_run_end()

Hook to be called at the end of the run.

Its return argument is returned from run.

Return type

Dict[int, Dict[str, Any]]

on_run_start(batch, optimizers, batch_idx)

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

Accepts all arguments passed to run.

Return type

None

reset()

Resets the internal state of the loop at the beginning of each call to run.

Example:

def reset(self):
    # reset your internal state or add custom logic
    # if you expect run() to be called multiple times
    self.current_iteration = 0
    self.outputs = []

Return type

None

property done: bool

Returns True when the last optimizer in the sequence has run.

Return type

bool