OptimizerLoop

class pytorch_lightning.loops.optimization.OptimizerLoop[source]

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)[source]

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)[source]

Optionally connect one or multiple loops to this one.

Linked loops should form a tree.

Return type

None

on_run_end()[source]

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)[source]

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()[source]

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