ManualOptimization

class pytorch_lightning.loops.optimization.ManualOptimization

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

A special loop implementing what is known in Lightning as Manual Optimization where the optimization happens entirely in the training_step() and therefore the user is responsible for back-propagating gradients and making calls to the optimizers.

This loop is a trivial case because it performs only a single iteration (calling directly into the module’s training_step()) and passing through the output(s).

output_result_cls

alias of pytorch_lightning.loops.optimization.manual_loop.ManualResult

advance(batch, batch_idx)

Performs the training step for manual optimization.

Parameters

• batch (Any) – the current tbptt split of the current batch

• batch_idx (int) – the index of the current batch

Return type

None

on_run_end()

Returns the result of this loop, i.e., the post-processed outputs from the training step.

Return type

Dict[str, Any]

on_run_start(*_, **__)

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

Property indicating when the loop is finished.

Example:

@property
def done(self):
    return self.trainer.global_step >= self.trainer.max_steps

Return type

bool