DDPStrategy

class lightning.pytorch.strategies.DDPStrategy(accelerator=None, parallel_devices=None, cluster_environment=None, checkpoint_io=None, precision_plugin=None, ddp_comm_state=None, ddp_comm_hook=None, ddp_comm_wrapper=None, model_averaging_period=None, process_group_backend=None, timeout=datetime.timedelta(seconds=1800), start_method='popen', **kwargs)

Bases: lightning.pytorch.strategies.parallel.ParallelStrategy

Strategy for multi-process single-device training on one or multiple nodes.

barrier(*args, **kwargs)

Synchronizes all processes which blocks processes until the whole group enters this function.

Parameters

name – an optional name to pass into barrier.

Return type

None

broadcast(obj, src=0)

Broadcasts an object to all processes.

Parameters

• obj (TypeVar(TBroadcast)) – the object to broadcast

• src (int) – source rank

Return type

TypeVar(TBroadcast)

model_to_device()

Moves the model to the correct device.

Return type

None

on_exception(exception)

Called when the trainer execution is interrupted by an exception.

Return type

None

optimizer_step(optimizer, closure, model=None, **kwargs)

Performs the actual optimizer step.

Parameters

• optimizer (Optimizer) – the optimizer performing the step

• closure (Callable[[], Any]) – closure calculating the loss value

• model (Union[LightningModule, Module, None]) – reference to the model, optionally defining optimizer step related hooks

• **kwargs – Any extra arguments to optimizer.step

Return type

Any

pre_backward(closure_loss)

Run before precision plugin executes backward.

Return type

None

predict_step(*args, **kwargs)

The actual predict step.

See predict_step() for more details

Return type

Union[Tensor, Dict[str, Any]]

reduce(tensor, group=None, reduce_op='mean')

Reduces a tensor from several distributed processes to one aggregated tensor.

Parameters

• tensor (Tensor) – the tensor to sync and reduce

• group (Optional[Any]) – the process group to gather results from. Defaults to all processes (world)

• reduce_op (Union[ReduceOp, str, None]) – the reduction operation. Defaults to ‘mean’/’avg’. Can also be a string ‘sum’ to calculate the sum during reduction.

Return type

Tensor

Returns

reduced value, except when the input was not a tensor the output remains is unchanged

setup(trainer)

Setup plugins for the trainer fit and creates optimizers.

Parameters

trainer (Trainer) – the trainer instance

Return type

None

setup_environment()

Setup any processes or distributed connections.

This is called before the LightningModule/DataModule setup hook which allows the user to access the accelerator environment before setup is complete.

Return type

None

teardown()

This method is called to teardown the training process.

It is the right place to release memory and free other resources.

Return type

None

test_step(*args, **kwargs)

The actual test step.

See test_step() for more details

Return type

Union[Tensor, Dict[str, Any], None]

training_step(*args, **kwargs)

The actual training step.

See training_step() for more details

Return type

Union[Tensor, Dict[str, Any]]

validation_step(*args, **kwargs)

The actual validation step.

See validation_step() for more details

Return type

Union[Tensor, Dict[str, Any], None]

property root_device: torch.device

Return the root device.

Return type

device