DDPSpawnStrategy

class pytorch_lightning.strategies.DDPSpawnStrategy(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, process_group_backend=None, timeout=datetime.timedelta(seconds=1800), start_method='spawn', **kwargs)

Bases: pytorch_lightning.strategies.parallel.ParallelStrategy

Spawns processes using the torch.multiprocessing.spawn() method and joins processes after training finishes.

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

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.