DataParallelStrategy

class pytorch_lightning.strategies.DataParallelStrategy(accelerator=None, parallel_devices=None, checkpoint_io=None, precision_plugin=None)

Bases: pytorch_lightning.strategies.parallel.ParallelStrategy

Implements data-parallel training in a single process, i.e., the model gets replicated to each device and each gets a split of the data.

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

batch_to_device(batch, device=None, dataloader_idx=0)

Moves the batch to the correct device.

The input and the output is the same type.

Parameters:

• batch (Any) – The batch of samples to move to the correct device

• device (Optional[device]) – The target device

• dataloader_idx (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

broadcast(obj, src=0)

Broadcasts an object to all processes.

Parameters:

• obj (~TBroadcast) – the object to broadcast

• src (int) – source rank

Return type:

~TBroadcast

model_to_device()

Moves the model to the correct device.

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(collection, group=None, reduce_op='mean')

Reduces a collection of tensors from all processes. It can be applied to just a single tensor.

Parameters:

• collection (~TReduce) – The collection of tensors to sync and reduce.

• group (Optional[Any]) – ignored for DP

• reduce_op (Union[ReduceOp, str, None]) – ignored for DP

Return type:

~TReduce

Returns:

Reduced tensor values or the same value if it was not or did not contain a tensor.

reduce_boolean_decision(decision)

Reduce a boolean decision across all processes.

Return type:

bool

setup(trainer)

Setup plugins for the trainer fit and creates optimizers.

Parameters:

trainer (Trainer) – the trainer instance

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