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.

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 (object) – the object to broadcast

• src (int) – source rank

Return type

object

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, *args, **kwargs)

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

Parameters

• collection (Union[Metric, Tensor, int, float, Mapping[str, Union[Metric, Tensor, int, float]]]) – The collection of tensors to sync and reduce.

• *args – ignored for DP

• **kwargs – ignored for DP

Return type

Union[Metric, Tensor, int, float, Mapping[str, Union[Metric, Tensor, int, float]]]

Returns

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

reduce_boolean_decision(decision)

Reduce the early stopping 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

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

Return the root device.