DataParallelStrategy

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

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)[source]

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)[source]

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)[source]

Broadcasts an object to all processes.

Parameters

obj (object) – the object to broadcast
src (int) – source rank

Return type

object

model_to_device()[source]

Moves the model to the correct device.

Return type: None

predict_step(*args, **kwargs)[source]

The actual predict step.

See predict_step() for more details

Return type: Union[Tensor, Dict[str, Any]]

reduce(collection, *args, **kwargs)[source]

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)[source]

Reduce the early stopping decision across all processes.

Return type: bool

setup(trainer)[source]

Setup plugins for the trainer fit and creates optimizers.

Parameters: trainer (Trainer) – the trainer instance
Return type: None

teardown()[source]

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)[source]

The actual test step.

See test_step() for more details

Return type: Union[Tensor, Dict[str, Any], None]

training_step(*args, **kwargs)[source]

The actual training step.

See training_step() for more details

Return type: Union[Tensor, Dict[str, Any]]

validation_step(*args, **kwargs)[source]

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.