DDPStrategy

class pytorch_lightning.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, **kwargs)[source]

Bases: pytorch_lightning.strategies.parallel.ParallelStrategy

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

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.
Return type: None

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.

optimizer_step(optimizer, opt_idx, closure, model=None, **kwargs)[source]

Performs the actual optimizer step.

Parameters

optimizer (Optimizer) – the optimizer performing the step
opt_idx (int) – index of the current optimizer
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)[source]

Run before precision plugin executes backward.

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]]

reconciliate_processes(trace)[source]

Function to re-conciliate processes on failure.

Return type: None

reduce(tensor, group=None, reduce_op='mean')[source]

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

Parameters

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]) – 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)[source]

Setup plugins for the trainer fit and creates optimizers.

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

setup_environment()[source]

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()[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: torch.device

Return the root device.

Return type: device