DDPStrategy

class lightning.pytorch.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, timeout=datetime.timedelta(seconds=1800), start_method='popen', **kwargs)

Bases: ParallelStrategy

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

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

on_exception(exception)

Called when the trainer execution is interrupted by an exception.

Return type:

None

optimizer_step(optimizer, closure, model=None, **kwargs)

Performs the actual optimizer step.

Parameters:

• optimizer (Optimizer) – the optimizer performing the step

• 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) – Any extra arguments to optimizer.step

Return type:

Any

pre_backward(closure_loss)

Run before precision plugin executes backward.

Return type:

None

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)

Sets up the accelerator, plugins and initializes the optimizers (if needed).

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

property is_distributed: bool

Legacy property kept for backwards compatibility.

property root_device: device

Return the root device.