DDPSpawnPlugin¶

class pytorch_lightning.plugins.training_type.DDPSpawnPlugin(parallel_devices=None, num_nodes=None, cluster_environment=None, sync_batchnorm=None, ddp_comm_state=None, ddp_comm_hook=None, ddp_comm_wrapper=None, **kwargs)[source]¶

Bases: pytorch_lightning.plugins.training_type.parallel.ParallelPlugin

Spawns processes using the torch.multiprocessing.spawn() method and joins processes after training finishes.

barrier(*args, **kwargs)[source]¶

Forces all possibly joined processes to wait for each other

Return type: None

broadcast(obj, src=0)[source]¶

Broadcasts an object to all processes

Return type: object

model_to_device()[source]¶: Moves the model to the correct device

post_dispatch()[source]¶: Hook to do something after the training/evaluation/prediction finishes.

pre_backward(closure_loss)[source]¶

Run before precision plugin executes backward

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(model)[source]¶: Called by the accelerator to finish setup.

property root_device¶: Returns the root device