TPUSpawnPlugin¶
- class pytorch_lightning.plugins.training_type.TPUSpawnPlugin(parallel_devices=None, debug=False, **_)[source]¶
Bases:
pytorch_lightning.plugins.training_type.ddp_spawn.DDPSpawnPlugin
Plugin for training multiple TPU devices using the
torch.multiprocessing.spawn()
method.- all_gather(tensor, group=None, sync_grads=False)[source]¶
Function to gather a tensor from several distributed processes :type _sphinx_paramlinks_pytorch_lightning.plugins.training_type.TPUSpawnPlugin.all_gather.tensor:
Tensor
:param _sphinx_paramlinks_pytorch_lightning.plugins.training_type.TPUSpawnPlugin.all_gather.tensor: tensor of shape (batch, …) :type _sphinx_paramlinks_pytorch_lightning.plugins.training_type.TPUSpawnPlugin.all_gather.group:Optional
[Any
] :param _sphinx_paramlinks_pytorch_lightning.plugins.training_type.TPUSpawnPlugin.all_gather.group: not available with TPUs :type _sphinx_paramlinks_pytorch_lightning.plugins.training_type.TPUSpawnPlugin.all_gather.sync_grads:bool
:param _sphinx_paramlinks_pytorch_lightning.plugins.training_type.TPUSpawnPlugin.all_gather.sync_grads: not available with TPUs- Return type
- Returns
A tensor of shape (world_size, batch, …)
- connect(model)[source]¶
Called by the accelerator to connect the accelerator and the model with this plugin
- Return type
- process_dataloader(dataloader)[source]¶
Wraps the dataloader if necessary
- Parameters
dataloader¶ (
DataLoader
) – iterable. Ideally of type:torch.utils.data.DataLoader
- Return type
- reduce(output, group=None, reduce_op=None)[source]¶
Reduces a tensor from several distributed processes to one aggregated tensor.
- Parameters
- Returns
reduced value, except when the input was not a tensor the output remains is unchanged
- reduce_boolean_decision(decision)[source]¶
Reduce the early stopping decision across all processes
- Return type
- save_checkpoint(checkpoint, filepath)[source]¶
Save model/training states as a checkpoint file through state-dump and file-write.
- 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
- property root_device: torch.device¶
Returns the root device
- property should_rank_save_checkpoint: bool¶
Returns whether the checkpoint should be saved (rank based)