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DDPShardedStrategy

class pytorch_lightning.strategies.DDPShardedStrategy(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, **kwargs)[source]

Bases: pytorch_lightning.strategies.ddp.DDPStrategy

Optimizer and gradient sharded training provided by FairScale.

block_backward_sync()[source]

Blocks syncing gradients behaviour on backwards pass.

This is useful for skipping sync when accumulating gradients, reducing communication overhead Returns: context manager with sync behaviour off

Return type

Generator

optimizer_state(optimizer)[source]

Returns state of an optimizer.

Allows for syncing/collating optimizer state from processes in custom plugins.

Return type

Optional[dict]

pre_backward(closure_loss)[source]

Run before precision plugin executes backward.

Return type

None

property lightning_module: Optional[pytorch_lightning.core.lightning.LightningModule]

Returns the pure LightningModule without potential wrappers.

Return type

Optional[LightningModule]

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