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, process_group_backend=None, timeout=datetime.timedelta(seconds=1800), **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

connect(model)[source]¶

Called by the accelerator to connect the accelerator and the model with this plugin.

Return type:: None

pre_backward(closure_loss)[source]¶

Run before precision plugin executes backward.

Return type:: None

setup(trainer)[source]¶

Setup plugins for the trainer fit and creates optimizers.

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