DDPStrategy

class mmengine._strategy.DDPStrategy(*, model_wrapper=None, sync_bn=None, **kwargs)

Distribution strategy for distributed data parallel training.

Parameters:

• model_wrapper (dict) – Dict for model wrapper. Defaults to None.

• sync_bn (str) – Type of sync batch norm. Defaults to None. Options are ‘torch’ and ‘mmcv’.

• **kwargs – Other arguments for BaseStrategy.

convert_model(model)

convert all BatchNorm layers in the model to SyncBatchNorm (SyncBN) or mmcv.ops.sync_bn.SyncBatchNorm (MMSyncBN) layers.

Parameters:

model (nn.Module) – Model to be converted.

Returns:

Converted model.

Return type:

nn.Module

save_checkpoint(filename, *, save_optimizer=True, save_param_scheduler=True, extra_ckpt=None, callback=None)

Save checkpoint to given filename.

Parameters:

• filename (str) – Filename to save checkpoint.

• save_optimizer (bool) –

• save_param_scheduler (bool) –

• extra_ckpt (dict | None) –

• callback (Callable | None) –

Keyword Arguments:

• save_optimizer (bool) – Whether to save the optimizer to the checkpoint. Defaults to True.

• save_param_scheduler (bool) – Whether to save the param_scheduler to the checkpoint. Defaults to True.

• extra_ckpt (dict, optional) – Extra checkpoint to save. Defaults to None.

• callback (callable, callable) – Callback function to modify the checkpoint before saving the checkpoint. Defaults to None.

Return type:

None