def resolve_training_type_plugin( self, training_type: TrainingTypePlugin) -> TrainingTypePlugin: # necessary for when the user has passed in a plugin if hasattr(training_type, 'parallel_devices') and not getattr( training_type, 'parallel_devices'): training_type.parallel_devices = self.parallel_devices if hasattr(training_type, 'num_processes'): training_type.num_processes = len(self.parallel_devices) if hasattr(training_type, 'cluster_environment') and getattr( training_type, 'cluster_environment') is None: training_type.cluster_environment = self.select_cluster_environment( ) if hasattr( training_type, 'num_nodes') and getattr(training_type, 'num_nodes') is None: training_type.num_nodes = self.num_nodes # Automatically set sync_batchnorm if None. # Useful for custom plugins. if hasattr(training_type, 'sync_batchnorm') and getattr( training_type, 'sync_batchnorm') is None: training_type.sync_batchnorm = self.sync_batchnorm return training_type
def resolve_training_type_plugin(self, training_type: TrainingTypePlugin) -> TrainingTypePlugin: # necessary for when the user has passed in a plugin if hasattr(training_type, 'parallel_devices') and getattr(training_type, 'parallel_devices') is None: training_type.parallel_devices = self.parallel_devices if hasattr(training_type, 'num_processes'): training_type.num_processes = len(self.parallel_devices) if hasattr(training_type, 'cluster_environment') and getattr(training_type, 'cluster_environment') is None: training_type.cluster_environment = self.select_cluster_environment() if hasattr(training_type, 'num_nodes'): # set num_nodes for training_type from trainer setting training_type.num_nodes = self.num_nodes if hasattr(training_type, 'sync_batchnorm'): # set sync_batchnorm for training_type from trainer setting training_type.sync_batchnorm = self.sync_batchnorm return training_type
def resolve_training_type_plugin(self, training_type: TrainingTypePlugin) -> TrainingTypePlugin: # necessary for when the user has passed in a plugin if hasattr(training_type, "parallel_devices") and getattr(training_type, "parallel_devices") is None: training_type.parallel_devices = self.parallel_devices if hasattr(training_type, "num_processes"): training_type.num_processes = len(self.parallel_devices) if hasattr(training_type, "cluster_environment") and getattr(training_type, "cluster_environment") is None: # transfer ownership of the cluster environment to the training type training_type.cluster_environment = self.cluster_environment self._cluster_environment = proxy(self.cluster_environment) if hasattr(training_type, "num_nodes"): # set num_nodes for training_type from trainer setting training_type.num_nodes = self.num_nodes if hasattr(training_type, "sync_batchnorm"): # set sync_batchnorm for training_type from trainer setting training_type.sync_batchnorm = self.sync_batchnorm return training_type