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 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
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
