snapchat.research.gbml.trained_model_metadata_pb2#

@generated by mypy-protobuf. Do not edit manually! isort:skip_file

Attributes#

DESCRIPTOR

global___TrainedModelMetadata

Classes#

TrainedModelMetadata

Abstract base class for protocol messages.

Module Contents#

class snapchat.research.gbml.trained_model_metadata_pb2.TrainedModelMetadata(*, trained_model_uri=..., scripted_model_uri=..., eval_metrics_uri=..., tensorboard_logs_uri=...)[source]#

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

Parameters:

  • trained_model_uri (str)

  • scripted_model_uri (str)

  • eval_metrics_uri (str)

  • tensorboard_logs_uri (str)

ClearField(field_name)[source]#

Clears the contents of a given field.

Inside a oneof group, clears the field set. If the name neither refers to a defined field or oneof group, ValueError is raised.

Parameters:

field_name (str) – The name of the field to check for presence.

Raises:

ValueError – if the field_name is not a member of this message.

Return type:

None

DESCRIPTOR: google.protobuf.descriptor.Descriptor[source]#
EVAL_METRICS_URI_FIELD_NUMBER: int[source]#
SCRIPTED_MODEL_URI_FIELD_NUMBER: int[source]#
TENSORBOARD_LOGS_URI_FIELD_NUMBER: int[source]#
TRAINED_MODEL_URI_FIELD_NUMBER: int[source]#
eval_metrics_uri: str[source]#

The path where evaluation metrics are stored

scripted_model_uri: str[source]#

The path to above model which is TorchScripted.

tensorboard_logs_uri: str[source]#

Path where tensorboard logs will be stored

trained_model_uri: str[source]#

The path to a trained PyTorch model.

snapchat.research.gbml.trained_model_metadata_pb2.DESCRIPTOR: google.protobuf.descriptor.FileDescriptor[source]#
snapchat.research.gbml.trained_model_metadata_pb2.global___TrainedModelMetadata[source]#