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A TFX component to do batch inference on a model with unlabelled examples.
tfx.components.BulkInferrer( examples=None, model=None, model_blessing=None, data_spec=None, model_spec=None, inference_result=None, instance_name=None )
BulkInferrer consumes examples data and a model, and produces the inference results to an external location as PredictionLog proto.
BulkInferrer will infer on validated model.
# Uses BulkInferrer to inference on examples. bulk_inferrer = BulkInferrer( examples=example_gen.outputs['examples'], model=trainer.outputs['model'])
examples: A Channel of type
standard_artifacts.Examples, usually produced by an ExampleGen component. required
model: A Channel of type
standard_artifacts.Model, usually produced by a Trainer component.
model_blessing: A Channel of type
standard_artifacts.ModelBlessing, usually produced by a ModelValidator component.
data_spec: bulk_inferrer_pb2.DataSpec instance that describes data selection. If any field is provided as a RuntimeParameter, data_spec should be constructed as a dict with the same field names as DataSpec proto message.
model_spec: bulk_inferrer_pb2.ModelSpec instance that describes model specification. If any field is provided as a RuntimeParameter, model_spec should be constructed as a dict with the same field names as ModelSpec proto message.
inference_result: Channel of type
standard_artifacts.InferenceResultto store the inference results.
instance_name: Optional name assigned to this specific instance of BulkInferrer. Required only if multiple BulkInferrer components are declared in the same pipeline.
component_id: DEPRECATED FUNCTION
component_type: DEPRECATED FUNCTION
id: Node id, unique across all TFX nodes in a pipeline.
If instance name is available, node_id will be:
. otherwise, node_id will be:
add_downstream_node( downstream_node )
add_upstream_node( upstream_node )
@classmethod from_json_dict( cls, dict_data )
Convert from dictionary data to an object.
@classmethod get_id( cls, instance_name=None )
Gets the id of a node.
This can be used during pipeline authoring time. For example: from tfx.components import Trainer
resolver = ResolverNode(..., model=Channel( type=Model, producer_component_id=Trainer.get_id('my_trainer')))
instance_name: (Optional) instance name of a node. If given, the instance name will be taken into consideration when generating the id.
an id for the node.
Convert from an object to a JSON serializable dictionary.