tfx.components.BulkInferrer

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A TFX component to do batch inference on a model with unlabelled examples.

Inherits From: BaseComponent

View aliases

Main aliases

tfx.components.bulk_inferrer.component.BulkInferrer

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.

Example

  # Uses BulkInferrer to inference on examples.
  bulk_inferrer = BulkInferrer(
      examples=example_gen.outputs['examples'],
      model=trainer.outputs['model'])

Args:

• 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.InferenceResult to 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.

Attributes:

• component_id: DEPRECATED FUNCTION

• component_type: DEPRECATED FUNCTION

• downstream_nodes

• exec_properties

• 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:

• inputs

• outputs

• type

• upstream_nodes

Child Classes

class DRIVER_CLASS

class SPEC_CLASS

Methods

add_downstream_node

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add_downstream_node(
    downstream_node
)

add_upstream_node

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add_upstream_node(
    upstream_node
)

from_json_dict

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@classmethod
from_json_dict(
    cls, dict_data
)

Convert from dictionary data to an object.

get_id

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@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')))

Args:

• instance_name: (Optional) instance name of a node. If given, the instance name will be taken into consideration when generating the id.

Returns:

an id for the node.

to_json_dict

View source

to_json_dict()

Convert from an object to a JSON serializable dictionary.

Class Variables

• EXECUTOR_SPEC