tfx.components.BulkInferrer

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

Inherits From: BaseComponent

tfx.components.BulkInferrer(
    examples: tfx.types.Channel = None,
    model: Optional[tfx.types.Channel] = None,
    model_blessing: Optional[tfx.types.Channel] = None,
    data_spec: Optional[Union[bulk_inferrer_pb2.DataSpec, Dict[Text, Any]]] = None,
    model_spec: Optional[Union[bulk_inferrer_pb2.ModelSpec, Dict[Text, Any]]] = None,
    inference_result: Optional[tfx.types.Channel] = None,
    instance_name: Optional[Text] = 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'])

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.

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

View source

add_downstream_node(
    downstream_node
)

Experimental: Add another component that must run after this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_upstream_node.

Args
downstream_node a component that must run after this node.

add_upstream_node

View source

add_upstream_node(
    upstream_node
)

Experimental: Add another component that must run before this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_downstream_node.

Args
upstream_node a component that must run before this node.

from_json_dict

View source

@classmethod
from_json_dict(
    dict_data: Dict[Text, Any]
) -> Any

Convert from dictionary data to an object.

get_id

View source

@classmethod
get_id(
    instance_name: Optional[Text] = 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() -> Dict[Text, Any]

Convert from an object to a JSON serializable dictionary.

Class Variables

  • EXECUTOR_SPEC