tfx.components.BulkInferrer

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

Inherits From: BaseComponent, BaseNode

View aliases

Main aliases

tfx.components.bulk_inferrer.component.BulkInferrer

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,
    output_example_spec: Optional[Union[bulk_inferrer_pb2.OutputExampleSpec, Dict[Text, Any]]] = None,
    inference_result: Optional[tfx.types.Channel] = None,
    output_examples: 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'])

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.
output_example_spec	bulk_inferrer_pb2.OutputExampleSpec instance, specify if you want BulkInferrer to output examples instead of inference result. If any field is provided as a RuntimeParameter, output_example_spec should be constructed as a dict with the same field names as OutputExampleSpec proto message.
inference_result	Channel of type standard_artifacts.InferenceResult to store the inference results, must not be specified when output_example_spec is set.
output_examples	Channel of type standard_artifacts.Examples to store the output examples, must not be specified when output_example_spec is unset. Check output_example_spec for details.
instance_name	Optional name assigned to this specific instance of BulkInferrer. Required only if multiple BulkInferrer components are declared in the same pipeline.

Raises
ValueError	Must not specify inference_result or output_examples depends on whether output_example_spec is set or not.

Attributes
component_id
component_type
downstream_nodes
exec_properties
id	Node id, unique across all TFX nodes in a pipeline. If id is set by the user, return it directly. otherwise, if instance name (deprecated) 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_class_type

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@classmethod
get_class_type() -> Text

get_id

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@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.

with_id

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with_id(
    id: Text
) -> "BaseNode"

with_platform_config

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with_platform_config(
    config: message.Message
) -> "BaseComponent"

Attaches a proto-form platform config to a component.

The config will be a per-node platform-specific config.

Args
config	platform config to attach to the component.

Returns
the same component itself.

Class Variables
EXECUTOR_SPEC	Instance of tfx.dsl.components.base.executor_spec.ExecutorClassSpec