tfx.components.experimental.data_view.binder_component.DataViewBinder

A component that binds a DataView to ExamplesArtifact.

Inherits From: BaseComponent

It takes as inputs a channel of Examples and a channel of DataView, and binds the DataView (i.e. attaching information from the DataView as custom properties) to the Examples in the input channel, producing new Examples Artifacts that are identical to the input Examples (including the uris), except for the additional information attached.

Example:

  # We assume Examples are imported by ExampleGen
  example_gen = ...
  # First, create a dataview:
  data_view_provider = TfGraphDataViewProvider(
      module_file=module_file,
      create_decoder_func='create_decoder')
  # Then, bind the DataView to Examples:
  data_view_binder = DataViewBinder(
      input_examples=example_gen.outputs['examples'],
      data_view=data_view_provider.outputs['data_view'],
      )
  # Downstream component can then consume the output of the DataViewBinder:
  stats_gen = StatisticsGen(
      examples=data_view_binder.outputs['output_examples'], ...)

spec types.ComponentSpec object for this component instance.
custom_executor_spec Optional custom executor spec overriding the default executor specified in the component attribute.
instance_name Optional unique identifying name for this instance of the component in the pipeline. Required if two instances of the same component is used in the 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

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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

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

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Convert from dictionary data to an object.

get_id

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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

Convert from an object to a JSON serializable dictionary.

EXECUTOR_SPEC tfx.components.base.executor_spec.ExecutorClassSpec