tfx.components.base.base_component.BaseComponent

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Base class for a TFX pipeline component.

Inherits From: BaseNode

An instance of a subclass of BaseComponent represents the parameters for a single execution of that TFX pipeline component.

All subclasses of BaseComponent must override the SPEC_CLASS field with the ComponentSpec subclass that defines the interface of this component.

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.

SPEC_CLASS a subclass of types.ComponentSpec used by this component (required).
EXECUTOR_SPEC an instance of executor_spec.ExecutorSpec which describes how to execute this component (required).
DRIVER_CLASS a subclass of base_driver.BaseDriver as a custom driver for this component (optional, defaults to base_driver.BaseDriver).
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

Methods

EXECUTOR_SPEC

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SPEC_CLASS

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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

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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

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Convert from an object to a JSON serializable dictionary.