tfx.components.ResolverNode

Deprecated ResolverNode alias constructor.

Inherits From: Resolver, BaseNode

View aliases

Main aliases

tfx.components.common_nodes.resolver_node.ResolverNode

tfx.components.ResolverNode(
    instance_name: Text,
    resolver_class: Type[tfx.dsl.components.common.resolver.ResolverStrategy],
    resolver_configs: Dict[Text, json_utils.JsonableType] = None,
    **kwargs
)

Used in the notebooks

Used in the tutorials
TFX Estimator Component Tutorial TFX Keras Component Tutorial

This class location is DEPRECATED and is provided temporarily for compatibility. Please use tfx.dsl.components.common.resolver.Resolver instead.

Args
instance_name	the name of the Resolver instance.
resolver_class	a ResolverStrategy subclass which contains the artifact resolution logic.
resolver_configs	a dict of key to Jsonable type representing configuration that will be used to construct the resolver strategy.
**kwargs	a key -> Channel dict, describing what are the Channels to be resolved. This is set by user through keyword args.

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

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

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to_json_dict() -> Dict[Text, Any]

Convert from an object to a JSON serializable dictionary.

with_id

View source

with_id(
    id: Text
) -> "BaseNode"