tfx.orchestration.kubeflow.node_wrapper.NodeWrapper

Wrapper of a node.

Inherits From: BaseNode

The wrapper is needed for container entrypoint to deserialize a component wihtout knowning it's original python class. This enables users to use container base component without re-compiling the tfx base image every time they change the component and spec definitions.

instance_name Optional unique identifying name for this instance of node in the pipeline. Required if two instances of the same node are used in the pipeline.
executor_spec Optional instance of executor_spec.ExecutorSpec which describes how to execute this node (optional, defaults to an empty executor indicates no-op.
driver_class Optional subclass of base_driver.BaseDriver as a custom driver for this node (optional, defaults to base_driver.BaseDriver). Nodes usually use the default driver class, but may override it.

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

Methods

add_downstream_node

View source

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

View source

Convert from dictionary data to an object.

get_id

View source

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.