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A component that binds a DataView to ExamplesArtifact.
Inherits From: BaseComponent
, BaseNode
tfx.components.experimental.data_view.binder_component.DataViewBinder(
input_examples: tfx.types.Channel
,
data_view: tfx.types.Channel
,
output_examples: Optional[tfx.types.Channel
] = None,
instance_name: Optional[Text] = None
)
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'], ...)
Args | |
---|---|
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
|
Deprecated. Please set id directly using with_id()
function or .id setter in the BaseNode class. The pipeline
assembling will fail if there are two nodes in the pipeline with the
same id.
|
Attributes | |
---|---|
component_id
|
|
component_type
|
|
downstream_nodes
|
|
exec_properties
|
|
id
|
Node id, unique across all TFX nodes in a pipeline.
If |
inputs
|
|
outputs
|
|
type
|
|
upstream_nodes
|
Child Classes
Methods
add_downstream_node
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
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
@classmethod
from_json_dict( dict_data: Dict[Text, Any] ) -> Any
Convert from dictionary data to an object.
get_id
@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
to_json_dict() -> Dict[Text, Any]
Convert from an object to a JSON serializable dictionary.
with_id
with_id(
id: Text
) -> "BaseNode"
with_platform_config
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 |
tfx.dsl.components.base.executor_spec.ExecutorClassSpec
|