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A TFX component to ingest examples from query based systems.
Inherits From: BaseComponent
, BaseNode
tfx.components.example_gen.component.QueryBasedExampleGen(
input_config: Union[example_gen_pb2.Input, Dict[Text, Any]],
output_config: Optional[Union[example_gen_pb2.Output, Dict[Text, Any]]] = None,
custom_config: Optional[Union[example_gen_pb2.CustomConfig, Dict[Text, Any]]] = None,
output_data_format: Optional[int] = example_gen_pb2.FORMAT_TF_EXAMPLE,
example_artifacts: Optional[tfx.types.Channel
] = None,
instance_name: Optional[Text] = None
)
The QueryBasedExampleGen component can be extended to ingest examples from query based systems such as Presto or Bigquery. The component will also convert the input data into tf.record](https://www.tensorflow.org/tutorials/load_data/tf_records) and generate train and eval example splits for downsteam components.
Example
_query = "SELECT * FROM `bigquery-public-data.chicago_taxi_trips.taxi_trips`"
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = BigQueryExampleGen(query=_query)
Args | |
---|---|
input_config
|
An example_gen_pb2.Input instance, providing input configuration. If any field is provided as a RuntimeParameter, input_config should be constructed as a dict with the same field names as Input proto message. required |
output_config
|
An example_gen_pb2.Output instance, providing output configuration. If unset, the default splits will be labeled as 'train' and 'eval' with a distribution ratio of 2:1. If any field is provided as a RuntimeParameter, output_config should be constructed as a dict with the same field names as Output proto message. |
custom_config
|
An example_gen_pb2.CustomConfig instance, providing custom configuration for ExampleGen. If any field is provided as a RuntimeParameter, output_config should be constructed as a dict. |
output_data_format
|
Payload format of generated data in output artifact, one of example_gen_pb2.PayloadFormat enum. |
example_artifacts
|
Channel of standard_artifacts.Examples for output
train and eval examples.
|
instance_name
|
Optional unique instance name. Required only if multiple ExampleGen components are declared in the same pipeline. |
Raises | |
---|---|
ValueError
|
The output_data_format value must be defined in the example_gen_pb2.PayloadFormat proto. |
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_class_type
@classmethod
get_class_type() -> Text
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 |
Instance of tfx.dsl.components.base.executor_spec.ExecutorClassSpec
|