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tfx.components.example_gen.component.QueryBasedExampleGen

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)

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.

ValueError The output_data_format value must be defined in the example_gen_pb2.PayloadFormat proto.

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

Child Classes

class DRIVER_CLASS

class SPEC_CLASS

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_class_type

View source

@classmethod
get_class_type() -> Text

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

View source

to_json_dict() -> Dict[Text, Any]

Convert from an object to a JSON serializable dictionary.

with_id

View source

with_id(
    id: Text
) -> "BaseNode"

with_platform_config

View source

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.

EXECUTOR_SPEC Instance of tfx.dsl.components.base.executor_spec.ExecutorClassSpec