tfx.components.ImportExampleGen

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Official TFX ImportExampleGen component.

Inherits From: FileBasedExampleGen, BaseComponent, BaseNode

View aliases

Main aliases

tfx.components.example_gen.import_example_gen.component.ImportExampleGen

tfx.components.ImportExampleGen(
    input: Optional[tfx.types.Channel] = None,
    input_base: Optional[Text] = None,
    input_config: Optional[Union[example_gen_pb2.Input, Dict[Text, Any]]] = None,
    output_config: Optional[Union[example_gen_pb2.Output, Dict[Text, Any]]] = None,
    range_config: Optional[Union[range_config_pb2.RangeConfig, Dict[Text, Any]]] = None,
    payload_format: Optional[int] = example_gen_pb2.FORMAT_TF_EXAMPLE,
    example_artifacts: Optional[tfx.types.Channel] = None,
    instance_name: Optional[Text] = None
)

Used in the notebooks

Used in the tutorials
Graph-based Neural Structured Learning in TFX

The ImportExampleGen component takes TFRecord files with TF Example data format, and generates train and eval examples for downsteam components. This component provides consistent and configurable partition, and it also shuffle the dataset for ML best practice.

Args
input: A Channel of type standard_artifacts.ExternalArtifact, which includes one artifact whose uri is an external directory containing the TFRecord files. (Deprecated by input_base)
input_base	an external directory containing the TFRecord files.
input_config	An example_gen_pb2.Input instance, providing input configuration. If unset, the files under input_base will be treated as a single split. 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.
output_config	An example_gen_pb2.Output instance, providing output configuration. If unset, default splits will be 'train' and 'eval' with size 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.
range_config	An optional range_config_pb2.RangeConfig instance, specifying the range of span values to consider. If unset, driver will default to searching for latest span with no restrictions.
payload_format	Payload format of input data. Should be one of example_gen_pb2.PayloadFormat enum. Note that payload format of output data is the same as input.
example_artifacts	Optional channel of 'ExamplesPath' for output train and eval examples.
instance_name	Optional unique instance name. Necessary if multiple ImportExampleGen components are declared in the same pipeline.

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

Child Classes

class DRIVER_CLASS

class SPEC_CLASS

Methods

add_downstream_node

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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

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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

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@classmethod
from_json_dict(
    dict_data: Dict[Text, Any]
) -> Any

Convert from dictionary data to an object.

get_class_type

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@classmethod
get_class_type() -> Text

get_id

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@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

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with_id(
    id: Text
) -> "BaseNode"

with_platform_config

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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