tfx.components.ImportExampleGen

Official TFX ImportExampleGen component.

Inherits From: FileBasedExampleGen

Used in the notebooks

Used in the tutorials

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.

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.
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.

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

Child Classes

class DRIVER_CLASS

class SPEC_CLASS

Methods

add_downstream_node

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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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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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Convert from dictionary data to an object.

get_id

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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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Convert from an object to a JSON serializable dictionary.

Class Variables

  • EXECUTOR_SPEC