tfx.components.FileBasedExampleGen

A TFX component to ingest examples from a file system.

Inherits From: BaseComponent

View aliases

Main aliases

tfx.components.example_gen.component.FileBasedExampleGen

tfx.components.FileBasedExampleGen(
    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,
    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,
    custom_executor_spec: Optional[tfx.components.base.executor_spec.ExecutorSpec] = None,
    instance_name: Optional[Text] = None
)

The FileBasedExampleGen component is an API for getting file-based records into TFX pipelines. It consumes external files to generate examples which will be used by other internal components like StatisticsGen or Trainers. The component will also convert the input data into tf.record and generate train and eval example splits for downsteam components.

Example

_taxi_root = os.path.join(os.environ['HOME'], 'taxi')
_data_root = os.path.join(_taxi_root, 'data', 'simple')
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = FileBasedExampleGen(input_base=_data_root)

Args
input	A Channel of type standard_artifacts.ExternalArtifact, which includes one artifact whose uri is an external directory containing the data files. (Deprecated by input_base)
input_base	an external directory containing the data files.
input_config	An example_gen_pb2.Input instance, providing input configuration. If unset, input files will be treated as a single split.
output_config	An example_gen_pb2.Output instance, providing the output configuration. If unset, default splits will be 'train' and 'eval' with size 2:1.
custom_config	An optional example_gen_pb2.CustomConfig instance, providing custom configuration for executor.
output_data_format	Payload format of generated data in output artifact, one of example_gen_pb2.PayloadFormat enum.
example_artifacts	Channel of 'ExamplesPath' for output train and eval examples.
custom_executor_spec	Optional custom executor spec overriding the default executor spec specified in the component attribute.
instance_name	Optional unique instance name. Required only if multiple ExampleGen components are declared in the same pipeline.

Attributes
component_id	DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: component_id is deprecated, use id instead
component_type	DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: component_type is deprecated, use type instead
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

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_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.

Class Variables

• EXECUTOR_SPEC