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tfx.components.FileBasedExampleGen

A TFX component to ingest examples from a file system.

Inherits From: BaseComponent, BaseNode

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,
    range_config: Optional[Union[range_config_pb2.RangeConfig, 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.dsl.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)

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.
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.
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.

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