tfx.components.Tuner

A TFX component for model hyperparameter tuning.

Inherits From: BaseComponent, BaseNode

View aliases

Main aliases

tfx.components.tuner.component.Tuner

tfx.components.Tuner(
    examples: tfx.types.Channel = None,
    schema: Optional[tfx.types.Channel] = None,
    transform_graph: Optional[tfx.types.Channel] = None,
    module_file: Optional[Text] = None,
    tuner_fn: Optional[Text] = None,
    train_args: trainer_pb2.TrainArgs = None,
    eval_args: trainer_pb2.EvalArgs = None,
    tune_args: Optional[tuner_pb2.TuneArgs] = None,
    custom_config: Optional[Dict[Text, Any]] = None,
    best_hyperparameters: Optional[tfx.types.Channel] = None,
    instance_name: Optional[Text] = None
)

Args
examples	A Channel of type standard_artifacts.Examples, serving as the source of examples that are used in tuning (required).
schema	An optional Channel of type standard_artifacts.Schema, serving as the schema of training and eval data. This is used when raw examples are provided.
transform_graph	An optional Channel of type standard_artifacts.TransformGraph, serving as the input transform graph if present. This is used when transformed examples are provided.
module_file	A path to python module file containing UDF tuner definition. The module_file must implement a function named tuner_fn at its top level. The function must have the following signature. def tuner_fn(fn_args: FnArgs) -> TunerFnResult: Exactly one of 'module_file' or 'tuner_fn' must be supplied.
tuner_fn	A python path to UDF model definition function. See 'module_file' for the required signature of the UDF. Exactly one of 'module_file' or 'tuner_fn' must be supplied.
train_args	A trainer_pb2.TrainArgs instance, containing args used for training. Currently only splits and num_steps are available. Default behavior (when splits is empty) is train on train split.
eval_args	A trainer_pb2.EvalArgs instance, containing args used for eval. Currently only splits and num_steps are available. Default behavior (when splits is empty) is evaluate on eval split.
tune_args	A tuner_pb2.TuneArgs instance, containing args used for tuning. Currently only num_parallel_trials is available.
custom_config	A dict which contains addtional training job parameters that will be passed into user module.
best_hyperparameters	Optional Channel of type standard_artifacts.HyperParameters for result of the best hparams.
instance_name	Optional unique instance name. Necessary if multiple Tuner 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

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.

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.

Class Variables
EXECUTOR_SPEC	tfx.dsl.components.base.executor_spec.ExecutorClassSpec