tfx.v1.extensions.google_cloud_ai_platform.Tuner

TFX component for model hyperparameter tuning on AI Platform Training.

Inherits From: Tuner, BaseComponent, BaseNode

tfx.v1.extensions.google_cloud_ai_platform.Tuner(
    examples: tfx.v1.types.BaseChannel,
    schema: Optional[tfx.v1.types.BaseChannel] = None,
    transform_graph: Optional[tfx.v1.types.BaseChannel] = None,
    base_model: Optional[tfx.v1.types.BaseChannel] = None,
    module_file: Optional[str] = None,
    tuner_fn: Optional[str] = None,
    train_args: Optional[tfx.v1.proto.TrainArgs] = None,
    eval_args: Optional[tfx.v1.proto.EvalArgs] = None,
    tune_args: Optional[tfx.v1.proto.TuneArgs] = None,
    custom_config: Optional[Dict[str, Any]] = None
)

examples A BaseChannel of type standard_artifacts.Examples, serving as the source of examples that are used in tuning (required).
schema An optional BaseChannel 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 BaseChannel of type standard_artifacts.TransformGraph, serving as the input transform graph if present. This is used when transformed examples are provided.
base_model A BaseChannel of type Model, containing model that will be used for training. This can be used for warmstart, transfer learning or model ensembling.
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.

outputs Component's output channel dict.

Methods

with_node_execution_options

with_node_execution_options(
    node_execution_options: utils.NodeExecutionOptions
) -> typing_extensions.Self

POST_EXECUTABLE_SPEC None
PRE_EXECUTABLE_SPEC None