Class TuningEvaluationSpec (1.48.0)

Version latest

• 1.48.0 (latest)
• 1.47.0
• 1.46.0
• 1.45.0
• 1.44.0
• 1.43.0
• 1.39.0
• 1.38.1
• 1.37.0
• 1.36.4
• 1.35.0
• 1.34.0
• 1.33.1
• 1.32.0
• 1.31.1
• 1.30.1
• 1.29.0
• 1.28.1
• 1.27.1
• 1.26.1
• 1.25.0
• 1.24.1
• 1.23.0
• 1.22.1
• 1.21.0
• 1.20.0
• 1.19.1
• 1.18.3
• 1.17.1
• 1.16.1
• 1.15.1
• 1.14.0
• 1.13.1
• 1.12.1
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.1
• 1.7.1
• 1.6.2
• 1.5.0
• 1.4.3
• 1.3.0
• 1.2.0
• 1.1.1
• 1.0.1
• 0.9.0
• 0.8.0
• 0.7.1
• 0.6.0
• 0.5.1
• 0.4.0
• 0.3.1

TuningEvaluationSpec(
    evaluation_data: typing.Optional[str] = None,
    evaluation_interval: typing.Optional[int] = None,
    enable_early_stopping: typing.Optional[bool] = None,
    enable_checkpoint_selection: typing.Optional[bool] = None,
    tensorboard: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard, str
        ]
    ] = None,
)

Specification for model evaluation to perform during tuning.

Attributes
Name	Description
evaluation_data	Optional[str] GCS URI of the evaluation dataset. This will run model evaluation as part of the tuning job.
evaluation_interval	Optional[int] The evaluation will run at every evaluation_interval tuning steps. Default: 20.
enable_early_stopping	Optional[bool] If True, the tuning may stop early before completing all the tuning steps. Requires evaluation_data.
enable_checkpoint_selection	Optional[bool] If set to True, the tuning process returns the best model checkpoint (based on model evaluation). If set to False, the latest model checkpoint is returned. If unset, the selection is only enabled for *-bison@001 models.
tensorboard	Optional[Union[google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard, str]] Vertex Tensorboard where to write the evaluation metrics. The Tensorboard must be in the same location as the tuning job.