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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
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google-native.aiplatform/v1beta1.Study

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Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
pulumi/pulumi-google-native

    Creates a Study. A resource name will be generated after creation of the Study. Auto-naming is currently not supported for this resource.

    Create Study Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new Study(name: string, args: StudyArgs, opts?: CustomResourceOptions);
    @overload
def Study(resource_name: str,
          args: StudyArgs,
          opts: Optional[ResourceOptions] = None)

@overload
def Study(resource_name: str,
          opts: Optional[ResourceOptions] = None,
          display_name: Optional[str] = None,
          study_spec: Optional[GoogleCloudAiplatformV1beta1StudySpecArgs] = None,
          location: Optional[str] = None,
          project: Optional[str] = None)
    func NewStudy(ctx *Context, name string, args StudyArgs, opts ...ResourceOption) (*Study, error)
    public Study(string name, StudyArgs args, CustomResourceOptions? opts = null)
    public Study(String name, StudyArgs args)
public Study(String name, StudyArgs args, CustomResourceOptions options)

    type: google-native:aiplatform/v1beta1:Study
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args StudyArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args StudyArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args StudyArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args StudyArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args StudyArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

    The following reference example uses placeholder values for all input properties.

    var google_nativeStudyResource = new GoogleNative.Aiplatform.V1Beta1.Study("google-nativeStudyResource", new()
{
    DisplayName = "string",
    StudySpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecArgs
    {
        Metrics = new[]
        {
            new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMetricSpecArgs
            {
                Goal = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoal.GoalTypeUnspecified,
                MetricId = "string",
                SafetyConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigArgs
                {
                    DesiredMinSafeTrialsFraction = 0,
                    SafetyThreshold = 0,
                },
            },
        },
        Parameters = new[]
        {
            new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecArgs
            {
                ParameterId = "string",
                CategoricalValueSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecArgs
                {
                    Values = new[]
                    {
                        "string",
                    },
                    DefaultValue = "string",
                },
                ConditionalParameterSpecs = new[]
                {
                    new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecArgs
                    {
                        ParameterSpec = googleCloudAiplatformV1beta1StudySpecParameterSpec,
                        ParentCategoricalValues = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionArgs
                        {
                            Values = new[]
                            {
                                "string",
                            },
                        },
                        ParentDiscreteValues = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionArgs
                        {
                            Values = new[]
                            {
                                0,
                            },
                        },
                        ParentIntValues = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionArgs
                        {
                            Values = new[]
                            {
                                "string",
                            },
                        },
                    },
                },
                DiscreteValueSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecArgs
                {
                    Values = new[]
                    {
                        0,
                    },
                    DefaultValue = 0,
                },
                DoubleValueSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecArgs
                {
                    MaxValue = 0,
                    MinValue = 0,
                    DefaultValue = 0,
                },
                IntegerValueSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecArgs
                {
                    MaxValue = "string",
                    MinValue = "string",
                    DefaultValue = "string",
                },
                ScaleType = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleType.ScaleTypeUnspecified,
            },
        },
        Algorithm = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1StudySpecAlgorithm.AlgorithmUnspecified,
        ConvexAutomatedStoppingSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecArgs
        {
            LearningRateParameterName = "string",
            MaxStepCount = "string",
            MinMeasurementCount = "string",
            MinStepCount = "string",
            UpdateAllStoppedTrials = false,
            UseElapsedDuration = false,
        },
        DecayCurveStoppingSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecArgs
        {
            UseElapsedDuration = false,
        },
        MeasurementSelectionType = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionType.MeasurementSelectionTypeUnspecified,
        MedianAutomatedStoppingSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecArgs
        {
            UseElapsedDuration = false,
        },
        ObservationNoise = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1StudySpecObservationNoise.ObservationNoiseUnspecified,
        StudyStoppingConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigArgs
        {
            MaxDurationNoProgress = "string",
            MaxNumTrials = 0,
            MaxNumTrialsNoProgress = 0,
            MaximumRuntimeConstraint = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudyTimeConstraintArgs
            {
                EndTime = "string",
                MaxDuration = "string",
            },
            MinNumTrials = 0,
            MinimumRuntimeConstraint = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudyTimeConstraintArgs
            {
                EndTime = "string",
                MaxDuration = "string",
            },
            ShouldStopAsap = false,
        },
        TransferLearningConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigArgs
        {
            DisableTransferLearning = false,
        },
    },
    Location = "string",
    Project = "string",
});

    example, err := aiplatformv1beta1.NewStudy(ctx, "google-nativeStudyResource", &aiplatformv1beta1.StudyArgs{
	DisplayName: pulumi.String("string"),
	StudySpec: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecArgs{
		Metrics: aiplatform.GoogleCloudAiplatformV1beta1StudySpecMetricSpecArray{
			&aiplatform.GoogleCloudAiplatformV1beta1StudySpecMetricSpecArgs{
				Goal:     aiplatformv1beta1.GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoalGoalTypeUnspecified,
				MetricId: pulumi.String("string"),
				SafetyConfig: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigArgs{
					DesiredMinSafeTrialsFraction: pulumi.Float64(0),
					SafetyThreshold:              pulumi.Float64(0),
				},
			},
		},
		Parameters: aiplatform.GoogleCloudAiplatformV1beta1StudySpecParameterSpecArray{
			&aiplatform.GoogleCloudAiplatformV1beta1StudySpecParameterSpecArgs{
				ParameterId: pulumi.String("string"),
				CategoricalValueSpec: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecArgs{
					Values: pulumi.StringArray{
						pulumi.String("string"),
					},
					DefaultValue: pulumi.String("string"),
				},
				ConditionalParameterSpecs: aiplatform.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecArray{
					&aiplatform.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecArgs{
						ParameterSpec: pulumi.Any(googleCloudAiplatformV1beta1StudySpecParameterSpec),
						ParentCategoricalValues: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionArgs{
							Values: pulumi.StringArray{
								pulumi.String("string"),
							},
						},
						ParentDiscreteValues: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionArgs{
							Values: pulumi.Float64Array{
								pulumi.Float64(0),
							},
						},
						ParentIntValues: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionArgs{
							Values: pulumi.StringArray{
								pulumi.String("string"),
							},
						},
					},
				},
				DiscreteValueSpec: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecArgs{
					Values: pulumi.Float64Array{
						pulumi.Float64(0),
					},
					DefaultValue: pulumi.Float64(0),
				},
				DoubleValueSpec: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecArgs{
					MaxValue:     pulumi.Float64(0),
					MinValue:     pulumi.Float64(0),
					DefaultValue: pulumi.Float64(0),
				},
				IntegerValueSpec: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecArgs{
					MaxValue:     pulumi.String("string"),
					MinValue:     pulumi.String("string"),
					DefaultValue: pulumi.String("string"),
				},
				ScaleType: aiplatformv1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleTypeScaleTypeUnspecified,
			},
		},
		Algorithm: aiplatformv1beta1.GoogleCloudAiplatformV1beta1StudySpecAlgorithmAlgorithmUnspecified,
		ConvexAutomatedStoppingSpec: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecArgs{
			LearningRateParameterName: pulumi.String("string"),
			MaxStepCount:              pulumi.String("string"),
			MinMeasurementCount:       pulumi.String("string"),
			MinStepCount:              pulumi.String("string"),
			UpdateAllStoppedTrials:    pulumi.Bool(false),
			UseElapsedDuration:        pulumi.Bool(false),
		},
		DecayCurveStoppingSpec: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecArgs{
			UseElapsedDuration: pulumi.Bool(false),
		},
		MeasurementSelectionType: aiplatformv1beta1.GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionTypeMeasurementSelectionTypeUnspecified,
		MedianAutomatedStoppingSpec: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecArgs{
			UseElapsedDuration: pulumi.Bool(false),
		},
		ObservationNoise: aiplatformv1beta1.GoogleCloudAiplatformV1beta1StudySpecObservationNoiseObservationNoiseUnspecified,
		StudyStoppingConfig: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigArgs{
			MaxDurationNoProgress:  pulumi.String("string"),
			MaxNumTrials:           pulumi.Int(0),
			MaxNumTrialsNoProgress: pulumi.Int(0),
			MaximumRuntimeConstraint: &aiplatform.GoogleCloudAiplatformV1beta1StudyTimeConstraintArgs{
				EndTime:     pulumi.String("string"),
				MaxDuration: pulumi.String("string"),
			},
			MinNumTrials: pulumi.Int(0),
			MinimumRuntimeConstraint: &aiplatform.GoogleCloudAiplatformV1beta1StudyTimeConstraintArgs{
				EndTime:     pulumi.String("string"),
				MaxDuration: pulumi.String("string"),
			},
			ShouldStopAsap: pulumi.Bool(false),
		},
		TransferLearningConfig: &aiplatform.GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigArgs{
			DisableTransferLearning: pulumi.Bool(false),
		},
	},
	Location: pulumi.String("string"),
	Project:  pulumi.String("string"),
})

    var google_nativeStudyResource = new Study("google-nativeStudyResource", StudyArgs.builder()
    .displayName("string")
    .studySpec(GoogleCloudAiplatformV1beta1StudySpecArgs.builder()
        .metrics(GoogleCloudAiplatformV1beta1StudySpecMetricSpecArgs.builder()
            .goal("GOAL_TYPE_UNSPECIFIED")
            .metricId("string")
            .safetyConfig(GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigArgs.builder()
                .desiredMinSafeTrialsFraction(0)
                .safetyThreshold(0)
                .build())
            .build())
        .parameters(GoogleCloudAiplatformV1beta1StudySpecParameterSpecArgs.builder()
            .parameterId("string")
            .categoricalValueSpec(GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecArgs.builder()
                .values("string")
                .defaultValue("string")
                .build())
            .conditionalParameterSpecs(GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecArgs.builder()
                .parameterSpec(googleCloudAiplatformV1beta1StudySpecParameterSpec)
                .parentCategoricalValues(GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionArgs.builder()
                    .values("string")
                    .build())
                .parentDiscreteValues(GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionArgs.builder()
                    .values(0)
                    .build())
                .parentIntValues(GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionArgs.builder()
                    .values("string")
                    .build())
                .build())
            .discreteValueSpec(GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecArgs.builder()
                .values(0)
                .defaultValue(0)
                .build())
            .doubleValueSpec(GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecArgs.builder()
                .maxValue(0)
                .minValue(0)
                .defaultValue(0)
                .build())
            .integerValueSpec(GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecArgs.builder()
                .maxValue("string")
                .minValue("string")
                .defaultValue("string")
                .build())
            .scaleType("SCALE_TYPE_UNSPECIFIED")
            .build())
        .algorithm("ALGORITHM_UNSPECIFIED")
        .convexAutomatedStoppingSpec(GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecArgs.builder()
            .learningRateParameterName("string")
            .maxStepCount("string")
            .minMeasurementCount("string")
            .minStepCount("string")
            .updateAllStoppedTrials(false)
            .useElapsedDuration(false)
            .build())
        .decayCurveStoppingSpec(GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecArgs.builder()
            .useElapsedDuration(false)
            .build())
        .measurementSelectionType("MEASUREMENT_SELECTION_TYPE_UNSPECIFIED")
        .medianAutomatedStoppingSpec(GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecArgs.builder()
            .useElapsedDuration(false)
            .build())
        .observationNoise("OBSERVATION_NOISE_UNSPECIFIED")
        .studyStoppingConfig(GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigArgs.builder()
            .maxDurationNoProgress("string")
            .maxNumTrials(0)
            .maxNumTrialsNoProgress(0)
            .maximumRuntimeConstraint(GoogleCloudAiplatformV1beta1StudyTimeConstraintArgs.builder()
                .endTime("string")
                .maxDuration("string")
                .build())
            .minNumTrials(0)
            .minimumRuntimeConstraint(GoogleCloudAiplatformV1beta1StudyTimeConstraintArgs.builder()
                .endTime("string")
                .maxDuration("string")
                .build())
            .shouldStopAsap(false)
            .build())
        .transferLearningConfig(GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigArgs.builder()
            .disableTransferLearning(false)
            .build())
        .build())
    .location("string")
    .project("string")
    .build());

    google_native_study_resource = google_native.aiplatform.v1beta1.Study("google-nativeStudyResource",
    display_name="string",
    study_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecArgs(
        metrics=[google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecMetricSpecArgs(
            goal=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoal.GOAL_TYPE_UNSPECIFIED,
            metric_id="string",
            safety_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigArgs(
                desired_min_safe_trials_fraction=0,
                safety_threshold=0,
            ),
        )],
        parameters=[google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecArgs(
            parameter_id="string",
            categorical_value_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecArgs(
                values=["string"],
                default_value="string",
            ),
            conditional_parameter_specs=[google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecArgs(
                parameter_spec=google_cloud_aiplatform_v1beta1_study_spec_parameter_spec,
                parent_categorical_values=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionArgs(
                    values=["string"],
                ),
                parent_discrete_values=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionArgs(
                    values=[0],
                ),
                parent_int_values=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionArgs(
                    values=["string"],
                ),
            )],
            discrete_value_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecArgs(
                values=[0],
                default_value=0,
            ),
            double_value_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecArgs(
                max_value=0,
                min_value=0,
                default_value=0,
            ),
            integer_value_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecArgs(
                max_value="string",
                min_value="string",
                default_value="string",
            ),
            scale_type=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleType.SCALE_TYPE_UNSPECIFIED,
        )],
        algorithm=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecAlgorithm.ALGORITHM_UNSPECIFIED,
        convex_automated_stopping_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecArgs(
            learning_rate_parameter_name="string",
            max_step_count="string",
            min_measurement_count="string",
            min_step_count="string",
            update_all_stopped_trials=False,
            use_elapsed_duration=False,
        ),
        decay_curve_stopping_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecArgs(
            use_elapsed_duration=False,
        ),
        measurement_selection_type=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionType.MEASUREMENT_SELECTION_TYPE_UNSPECIFIED,
        median_automated_stopping_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecArgs(
            use_elapsed_duration=False,
        ),
        observation_noise=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecObservationNoise.OBSERVATION_NOISE_UNSPECIFIED,
        study_stopping_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigArgs(
            max_duration_no_progress="string",
            max_num_trials=0,
            max_num_trials_no_progress=0,
            maximum_runtime_constraint=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudyTimeConstraintArgs(
                end_time="string",
                max_duration="string",
            ),
            min_num_trials=0,
            minimum_runtime_constraint=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudyTimeConstraintArgs(
                end_time="string",
                max_duration="string",
            ),
            should_stop_asap=False,
        ),
        transfer_learning_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigArgs(
            disable_transfer_learning=False,
        ),
    ),
    location="string",
    project="string")

    const google_nativeStudyResource = new google_native.aiplatform.v1beta1.Study("google-nativeStudyResource", {
    displayName: "string",
    studySpec: {
        metrics: [{
            goal: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoal.GoalTypeUnspecified,
            metricId: "string",
            safetyConfig: {
                desiredMinSafeTrialsFraction: 0,
                safetyThreshold: 0,
            },
        }],
        parameters: [{
            parameterId: "string",
            categoricalValueSpec: {
                values: ["string"],
                defaultValue: "string",
            },
            conditionalParameterSpecs: [{
                parameterSpec: googleCloudAiplatformV1beta1StudySpecParameterSpec,
                parentCategoricalValues: {
                    values: ["string"],
                },
                parentDiscreteValues: {
                    values: [0],
                },
                parentIntValues: {
                    values: ["string"],
                },
            }],
            discreteValueSpec: {
                values: [0],
                defaultValue: 0,
            },
            doubleValueSpec: {
                maxValue: 0,
                minValue: 0,
                defaultValue: 0,
            },
            integerValueSpec: {
                maxValue: "string",
                minValue: "string",
                defaultValue: "string",
            },
            scaleType: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleType.ScaleTypeUnspecified,
        }],
        algorithm: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecAlgorithm.AlgorithmUnspecified,
        convexAutomatedStoppingSpec: {
            learningRateParameterName: "string",
            maxStepCount: "string",
            minMeasurementCount: "string",
            minStepCount: "string",
            updateAllStoppedTrials: false,
            useElapsedDuration: false,
        },
        decayCurveStoppingSpec: {
            useElapsedDuration: false,
        },
        measurementSelectionType: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionType.MeasurementSelectionTypeUnspecified,
        medianAutomatedStoppingSpec: {
            useElapsedDuration: false,
        },
        observationNoise: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StudySpecObservationNoise.ObservationNoiseUnspecified,
        studyStoppingConfig: {
            maxDurationNoProgress: "string",
            maxNumTrials: 0,
            maxNumTrialsNoProgress: 0,
            maximumRuntimeConstraint: {
                endTime: "string",
                maxDuration: "string",
            },
            minNumTrials: 0,
            minimumRuntimeConstraint: {
                endTime: "string",
                maxDuration: "string",
            },
            shouldStopAsap: false,
        },
        transferLearningConfig: {
            disableTransferLearning: false,
        },
    },
    location: "string",
    project: "string",
});

    type: google-native:aiplatform/v1beta1:Study
properties:
    displayName: string
    location: string
    project: string
    studySpec:
        algorithm: ALGORITHM_UNSPECIFIED
        convexAutomatedStoppingSpec:
            learningRateParameterName: string
            maxStepCount: string
            minMeasurementCount: string
            minStepCount: string
            updateAllStoppedTrials: false
            useElapsedDuration: false
        decayCurveStoppingSpec:
            useElapsedDuration: false
        measurementSelectionType: MEASUREMENT_SELECTION_TYPE_UNSPECIFIED
        medianAutomatedStoppingSpec:
            useElapsedDuration: false
        metrics:
            - goal: GOAL_TYPE_UNSPECIFIED
              metricId: string
              safetyConfig:
                desiredMinSafeTrialsFraction: 0
                safetyThreshold: 0
        observationNoise: OBSERVATION_NOISE_UNSPECIFIED
        parameters:
            - categoricalValueSpec:
                defaultValue: string
                values:
                    - string
              conditionalParameterSpecs:
                - parameterSpec: ${googleCloudAiplatformV1beta1StudySpecParameterSpec}
                  parentCategoricalValues:
                    values:
                        - string
                  parentDiscreteValues:
                    values:
                        - 0
                  parentIntValues:
                    values:
                        - string
              discreteValueSpec:
                defaultValue: 0
                values:
                    - 0
              doubleValueSpec:
                defaultValue: 0
                maxValue: 0
                minValue: 0
              integerValueSpec:
                defaultValue: string
                maxValue: string
                minValue: string
              parameterId: string
              scaleType: SCALE_TYPE_UNSPECIFIED
        studyStoppingConfig:
            maxDurationNoProgress: string
            maxNumTrials: 0
            maxNumTrialsNoProgress: 0
            maximumRuntimeConstraint:
                endTime: string
                maxDuration: string
            minNumTrials: 0
            minimumRuntimeConstraint:
                endTime: string
                maxDuration: string
            shouldStopAsap: false
        transferLearningConfig:
            disableTransferLearning: false

    Study Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    The Study resource accepts the following input properties:

    DisplayName string
    Describes the Study, default value is empty string.
    StudySpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpec
    Configuration of the Study.
    Location string
    Project string
    DisplayName string
    Describes the Study, default value is empty string.
    StudySpec GoogleCloudAiplatformV1beta1StudySpecArgs
    Configuration of the Study.
    Location string
    Project string
    displayName String
    Describes the Study, default value is empty string.
    studySpec GoogleCloudAiplatformV1beta1StudySpec
    Configuration of the Study.
    location String
    project String
    displayName string
    Describes the Study, default value is empty string.
    studySpec GoogleCloudAiplatformV1beta1StudySpec
    Configuration of the Study.
    location string
    project string
    display_name str
    Describes the Study, default value is empty string.
    study_spec GoogleCloudAiplatformV1beta1StudySpecArgs
    Configuration of the Study.
    location str
    project str
    displayName String
    Describes the Study, default value is empty string.
    studySpec Property Map
    Configuration of the Study.
    location String
    project String

    Outputs

    All input properties are implicitly available as output properties. Additionally, the Study resource produces the following output properties:

    CreateTime string
    Time at which the study was created.
    Id string
    The provider-assigned unique ID for this managed resource.
    InactiveReason string
    A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
    Name string
    The name of a study. The study's globally unique identifier. Format: projects/{project}/locations/{location}/studies/{study}
    State string
    The detailed state of a Study.
    CreateTime string
    Time at which the study was created.
    Id string
    The provider-assigned unique ID for this managed resource.
    InactiveReason string
    A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
    Name string
    The name of a study. The study's globally unique identifier. Format: projects/{project}/locations/{location}/studies/{study}
    State string
    The detailed state of a Study.
    createTime String
    Time at which the study was created.
    id String
    The provider-assigned unique ID for this managed resource.
    inactiveReason String
    A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
    name String
    The name of a study. The study's globally unique identifier. Format: projects/{project}/locations/{location}/studies/{study}
    state String
    The detailed state of a Study.
    createTime string
    Time at which the study was created.
    id string
    The provider-assigned unique ID for this managed resource.
    inactiveReason string
    A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
    name string
    The name of a study. The study's globally unique identifier. Format: projects/{project}/locations/{location}/studies/{study}
    state string
    The detailed state of a Study.
    create_time str
    Time at which the study was created.
    id str
    The provider-assigned unique ID for this managed resource.
    inactive_reason str
    A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
    name str
    The name of a study. The study's globally unique identifier. Format: projects/{project}/locations/{location}/studies/{study}
    state str
    The detailed state of a Study.
    createTime String
    Time at which the study was created.
    id String
    The provider-assigned unique ID for this managed resource.
    inactiveReason String
    A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
    name String
    The name of a study. The study's globally unique identifier. Format: projects/{project}/locations/{location}/studies/{study}
    state String
    The detailed state of a Study.

    Supporting Types

    GoogleCloudAiplatformV1beta1StudySpec, GoogleCloudAiplatformV1beta1StudySpecArgs

    Metrics List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMetricSpec>
    Metric specs for the Study.
    Parameters List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpec>
    The set of parameters to tune.
    Algorithm Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1StudySpecAlgorithm
    The search algorithm specified for the Study.
    ConvexAutomatedStoppingSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpec
    The automated early stopping spec using convex stopping rule.
    ConvexStopConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecConvexStopConfig
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    DecayCurveStoppingSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpec
    The automated early stopping spec using decay curve rule.
    MeasurementSelectionType Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionType
    Describe which measurement selection type will be used
    MedianAutomatedStoppingSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpec
    The automated early stopping spec using median rule.
    ObservationNoise Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1StudySpecObservationNoise
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    StudyStoppingConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfig
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    TransferLearningConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfig
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    Metrics []GoogleCloudAiplatformV1beta1StudySpecMetricSpec
    Metric specs for the Study.
    Parameters []GoogleCloudAiplatformV1beta1StudySpecParameterSpec
    The set of parameters to tune.
    Algorithm GoogleCloudAiplatformV1beta1StudySpecAlgorithm
    The search algorithm specified for the Study.
    ConvexAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpec
    The automated early stopping spec using convex stopping rule.
    ConvexStopConfig GoogleCloudAiplatformV1beta1StudySpecConvexStopConfig
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    DecayCurveStoppingSpec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpec
    The automated early stopping spec using decay curve rule.
    MeasurementSelectionType GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionType
    Describe which measurement selection type will be used
    MedianAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpec
    The automated early stopping spec using median rule.
    ObservationNoise GoogleCloudAiplatformV1beta1StudySpecObservationNoise
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    StudyStoppingConfig GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfig
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    TransferLearningConfig GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfig
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    metrics List<GoogleCloudAiplatformV1beta1StudySpecMetricSpec>
    Metric specs for the Study.
    parameters List<GoogleCloudAiplatformV1beta1StudySpecParameterSpec>
    The set of parameters to tune.
    algorithm GoogleCloudAiplatformV1beta1StudySpecAlgorithm
    The search algorithm specified for the Study.
    convexAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpec
    The automated early stopping spec using convex stopping rule.
    convexStopConfig GoogleCloudAiplatformV1beta1StudySpecConvexStopConfig
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decayCurveStoppingSpec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpec
    The automated early stopping spec using decay curve rule.
    measurementSelectionType GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionType
    Describe which measurement selection type will be used
    medianAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpec
    The automated early stopping spec using median rule.
    observationNoise GoogleCloudAiplatformV1beta1StudySpecObservationNoise
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    studyStoppingConfig GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfig
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transferLearningConfig GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfig
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    metrics GoogleCloudAiplatformV1beta1StudySpecMetricSpec[]
    Metric specs for the Study.
    parameters GoogleCloudAiplatformV1beta1StudySpecParameterSpec[]
    The set of parameters to tune.
    algorithm GoogleCloudAiplatformV1beta1StudySpecAlgorithm
    The search algorithm specified for the Study.
    convexAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpec
    The automated early stopping spec using convex stopping rule.
    convexStopConfig GoogleCloudAiplatformV1beta1StudySpecConvexStopConfig
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decayCurveStoppingSpec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpec
    The automated early stopping spec using decay curve rule.
    measurementSelectionType GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionType
    Describe which measurement selection type will be used
    medianAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpec
    The automated early stopping spec using median rule.
    observationNoise GoogleCloudAiplatformV1beta1StudySpecObservationNoise
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    studyStoppingConfig GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfig
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transferLearningConfig GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfig
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    metrics Sequence[GoogleCloudAiplatformV1beta1StudySpecMetricSpec]
    Metric specs for the Study.
    parameters Sequence[GoogleCloudAiplatformV1beta1StudySpecParameterSpec]
    The set of parameters to tune.
    algorithm GoogleCloudAiplatformV1beta1StudySpecAlgorithm
    The search algorithm specified for the Study.
    convex_automated_stopping_spec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpec
    The automated early stopping spec using convex stopping rule.
    convex_stop_config GoogleCloudAiplatformV1beta1StudySpecConvexStopConfig
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decay_curve_stopping_spec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpec
    The automated early stopping spec using decay curve rule.
    measurement_selection_type GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionType
    Describe which measurement selection type will be used
    median_automated_stopping_spec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpec
    The automated early stopping spec using median rule.
    observation_noise GoogleCloudAiplatformV1beta1StudySpecObservationNoise
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    study_stopping_config GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfig
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transfer_learning_config GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfig
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    metrics List<Property Map>
    Metric specs for the Study.
    parameters List<Property Map>
    The set of parameters to tune.
    algorithm "ALGORITHM_UNSPECIFIED" | "GRID_SEARCH" | "RANDOM_SEARCH"
    The search algorithm specified for the Study.
    convexAutomatedStoppingSpec Property Map
    The automated early stopping spec using convex stopping rule.
    convexStopConfig Property Map
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decayCurveStoppingSpec Property Map
    The automated early stopping spec using decay curve rule.
    measurementSelectionType "MEASUREMENT_SELECTION_TYPE_UNSPECIFIED" | "LAST_MEASUREMENT" | "BEST_MEASUREMENT"
    Describe which measurement selection type will be used
    medianAutomatedStoppingSpec Property Map
    The automated early stopping spec using median rule.
    observationNoise "OBSERVATION_NOISE_UNSPECIFIED" | "LOW" | "HIGH"
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    studyStoppingConfig Property Map
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transferLearningConfig Property Map
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob

    GoogleCloudAiplatformV1beta1StudySpecAlgorithm, GoogleCloudAiplatformV1beta1StudySpecAlgorithmArgs

    AlgorithmUnspecified
    ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
    GridSearch
    GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE.
    RandomSearch
    RANDOM_SEARCHSimple random search within the feasible space.
    GoogleCloudAiplatformV1beta1StudySpecAlgorithmAlgorithmUnspecified
    ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
    GoogleCloudAiplatformV1beta1StudySpecAlgorithmGridSearch
    GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE.
    GoogleCloudAiplatformV1beta1StudySpecAlgorithmRandomSearch
    RANDOM_SEARCHSimple random search within the feasible space.
    AlgorithmUnspecified
    ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
    GridSearch
    GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE.
    RandomSearch
    RANDOM_SEARCHSimple random search within the feasible space.
    AlgorithmUnspecified
    ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
    GridSearch
    GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE.
    RandomSearch
    RANDOM_SEARCHSimple random search within the feasible space.
    ALGORITHM_UNSPECIFIED
    ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
    GRID_SEARCH
    GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE.
    RANDOM_SEARCH
    RANDOM_SEARCHSimple random search within the feasible space.
    "ALGORITHM_UNSPECIFIED"
    ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
    "GRID_SEARCH"
    GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE.
    "RANDOM_SEARCH"
    RANDOM_SEARCHSimple random search within the feasible space.

    GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpec, GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecArgs

    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxStepCount string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinMeasurementCount string
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    MinStepCount string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    UpdateAllStoppedTrials bool
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    UseElapsedDuration bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxStepCount string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinMeasurementCount string
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    MinStepCount string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    UpdateAllStoppedTrials bool
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    UseElapsedDuration bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxStepCount String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    minMeasurementCount String
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    minStepCount String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    updateAllStoppedTrials Boolean
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    useElapsedDuration Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxStepCount string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    minMeasurementCount string
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    minStepCount string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    updateAllStoppedTrials boolean
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    useElapsedDuration boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learning_rate_parameter_name str
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    max_step_count str
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    min_measurement_count str
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    min_step_count str
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    update_all_stopped_trials bool
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    use_elapsed_duration bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxStepCount String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    minMeasurementCount String
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    minStepCount String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    updateAllStoppedTrials Boolean
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    useElapsedDuration Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.

    GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse, GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponseArgs

    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxStepCount string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinMeasurementCount string
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    MinStepCount string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    UpdateAllStoppedTrials bool
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    UseElapsedDuration bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxStepCount string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinMeasurementCount string
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    MinStepCount string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    UpdateAllStoppedTrials bool
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    UseElapsedDuration bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxStepCount String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    minMeasurementCount String
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    minStepCount String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    updateAllStoppedTrials Boolean
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    useElapsedDuration Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxStepCount string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    minMeasurementCount string
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    minStepCount string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    updateAllStoppedTrials boolean
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    useElapsedDuration boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learning_rate_parameter_name str
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    max_step_count str
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    min_measurement_count str
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    min_step_count str
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    update_all_stopped_trials bool
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    use_elapsed_duration bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxStepCount String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
    minMeasurementCount String
    The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
    minStepCount String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
    updateAllStoppedTrials Boolean
    ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
    useElapsedDuration Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.

    GoogleCloudAiplatformV1beta1StudySpecConvexStopConfig, GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigArgs

    AutoregressiveOrder string
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxNumSteps string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinNumSteps string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    UseSeconds bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    AutoregressiveOrder string
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxNumSteps string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinNumSteps string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    UseSeconds bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressiveOrder String
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxNumSteps String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    minNumSteps String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    useSeconds Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressiveOrder string
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxNumSteps string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    minNumSteps string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    useSeconds boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressive_order str
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learning_rate_parameter_name str
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    max_num_steps str
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    min_num_steps str
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    use_seconds bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressiveOrder String
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxNumSteps String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    minNumSteps String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    useSeconds Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.

    GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse, GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponseArgs

    AutoregressiveOrder string
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxNumSteps string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinNumSteps string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    UseSeconds bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    AutoregressiveOrder string
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    LearningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    MaxNumSteps string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    MinNumSteps string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    UseSeconds bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressiveOrder String
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxNumSteps String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    minNumSteps String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    useSeconds Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressiveOrder string
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learningRateParameterName string
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxNumSteps string
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    minNumSteps string
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    useSeconds boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressive_order str
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learning_rate_parameter_name str
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    max_num_steps str
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    min_num_steps str
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    use_seconds bool
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
    autoregressiveOrder String
    The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
    learningRateParameterName String
    The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
    maxNumSteps String
    Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
    minNumSteps String
    Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
    useSeconds Boolean
    This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.

    GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpec, GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecArgs

    UseElapsedDuration bool
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    UseElapsedDuration bool
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    useElapsedDuration Boolean
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    useElapsedDuration boolean
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    use_elapsed_duration bool
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    useElapsedDuration Boolean
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.

    GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse, GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponseArgs

    UseElapsedDuration bool
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    UseElapsedDuration bool
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    useElapsedDuration Boolean
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    useElapsedDuration boolean
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    use_elapsed_duration bool
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
    useElapsedDuration Boolean
    True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.

    GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionType, GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionTypeArgs

    MeasurementSelectionTypeUnspecified
    MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
    LastMeasurement
    LAST_MEASUREMENTUse the last measurement reported.
    BestMeasurement
    BEST_MEASUREMENTUse the best measurement reported.
    GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionTypeMeasurementSelectionTypeUnspecified
    MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
    GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionTypeLastMeasurement
    LAST_MEASUREMENTUse the last measurement reported.
    GoogleCloudAiplatformV1beta1StudySpecMeasurementSelectionTypeBestMeasurement
    BEST_MEASUREMENTUse the best measurement reported.
    MeasurementSelectionTypeUnspecified
    MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
    LastMeasurement
    LAST_MEASUREMENTUse the last measurement reported.
    BestMeasurement
    BEST_MEASUREMENTUse the best measurement reported.
    MeasurementSelectionTypeUnspecified
    MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
    LastMeasurement
    LAST_MEASUREMENTUse the last measurement reported.
    BestMeasurement
    BEST_MEASUREMENTUse the best measurement reported.
    MEASUREMENT_SELECTION_TYPE_UNSPECIFIED
    MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
    LAST_MEASUREMENT
    LAST_MEASUREMENTUse the last measurement reported.
    BEST_MEASUREMENT
    BEST_MEASUREMENTUse the best measurement reported.
    "MEASUREMENT_SELECTION_TYPE_UNSPECIFIED"
    MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
    "LAST_MEASUREMENT"
    LAST_MEASUREMENTUse the last measurement reported.
    "BEST_MEASUREMENT"
    BEST_MEASUREMENTUse the best measurement reported.

    GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpec, GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecArgs

    UseElapsedDuration bool
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    UseElapsedDuration bool
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    useElapsedDuration Boolean
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    useElapsedDuration boolean
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    use_elapsed_duration bool
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    useElapsedDuration Boolean
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.

    GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse, GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponseArgs

    UseElapsedDuration bool
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    UseElapsedDuration bool
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    useElapsedDuration Boolean
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    useElapsedDuration boolean
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    use_elapsed_duration bool
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
    useElapsedDuration Boolean
    True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.

    GoogleCloudAiplatformV1beta1StudySpecMetricSpec, GoogleCloudAiplatformV1beta1StudySpecMetricSpecArgs

    Goal Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoal
    The optimization goal of the metric.
    MetricId string
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    SafetyConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfig
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    Goal GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoal
    The optimization goal of the metric.
    MetricId string
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    SafetyConfig GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfig
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoal
    The optimization goal of the metric.
    metricId String
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safetyConfig GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfig
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoal
    The optimization goal of the metric.
    metricId string
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safetyConfig GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfig
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoal
    The optimization goal of the metric.
    metric_id str
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safety_config GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfig
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal "GOAL_TYPE_UNSPECIFIED" | "MAXIMIZE" | "MINIMIZE"
    The optimization goal of the metric.
    metricId String
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safetyConfig Property Map
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.

    GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoal, GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoalArgs

    GoalTypeUnspecified
    GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
    Maximize
    MAXIMIZEMaximize the goal metric.
    Minimize
    MINIMIZEMinimize the goal metric.
    GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoalGoalTypeUnspecified
    GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
    GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoalMaximize
    MAXIMIZEMaximize the goal metric.
    GoogleCloudAiplatformV1beta1StudySpecMetricSpecGoalMinimize
    MINIMIZEMinimize the goal metric.
    GoalTypeUnspecified
    GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
    Maximize
    MAXIMIZEMaximize the goal metric.
    Minimize
    MINIMIZEMinimize the goal metric.
    GoalTypeUnspecified
    GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
    Maximize
    MAXIMIZEMaximize the goal metric.
    Minimize
    MINIMIZEMinimize the goal metric.
    GOAL_TYPE_UNSPECIFIED
    GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
    MAXIMIZE
    MAXIMIZEMaximize the goal metric.
    MINIMIZE
    MINIMIZEMinimize the goal metric.
    "GOAL_TYPE_UNSPECIFIED"
    GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
    "MAXIMIZE"
    MAXIMIZEMaximize the goal metric.
    "MINIMIZE"
    MINIMIZEMinimize the goal metric.

    GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse, GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponseArgs

    Goal string
    The optimization goal of the metric.
    MetricId string
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    SafetyConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    Goal string
    The optimization goal of the metric.
    MetricId string
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    SafetyConfig GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal String
    The optimization goal of the metric.
    metricId String
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safetyConfig GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal string
    The optimization goal of the metric.
    metricId string
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safetyConfig GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal str
    The optimization goal of the metric.
    metric_id str
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safety_config GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
    goal String
    The optimization goal of the metric.
    metricId String
    The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
    safetyConfig Property Map
    Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.

    GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfig, GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigArgs

    DesiredMinSafeTrialsFraction double
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    SafetyThreshold double
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    DesiredMinSafeTrialsFraction float64
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    SafetyThreshold float64
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desiredMinSafeTrialsFraction Double
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safetyThreshold Double
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desiredMinSafeTrialsFraction number
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safetyThreshold number
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desired_min_safe_trials_fraction float
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safety_threshold float
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desiredMinSafeTrialsFraction Number
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safetyThreshold Number
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.

    GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse, GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponseArgs

    DesiredMinSafeTrialsFraction double
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    SafetyThreshold double
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    DesiredMinSafeTrialsFraction float64
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    SafetyThreshold float64
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desiredMinSafeTrialsFraction Double
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safetyThreshold Double
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desiredMinSafeTrialsFraction number
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safetyThreshold number
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desired_min_safe_trials_fraction float
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safety_threshold float
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
    desiredMinSafeTrialsFraction Number
    Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
    safetyThreshold Number
    Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.

    GoogleCloudAiplatformV1beta1StudySpecObservationNoise, GoogleCloudAiplatformV1beta1StudySpecObservationNoiseArgs

    ObservationNoiseUnspecified
    OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
    Low
    LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
    High
    HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
    GoogleCloudAiplatformV1beta1StudySpecObservationNoiseObservationNoiseUnspecified
    OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
    GoogleCloudAiplatformV1beta1StudySpecObservationNoiseLow
    LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
    GoogleCloudAiplatformV1beta1StudySpecObservationNoiseHigh
    HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
    ObservationNoiseUnspecified
    OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
    Low
    LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
    High
    HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
    ObservationNoiseUnspecified
    OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
    Low
    LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
    High
    HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
    OBSERVATION_NOISE_UNSPECIFIED
    OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
    LOW
    LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
    HIGH
    HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
    "OBSERVATION_NOISE_UNSPECIFIED"
    OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
    "LOW"
    LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
    "HIGH"
    HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpec, GoogleCloudAiplatformV1beta1StudySpecParameterSpecArgs

    ParameterId string
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    CategoricalValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpec
    The value spec for a 'CATEGORICAL' parameter.
    ConditionalParameterSpecs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpec>
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    DiscreteValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpec
    The value spec for a 'DISCRETE' parameter.
    DoubleValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpec
    The value spec for a 'DOUBLE' parameter.
    IntegerValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpec
    The value spec for an 'INTEGER' parameter.
    ScaleType Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleType
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    ParameterId string
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    CategoricalValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpec
    The value spec for a 'CATEGORICAL' parameter.
    ConditionalParameterSpecs []GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpec
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    DiscreteValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpec
    The value spec for a 'DISCRETE' parameter.
    DoubleValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpec
    The value spec for a 'DOUBLE' parameter.
    IntegerValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpec
    The value spec for an 'INTEGER' parameter.
    ScaleType GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleType
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    parameterId String
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    categoricalValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpec
    The value spec for a 'CATEGORICAL' parameter.
    conditionalParameterSpecs List<GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpec>
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discreteValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpec
    The value spec for a 'DISCRETE' parameter.
    doubleValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpec
    The value spec for a 'DOUBLE' parameter.
    integerValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpec
    The value spec for an 'INTEGER' parameter.
    scaleType GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleType
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    parameterId string
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    categoricalValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpec
    The value spec for a 'CATEGORICAL' parameter.
    conditionalParameterSpecs GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpec[]
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discreteValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpec
    The value spec for a 'DISCRETE' parameter.
    doubleValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpec
    The value spec for a 'DOUBLE' parameter.
    integerValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpec
    The value spec for an 'INTEGER' parameter.
    scaleType GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleType
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    parameter_id str
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    categorical_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpec
    The value spec for a 'CATEGORICAL' parameter.
    conditional_parameter_specs Sequence[GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpec]
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discrete_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpec
    The value spec for a 'DISCRETE' parameter.
    double_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpec
    The value spec for a 'DOUBLE' parameter.
    integer_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpec
    The value spec for an 'INTEGER' parameter.
    scale_type GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleType
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    parameterId String
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    categoricalValueSpec Property Map
    The value spec for a 'CATEGORICAL' parameter.
    conditionalParameterSpecs List<Property Map>
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discreteValueSpec Property Map
    The value spec for a 'DISCRETE' parameter.
    doubleValueSpec Property Map
    The value spec for a 'DOUBLE' parameter.
    integerValueSpec Property Map
    The value spec for an 'INTEGER' parameter.
    scaleType "SCALE_TYPE_UNSPECIFIED" | "UNIT_LINEAR_SCALE" | "UNIT_LOG_SCALE" | "UNIT_REVERSE_LOG_SCALE"
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpec, GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecArgs

    Values List<string>
    The list of possible categories.
    DefaultValue string
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Values []string
    The list of possible categories.
    DefaultValue string
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<String>
    The list of possible categories.
    defaultValue String
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values string[]
    The list of possible categories.
    defaultValue string
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values Sequence[str]
    The list of possible categories.
    default_value str
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<String>
    The list of possible categories.
    defaultValue String
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse, GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponseArgs

    DefaultValue string
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Values List<string>
    The list of possible categories.
    DefaultValue string
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Values []string
    The list of possible categories.
    defaultValue String
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<String>
    The list of possible categories.
    defaultValue string
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values string[]
    The list of possible categories.
    default_value str
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values Sequence[str]
    The list of possible categories.
    defaultValue String
    A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<String>
    The list of possible categories.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpec, GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecArgs

    ParameterSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpec
    The spec for a conditional parameter.
    ParentCategoricalValues Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueCondition
    The spec for matching values from a parent parameter of CATEGORICAL type.
    ParentDiscreteValues Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueCondition
    The spec for matching values from a parent parameter of DISCRETE type.
    ParentIntValues Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueCondition
    The spec for matching values from a parent parameter of INTEGER type.
    parameterSpec Property Map
    The spec for a conditional parameter.
    parentCategoricalValues Property Map
    The spec for matching values from a parent parameter of CATEGORICAL type.
    parentDiscreteValues Property Map
    The spec for matching values from a parent parameter of DISCRETE type.
    parentIntValues Property Map
    The spec for matching values from a parent parameter of INTEGER type.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueCondition, GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionArgs

    Values List<string>
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    Values []string
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values string[]
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values Sequence[str]
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse, GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponseArgs

    Values List<string>
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    Values []string
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values string[]
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values Sequence[str]
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueCondition, GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionArgs

    Values List<double>
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    Values []float64
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values List<Double>
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values number[]
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values Sequence[float]
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values List<Number>
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse, GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponseArgs

    Values List<double>
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    Values []float64
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values List<Double>
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values number[]
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values Sequence[float]
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
    values List<Number>
    Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueCondition, GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionArgs

    Values List<string>
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    Values []string
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values string[]
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values Sequence[str]
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse, GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponseArgs

    Values List<string>
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    Values []string
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values string[]
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values Sequence[str]
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.
    values List<String>
    Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse, GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponseArgs

    ParameterSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse
    The spec for a conditional parameter.
    ParentCategoricalValues Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse
    The spec for matching values from a parent parameter of CATEGORICAL type.
    ParentDiscreteValues Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse
    The spec for matching values from a parent parameter of DISCRETE type.
    ParentIntValues Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse
    The spec for matching values from a parent parameter of INTEGER type.
    parameterSpec Property Map
    The spec for a conditional parameter.
    parentCategoricalValues Property Map
    The spec for matching values from a parent parameter of CATEGORICAL type.
    parentDiscreteValues Property Map
    The spec for matching values from a parent parameter of DISCRETE type.
    parentIntValues Property Map
    The spec for matching values from a parent parameter of INTEGER type.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpec, GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecArgs

    Values List<double>
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    DefaultValue double
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Values []float64
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    DefaultValue float64
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<Double>
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    defaultValue Double
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values number[]
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    defaultValue number
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values Sequence[float]
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    default_value float
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<Number>
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    defaultValue Number
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse, GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponseArgs

    DefaultValue double
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Values List<double>
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    DefaultValue float64
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Values []float64
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    defaultValue Double
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<Double>
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    defaultValue number
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values number[]
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    default_value float
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values Sequence[float]
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
    defaultValue Number
    A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    values List<Number>
    A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpec, GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecArgs

    MaxValue double
    Inclusive maximum value of the parameter.
    MinValue double
    Inclusive minimum value of the parameter.
    DefaultValue double
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    MaxValue float64
    Inclusive maximum value of the parameter.
    MinValue float64
    Inclusive minimum value of the parameter.
    DefaultValue float64
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue Double
    Inclusive maximum value of the parameter.
    minValue Double
    Inclusive minimum value of the parameter.
    defaultValue Double
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue number
    Inclusive maximum value of the parameter.
    minValue number
    Inclusive minimum value of the parameter.
    defaultValue number
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    max_value float
    Inclusive maximum value of the parameter.
    min_value float
    Inclusive minimum value of the parameter.
    default_value float
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue Number
    Inclusive maximum value of the parameter.
    minValue Number
    Inclusive minimum value of the parameter.
    defaultValue Number
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse, GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponseArgs

    DefaultValue double
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    MaxValue double
    Inclusive maximum value of the parameter.
    MinValue double
    Inclusive minimum value of the parameter.
    DefaultValue float64
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    MaxValue float64
    Inclusive maximum value of the parameter.
    MinValue float64
    Inclusive minimum value of the parameter.
    defaultValue Double
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue Double
    Inclusive maximum value of the parameter.
    minValue Double
    Inclusive minimum value of the parameter.
    defaultValue number
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue number
    Inclusive maximum value of the parameter.
    minValue number
    Inclusive minimum value of the parameter.
    default_value float
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    max_value float
    Inclusive maximum value of the parameter.
    min_value float
    Inclusive minimum value of the parameter.
    defaultValue Number
    A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue Number
    Inclusive maximum value of the parameter.
    minValue Number
    Inclusive minimum value of the parameter.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpec, GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecArgs

    MaxValue string
    Inclusive maximum value of the parameter.
    MinValue string
    Inclusive minimum value of the parameter.
    DefaultValue string
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    MaxValue string
    Inclusive maximum value of the parameter.
    MinValue string
    Inclusive minimum value of the parameter.
    DefaultValue string
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue String
    Inclusive maximum value of the parameter.
    minValue String
    Inclusive minimum value of the parameter.
    defaultValue String
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue string
    Inclusive maximum value of the parameter.
    minValue string
    Inclusive minimum value of the parameter.
    defaultValue string
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    max_value str
    Inclusive maximum value of the parameter.
    min_value str
    Inclusive minimum value of the parameter.
    default_value str
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue String
    Inclusive maximum value of the parameter.
    minValue String
    Inclusive minimum value of the parameter.
    defaultValue String
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse, GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponseArgs

    DefaultValue string
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    MaxValue string
    Inclusive maximum value of the parameter.
    MinValue string
    Inclusive minimum value of the parameter.
    DefaultValue string
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    MaxValue string
    Inclusive maximum value of the parameter.
    MinValue string
    Inclusive minimum value of the parameter.
    defaultValue String
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue String
    Inclusive maximum value of the parameter.
    minValue String
    Inclusive minimum value of the parameter.
    defaultValue string
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue string
    Inclusive maximum value of the parameter.
    minValue string
    Inclusive minimum value of the parameter.
    default_value str
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    max_value str
    Inclusive maximum value of the parameter.
    min_value str
    Inclusive minimum value of the parameter.
    defaultValue String
    A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    maxValue String
    Inclusive maximum value of the parameter.
    minValue String
    Inclusive minimum value of the parameter.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse, GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponseArgs

    CategoricalValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse
    The value spec for a 'CATEGORICAL' parameter.
    ConditionalParameterSpecs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse>
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    DiscreteValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse
    The value spec for a 'DISCRETE' parameter.
    DoubleValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse
    The value spec for a 'DOUBLE' parameter.
    IntegerValueSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse
    The value spec for an 'INTEGER' parameter.
    ParameterId string
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    ScaleType string
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    CategoricalValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse
    The value spec for a 'CATEGORICAL' parameter.
    ConditionalParameterSpecs []GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    DiscreteValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse
    The value spec for a 'DISCRETE' parameter.
    DoubleValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse
    The value spec for a 'DOUBLE' parameter.
    IntegerValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse
    The value spec for an 'INTEGER' parameter.
    ParameterId string
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    ScaleType string
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    categoricalValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse
    The value spec for a 'CATEGORICAL' parameter.
    conditionalParameterSpecs List<GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse>
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discreteValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse
    The value spec for a 'DISCRETE' parameter.
    doubleValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse
    The value spec for a 'DOUBLE' parameter.
    integerValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse
    The value spec for an 'INTEGER' parameter.
    parameterId String
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    scaleType String
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    categoricalValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse
    The value spec for a 'CATEGORICAL' parameter.
    conditionalParameterSpecs GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse[]
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discreteValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse
    The value spec for a 'DISCRETE' parameter.
    doubleValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse
    The value spec for a 'DOUBLE' parameter.
    integerValueSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse
    The value spec for an 'INTEGER' parameter.
    parameterId string
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    scaleType string
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    categorical_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse
    The value spec for a 'CATEGORICAL' parameter.
    conditional_parameter_specs Sequence[GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse]
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discrete_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse
    The value spec for a 'DISCRETE' parameter.
    double_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse
    The value spec for a 'DOUBLE' parameter.
    integer_value_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse
    The value spec for an 'INTEGER' parameter.
    parameter_id str
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    scale_type str
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
    categoricalValueSpec Property Map
    The value spec for a 'CATEGORICAL' parameter.
    conditionalParameterSpecs List<Property Map>
    A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
    discreteValueSpec Property Map
    The value spec for a 'DISCRETE' parameter.
    doubleValueSpec Property Map
    The value spec for a 'DOUBLE' parameter.
    integerValueSpec Property Map
    The value spec for an 'INTEGER' parameter.
    parameterId String
    The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
    scaleType String
    How the parameter should be scaled. Leave unset for CATEGORICAL parameters.

    GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleType, GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleTypeArgs

    ScaleTypeUnspecified
    SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
    UnitLinearScale
    UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
    UnitLogScale
    UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
    UnitReverseLogScale
    UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
    GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleTypeScaleTypeUnspecified
    SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
    GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleTypeUnitLinearScale
    UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
    GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleTypeUnitLogScale
    UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
    GoogleCloudAiplatformV1beta1StudySpecParameterSpecScaleTypeUnitReverseLogScale
    UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
    ScaleTypeUnspecified
    SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
    UnitLinearScale
    UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
    UnitLogScale
    UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
    UnitReverseLogScale
    UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
    ScaleTypeUnspecified
    SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
    UnitLinearScale
    UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
    UnitLogScale
    UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
    UnitReverseLogScale
    UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
    SCALE_TYPE_UNSPECIFIED
    SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
    UNIT_LINEAR_SCALE
    UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
    UNIT_LOG_SCALE
    UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
    UNIT_REVERSE_LOG_SCALE
    UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
    "SCALE_TYPE_UNSPECIFIED"
    SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
    "UNIT_LINEAR_SCALE"
    UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
    "UNIT_LOG_SCALE"
    UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
    "UNIT_REVERSE_LOG_SCALE"
    UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.

    GoogleCloudAiplatformV1beta1StudySpecResponse, GoogleCloudAiplatformV1beta1StudySpecResponseArgs

    Algorithm string
    The search algorithm specified for the Study.
    ConvexAutomatedStoppingSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse
    The automated early stopping spec using convex stopping rule.
    ConvexStopConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    DecayCurveStoppingSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse
    The automated early stopping spec using decay curve rule.
    MeasurementSelectionType string
    Describe which measurement selection type will be used
    MedianAutomatedStoppingSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse
    The automated early stopping spec using median rule.
    Metrics List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse>
    Metric specs for the Study.
    ObservationNoise string
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Parameters List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse>
    The set of parameters to tune.
    StudyStoppingConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    TransferLearningConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    Algorithm string
    The search algorithm specified for the Study.
    ConvexAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse
    The automated early stopping spec using convex stopping rule.
    ConvexStopConfig GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    DecayCurveStoppingSpec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse
    The automated early stopping spec using decay curve rule.
    MeasurementSelectionType string
    Describe which measurement selection type will be used
    MedianAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse
    The automated early stopping spec using median rule.
    Metrics []GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse
    Metric specs for the Study.
    ObservationNoise string
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    Parameters []GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse
    The set of parameters to tune.
    StudyStoppingConfig GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    TransferLearningConfig GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    algorithm String
    The search algorithm specified for the Study.
    convexAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse
    The automated early stopping spec using convex stopping rule.
    convexStopConfig GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decayCurveStoppingSpec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse
    The automated early stopping spec using decay curve rule.
    measurementSelectionType String
    Describe which measurement selection type will be used
    medianAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse
    The automated early stopping spec using median rule.
    metrics List<GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse>
    Metric specs for the Study.
    observationNoise String
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    parameters List<GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse>
    The set of parameters to tune.
    studyStoppingConfig GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transferLearningConfig GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    algorithm string
    The search algorithm specified for the Study.
    convexAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse
    The automated early stopping spec using convex stopping rule.
    convexStopConfig GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decayCurveStoppingSpec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse
    The automated early stopping spec using decay curve rule.
    measurementSelectionType string
    Describe which measurement selection type will be used
    medianAutomatedStoppingSpec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse
    The automated early stopping spec using median rule.
    metrics GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse[]
    Metric specs for the Study.
    observationNoise string
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    parameters GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse[]
    The set of parameters to tune.
    studyStoppingConfig GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transferLearningConfig GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    algorithm str
    The search algorithm specified for the Study.
    convex_automated_stopping_spec GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse
    The automated early stopping spec using convex stopping rule.
    convex_stop_config GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decay_curve_stopping_spec GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse
    The automated early stopping spec using decay curve rule.
    measurement_selection_type str
    Describe which measurement selection type will be used
    median_automated_stopping_spec GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse
    The automated early stopping spec using median rule.
    metrics Sequence[GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse]
    Metric specs for the Study.
    observation_noise str
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    parameters Sequence[GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse]
    The set of parameters to tune.
    study_stopping_config GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transfer_learning_config GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
    algorithm String
    The search algorithm specified for the Study.
    convexAutomatedStoppingSpec Property Map
    The automated early stopping spec using convex stopping rule.
    convexStopConfig Property Map
    Deprecated. The automated early stopping using convex stopping rule.

    Deprecated: Deprecated. The automated early stopping using convex stopping rule.

    decayCurveStoppingSpec Property Map
    The automated early stopping spec using decay curve rule.
    measurementSelectionType String
    Describe which measurement selection type will be used
    medianAutomatedStoppingSpec Property Map
    The automated early stopping spec using median rule.
    metrics List<Property Map>
    Metric specs for the Study.
    observationNoise String
    The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
    parameters List<Property Map>
    The set of parameters to tune.
    studyStoppingConfig Property Map
    Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
    transferLearningConfig Property Map
    The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob

    GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfig, GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigArgs

    MaxDurationNoProgress string
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    MaxNumTrials int
    If there are more than this many trials, stop the study.
    MaxNumTrialsNoProgress int
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    MaximumRuntimeConstraint Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudyTimeConstraint
    If the specified time or duration has passed, stop the study.
    MinNumTrials int
    If there are fewer than this many COMPLETED trials, do not stop the study.
    MinimumRuntimeConstraint Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudyTimeConstraint
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    ShouldStopAsap bool
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    MaxDurationNoProgress string
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    MaxNumTrials int
    If there are more than this many trials, stop the study.
    MaxNumTrialsNoProgress int
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    MaximumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraint
    If the specified time or duration has passed, stop the study.
    MinNumTrials int
    If there are fewer than this many COMPLETED trials, do not stop the study.
    MinimumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraint
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    ShouldStopAsap bool
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    maxDurationNoProgress String
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    maxNumTrials Integer
    If there are more than this many trials, stop the study.
    maxNumTrialsNoProgress Integer
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraint
    If the specified time or duration has passed, stop the study.
    minNumTrials Integer
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraint
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    shouldStopAsap Boolean
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    maxDurationNoProgress string
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    maxNumTrials number
    If there are more than this many trials, stop the study.
    maxNumTrialsNoProgress number
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraint
    If the specified time or duration has passed, stop the study.
    minNumTrials number
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraint
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    shouldStopAsap boolean
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    max_duration_no_progress str
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    max_num_trials int
    If there are more than this many trials, stop the study.
    max_num_trials_no_progress int
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximum_runtime_constraint GoogleCloudAiplatformV1beta1StudyTimeConstraint
    If the specified time or duration has passed, stop the study.
    min_num_trials int
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimum_runtime_constraint GoogleCloudAiplatformV1beta1StudyTimeConstraint
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    should_stop_asap bool
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    maxDurationNoProgress String
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    maxNumTrials Number
    If there are more than this many trials, stop the study.
    maxNumTrialsNoProgress Number
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximumRuntimeConstraint Property Map
    If the specified time or duration has passed, stop the study.
    minNumTrials Number
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimumRuntimeConstraint Property Map
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    shouldStopAsap Boolean
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).

    GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse, GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponseArgs

    MaxDurationNoProgress string
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    MaxNumTrials int
    If there are more than this many trials, stop the study.
    MaxNumTrialsNoProgress int
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    MaximumRuntimeConstraint Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    If the specified time or duration has passed, stop the study.
    MinNumTrials int
    If there are fewer than this many COMPLETED trials, do not stop the study.
    MinimumRuntimeConstraint Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    ShouldStopAsap bool
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    MaxDurationNoProgress string
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    MaxNumTrials int
    If there are more than this many trials, stop the study.
    MaxNumTrialsNoProgress int
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    MaximumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    If the specified time or duration has passed, stop the study.
    MinNumTrials int
    If there are fewer than this many COMPLETED trials, do not stop the study.
    MinimumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    ShouldStopAsap bool
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    maxDurationNoProgress String
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    maxNumTrials Integer
    If there are more than this many trials, stop the study.
    maxNumTrialsNoProgress Integer
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    If the specified time or duration has passed, stop the study.
    minNumTrials Integer
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    shouldStopAsap Boolean
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    maxDurationNoProgress string
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    maxNumTrials number
    If there are more than this many trials, stop the study.
    maxNumTrialsNoProgress number
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    If the specified time or duration has passed, stop the study.
    minNumTrials number
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimumRuntimeConstraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    shouldStopAsap boolean
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    max_duration_no_progress str
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    max_num_trials int
    If there are more than this many trials, stop the study.
    max_num_trials_no_progress int
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximum_runtime_constraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    If the specified time or duration has passed, stop the study.
    min_num_trials int
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimum_runtime_constraint GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    should_stop_asap bool
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
    maxDurationNoProgress String
    If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
    maxNumTrials Number
    If there are more than this many trials, stop the study.
    maxNumTrialsNoProgress Number
    If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
    maximumRuntimeConstraint Property Map
    If the specified time or duration has passed, stop the study.
    minNumTrials Number
    If there are fewer than this many COMPLETED trials, do not stop the study.
    minimumRuntimeConstraint Property Map
    Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
    shouldStopAsap Boolean
    If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).

    GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfig, GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigArgs

    DisableTransferLearning bool
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    DisableTransferLearning bool
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    disableTransferLearning Boolean
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    disableTransferLearning boolean
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    disable_transfer_learning bool
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    disableTransferLearning Boolean
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.

    GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse, GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponseArgs

    DisableTransferLearning bool
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    PriorStudyNames List<string>
    Names of previously completed studies
    DisableTransferLearning bool
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    PriorStudyNames []string
    Names of previously completed studies
    disableTransferLearning Boolean
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    priorStudyNames List<String>
    Names of previously completed studies
    disableTransferLearning boolean
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    priorStudyNames string[]
    Names of previously completed studies
    disable_transfer_learning bool
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    prior_study_names Sequence[str]
    Names of previously completed studies
    disableTransferLearning Boolean
    Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
    priorStudyNames List<String>
    Names of previously completed studies

    GoogleCloudAiplatformV1beta1StudyTimeConstraint, GoogleCloudAiplatformV1beta1StudyTimeConstraintArgs

    EndTime string
    Compares the wallclock time to this time. Must use UTC timezone.
    MaxDuration string
    Counts the wallclock time passed since the creation of this Study.
    EndTime string
    Compares the wallclock time to this time. Must use UTC timezone.
    MaxDuration string
    Counts the wallclock time passed since the creation of this Study.
    endTime String
    Compares the wallclock time to this time. Must use UTC timezone.
    maxDuration String
    Counts the wallclock time passed since the creation of this Study.
    endTime string
    Compares the wallclock time to this time. Must use UTC timezone.
    maxDuration string
    Counts the wallclock time passed since the creation of this Study.
    end_time str
    Compares the wallclock time to this time. Must use UTC timezone.
    max_duration str
    Counts the wallclock time passed since the creation of this Study.
    endTime String
    Compares the wallclock time to this time. Must use UTC timezone.
    maxDuration String
    Counts the wallclock time passed since the creation of this Study.

    GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse, GoogleCloudAiplatformV1beta1StudyTimeConstraintResponseArgs

    EndTime string
    Compares the wallclock time to this time. Must use UTC timezone.
    MaxDuration string
    Counts the wallclock time passed since the creation of this Study.
    EndTime string
    Compares the wallclock time to this time. Must use UTC timezone.
    MaxDuration string
    Counts the wallclock time passed since the creation of this Study.
    endTime String
    Compares the wallclock time to this time. Must use UTC timezone.
    maxDuration String
    Counts the wallclock time passed since the creation of this Study.
    endTime string
    Compares the wallclock time to this time. Must use UTC timezone.
    maxDuration string
    Counts the wallclock time passed since the creation of this Study.
    end_time str
    Compares the wallclock time to this time. Must use UTC timezone.
    max_duration str
    Counts the wallclock time passed since the creation of this Study.
    endTime String
    Compares the wallclock time to this time. Must use UTC timezone.
    maxDuration String
    Counts the wallclock time passed since the creation of this Study.

    Package Details

    Repository
    Google Cloud Native pulumi/pulumi-google-native
    License
    Apache-2.0
    google-native logo

    Google Cloud Native is in preview. Google Cloud Classic is fully supported.

    Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
    pulumi/pulumi-google-native