Google Cloud Native v0.32.0, Nov 29 23

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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.getStudy

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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

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Using getStudy

Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

function getStudy(args: GetStudyArgs, opts?: InvokeOptions): Promise<GetStudyResult>
function getStudyOutput(args: GetStudyOutputArgs, opts?: InvokeOptions): Output<GetStudyResult>

def get_study(location: Optional[str] = None,
              project: Optional[str] = None,
              study_id: Optional[str] = None,
              opts: Optional[InvokeOptions] = None) -> GetStudyResult
def get_study_output(location: Optional[pulumi.Input[str]] = None,
              project: Optional[pulumi.Input[str]] = None,
              study_id: Optional[pulumi.Input[str]] = None,
              opts: Optional[InvokeOptions] = None) -> Output[GetStudyResult]

func LookupStudy(ctx *Context, args *LookupStudyArgs, opts ...InvokeOption) (*LookupStudyResult, error)
func LookupStudyOutput(ctx *Context, args *LookupStudyOutputArgs, opts ...InvokeOption) LookupStudyResultOutput

> Note: This function is named LookupStudy in the Go SDK.

public static class GetStudy 
{
    public static Task<GetStudyResult> InvokeAsync(GetStudyArgs args, InvokeOptions? opts = null)
    public static Output<GetStudyResult> Invoke(GetStudyInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetStudyResult> getStudy(GetStudyArgs args, InvokeOptions options)
public static Output<GetStudyResult> getStudy(GetStudyArgs args, InvokeOptions options)

fn::invoke:
  function: google-native:aiplatform/v1beta1:getStudy
  arguments:
    # arguments dictionary

The following arguments are supported:

Location string
StudyId string
Project string

Location string
StudyId string
Project string

location String
studyId String
project String

location string
studyId string
project string

location str
study_id str
project str

location String
studyId String
project String

getStudy Result

The following output properties are available:

CreateTime string: Time at which the study was created.
DisplayName string: Describes the Study, default value is empty string.
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.
StudySpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1StudySpecResponse: Configuration of the Study.

CreateTime string: Time at which the study was created.
DisplayName string: Describes the Study, default value is empty string.
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.
StudySpec GoogleCloudAiplatformV1beta1StudySpecResponse: Configuration of the Study.

createTime String: Time at which the study was created.
displayName String: Describes the Study, default value is empty string.
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.
studySpec GoogleCloudAiplatformV1beta1StudySpecResponse: Configuration of the Study.

createTime string: Time at which the study was created.
displayName string: Describes the Study, default value is empty string.
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.
studySpec GoogleCloudAiplatformV1beta1StudySpecResponse: Configuration of the Study.

create_time str: Time at which the study was created.
display_name str: Describes the Study, default value is empty string.
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.
study_spec GoogleCloudAiplatformV1beta1StudySpecResponse: Configuration of the Study.

createTime String: Time at which the study was created.
displayName String: Describes the Study, default value is empty string.
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.
studySpec Property Map: Configuration of the Study.

Supporting Types

GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse

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.

GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse

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.

GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse

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.

GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse

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.

GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse

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.

GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse

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.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse

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.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse

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.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse

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.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse

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

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 GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse: The spec for a conditional parameter.
ParentCategoricalValues GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse: The spec for matching values from a parent parameter of CATEGORICAL type.
ParentDiscreteValues GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse: The spec for matching values from a parent parameter of DISCRETE type.
ParentIntValues GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse: The spec for matching values from a parent parameter of INTEGER type.

parameterSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse: The spec for a conditional parameter.
parentCategoricalValues GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse: The spec for matching values from a parent parameter of CATEGORICAL type.
parentDiscreteValues GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse: The spec for matching values from a parent parameter of DISCRETE type.
parentIntValues GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse: The spec for matching values from a parent parameter of INTEGER type.

parameterSpec GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse: The spec for a conditional parameter.
parentCategoricalValues GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse: The spec for matching values from a parent parameter of CATEGORICAL type.
parentDiscreteValues GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse: The spec for matching values from a parent parameter of DISCRETE type.
parentIntValues GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse: The spec for matching values from a parent parameter of INTEGER type.

parameter_spec GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse: The spec for a conditional parameter.
parent_categorical_values GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse: The spec for matching values from a parent parameter of CATEGORICAL type.
parent_discrete_values GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse: The spec for matching values from a parent parameter of DISCRETE type.
parent_int_values 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.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse

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.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse

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.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse

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

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.

GoogleCloudAiplatformV1beta1StudySpecResponse

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

GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse

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).

GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse

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

GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse

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 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

google-native.aiplatform/v1beta1.getStudy

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Using getStudy

getStudy Result

Supporting Types

GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpecResponse

GoogleCloudAiplatformV1beta1StudySpecConvexStopConfigResponse

GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpecResponse

GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpecResponse

GoogleCloudAiplatformV1beta1StudySpecMetricSpecResponse

GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfigResponse

GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpecResponse

GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse

GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse

GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse

GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecResponse

GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpecResponse

GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpecResponse

GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpecResponse

GoogleCloudAiplatformV1beta1StudySpecParameterSpecResponse

GoogleCloudAiplatformV1beta1StudySpecResponse

GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfigResponse

GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfigResponse

GoogleCloudAiplatformV1beta1StudyTimeConstraintResponse

Package Details

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