com.google.cloud.aiplatform.v1

Interface StudySpec.ConvexAutomatedStoppingSpecOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

StudySpec.ConvexAutomatedStoppingSpec, StudySpec.ConvexAutomatedStoppingSpec.Builder

Enclosing class:

StudySpec

public static interface StudySpec.ConvexAutomatedStoppingSpecOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method and Description
String	getLearningRateParameterName() The hyper-parameter name used in the tuning job that stands for learning rate.
com.google.protobuf.ByteString	getLearningRateParameterNameBytes() The hyper-parameter name used in the tuning job that stands for learning rate.
long	getMaxStepCount() Steps used in predicting the final objective for early stopped trials.
long	getMinMeasurementCount() The minimal number of measurements in a Trial.
long	getMinStepCount() Minimum number of steps for a trial to complete.
boolean	getUpdateAllStoppedTrials() ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model.
boolean	getUseElapsedDuration() This bool determines whether or not the rule is applied based on elapsed_secs or steps.
boolean	hasUpdateAllStoppedTrials() ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model.

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Method Detail

getMaxStepCount

long getMaxStepCount()

 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.
 

int64 max_step_count = 1;

Returns:

The maxStepCount.

getMinStepCount

long getMinStepCount()

 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.
 

int64 min_step_count = 2;

Returns:

The minStepCount.

getMinMeasurementCount

long getMinMeasurementCount()

 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.
 

int64 min_measurement_count = 3;

Returns:

The minMeasurementCount.

getLearningRateParameterName

String getLearningRateParameterName()

 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.
 

string learning_rate_parameter_name = 4;

Returns:

The learningRateParameterName.

getLearningRateParameterNameBytes

com.google.protobuf.ByteString getLearningRateParameterNameBytes()

 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.
 

string learning_rate_parameter_name = 4;

Returns:

The bytes for learningRateParameterName.

getUseElapsedDuration

boolean getUseElapsedDuration()

 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.
 

bool use_elapsed_duration = 5;

Returns:

The useElapsedDuration.

hasUpdateAllStoppedTrials

boolean hasUpdateAllStoppedTrials()

 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.
 

optional bool update_all_stopped_trials = 6;

Returns:

Whether the updateAllStoppedTrials field is set.

getUpdateAllStoppedTrials

boolean getUpdateAllStoppedTrials()

 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.
 

optional bool update_all_stopped_trials = 6;

Returns:

The updateAllStoppedTrials.