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<h1><a href="aiplatform_v1beta1.html">Vertex AI API</a> . <a href="aiplatform_v1beta1.projects.html">projects</a> . <a href="aiplatform_v1beta1.projects.locations.html">locations</a> . <a href="aiplatform_v1beta1.projects.locations.studies.html">studies</a></h1>
<h2>Instance Methods</h2>
<p class="toc_element">
<code><a href="aiplatform_v1beta1.projects.locations.studies.operations.html">operations()</a></code>
</p>
<p class="firstline">Returns the operations Resource.</p>
<p class="toc_element">
<code><a href="aiplatform_v1beta1.projects.locations.studies.trials.html">trials()</a></code>
</p>
<p class="firstline">Returns the trials Resource.</p>
<p class="toc_element">
<code><a href="#close">close()</a></code></p>
<p class="firstline">Close httplib2 connections.</p>
<p class="toc_element">
<code><a href="#create">create(parent, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Creates a Study. A resource name will be generated after creation of the Study.</p>
<p class="toc_element">
<code><a href="#delete">delete(name, x__xgafv=None)</a></code></p>
<p class="firstline">Deletes a Study.</p>
<p class="toc_element">
<code><a href="#get">get(name, x__xgafv=None)</a></code></p>
<p class="firstline">Gets a Study by name.</p>
<p class="toc_element">
<code><a href="#list">list(parent, pageSize=None, pageToken=None, x__xgafv=None)</a></code></p>
<p class="firstline">Lists all the studies in a region for an associated project.</p>
<p class="toc_element">
<code><a href="#list_next">list_next()</a></code></p>
<p class="firstline">Retrieves the next page of results.</p>
<p class="toc_element">
<code><a href="#lookup">lookup(parent, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Looks a study up using the user-defined display_name field instead of the fully qualified resource name.</p>
<h3>Method Details</h3>
<div class="method">
<code class="details" id="close">close()</code>
<pre>Close httplib2 connections.</pre>
</div>
<div class="method">
<code class="details" id="create">create(parent, body=None, x__xgafv=None)</code>
<pre>Creates a Study. A resource name will be generated after creation of the Study.
Args:
parent: string, Required. The resource name of the Location to create the CustomJob in. Format: `projects/{project}/locations/{location}` (required)
body: object, The request body.
The object takes the form of:
{ # A message representing a Study.
"createTime": "A String", # Output only. Time at which the study was created.
"displayName": "A String", # Required. Describes the Study, default value is empty string.
"inactiveReason": "A String", # Output only. A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
"name": "A String", # Output only. The name of a study. The study's globally unique identifier. Format: `projects/{project}/locations/{location}/studies/{study}`
"state": "A String", # Output only. The detailed state of a Study.
"studySpec": { # Represents specification of a Study. # Required. Configuration of the Study.
"algorithm": "A String", # The search algorithm specified for the Study.
"convexAutomatedStoppingSpec": { # Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model. # The automated early stopping spec using convex stopping rule.
"learningRateParameterName": "A 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": "A 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": "A 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": "A 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": True or False, # 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": True or False, # 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.
},
"convexStopConfig": { # Configuration for ConvexStopPolicy. # Deprecated. The automated early stopping using convex stopping rule.
"autoregressiveOrder": "A 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": "A 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": "A 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": "A 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": True or False, # 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.
},
"decayCurveStoppingSpec": { # The decay curve automated stopping rule builds a Gaussian Process Regressor to predict the final objective value of a Trial based on the already completed Trials and the intermediate measurements of the current Trial. Early stopping is requested for the current Trial if there is very low probability to exceed the optimal value found so far. # The automated early stopping spec using decay curve rule.
"useElapsedDuration": True or False, # 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.
},
"measurementSelectionType": "A String", # Describe which measurement selection type will be used
"medianAutomatedStoppingSpec": { # The median automated stopping rule stops a pending Trial if the Trial's best objective_value is strictly below the median 'performance' of all completed Trials reported up to the Trial's last measurement. Currently, 'performance' refers to the running average of the objective values reported by the Trial in each measurement. # The automated early stopping spec using median rule.
"useElapsedDuration": True or False, # 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.
},
"metrics": [ # Required. Metric specs for the Study.
{ # Represents a metric to optimize.
"goal": "A String", # Required. The optimization goal of the metric.
"metricId": "A String", # Required. The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
"safetyConfig": { # Used in safe optimization to specify threshold levels and risk tolerance. # Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
"desiredMinSafeTrialsFraction": 3.14, # 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": 3.14, # Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
},
},
],
"observationNoise": "A String", # The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
"parameters": [ # Required. The set of parameters to tune.
{ # Represents a single parameter to optimize.
"categoricalValueSpec": { # Value specification for a parameter in `CATEGORICAL` type. # The value spec for a 'CATEGORICAL' parameter.
"defaultValue": "A 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": [ # Required. The list of possible categories.
"A String",
],
},
"conditionalParameterSpecs": [ # 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.
{ # Represents a parameter spec with condition from its parent parameter.
"parameterSpec": # Object with schema name: GoogleCloudAiplatformV1beta1StudySpecParameterSpec # Required. The spec for a conditional parameter.
"parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter. # The spec for matching values from a parent parameter of `CATEGORICAL` type.
"values": [ # Required. Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in `categorical_value_spec` of parent parameter.
"A String",
],
},
"parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter. # The spec for matching values from a parent parameter of `DISCRETE` type.
"values": [ # Required. 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.
3.14,
],
},
"parentIntValues": { # Represents the spec to match integer values from parent parameter. # The spec for matching values from a parent parameter of `INTEGER` type.
"values": [ # Required. Matches values of the parent parameter of 'INTEGER' type. All values must lie in `integer_value_spec` of parent parameter.
"A String",
],
},
},
],
"discreteValueSpec": { # Value specification for a parameter in `DISCRETE` type. # The value spec for a 'DISCRETE' parameter.
"defaultValue": 3.14, # 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": [ # Required. 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.
3.14,
],
},
"doubleValueSpec": { # Value specification for a parameter in `DOUBLE` type. # The value spec for a 'DOUBLE' parameter.
"defaultValue": 3.14, # 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": 3.14, # Required. Inclusive maximum value of the parameter.
"minValue": 3.14, # Required. Inclusive minimum value of the parameter.
},
"integerValueSpec": { # Value specification for a parameter in `INTEGER` type. # The value spec for an 'INTEGER' parameter.
"defaultValue": "A 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": "A String", # Required. Inclusive maximum value of the parameter.
"minValue": "A String", # Required. Inclusive minimum value of the parameter.
},
"parameterId": "A String", # Required. The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
"scaleType": "A String", # How the parameter should be scaled. Leave unset for `CATEGORICAL` parameters.
},
],
"studyStoppingConfig": { # The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection. # Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
"maxDurationNoProgress": "A String", # If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
"maxNumTrials": 42, # If there are more than this many trials, stop the study.
"maxNumTrialsNoProgress": 42, # If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
"maximumRuntimeConstraint": { # Time-based Constraint for Study # If the specified time or duration has passed, stop the study.
"endTime": "A String", # Compares the wallclock time to this time. Must use UTC timezone.
"maxDuration": "A String", # Counts the wallclock time passed since the creation of this Study.
},
"minNumTrials": 42, # If there are fewer than this many COMPLETED trials, do not stop the study.
"minimumRuntimeConstraint": { # Time-based Constraint for Study # 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.
"endTime": "A String", # Compares the wallclock time to this time. Must use UTC timezone.
"maxDuration": "A String", # Counts the wallclock time passed since the creation of this Study.
},
"shouldStopAsap": True or False, # 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).
},
"transferLearningConfig": { # This contains flag for manually disabling transfer learning for a study. The names of prior studies being used for transfer learning (if any) are also listed here. # The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
"disableTransferLearning": True or False, # 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": [ # Output only. Names of previously completed studies
"A String",
],
},
},
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # A message representing a Study.
"createTime": "A String", # Output only. Time at which the study was created.
"displayName": "A String", # Required. Describes the Study, default value is empty string.
"inactiveReason": "A String", # Output only. A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
"name": "A String", # Output only. The name of a study. The study's globally unique identifier. Format: `projects/{project}/locations/{location}/studies/{study}`
"state": "A String", # Output only. The detailed state of a Study.
"studySpec": { # Represents specification of a Study. # Required. Configuration of the Study.
"algorithm": "A String", # The search algorithm specified for the Study.
"convexAutomatedStoppingSpec": { # Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model. # The automated early stopping spec using convex stopping rule.
"learningRateParameterName": "A 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": "A 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": "A 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": "A 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": True or False, # 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": True or False, # 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.
},
"convexStopConfig": { # Configuration for ConvexStopPolicy. # Deprecated. The automated early stopping using convex stopping rule.
"autoregressiveOrder": "A 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": "A 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": "A 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": "A 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": True or False, # 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.
},
"decayCurveStoppingSpec": { # The decay curve automated stopping rule builds a Gaussian Process Regressor to predict the final objective value of a Trial based on the already completed Trials and the intermediate measurements of the current Trial. Early stopping is requested for the current Trial if there is very low probability to exceed the optimal value found so far. # The automated early stopping spec using decay curve rule.
"useElapsedDuration": True or False, # 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.
},
"measurementSelectionType": "A String", # Describe which measurement selection type will be used
"medianAutomatedStoppingSpec": { # The median automated stopping rule stops a pending Trial if the Trial's best objective_value is strictly below the median 'performance' of all completed Trials reported up to the Trial's last measurement. Currently, 'performance' refers to the running average of the objective values reported by the Trial in each measurement. # The automated early stopping spec using median rule.
"useElapsedDuration": True or False, # 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.
},
"metrics": [ # Required. Metric specs for the Study.
{ # Represents a metric to optimize.
"goal": "A String", # Required. The optimization goal of the metric.
"metricId": "A String", # Required. The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
"safetyConfig": { # Used in safe optimization to specify threshold levels and risk tolerance. # Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
"desiredMinSafeTrialsFraction": 3.14, # 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": 3.14, # Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
},
},
],
"observationNoise": "A String", # The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
"parameters": [ # Required. The set of parameters to tune.
{ # Represents a single parameter to optimize.
"categoricalValueSpec": { # Value specification for a parameter in `CATEGORICAL` type. # The value spec for a 'CATEGORICAL' parameter.
"defaultValue": "A 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": [ # Required. The list of possible categories.
"A String",
],
},
"conditionalParameterSpecs": [ # 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.
{ # Represents a parameter spec with condition from its parent parameter.
"parameterSpec": # Object with schema name: GoogleCloudAiplatformV1beta1StudySpecParameterSpec # Required. The spec for a conditional parameter.
"parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter. # The spec for matching values from a parent parameter of `CATEGORICAL` type.
"values": [ # Required. Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in `categorical_value_spec` of parent parameter.
"A String",
],
},
"parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter. # The spec for matching values from a parent parameter of `DISCRETE` type.
"values": [ # Required. 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.
3.14,
],
},
"parentIntValues": { # Represents the spec to match integer values from parent parameter. # The spec for matching values from a parent parameter of `INTEGER` type.
"values": [ # Required. Matches values of the parent parameter of 'INTEGER' type. All values must lie in `integer_value_spec` of parent parameter.
"A String",
],
},
},
],
"discreteValueSpec": { # Value specification for a parameter in `DISCRETE` type. # The value spec for a 'DISCRETE' parameter.
"defaultValue": 3.14, # 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": [ # Required. 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.
3.14,
],
},
"doubleValueSpec": { # Value specification for a parameter in `DOUBLE` type. # The value spec for a 'DOUBLE' parameter.
"defaultValue": 3.14, # 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": 3.14, # Required. Inclusive maximum value of the parameter.
"minValue": 3.14, # Required. Inclusive minimum value of the parameter.
},
"integerValueSpec": { # Value specification for a parameter in `INTEGER` type. # The value spec for an 'INTEGER' parameter.
"defaultValue": "A 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": "A String", # Required. Inclusive maximum value of the parameter.
"minValue": "A String", # Required. Inclusive minimum value of the parameter.
},
"parameterId": "A String", # Required. The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
"scaleType": "A String", # How the parameter should be scaled. Leave unset for `CATEGORICAL` parameters.
},
],
"studyStoppingConfig": { # The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection. # Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
"maxDurationNoProgress": "A String", # If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
"maxNumTrials": 42, # If there are more than this many trials, stop the study.
"maxNumTrialsNoProgress": 42, # If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
"maximumRuntimeConstraint": { # Time-based Constraint for Study # If the specified time or duration has passed, stop the study.
"endTime": "A String", # Compares the wallclock time to this time. Must use UTC timezone.
"maxDuration": "A String", # Counts the wallclock time passed since the creation of this Study.
},
"minNumTrials": 42, # If there are fewer than this many COMPLETED trials, do not stop the study.
"minimumRuntimeConstraint": { # Time-based Constraint for Study # 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.
"endTime": "A String", # Compares the wallclock time to this time. Must use UTC timezone.
"maxDuration": "A String", # Counts the wallclock time passed since the creation of this Study.
},
"shouldStopAsap": True or False, # 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).
},
"transferLearningConfig": { # This contains flag for manually disabling transfer learning for a study. The names of prior studies being used for transfer learning (if any) are also listed here. # The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
"disableTransferLearning": True or False, # 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": [ # Output only. Names of previously completed studies
"A String",
],
},
},
}</pre>
</div>
<div class="method">
<code class="details" id="delete">delete(name, x__xgafv=None)</code>
<pre>Deletes a Study.
Args:
name: string, Required. The name of the Study resource to be deleted. Format: `projects/{project}/locations/{location}/studies/{study}` (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }
}</pre>
</div>
<div class="method">
<code class="details" id="get">get(name, x__xgafv=None)</code>
<pre>Gets a Study by name.
Args:
name: string, Required. The name of the Study resource. Format: `projects/{project}/locations/{location}/studies/{study}` (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # A message representing a Study.
"createTime": "A String", # Output only. Time at which the study was created.
"displayName": "A String", # Required. Describes the Study, default value is empty string.
"inactiveReason": "A String", # Output only. A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
"name": "A String", # Output only. The name of a study. The study's globally unique identifier. Format: `projects/{project}/locations/{location}/studies/{study}`
"state": "A String", # Output only. The detailed state of a Study.
"studySpec": { # Represents specification of a Study. # Required. Configuration of the Study.
"algorithm": "A String", # The search algorithm specified for the Study.
"convexAutomatedStoppingSpec": { # Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model. # The automated early stopping spec using convex stopping rule.
"learningRateParameterName": "A 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": "A 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": "A 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": "A 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": True or False, # 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": True or False, # 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.
},
"convexStopConfig": { # Configuration for ConvexStopPolicy. # Deprecated. The automated early stopping using convex stopping rule.
"autoregressiveOrder": "A 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": "A 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": "A 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": "A 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": True or False, # 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.
},
"decayCurveStoppingSpec": { # The decay curve automated stopping rule builds a Gaussian Process Regressor to predict the final objective value of a Trial based on the already completed Trials and the intermediate measurements of the current Trial. Early stopping is requested for the current Trial if there is very low probability to exceed the optimal value found so far. # The automated early stopping spec using decay curve rule.
"useElapsedDuration": True or False, # 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.
},
"measurementSelectionType": "A String", # Describe which measurement selection type will be used
"medianAutomatedStoppingSpec": { # The median automated stopping rule stops a pending Trial if the Trial's best objective_value is strictly below the median 'performance' of all completed Trials reported up to the Trial's last measurement. Currently, 'performance' refers to the running average of the objective values reported by the Trial in each measurement. # The automated early stopping spec using median rule.
"useElapsedDuration": True or False, # 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.
},
"metrics": [ # Required. Metric specs for the Study.
{ # Represents a metric to optimize.
"goal": "A String", # Required. The optimization goal of the metric.
"metricId": "A String", # Required. The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
"safetyConfig": { # Used in safe optimization to specify threshold levels and risk tolerance. # Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
"desiredMinSafeTrialsFraction": 3.14, # 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": 3.14, # Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
},
},
],
"observationNoise": "A String", # The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
"parameters": [ # Required. The set of parameters to tune.
{ # Represents a single parameter to optimize.
"categoricalValueSpec": { # Value specification for a parameter in `CATEGORICAL` type. # The value spec for a 'CATEGORICAL' parameter.
"defaultValue": "A 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": [ # Required. The list of possible categories.
"A String",
],
},
"conditionalParameterSpecs": [ # 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.
{ # Represents a parameter spec with condition from its parent parameter.
"parameterSpec": # Object with schema name: GoogleCloudAiplatformV1beta1StudySpecParameterSpec # Required. The spec for a conditional parameter.
"parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter. # The spec for matching values from a parent parameter of `CATEGORICAL` type.
"values": [ # Required. Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in `categorical_value_spec` of parent parameter.
"A String",
],
},
"parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter. # The spec for matching values from a parent parameter of `DISCRETE` type.
"values": [ # Required. 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.
3.14,
],
},
"parentIntValues": { # Represents the spec to match integer values from parent parameter. # The spec for matching values from a parent parameter of `INTEGER` type.
"values": [ # Required. Matches values of the parent parameter of 'INTEGER' type. All values must lie in `integer_value_spec` of parent parameter.
"A String",
],
},
},
],
"discreteValueSpec": { # Value specification for a parameter in `DISCRETE` type. # The value spec for a 'DISCRETE' parameter.
"defaultValue": 3.14, # 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": [ # Required. 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.
3.14,
],
},
"doubleValueSpec": { # Value specification for a parameter in `DOUBLE` type. # The value spec for a 'DOUBLE' parameter.
"defaultValue": 3.14, # 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": 3.14, # Required. Inclusive maximum value of the parameter.
"minValue": 3.14, # Required. Inclusive minimum value of the parameter.
},
"integerValueSpec": { # Value specification for a parameter in `INTEGER` type. # The value spec for an 'INTEGER' parameter.
"defaultValue": "A 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": "A String", # Required. Inclusive maximum value of the parameter.
"minValue": "A String", # Required. Inclusive minimum value of the parameter.
},
"parameterId": "A String", # Required. The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
"scaleType": "A String", # How the parameter should be scaled. Leave unset for `CATEGORICAL` parameters.
},
],
"studyStoppingConfig": { # The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection. # Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
"maxDurationNoProgress": "A String", # If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
"maxNumTrials": 42, # If there are more than this many trials, stop the study.
"maxNumTrialsNoProgress": 42, # If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
"maximumRuntimeConstraint": { # Time-based Constraint for Study # If the specified time or duration has passed, stop the study.
"endTime": "A String", # Compares the wallclock time to this time. Must use UTC timezone.
"maxDuration": "A String", # Counts the wallclock time passed since the creation of this Study.
},
"minNumTrials": 42, # If there are fewer than this many COMPLETED trials, do not stop the study.
"minimumRuntimeConstraint": { # Time-based Constraint for Study # 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.
"endTime": "A String", # Compares the wallclock time to this time. Must use UTC timezone.
"maxDuration": "A String", # Counts the wallclock time passed since the creation of this Study.
},
"shouldStopAsap": True or False, # 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).
},
"transferLearningConfig": { # This contains flag for manually disabling transfer learning for a study. The names of prior studies being used for transfer learning (if any) are also listed here. # The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
"disableTransferLearning": True or False, # 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": [ # Output only. Names of previously completed studies
"A String",
],
},
},
}</pre>
</div>
<div class="method">
<code class="details" id="list">list(parent, pageSize=None, pageToken=None, x__xgafv=None)</code>
<pre>Lists all the studies in a region for an associated project.
Args:
parent: string, Required. The resource name of the Location to list the Study from. Format: `projects/{project}/locations/{location}` (required)
pageSize: integer, Optional. The maximum number of studies to return per "page" of results. If unspecified, service will pick an appropriate default.
pageToken: string, Optional. A page token to request the next page of results. If unspecified, there are no subsequent pages.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Response message for VizierService.ListStudies.
"nextPageToken": "A String", # Passes this token as the `page_token` field of the request for a subsequent call. If this field is omitted, there are no subsequent pages.
"studies": [ # The studies associated with the project.
{ # A message representing a Study.
"createTime": "A String", # Output only. Time at which the study was created.
"displayName": "A String", # Required. Describes the Study, default value is empty string.
"inactiveReason": "A String", # Output only. A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
"name": "A String", # Output only. The name of a study. The study's globally unique identifier. Format: `projects/{project}/locations/{location}/studies/{study}`
"state": "A String", # Output only. The detailed state of a Study.
"studySpec": { # Represents specification of a Study. # Required. Configuration of the Study.
"algorithm": "A String", # The search algorithm specified for the Study.
"convexAutomatedStoppingSpec": { # Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model. # The automated early stopping spec using convex stopping rule.
"learningRateParameterName": "A 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": "A 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": "A 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": "A 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": True or False, # 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": True or False, # 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.
},
"convexStopConfig": { # Configuration for ConvexStopPolicy. # Deprecated. The automated early stopping using convex stopping rule.
"autoregressiveOrder": "A 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": "A 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": "A 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": "A 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": True or False, # 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.
},
"decayCurveStoppingSpec": { # The decay curve automated stopping rule builds a Gaussian Process Regressor to predict the final objective value of a Trial based on the already completed Trials and the intermediate measurements of the current Trial. Early stopping is requested for the current Trial if there is very low probability to exceed the optimal value found so far. # The automated early stopping spec using decay curve rule.
"useElapsedDuration": True or False, # 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.
},
"measurementSelectionType": "A String", # Describe which measurement selection type will be used
"medianAutomatedStoppingSpec": { # The median automated stopping rule stops a pending Trial if the Trial's best objective_value is strictly below the median 'performance' of all completed Trials reported up to the Trial's last measurement. Currently, 'performance' refers to the running average of the objective values reported by the Trial in each measurement. # The automated early stopping spec using median rule.
"useElapsedDuration": True or False, # 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.
},
"metrics": [ # Required. Metric specs for the Study.
{ # Represents a metric to optimize.
"goal": "A String", # Required. The optimization goal of the metric.
"metricId": "A String", # Required. The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
"safetyConfig": { # Used in safe optimization to specify threshold levels and risk tolerance. # Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
"desiredMinSafeTrialsFraction": 3.14, # 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": 3.14, # Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
},
},
],
"observationNoise": "A String", # The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
"parameters": [ # Required. The set of parameters to tune.
{ # Represents a single parameter to optimize.
"categoricalValueSpec": { # Value specification for a parameter in `CATEGORICAL` type. # The value spec for a 'CATEGORICAL' parameter.
"defaultValue": "A 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": [ # Required. The list of possible categories.
"A String",
],
},
"conditionalParameterSpecs": [ # 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.
{ # Represents a parameter spec with condition from its parent parameter.
"parameterSpec": # Object with schema name: GoogleCloudAiplatformV1beta1StudySpecParameterSpec # Required. The spec for a conditional parameter.
"parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter. # The spec for matching values from a parent parameter of `CATEGORICAL` type.
"values": [ # Required. Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in `categorical_value_spec` of parent parameter.
"A String",
],
},
"parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter. # The spec for matching values from a parent parameter of `DISCRETE` type.
"values": [ # Required. 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.
3.14,
],
},
"parentIntValues": { # Represents the spec to match integer values from parent parameter. # The spec for matching values from a parent parameter of `INTEGER` type.
"values": [ # Required. Matches values of the parent parameter of 'INTEGER' type. All values must lie in `integer_value_spec` of parent parameter.
"A String",
],
},
},
],
"discreteValueSpec": { # Value specification for a parameter in `DISCRETE` type. # The value spec for a 'DISCRETE' parameter.
"defaultValue": 3.14, # 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": [ # Required. 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.
3.14,
],
},
"doubleValueSpec": { # Value specification for a parameter in `DOUBLE` type. # The value spec for a 'DOUBLE' parameter.
"defaultValue": 3.14, # 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": 3.14, # Required. Inclusive maximum value of the parameter.
"minValue": 3.14, # Required. Inclusive minimum value of the parameter.
},
"integerValueSpec": { # Value specification for a parameter in `INTEGER` type. # The value spec for an 'INTEGER' parameter.
"defaultValue": "A 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": "A String", # Required. Inclusive maximum value of the parameter.
"minValue": "A String", # Required. Inclusive minimum value of the parameter.
},
"parameterId": "A String", # Required. The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
"scaleType": "A String", # How the parameter should be scaled. Leave unset for `CATEGORICAL` parameters.
},
],
"studyStoppingConfig": { # The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection. # Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
"maxDurationNoProgress": "A String", # If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
"maxNumTrials": 42, # If there are more than this many trials, stop the study.
"maxNumTrialsNoProgress": 42, # If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
"maximumRuntimeConstraint": { # Time-based Constraint for Study # If the specified time or duration has passed, stop the study.
"endTime": "A String", # Compares the wallclock time to this time. Must use UTC timezone.
"maxDuration": "A String", # Counts the wallclock time passed since the creation of this Study.
},
"minNumTrials": 42, # If there are fewer than this many COMPLETED trials, do not stop the study.
"minimumRuntimeConstraint": { # Time-based Constraint for Study # 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.
"endTime": "A String", # Compares the wallclock time to this time. Must use UTC timezone.
"maxDuration": "A String", # Counts the wallclock time passed since the creation of this Study.
},
"shouldStopAsap": True or False, # 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).
},
"transferLearningConfig": { # This contains flag for manually disabling transfer learning for a study. The names of prior studies being used for transfer learning (if any) are also listed here. # The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
"disableTransferLearning": True or False, # 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": [ # Output only. Names of previously completed studies
"A String",
],
},
},
},
],
}</pre>
</div>
<div class="method">
<code class="details" id="list_next">list_next()</code>
<pre>Retrieves the next page of results.
Args:
previous_request: The request for the previous page. (required)
previous_response: The response from the request for the previous page. (required)
Returns:
A request object that you can call 'execute()' on to request the next
page. Returns None if there are no more items in the collection.
</pre>
</div>
<div class="method">
<code class="details" id="lookup">lookup(parent, body=None, x__xgafv=None)</code>
<pre>Looks a study up using the user-defined display_name field instead of the fully qualified resource name.
Args:
parent: string, Required. The resource name of the Location to get the Study from. Format: `projects/{project}/locations/{location}` (required)
body: object, The request body.
The object takes the form of:
{ # Request message for VizierService.LookupStudy.
"displayName": "A String", # Required. The user-defined display name of the Study
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # A message representing a Study.
"createTime": "A String", # Output only. Time at which the study was created.
"displayName": "A String", # Required. Describes the Study, default value is empty string.
"inactiveReason": "A String", # Output only. A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
"name": "A String", # Output only. The name of a study. The study's globally unique identifier. Format: `projects/{project}/locations/{location}/studies/{study}`
"state": "A String", # Output only. The detailed state of a Study.
"studySpec": { # Represents specification of a Study. # Required. Configuration of the Study.
"algorithm": "A String", # The search algorithm specified for the Study.
"convexAutomatedStoppingSpec": { # Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model. # The automated early stopping spec using convex stopping rule.
"learningRateParameterName": "A 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": "A 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": "A 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": "A 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": True or False, # 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": True or False, # 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.
},
"convexStopConfig": { # Configuration for ConvexStopPolicy. # Deprecated. The automated early stopping using convex stopping rule.
"autoregressiveOrder": "A 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": "A 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": "A 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": "A 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": True or False, # 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.
},
"decayCurveStoppingSpec": { # The decay curve automated stopping rule builds a Gaussian Process Regressor to predict the final objective value of a Trial based on the already completed Trials and the intermediate measurements of the current Trial. Early stopping is requested for the current Trial if there is very low probability to exceed the optimal value found so far. # The automated early stopping spec using decay curve rule.
"useElapsedDuration": True or False, # 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.
},
"measurementSelectionType": "A String", # Describe which measurement selection type will be used
"medianAutomatedStoppingSpec": { # The median automated stopping rule stops a pending Trial if the Trial's best objective_value is strictly below the median 'performance' of all completed Trials reported up to the Trial's last measurement. Currently, 'performance' refers to the running average of the objective values reported by the Trial in each measurement. # The automated early stopping spec using median rule.
"useElapsedDuration": True or False, # 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.
},
"metrics": [ # Required. Metric specs for the Study.
{ # Represents a metric to optimize.
"goal": "A String", # Required. The optimization goal of the metric.
"metricId": "A String", # Required. The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
"safetyConfig": { # Used in safe optimization to specify threshold levels and risk tolerance. # Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
"desiredMinSafeTrialsFraction": 3.14, # 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": 3.14, # Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
},
},
],
"observationNoise": "A String", # The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
"parameters": [ # Required. The set of parameters to tune.
{ # Represents a single parameter to optimize.
"categoricalValueSpec": { # Value specification for a parameter in `CATEGORICAL` type. # The value spec for a 'CATEGORICAL' parameter.
"defaultValue": "A 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": [ # Required. The list of possible categories.
"A String",
],
},
"conditionalParameterSpecs": [ # 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.
{ # Represents a parameter spec with condition from its parent parameter.
"parameterSpec": # Object with schema name: GoogleCloudAiplatformV1beta1StudySpecParameterSpec # Required. The spec for a conditional parameter.
"parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter. # The spec for matching values from a parent parameter of `CATEGORICAL` type.
"values": [ # Required. Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in `categorical_value_spec` of parent parameter.
"A String",
],
},
"parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter. # The spec for matching values from a parent parameter of `DISCRETE` type.
"values": [ # Required. 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.
3.14,
],
},
"parentIntValues": { # Represents the spec to match integer values from parent parameter. # The spec for matching values from a parent parameter of `INTEGER` type.
"values": [ # Required. Matches values of the parent parameter of 'INTEGER' type. All values must lie in `integer_value_spec` of parent parameter.
"A String",
],
},
},
],
"discreteValueSpec": { # Value specification for a parameter in `DISCRETE` type. # The value spec for a 'DISCRETE' parameter.
"defaultValue": 3.14, # 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": [ # Required. 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.
3.14,
],
},
"doubleValueSpec": { # Value specification for a parameter in `DOUBLE` type. # The value spec for a 'DOUBLE' parameter.
"defaultValue": 3.14, # 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": 3.14, # Required. Inclusive maximum value of the parameter.
"minValue": 3.14, # Required. Inclusive minimum value of the parameter.
},
"integerValueSpec": { # Value specification for a parameter in `INTEGER` type. # The value spec for an 'INTEGER' parameter.
"defaultValue": "A 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": "A String", # Required. Inclusive maximum value of the parameter.
"minValue": "A String", # Required. Inclusive minimum value of the parameter.
},
"parameterId": "A String", # Required. The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
"scaleType": "A String", # How the parameter should be scaled. Leave unset for `CATEGORICAL` parameters.
},
],
"studyStoppingConfig": { # The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection. # Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
"maxDurationNoProgress": "A String", # If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
"maxNumTrials": 42, # If there are more than this many trials, stop the study.
"maxNumTrialsNoProgress": 42, # If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
"maximumRuntimeConstraint": { # Time-based Constraint for Study # If the specified time or duration has passed, stop the study.
"endTime": "A String", # Compares the wallclock time to this time. Must use UTC timezone.
"maxDuration": "A String", # Counts the wallclock time passed since the creation of this Study.
},
"minNumTrials": 42, # If there are fewer than this many COMPLETED trials, do not stop the study.
"minimumRuntimeConstraint": { # Time-based Constraint for Study # 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.
"endTime": "A String", # Compares the wallclock time to this time. Must use UTC timezone.
"maxDuration": "A String", # Counts the wallclock time passed since the creation of this Study.
},
"shouldStopAsap": True or False, # 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).
},
"transferLearningConfig": { # This contains flag for manually disabling transfer learning for a study. The names of prior studies being used for transfer learning (if any) are also listed here. # The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob
"disableTransferLearning": True or False, # 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": [ # Output only. Names of previously completed studies
"A String",
],
},
},
}</pre>
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