Returns the trials Resource.
create(parent=None, body=None, studyId=None, x__xgafv=None)
Creates a study.
Deletes a study.
Gets a study.
list(parent=None, x__xgafv=None)
Lists all the studies in a region for an associated project.
create(parent=None, body=None, studyId=None, x__xgafv=None)
Creates a study.
Args:
parent: string, Required. The project and location that the study belongs to.
Format: projects/{project}/locations/{location} (required)
body: object, The request body.
The object takes the form of:
{ # A message representing a Study.
"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.
"state": "A String", # Output only. The detailed state of a study.
"name": "A String", # Output only. The name of a study.
"studyConfig": { # Represents configuration of a study. # Required. Configuration of the study.
"metrics": [ # Metric specs for the study.
{ # Represents a metric to optimize.
"metric": "A String", # Required. The name of the metric.
"goal": "A String", # Required. The optimization goal of the metric.
},
],
"automatedStoppingConfig": { # Configuration for Automated Early Stopping of Trials. If no # Configuration for automated stopping of unpromising Trials.
# implementation_config is set, automated early stopping will not be run.
"decayCurveStoppingConfig": {
"useElapsedTime": True or False, # If true, measurement.elapsed_time is used as the x-axis of each
# Trials Decay Curve. Otherwise, Measurement.steps will be used as the
# x-axis.
},
"medianAutomatedStoppingConfig": { # 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.
"useElapsedTime": True or False, # If true, the median automated stopping rule applies to
# measurement.use_elapsed_time, which means the elapsed_time field of
# the current trial's
# latest measurement is used to compute the median objective
# value for each completed trial.
},
},
"parameters": [ # Required. The set of parameters to tune.
{ # Represents a single parameter to optimize.
"discreteValueSpec": { # The value spec for a 'DISCRETE' parameter.
"values": [ # Must be specified if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. 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,
],
},
"categoricalValueSpec": { # The value spec for a 'CATEGORICAL' parameter.
"values": [ # Must be specified if type is `CATEGORICAL`.
# The list of possible categories.
"A String",
],
},
"parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'CATEGORICAL'.
# All values must exist in `categorical_value_spec` of parent parameter.
"A String",
],
},
"integerValueSpec": { # The value spec for an 'INTEGER' parameter.
"maxValue": "A String", # Must be specified if type is `INTEGER`. Maximum value of the parameter.
"minValue": "A String", # Must be specified if type is `INTEGER`. Minimum value of the parameter.
},
"parentIntValues": { # Represents the spec to match integer values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'INTEGER'.
# All values must lie in `integer_value_spec` of parent parameter.
"A String",
],
},
"parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'DISCRETE'.
# All values must exist in `discrete_value_spec` of parent parameter.
3.14,
],
},
"childParameterSpecs": [ # A child node is active if the parameter's value matches the child node's
# matching_parent_values.
#
# If two items in child_parameter_specs have the same name, they must have
# disjoint matching_parent_values.
# Object with schema name: GoogleCloudMlV1_StudyConfig_ParameterSpec
],
"doubleValueSpec": { # The value spec for a 'DOUBLE' parameter.
"maxValue": 3.14, # Must be specified if type is `DOUBLE`. Maximum value of the parameter.
"minValue": 3.14, # Must be specified if type is `DOUBLE`. Minimum value of the parameter.
},
"parameter": "A String", # Required. The parameter name must be unique amongst all ParameterSpecs.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # How the parameter should be scaled.
# Leave unset for categorical parameters.
},
],
"algorithm": "A String", # The search algorithm specified for the study.
},
"createTime": "A String", # Output only. Time at which the study was created.
}
studyId: string, Required. The ID to use for the study, which will become the final component of
the study's resource name.
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.
"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.
"state": "A String", # Output only. The detailed state of a study.
"name": "A String", # Output only. The name of a study.
"studyConfig": { # Represents configuration of a study. # Required. Configuration of the study.
"metrics": [ # Metric specs for the study.
{ # Represents a metric to optimize.
"metric": "A String", # Required. The name of the metric.
"goal": "A String", # Required. The optimization goal of the metric.
},
],
"automatedStoppingConfig": { # Configuration for Automated Early Stopping of Trials. If no # Configuration for automated stopping of unpromising Trials.
# implementation_config is set, automated early stopping will not be run.
"decayCurveStoppingConfig": {
"useElapsedTime": True or False, # If true, measurement.elapsed_time is used as the x-axis of each
# Trials Decay Curve. Otherwise, Measurement.steps will be used as the
# x-axis.
},
"medianAutomatedStoppingConfig": { # 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.
"useElapsedTime": True or False, # If true, the median automated stopping rule applies to
# measurement.use_elapsed_time, which means the elapsed_time field of
# the current trial's
# latest measurement is used to compute the median objective
# value for each completed trial.
},
},
"parameters": [ # Required. The set of parameters to tune.
{ # Represents a single parameter to optimize.
"discreteValueSpec": { # The value spec for a 'DISCRETE' parameter.
"values": [ # Must be specified if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. 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,
],
},
"categoricalValueSpec": { # The value spec for a 'CATEGORICAL' parameter.
"values": [ # Must be specified if type is `CATEGORICAL`.
# The list of possible categories.
"A String",
],
},
"parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'CATEGORICAL'.
# All values must exist in `categorical_value_spec` of parent parameter.
"A String",
],
},
"integerValueSpec": { # The value spec for an 'INTEGER' parameter.
"maxValue": "A String", # Must be specified if type is `INTEGER`. Maximum value of the parameter.
"minValue": "A String", # Must be specified if type is `INTEGER`. Minimum value of the parameter.
},
"parentIntValues": { # Represents the spec to match integer values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'INTEGER'.
# All values must lie in `integer_value_spec` of parent parameter.
"A String",
],
},
"parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'DISCRETE'.
# All values must exist in `discrete_value_spec` of parent parameter.
3.14,
],
},
"childParameterSpecs": [ # A child node is active if the parameter's value matches the child node's
# matching_parent_values.
#
# If two items in child_parameter_specs have the same name, they must have
# disjoint matching_parent_values.
# Object with schema name: GoogleCloudMlV1_StudyConfig_ParameterSpec
],
"doubleValueSpec": { # The value spec for a 'DOUBLE' parameter.
"maxValue": 3.14, # Must be specified if type is `DOUBLE`. Maximum value of the parameter.
"minValue": 3.14, # Must be specified if type is `DOUBLE`. Minimum value of the parameter.
},
"parameter": "A String", # Required. The parameter name must be unique amongst all ParameterSpecs.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # How the parameter should be scaled.
# Leave unset for categorical parameters.
},
],
"algorithm": "A String", # The search algorithm specified for the study.
},
"createTime": "A String", # Output only. Time at which the study was created.
}
delete(name=*, x__xgafv=None)
Deletes a study.
Args:
name: string, Required. The study name. (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);
# }
#
# The JSON representation for `Empty` is empty JSON object `{}`.
}
get(name=*, x__xgafv=None)
Gets a study.
Args:
name: string, Required. The study name. (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.
"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.
"state": "A String", # Output only. The detailed state of a study.
"name": "A String", # Output only. The name of a study.
"studyConfig": { # Represents configuration of a study. # Required. Configuration of the study.
"metrics": [ # Metric specs for the study.
{ # Represents a metric to optimize.
"metric": "A String", # Required. The name of the metric.
"goal": "A String", # Required. The optimization goal of the metric.
},
],
"automatedStoppingConfig": { # Configuration for Automated Early Stopping of Trials. If no # Configuration for automated stopping of unpromising Trials.
# implementation_config is set, automated early stopping will not be run.
"decayCurveStoppingConfig": {
"useElapsedTime": True or False, # If true, measurement.elapsed_time is used as the x-axis of each
# Trials Decay Curve. Otherwise, Measurement.steps will be used as the
# x-axis.
},
"medianAutomatedStoppingConfig": { # 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.
"useElapsedTime": True or False, # If true, the median automated stopping rule applies to
# measurement.use_elapsed_time, which means the elapsed_time field of
# the current trial's
# latest measurement is used to compute the median objective
# value for each completed trial.
},
},
"parameters": [ # Required. The set of parameters to tune.
{ # Represents a single parameter to optimize.
"discreteValueSpec": { # The value spec for a 'DISCRETE' parameter.
"values": [ # Must be specified if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. 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,
],
},
"categoricalValueSpec": { # The value spec for a 'CATEGORICAL' parameter.
"values": [ # Must be specified if type is `CATEGORICAL`.
# The list of possible categories.
"A String",
],
},
"parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'CATEGORICAL'.
# All values must exist in `categorical_value_spec` of parent parameter.
"A String",
],
},
"integerValueSpec": { # The value spec for an 'INTEGER' parameter.
"maxValue": "A String", # Must be specified if type is `INTEGER`. Maximum value of the parameter.
"minValue": "A String", # Must be specified if type is `INTEGER`. Minimum value of the parameter.
},
"parentIntValues": { # Represents the spec to match integer values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'INTEGER'.
# All values must lie in `integer_value_spec` of parent parameter.
"A String",
],
},
"parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'DISCRETE'.
# All values must exist in `discrete_value_spec` of parent parameter.
3.14,
],
},
"childParameterSpecs": [ # A child node is active if the parameter's value matches the child node's
# matching_parent_values.
#
# If two items in child_parameter_specs have the same name, they must have
# disjoint matching_parent_values.
# Object with schema name: GoogleCloudMlV1_StudyConfig_ParameterSpec
],
"doubleValueSpec": { # The value spec for a 'DOUBLE' parameter.
"maxValue": 3.14, # Must be specified if type is `DOUBLE`. Maximum value of the parameter.
"minValue": 3.14, # Must be specified if type is `DOUBLE`. Minimum value of the parameter.
},
"parameter": "A String", # Required. The parameter name must be unique amongst all ParameterSpecs.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # How the parameter should be scaled.
# Leave unset for categorical parameters.
},
],
"algorithm": "A String", # The search algorithm specified for the study.
},
"createTime": "A String", # Output only. Time at which the study was created.
}
list(parent=None, x__xgafv=None)
Lists all the studies in a region for an associated project.
Args:
parent: string, Required. The project and location that the study belongs to.
Format: projects/{project}/locations/{location} (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{
"studies": [ # The studies associated with the project.
{ # A message representing a Study.
"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.
"state": "A String", # Output only. The detailed state of a study.
"name": "A String", # Output only. The name of a study.
"studyConfig": { # Represents configuration of a study. # Required. Configuration of the study.
"metrics": [ # Metric specs for the study.
{ # Represents a metric to optimize.
"metric": "A String", # Required. The name of the metric.
"goal": "A String", # Required. The optimization goal of the metric.
},
],
"automatedStoppingConfig": { # Configuration for Automated Early Stopping of Trials. If no # Configuration for automated stopping of unpromising Trials.
# implementation_config is set, automated early stopping will not be run.
"decayCurveStoppingConfig": {
"useElapsedTime": True or False, # If true, measurement.elapsed_time is used as the x-axis of each
# Trials Decay Curve. Otherwise, Measurement.steps will be used as the
# x-axis.
},
"medianAutomatedStoppingConfig": { # 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.
"useElapsedTime": True or False, # If true, the median automated stopping rule applies to
# measurement.use_elapsed_time, which means the elapsed_time field of
# the current trial's
# latest measurement is used to compute the median objective
# value for each completed trial.
},
},
"parameters": [ # Required. The set of parameters to tune.
{ # Represents a single parameter to optimize.
"discreteValueSpec": { # The value spec for a 'DISCRETE' parameter.
"values": [ # Must be specified if type is `DISCRETE`.
# A list of feasible points.
# The list should be in strictly increasing order. 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,
],
},
"categoricalValueSpec": { # The value spec for a 'CATEGORICAL' parameter.
"values": [ # Must be specified if type is `CATEGORICAL`.
# The list of possible categories.
"A String",
],
},
"parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'CATEGORICAL'.
# All values must exist in `categorical_value_spec` of parent parameter.
"A String",
],
},
"integerValueSpec": { # The value spec for an 'INTEGER' parameter.
"maxValue": "A String", # Must be specified if type is `INTEGER`. Maximum value of the parameter.
"minValue": "A String", # Must be specified if type is `INTEGER`. Minimum value of the parameter.
},
"parentIntValues": { # Represents the spec to match integer values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'INTEGER'.
# All values must lie in `integer_value_spec` of parent parameter.
"A String",
],
},
"parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter.
"values": [ # Matches values of the parent parameter with type 'DISCRETE'.
# All values must exist in `discrete_value_spec` of parent parameter.
3.14,
],
},
"childParameterSpecs": [ # A child node is active if the parameter's value matches the child node's
# matching_parent_values.
#
# If two items in child_parameter_specs have the same name, they must have
# disjoint matching_parent_values.
# Object with schema name: GoogleCloudMlV1_StudyConfig_ParameterSpec
],
"doubleValueSpec": { # The value spec for a 'DOUBLE' parameter.
"maxValue": 3.14, # Must be specified if type is `DOUBLE`. Maximum value of the parameter.
"minValue": 3.14, # Must be specified if type is `DOUBLE`. Minimum value of the parameter.
},
"parameter": "A String", # Required. The parameter name must be unique amongst all ParameterSpecs.
"type": "A String", # Required. The type of the parameter.
"scaleType": "A String", # How the parameter should be scaled.
# Leave unset for categorical parameters.
},
],
"algorithm": "A String", # The search algorithm specified for the study.
},
"createTime": "A String", # Output only. Time at which the study was created.
},
],
}