addMeasurement(name=*, body=None, x__xgafv=None)
Adds a measurement of the objective metrics to a trial. This measurement
checkEarlyStoppingState(name=*, body=None, x__xgafv=None)
Checks whether a trial should stop or not. Returns a
complete(name=*, body=None, x__xgafv=None)
Marks a trial as complete.
create(parent=*, body=None, x__xgafv=None)
Adds a user provided trial to a study.
Deletes a trial.
Gets a trial.
Lists the trials associated with a study.
stop(name=*, body=None, x__xgafv=None)
Stops a trial.
suggest(parent=*, body=None, x__xgafv=None)
Adds one or more trials to a study, with parameter values
addMeasurement(name=*, body=None, x__xgafv=None)
Adds a measurement of the objective metrics to a trial. This measurement
is assumed to have been taken before the trial is complete.
Args:
name: string, Required. The trial name. (required)
body: object, The request body.
The object takes the form of:
{ # The request message for the AddTrialMeasurement service method.
"measurement": { # A message representing a measurement. # Required. The measurement to be added to a trial.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
}
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 trial.
"infeasibleReason": "A String", # Output only. A human readable string describing why the trial is
# infeasible. This should only be set if trial_infeasible is true.
"finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
"name": "A String", # Output only. Name of the trial assigned by the service.
"parameters": [ # The parameters of the trial.
{ # A message representing a parameter to be tuned. Contains the name of
# the parameter and the suggested value to use for this trial.
"stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
"intValue": "A String", # Must be set if ParameterType is INTEGER
"floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
"parameter": "A String", # The name of the parameter.
},
],
"measurements": [ # A list of measurements that are strictly lexicographically
# ordered by their induced tuples (steps, elapsed_time).
# These are used for early stopping computations.
{ # A message representing a measurement.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
],
"clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
"state": "A String", # The detailed state of a trial.
"startTime": "A String", # Output only. Time at which the trial was started.
"endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
"trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
}
checkEarlyStoppingState(name=*, body=None, x__xgafv=None)
Checks whether a trial should stop or not. Returns a
long-running operation. When the operation is successful,
it will contain a
CheckTrialEarlyStoppingStateResponse.
Args:
name: string, Required. The trial name. (required)
body: object, The request body.
The object takes the form of:
{ # The request message for the CheckTrialEarlyStoppingState service method.
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # This resource represents a long-running operation that is the result of a
# network API call.
"metadata": { # Service-specific metadata associated with the operation. It typically
# contains progress information and common metadata such as create time.
# Some services might not provide such metadata. Any method that returns a
# long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"error": { # The `Status` type defines a logical error model that is suitable for # The error result of the operation in case of failure or cancellation.
# different programming environments, including REST APIs and RPC APIs. It is
# used by [gRPC](https://github.com/grpc). Each `Status` message contains
# three pieces of data: error code, error message, and error details.
#
# You can find out more about this error model and how to work with it in the
# [API Design Guide](https://cloud.google.com/apis/design/errors).
"message": "A String", # A developer-facing error message, which should be in English. Any
# user-facing error message should be localized and sent in the
# google.rpc.Status.details field, or localized by the client.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of
# message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
},
"done": True or False, # If the value is `false`, it means the operation is still in progress.
# If `true`, the operation is completed, and either `error` or `response` is
# available.
"response": { # The normal response of the operation in case of success. If the original
# method returns no data on success, such as `Delete`, the response is
# `google.protobuf.Empty`. If the original method is standard
# `Get`/`Create`/`Update`, the response should be the resource. For other
# methods, the response should have the type `XxxResponse`, where `Xxx`
# is the original method name. For example, if the original method name
# is `TakeSnapshot()`, the inferred response type is
# `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that
# originally returns it. If you use the default HTTP mapping, the
# `name` should be a resource name ending with `operations/{unique_id}`.
}
complete(name=*, body=None, x__xgafv=None)
Marks a trial as complete.
Args:
name: string, Required. The trial name.metat (required)
body: object, The request body.
The object takes the form of:
{ # The request message for the CompleteTrial service method.
"infeasibleReason": "A String", # Optional. A human readable reason why the trial was infeasible. This should
# only be provided if `trial_infeasible` is true.
"finalMeasurement": { # A message representing a measurement. # Optional. If provided, it will be used as the completed trial's
# final_measurement; Otherwise, the service will auto-select a
# previously reported measurement as the final-measurement
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
"trialInfeasible": True or False, # Optional. True if the trial cannot be run with the given Parameter, and
# final_measurement will be ignored.
}
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 trial.
"infeasibleReason": "A String", # Output only. A human readable string describing why the trial is
# infeasible. This should only be set if trial_infeasible is true.
"finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
"name": "A String", # Output only. Name of the trial assigned by the service.
"parameters": [ # The parameters of the trial.
{ # A message representing a parameter to be tuned. Contains the name of
# the parameter and the suggested value to use for this trial.
"stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
"intValue": "A String", # Must be set if ParameterType is INTEGER
"floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
"parameter": "A String", # The name of the parameter.
},
],
"measurements": [ # A list of measurements that are strictly lexicographically
# ordered by their induced tuples (steps, elapsed_time).
# These are used for early stopping computations.
{ # A message representing a measurement.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
],
"clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
"state": "A String", # The detailed state of a trial.
"startTime": "A String", # Output only. Time at which the trial was started.
"endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
"trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
}
create(parent=*, body=None, x__xgafv=None)
Adds a user provided trial to a study.
Args:
parent: string, Required. The name of the study that the trial belongs to. (required)
body: object, The request body.
The object takes the form of:
{ # A message representing a trial.
"infeasibleReason": "A String", # Output only. A human readable string describing why the trial is
# infeasible. This should only be set if trial_infeasible is true.
"finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
"name": "A String", # Output only. Name of the trial assigned by the service.
"parameters": [ # The parameters of the trial.
{ # A message representing a parameter to be tuned. Contains the name of
# the parameter and the suggested value to use for this trial.
"stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
"intValue": "A String", # Must be set if ParameterType is INTEGER
"floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
"parameter": "A String", # The name of the parameter.
},
],
"measurements": [ # A list of measurements that are strictly lexicographically
# ordered by their induced tuples (steps, elapsed_time).
# These are used for early stopping computations.
{ # A message representing a measurement.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
],
"clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
"state": "A String", # The detailed state of a trial.
"startTime": "A String", # Output only. Time at which the trial was started.
"endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
"trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
}
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 trial.
"infeasibleReason": "A String", # Output only. A human readable string describing why the trial is
# infeasible. This should only be set if trial_infeasible is true.
"finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
"name": "A String", # Output only. Name of the trial assigned by the service.
"parameters": [ # The parameters of the trial.
{ # A message representing a parameter to be tuned. Contains the name of
# the parameter and the suggested value to use for this trial.
"stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
"intValue": "A String", # Must be set if ParameterType is INTEGER
"floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
"parameter": "A String", # The name of the parameter.
},
],
"measurements": [ # A list of measurements that are strictly lexicographically
# ordered by their induced tuples (steps, elapsed_time).
# These are used for early stopping computations.
{ # A message representing a measurement.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
],
"clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
"state": "A String", # The detailed state of a trial.
"startTime": "A String", # Output only. Time at which the trial was started.
"endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
"trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
}
delete(name=*, x__xgafv=None)
Deletes a trial.
Args:
name: string, Required. The trial 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 trial.
Args:
name: string, Required. The trial 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 trial.
"infeasibleReason": "A String", # Output only. A human readable string describing why the trial is
# infeasible. This should only be set if trial_infeasible is true.
"finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
"name": "A String", # Output only. Name of the trial assigned by the service.
"parameters": [ # The parameters of the trial.
{ # A message representing a parameter to be tuned. Contains the name of
# the parameter and the suggested value to use for this trial.
"stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
"intValue": "A String", # Must be set if ParameterType is INTEGER
"floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
"parameter": "A String", # The name of the parameter.
},
],
"measurements": [ # A list of measurements that are strictly lexicographically
# ordered by their induced tuples (steps, elapsed_time).
# These are used for early stopping computations.
{ # A message representing a measurement.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
],
"clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
"state": "A String", # The detailed state of a trial.
"startTime": "A String", # Output only. Time at which the trial was started.
"endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
"trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
}
list(parent=*, x__xgafv=None)
Lists the trials associated with a study.
Args:
parent: string, Required. The name of the study that the trial belongs to. (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # The response message for the ListTrials method.
"trials": [ # The trials associated with the study.
{ # A message representing a trial.
"infeasibleReason": "A String", # Output only. A human readable string describing why the trial is
# infeasible. This should only be set if trial_infeasible is true.
"finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
"name": "A String", # Output only. Name of the trial assigned by the service.
"parameters": [ # The parameters of the trial.
{ # A message representing a parameter to be tuned. Contains the name of
# the parameter and the suggested value to use for this trial.
"stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
"intValue": "A String", # Must be set if ParameterType is INTEGER
"floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
"parameter": "A String", # The name of the parameter.
},
],
"measurements": [ # A list of measurements that are strictly lexicographically
# ordered by their induced tuples (steps, elapsed_time).
# These are used for early stopping computations.
{ # A message representing a measurement.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
],
"clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
"state": "A String", # The detailed state of a trial.
"startTime": "A String", # Output only. Time at which the trial was started.
"endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
"trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
},
],
}
stop(name=*, body=None, x__xgafv=None)
Stops a trial.
Args:
name: string, Required. The trial name. (required)
body: object, The request body.
The object takes the form of:
{
}
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 trial.
"infeasibleReason": "A String", # Output only. A human readable string describing why the trial is
# infeasible. This should only be set if trial_infeasible is true.
"finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
"name": "A String", # Output only. Name of the trial assigned by the service.
"parameters": [ # The parameters of the trial.
{ # A message representing a parameter to be tuned. Contains the name of
# the parameter and the suggested value to use for this trial.
"stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
"intValue": "A String", # Must be set if ParameterType is INTEGER
"floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
"parameter": "A String", # The name of the parameter.
},
],
"measurements": [ # A list of measurements that are strictly lexicographically
# ordered by their induced tuples (steps, elapsed_time).
# These are used for early stopping computations.
{ # A message representing a measurement.
"metrics": [ # Provides a list of metrics that act as inputs into the objective
# function.
{ # A message representing a metric in the measurement.
"metric": "A String", # Required. Metric name.
"value": 3.14, # Required. The value for this metric.
},
],
"elapsedTime": "A String", # Output only. Time that the trial has been running at the point of
# this measurement.
"stepCount": "A String", # The number of steps a machine learning model has been trained for.
# Must be non-negative.
},
],
"clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
"state": "A String", # The detailed state of a trial.
"startTime": "A String", # Output only. Time at which the trial was started.
"endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
"trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
}
suggest(parent=*, body=None, x__xgafv=None)
Adds one or more trials to a study, with parameter values
suggested by AI Platform Optimizer. Returns a long-running
operation associated with the generation of trial suggestions.
When this long-running operation succeeds, it will contain
a SuggestTrialsResponse.
Args:
parent: string, Required. The name of the study that the trial belongs to. (required)
body: object, The request body.
The object takes the form of:
{ # The request message for the SuggestTrial service method.
"suggestionCount": 42, # Required. The number of suggestions requested.
"clientId": "A String", # Required. The identifier of the client that is requesting the suggestion.
#
# If multiple SuggestTrialsRequests have the same `client_id`,
# the service will return the identical suggested trial if the trial is
# pending, and provide a new trial if the last suggested trial was completed.
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # This resource represents a long-running operation that is the result of a
# network API call.
"metadata": { # Service-specific metadata associated with the operation. It typically
# contains progress information and common metadata such as create time.
# Some services might not provide such metadata. Any method that returns a
# long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"error": { # The `Status` type defines a logical error model that is suitable for # The error result of the operation in case of failure or cancellation.
# different programming environments, including REST APIs and RPC APIs. It is
# used by [gRPC](https://github.com/grpc). Each `Status` message contains
# three pieces of data: error code, error message, and error details.
#
# You can find out more about this error model and how to work with it in the
# [API Design Guide](https://cloud.google.com/apis/design/errors).
"message": "A String", # A developer-facing error message, which should be in English. Any
# user-facing error message should be localized and sent in the
# google.rpc.Status.details field, or localized by the client.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of
# message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
},
"done": True or False, # If the value is `false`, it means the operation is still in progress.
# If `true`, the operation is completed, and either `error` or `response` is
# available.
"response": { # The normal response of the operation in case of success. If the original
# method returns no data on success, such as `Delete`, the response is
# `google.protobuf.Empty`. If the original method is standard
# `Get`/`Create`/`Update`, the response should be the resource. For other
# methods, the response should have the type `XxxResponse`, where `Xxx`
# is the original method name. For example, if the original method name
# is `TakeSnapshot()`, the inferred response type is
# `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that
# originally returns it. If you use the default HTTP mapping, the
# `name` should be a resource name ending with `operations/{unique_id}`.
}