Returns the operations Resource.
addTrialMeasurement(trialName, 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.
checkTrialEarlyStoppingState(trialName, 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.
Close httplib2 connections.
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.
list(parent, pageSize=None, pageToken=None, x__xgafv=None)
Lists the Trials associated with a Study.
listOptimalTrials(parent, body=None, x__xgafv=None)
Lists the pareto-optimal Trials for multi-objective Study or the optimal Trials for single-objective Study. The definition of pareto-optimal can be checked in wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency
Retrieves the next page of results.
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 suggested by Vertex AI Vizier. Returns a long-running operation associated with the generation of Trial suggestions. When this long-running operation succeeds, it will contain a SuggestTrialsResponse.
addTrialMeasurement(trialName, 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:
trialName: string, Required. The name of the trial to add measurement. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` (required)
body: object, The request body.
The object takes the form of:
{ # Request message for VizierService.AddTrialMeasurement.
"measurement": { # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values. # Required. The measurement to be added to a Trial.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the 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. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.
"clientId": "A String", # Output only. The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
"customJob": "A String", # Output only. The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
"endTime": "A String", # Output only. Time when the Trial's status changed to `SUCCEEDED` or `INFEASIBLE`.
"finalMeasurement": { # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values. # Output only. The final measurement containing the objective value.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
"id": "A String", # Output only. The identifier of the Trial assigned by the service.
"infeasibleReason": "A String", # Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is `INFEASIBLE`.
"measurements": [ # Output only. A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
{ # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
],
"name": "A String", # Output only. Resource name of the Trial assigned by the service.
"parameters": [ # Output only. The parameters of the Trial.
{ # A message representing a parameter to be tuned.
"parameterId": "A String", # Output only. The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
"value": "", # Output only. The value of the parameter. `number_value` will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. `string_value` will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
},
],
"startTime": "A String", # Output only. Time when the Trial was started.
"state": "A String", # Output only. The detailed state of the Trial.
"webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is `true`. The keys are names of each node used for the trial; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
"a_key": "A String",
},
}
checkTrialEarlyStoppingState(trialName, 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:
trialName: string, Required. The Trial's name. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` (required)
body: object, The request body.
The object takes the form of:
{ # Request message for VizierService.CheckTrialEarlyStoppingState.
}
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.
"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.
"error": { # The `Status` type defines a logical error model that is suitable for 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). # The error result of the operation in case of failure or cancellation.
"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.
},
],
"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.
},
"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.
},
"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}`.
"response": { # The normal, successful response of the operation. 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.
},
}
close()
Close httplib2 connections.
complete(name, body=None, x__xgafv=None)
Marks a Trial as complete.
Args:
name: string, Required. The Trial's name. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` (required)
body: object, The request body.
The object takes the form of:
{ # Request message for VizierService.CompleteTrial.
"finalMeasurement": { # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values. # 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
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
"infeasibleReason": "A String", # Optional. A human readable reason why the trial was infeasible. This should only be provided if `trial_infeasible` is true.
"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. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.
"clientId": "A String", # Output only. The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
"customJob": "A String", # Output only. The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
"endTime": "A String", # Output only. Time when the Trial's status changed to `SUCCEEDED` or `INFEASIBLE`.
"finalMeasurement": { # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values. # Output only. The final measurement containing the objective value.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
"id": "A String", # Output only. The identifier of the Trial assigned by the service.
"infeasibleReason": "A String", # Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is `INFEASIBLE`.
"measurements": [ # Output only. A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
{ # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
],
"name": "A String", # Output only. Resource name of the Trial assigned by the service.
"parameters": [ # Output only. The parameters of the Trial.
{ # A message representing a parameter to be tuned.
"parameterId": "A String", # Output only. The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
"value": "", # Output only. The value of the parameter. `number_value` will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. `string_value` will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
},
],
"startTime": "A String", # Output only. Time when the Trial was started.
"state": "A String", # Output only. The detailed state of the Trial.
"webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is `true`. The keys are names of each node used for the trial; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
"a_key": "A String",
},
}
create(parent, body=None, x__xgafv=None)
Adds a user provided Trial to a Study.
Args:
parent: string, Required. The resource name of the Study to create the Trial in. Format: `projects/{project}/locations/{location}/studies/{study}` (required)
body: object, The request body.
The object takes the form of:
{ # A message representing a Trial. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.
"clientId": "A String", # Output only. The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
"customJob": "A String", # Output only. The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
"endTime": "A String", # Output only. Time when the Trial's status changed to `SUCCEEDED` or `INFEASIBLE`.
"finalMeasurement": { # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values. # Output only. The final measurement containing the objective value.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
"id": "A String", # Output only. The identifier of the Trial assigned by the service.
"infeasibleReason": "A String", # Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is `INFEASIBLE`.
"measurements": [ # Output only. A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
{ # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
],
"name": "A String", # Output only. Resource name of the Trial assigned by the service.
"parameters": [ # Output only. The parameters of the Trial.
{ # A message representing a parameter to be tuned.
"parameterId": "A String", # Output only. The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
"value": "", # Output only. The value of the parameter. `number_value` will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. `string_value` will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
},
],
"startTime": "A String", # Output only. Time when the Trial was started.
"state": "A String", # Output only. The detailed state of the Trial.
"webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is `true`. The keys are names of each node used for the trial; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
"a_key": "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 Trial. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.
"clientId": "A String", # Output only. The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
"customJob": "A String", # Output only. The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
"endTime": "A String", # Output only. Time when the Trial's status changed to `SUCCEEDED` or `INFEASIBLE`.
"finalMeasurement": { # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values. # Output only. The final measurement containing the objective value.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
"id": "A String", # Output only. The identifier of the Trial assigned by the service.
"infeasibleReason": "A String", # Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is `INFEASIBLE`.
"measurements": [ # Output only. A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
{ # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
],
"name": "A String", # Output only. Resource name of the Trial assigned by the service.
"parameters": [ # Output only. The parameters of the Trial.
{ # A message representing a parameter to be tuned.
"parameterId": "A String", # Output only. The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
"value": "", # Output only. The value of the parameter. `number_value` will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. `string_value` will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
},
],
"startTime": "A String", # Output only. Time when the Trial was started.
"state": "A String", # Output only. The detailed state of the Trial.
"webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is `true`. The keys are names of each node used for the trial; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
"a_key": "A String",
},
}
delete(name, x__xgafv=None)
Deletes a Trial.
Args:
name: string, Required. The Trial's name. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` (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); }
}
get(name, x__xgafv=None)
Gets a Trial.
Args:
name: string, Required. The name of the Trial resource. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` (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. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.
"clientId": "A String", # Output only. The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
"customJob": "A String", # Output only. The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
"endTime": "A String", # Output only. Time when the Trial's status changed to `SUCCEEDED` or `INFEASIBLE`.
"finalMeasurement": { # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values. # Output only. The final measurement containing the objective value.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
"id": "A String", # Output only. The identifier of the Trial assigned by the service.
"infeasibleReason": "A String", # Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is `INFEASIBLE`.
"measurements": [ # Output only. A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
{ # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
],
"name": "A String", # Output only. Resource name of the Trial assigned by the service.
"parameters": [ # Output only. The parameters of the Trial.
{ # A message representing a parameter to be tuned.
"parameterId": "A String", # Output only. The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
"value": "", # Output only. The value of the parameter. `number_value` will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. `string_value` will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
},
],
"startTime": "A String", # Output only. Time when the Trial was started.
"state": "A String", # Output only. The detailed state of the Trial.
"webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is `true`. The keys are names of each node used for the trial; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
"a_key": "A String",
},
}
list(parent, pageSize=None, pageToken=None, x__xgafv=None)
Lists the Trials associated with a Study.
Args:
parent: string, Required. The resource name of the Study to list the Trial from. Format: `projects/{project}/locations/{location}/studies/{study}` (required)
pageSize: integer, Optional. The number of Trials to retrieve per "page" of results. If unspecified, the 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.ListTrials.
"nextPageToken": "A String", # Pass this token as the `page_token` field of the request for a subsequent call. If this field is omitted, there are no subsequent pages.
"trials": [ # The Trials associated with the Study.
{ # A message representing a Trial. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.
"clientId": "A String", # Output only. The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
"customJob": "A String", # Output only. The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
"endTime": "A String", # Output only. Time when the Trial's status changed to `SUCCEEDED` or `INFEASIBLE`.
"finalMeasurement": { # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values. # Output only. The final measurement containing the objective value.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
"id": "A String", # Output only. The identifier of the Trial assigned by the service.
"infeasibleReason": "A String", # Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is `INFEASIBLE`.
"measurements": [ # Output only. A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
{ # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
],
"name": "A String", # Output only. Resource name of the Trial assigned by the service.
"parameters": [ # Output only. The parameters of the Trial.
{ # A message representing a parameter to be tuned.
"parameterId": "A String", # Output only. The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
"value": "", # Output only. The value of the parameter. `number_value` will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. `string_value` will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
},
],
"startTime": "A String", # Output only. Time when the Trial was started.
"state": "A String", # Output only. The detailed state of the Trial.
"webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is `true`. The keys are names of each node used for the trial; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
"a_key": "A String",
},
},
],
}
listOptimalTrials(parent, body=None, x__xgafv=None)
Lists the pareto-optimal Trials for multi-objective Study or the optimal Trials for single-objective Study. The definition of pareto-optimal can be checked in wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency
Args:
parent: string, Required. The name of the Study that the optimal Trial belongs to. (required)
body: object, The request body.
The object takes the form of:
{ # Request message for VizierService.ListOptimalTrials.
}
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.ListOptimalTrials.
"optimalTrials": [ # The pareto-optimal Trials for multiple objective Study or the optimal trial for single objective Study. The definition of pareto-optimal can be checked in wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency
{ # A message representing a Trial. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.
"clientId": "A String", # Output only. The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
"customJob": "A String", # Output only. The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
"endTime": "A String", # Output only. Time when the Trial's status changed to `SUCCEEDED` or `INFEASIBLE`.
"finalMeasurement": { # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values. # Output only. The final measurement containing the objective value.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
"id": "A String", # Output only. The identifier of the Trial assigned by the service.
"infeasibleReason": "A String", # Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is `INFEASIBLE`.
"measurements": [ # Output only. A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
{ # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
],
"name": "A String", # Output only. Resource name of the Trial assigned by the service.
"parameters": [ # Output only. The parameters of the Trial.
{ # A message representing a parameter to be tuned.
"parameterId": "A String", # Output only. The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
"value": "", # Output only. The value of the parameter. `number_value` will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. `string_value` will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
},
],
"startTime": "A String", # Output only. Time when the Trial was started.
"state": "A String", # Output only. The detailed state of the Trial.
"webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is `true`. The keys are names of each node used for the trial; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
"a_key": "A String",
},
},
],
}
list_next()
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.
stop(name, body=None, x__xgafv=None)
Stops a Trial.
Args:
name: string, Required. The Trial's name. Format: `projects/{project}/locations/{location}/studies/{study}/trials/{trial}` (required)
body: object, The request body.
The object takes the form of:
{ # Request message for VizierService.StopTrial.
}
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. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.
"clientId": "A String", # Output only. The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
"customJob": "A String", # Output only. The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
"endTime": "A String", # Output only. Time when the Trial's status changed to `SUCCEEDED` or `INFEASIBLE`.
"finalMeasurement": { # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values. # Output only. The final measurement containing the objective value.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
"id": "A String", # Output only. The identifier of the Trial assigned by the service.
"infeasibleReason": "A String", # Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is `INFEASIBLE`.
"measurements": [ # Output only. A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
{ # A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.
"elapsedDuration": "A String", # Output only. Time that the Trial has been running at the point of this Measurement.
"metrics": [ # Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
{ # A message representing a metric in the measurement.
"metricId": "A String", # Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
"value": 3.14, # Output only. The value for this metric.
},
],
"stepCount": "A String", # Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
},
],
"name": "A String", # Output only. Resource name of the Trial assigned by the service.
"parameters": [ # Output only. The parameters of the Trial.
{ # A message representing a parameter to be tuned.
"parameterId": "A String", # Output only. The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
"value": "", # Output only. The value of the parameter. `number_value` will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. `string_value` will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
},
],
"startTime": "A String", # Output only. Time when the Trial was started.
"state": "A String", # Output only. The detailed state of the Trial.
"webAccessUris": { # Output only. URIs for accessing [interactive shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell) (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is `true`. The keys are names of each node used for the trial; for example, `workerpool0-0` for the primary node, `workerpool1-0` for the first node in the second worker pool, and `workerpool1-1` for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
"a_key": "A String",
},
}
suggest(parent, body=None, x__xgafv=None)
Adds one or more Trials to a Study, with parameter values suggested by Vertex AI Vizier. 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 project and location that the Study belongs to. Format: `projects/{project}/locations/{location}/studies/{study}` (required)
body: object, The request body.
The object takes the form of:
{ # Request message for VizierService.SuggestTrials.
"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.
"contexts": [ # Optional. This allows you to specify the "context" for a Trial; a context is a slice (a subspace) of the search space. Typical uses for contexts: 1) You are using Vizier to tune a server for best performance, but there's a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical. 2) Imagine you're optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient. 3) You want to do a fair A/B test efficiently. Specify the "A" and "B" conditions as contexts, and Vizier will generalize between "A" and "B" conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if "A" and "B" were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that's the asynchronous option where you don't need a close association between contexts and suggestions. NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don't supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It's an error if they don't. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.
{
"description": "A String", # A human-readable field which can store a description of this context. This will become part of the resulting Trial's description field.
"parameters": [ # If/when a Trial is generated or selected from this Context, its Parameters will match any parameters specified here. (I.e. if this context specifies parameter name:'a' int_value:3, then a resulting Trial will have int_value:3 for its parameter named 'a'.) Note that we first attempt to match existing REQUESTED Trials with contexts, and if there are no matches, we generate suggestions in the subspace defined by the parameters specified here. NOTE: a Context without any Parameters matches the entire feasible search space.
{ # A message representing a parameter to be tuned.
"parameterId": "A String", # Output only. The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
"value": "", # Output only. The value of the parameter. `number_value` will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'. `string_value` will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
},
],
},
],
"suggestionCount": 42, # Required. The number of suggestions requested. It must be positive.
}
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.
"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.
"error": { # The `Status` type defines a logical error model that is suitable for 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). # The error result of the operation in case of failure or cancellation.
"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.
},
],
"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.
},
"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.
},
"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}`.
"response": { # The normal, successful response of the operation. 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.
},
}