Types overview

CloudAiLargeModelsVisionFilteredText

Details for filtered input text.

Fields
category	enum Confidence level
Enum type. Can be one of the following:
RAI_CATEGORY_UNSPECIFIED	(No description provided)
OBSCENE	(No description provided)
SEXUALLY_EXPLICIT	Porn
IDENTITY_ATTACK	Hate
VIOLENCE_ABUSE	(No description provided)
CSAI	(No description provided)
SPII	(No description provided)
CELEBRITY	(No description provided)
FACE_IMG	(No description provided)
WATERMARK_IMG	(No description provided)
MEMORIZATION_IMG	(No description provided)
CSAI_IMG	(No description provided)
PORN_IMG	(No description provided)
VIOLENCE_IMG	(No description provided)
CHILD_IMG	(No description provided)
TOXIC	(No description provided)
SENSITIVE_WORD	(No description provided)
PERSON_IMG	(No description provided)
ICA_IMG	(No description provided)
SEXUAL_IMG	(No description provided)
IU_IMG	(No description provided)
RACY_IMG	(No description provided)
PEDO_IMG	(No description provided)
DEATH_HARM_TRAGEDY	SafetyAttributes returned but not filtered on
HEALTH	(No description provided)
FIREARMS_WEAPONS	(No description provided)
RELIGIOUS_BELIEF	(No description provided)
ILLICIT_DRUGS	(No description provided)
WAR_CONFLICT	(No description provided)
POLITICS	(No description provided)
HATE_SYMBOL_IMG	End of list
CHILD_TEXT	(No description provided)
DANGEROUS_CONTENT	Text category from SafetyCat v3
RECITATION_TEXT	(No description provided)
CELEBRITY_IMG	(No description provided)
WATERMARK_IMG_REMOVAL	Error message when user attempts to remove watermark from editing image
confidence	enum Filtered category
Enum type. Can be one of the following:
CONFIDENCE_UNSPECIFIED	(No description provided)
CONFIDENCE_LOW	(No description provided)
CONFIDENCE_MEDIUM	(No description provided)
CONFIDENCE_HIGH	(No description provided)
prompt	string Input prompt
score	number (double format) Score for category

CloudAiLargeModelsVisionGenerateVideoResponse

Generate video response.

Fields
generatedSamples[]	object (CloudAiLargeModelsVisionMedia) The generates samples.
raiErrorMessage	string Returns rai error message for filtered videos.
raiMediaFilteredCount	integer (int32 format) Returns if any videos were filtered due to RAI policies.
raiMediaFilteredReasons[]	string Returns rai failure reasons if any.
raiTextFilteredReason	object (CloudAiLargeModelsVisionFilteredText) Returns filtered text rai info.

CloudAiLargeModelsVisionImage

Image.

Fields
encoding	string Image encoding, encoded as "image/png" or "image/jpg".
image	string (bytes format) Raw bytes.
imageRaiScores	object (CloudAiLargeModelsVisionImageRAIScores) RAI scores for generated image.
raiInfo	object (CloudAiLargeModelsVisionRaiInfo) RAI info for image.
semanticFilterResponse	object (CloudAiLargeModelsVisionSemanticFilterResponse) Semantic filter info for image.
text	string Text/Expanded text input for imagen.
uri	string Path to another storage (typically Google Cloud Storage).

CloudAiLargeModelsVisionImageRAIScores

RAI scores for generated image returned.

Fields
agileWatermarkDetectionScore	number (double format) Agile watermark score for image.

CloudAiLargeModelsVisionMedia

Media.

Fields
image	object (CloudAiLargeModelsVisionImage) Image.
video	object (CloudAiLargeModelsVisionVideo) Video

CloudAiLargeModelsVisionNamedBoundingBox

(No description provided)

Fields
classes[]	string (No description provided)
entities[]	string (No description provided)
scores[]	number (float format) (No description provided)
x1	number (float format) (No description provided)
x2	number (float format) (No description provided)
y1	number (float format) (No description provided)
y2	number (float format) (No description provided)

CloudAiLargeModelsVisionRaiInfo

(No description provided)

Fields
raiCategories[]	string List of rai categories' information to return
scores[]	number (float format) List of rai scores mapping to the rai categories. Rounded to 1 decimal place.

CloudAiLargeModelsVisionSemanticFilterResponse

(No description provided)

Fields
namedBoundingBoxes[]	object (CloudAiLargeModelsVisionNamedBoundingBox) Class labels of the bounding boxes that failed the semantic filtering. Bounding box coordinates.
passedSemanticFilter	boolean This response is added when semantic filter config is turned on in EditConfig. It reports if this image is passed semantic filter response. If passed_semantic_filter is false, the bounding box information will be populated for user to check what caused the semantic filter to fail.

CloudAiLargeModelsVisionVideo

Video

Fields
uri	string Path to another storage (typically Google Cloud Storage).
video	string (bytes format) Raw bytes.

GoogleApiHttpBody

Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged.

Fields
contentType	string The HTTP Content-Type header value specifying the content type of the body.
data	string (bytes format) The HTTP request/response body as raw binary.
extensions[]	object Application specific response metadata. Must be set in the first response for streaming APIs.

GoogleCloudAiplatformV1beta1ActiveLearningConfig

Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy.

Fields
maxDataItemCount	string (int64 format) Max number of human labeled DataItems.
maxDataItemPercentage	integer (int32 format) Max percent of total DataItems for human labeling.
sampleConfig	object (GoogleCloudAiplatformV1beta1SampleConfig) Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
trainingConfig	object (GoogleCloudAiplatformV1beta1TrainingConfig) CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.

GoogleCloudAiplatformV1beta1AddContextArtifactsAndExecutionsRequest

Request message for MetadataService.AddContextArtifactsAndExecutions.

Fields
artifacts[]	string The resource names of the Artifacts to attribute to the Context. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}
executions[]	string The resource names of the Executions to associate with the Context. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}

GoogleCloudAiplatformV1beta1AddContextChildrenRequest

Request message for MetadataService.AddContextChildren.

Fields
childContexts[]	string The resource names of the child Contexts.

GoogleCloudAiplatformV1beta1AddExecutionEventsRequest

Request message for MetadataService.AddExecutionEvents.

Fields
events[]	object (GoogleCloudAiplatformV1beta1Event) The Events to create and add.

GoogleCloudAiplatformV1beta1AddTrialMeasurementRequest

Request message for VizierService.AddTrialMeasurement.

Fields
measurement	object (GoogleCloudAiplatformV1beta1Measurement) Required. The measurement to be added to a Trial.

GoogleCloudAiplatformV1beta1Annotation

Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.

Fields
annotationSource	object (GoogleCloudAiplatformV1beta1UserActionReference) Output only. The source of the Annotation.
createTime	string (Timestamp format) Output only. Timestamp when this Annotation was created.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) Optional. The labels with user-defined metadata to organize your Annotations. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Annotation(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Annotation: * "aiplatform.googleapis.com/annotation_set_name": optional, name of the UI's annotation set this Annotation belongs to. If not set, the Annotation is not visible in the UI. * "aiplatform.googleapis.com/payload_schema": output only, its value is the payload_schema's title.
name	string Output only. Resource name of the Annotation.
payload	any Required. The schema of the payload can be found in payload_schema.
payloadSchemaUri	string Required. Google Cloud Storage URI points to a YAML file describing payload. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with the parent Dataset's metadata.
updateTime	string (Timestamp format) Output only. Timestamp when this Annotation was last updated.

GoogleCloudAiplatformV1beta1AnnotationSpec

Identifies a concept with which DataItems may be annotated with.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this AnnotationSpec was created.
displayName	string Required. The user-defined name of the AnnotationSpec. The name can be up to 128 characters long and can consist of any UTF-8 characters.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
name	string Output only. Resource name of the AnnotationSpec.
updateTime	string (Timestamp format) Output only. Timestamp when AnnotationSpec was last updated.

GoogleCloudAiplatformV1beta1Artifact

Instance of a general artifact.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this Artifact was created.
description	string Description of the Artifact
displayName	string User provided display name of the Artifact. May be up to 128 Unicode characters.
etag	string An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your Artifacts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Artifact (System labels are excluded).
metadata	map (key: string, value: any) Properties of the Artifact. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.
name	string Output only. The resource name of the Artifact.
schemaTitle	string The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
schemaVersion	string The version of the schema in schema_name to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
state	enum The state of this Artifact. This is a property of the Artifact, and does not imply or capture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines), and the system does not prescribe or check the validity of state transitions.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	Unspecified state for the Artifact.
PENDING	A state used by systems like Vertex AI Pipelines to indicate that the underlying data item represented by this Artifact is being created.
LIVE	A state indicating that the Artifact should exist, unless something external to the system deletes it.
updateTime	string (Timestamp format) Output only. Timestamp when this Artifact was last updated.
uri	string The uniform resource identifier of the artifact file. May be empty if there is no actual artifact file.

GoogleCloudAiplatformV1beta1AssignNotebookRuntimeOperationMetadata

Metadata information for NotebookService.AssignNotebookRuntime.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.
progressMessage	string A human-readable message that shows the intermediate progress details of NotebookRuntime.

GoogleCloudAiplatformV1beta1AssignNotebookRuntimeRequest

Request message for NotebookService.AssignNotebookRuntime.

Fields
notebookRuntime	object (GoogleCloudAiplatformV1beta1NotebookRuntime) Required. Provide runtime specific information (e.g. runtime owner, notebook id) used for NotebookRuntime assignment.
notebookRuntimeId	string Optional. User specified ID for the notebook runtime.
notebookRuntimeTemplate	string Required. The resource name of the NotebookRuntimeTemplate based on which a NotebookRuntime will be assigned (reuse or create a new one).

GoogleCloudAiplatformV1beta1Attribution

Attribution that explains a particular prediction output.

Fields
approximationError	number (double format) Output only. Error of feature_attributions caused by approximation used in the explanation method. Lower value means more precise attributions. * For Sampled Shapley attribution, increasing path_count might reduce the error. * For Integrated Gradients attribution, increasing step_count might reduce the error. * For XRAI attribution, increasing step_count might reduce the error. See this introduction for more information.
baselineOutputValue	number (double format) Output only. Model predicted output if the input instance is constructed from the baselines of all the features defined in ExplanationMetadata.inputs. The field name of the output is determined by the key in ExplanationMetadata.outputs. If the Model's predicted output has multiple dimensions (rank > 1), this is the value in the output located by output_index. If there are multiple baselines, their output values are averaged.
featureAttributions	any Output only. Attributions of each explained feature. Features are extracted from the prediction instances according to explanation metadata for inputs. The value is a struct, whose keys are the name of the feature. The values are how much the feature in the instance contributed to the predicted result. The format of the value is determined by the feature's input format: * If the feature is a scalar value, the attribution value is a floating number. * If the feature is an array of scalar values, the attribution value is an array. * If the feature is a struct, the attribution value is a struct. The keys in the attribution value struct are the same as the keys in the feature struct. The formats of the values in the attribution struct are determined by the formats of the values in the feature struct. The ExplanationMetadata.feature_attributions_schema_uri field, pointed to by the ExplanationSpec field of the Endpoint.deployed_models object, points to the schema file that describes the features and their attribution values (if it is populated).
instanceOutputValue	number (double format) Output only. Model predicted output on the corresponding explanation instance. The field name of the output is determined by the key in ExplanationMetadata.outputs. If the Model predicted output has multiple dimensions, this is the value in the output located by output_index.
outputDisplayName	string Output only. The display name of the output identified by output_index. For example, the predicted class name by a multi-classification Model. This field is only populated iff the Model predicts display names as a separate field along with the explained output. The predicted display name must has the same shape of the explained output, and can be located using output_index.
outputIndex[]	integer (int32 format) Output only. The index that locates the explained prediction output. If the prediction output is a scalar value, output_index is not populated. If the prediction output has multiple dimensions, the length of the output_index list is the same as the number of dimensions of the output. The i-th element in output_index is the element index of the i-th dimension of the output vector. Indices start from 0.
outputName	string Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs.

GoogleCloudAiplatformV1beta1AuthConfig

Auth configuration to run the extension.

Fields
apiKeyConfig	object (GoogleCloudAiplatformV1beta1AuthConfigApiKeyConfig) Config for API key auth.
authType	enum Type of auth scheme.
Enum type. Can be one of the following:
AUTH_TYPE_UNSPECIFIED	(No description provided)
NO_AUTH	No Auth.
API_KEY_AUTH	API Key Auth.
HTTP_BASIC_AUTH	HTTP Basic Auth.
GOOGLE_SERVICE_ACCOUNT_AUTH	Google Service Account Auth.
OAUTH	OAuth auth.
OIDC_AUTH	OpenID Connect (OIDC) Auth.
googleServiceAccountConfig	object (GoogleCloudAiplatformV1beta1AuthConfigGoogleServiceAccountConfig) Config for Google Service Account auth.
httpBasicAuthConfig	object (GoogleCloudAiplatformV1beta1AuthConfigHttpBasicAuthConfig) Config for HTTP Basic auth.
oauthConfig	object (GoogleCloudAiplatformV1beta1AuthConfigOauthConfig) Config for user oauth.
oidcConfig	object (GoogleCloudAiplatformV1beta1AuthConfigOidcConfig) Config for user OIDC auth.

GoogleCloudAiplatformV1beta1AuthConfigApiKeyConfig

Config for authentication with API key.

Fields
apiKeySecret	string Required. The name of the SecretManager secret version resource storing the API key. Format: projects/{project}/secrets/{secrete}/versions/{version} - If specified, the secretmanager.versions.access permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
httpElementLocation	enum Required. The location of the API key.
Enum type. Can be one of the following:
HTTP_IN_UNSPECIFIED	(No description provided)
HTTP_IN_QUERY	Element is in the HTTP request query.
HTTP_IN_HEADER	Element is in the HTTP request header.
HTTP_IN_PATH	Element is in the HTTP request path.
HTTP_IN_BODY	Element is in the HTTP request body.
HTTP_IN_COOKIE	Element is in the HTTP request cookie.
name	string Required. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.

GoogleCloudAiplatformV1beta1AuthConfigGoogleServiceAccountConfig

Config for Google Service Account Authentication.

Fields

serviceAccount

string Optional. The service account that the extension execution service runs as. - If the service account is specified, the iam.serviceAccounts.getAccessToken permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.

GoogleCloudAiplatformV1beta1AuthConfigHttpBasicAuthConfig

Config for HTTP Basic Authentication.

Fields
credentialSecret	string Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: projects/{project}/secrets/{secrete}/versions/{version} - If specified, the secretmanager.versions.access permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.

GoogleCloudAiplatformV1beta1AuthConfigOauthConfig

Config for user oauth.

Fields
accessToken	string Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
serviceAccount	string The service account used to generate access tokens for executing the Extension. - If the service account is specified, the iam.serviceAccounts.getAccessToken permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.

GoogleCloudAiplatformV1beta1AuthConfigOidcConfig

Config for user OIDC auth.

Fields
idToken	string OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
serviceAccount	string The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant iam.serviceAccounts.getOpenIdToken permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).

GoogleCloudAiplatformV1beta1AutomaticResources

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines.

Fields
maxReplicaCount	integer (int32 format) Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.
minReplicaCount	integer (int32 format) Immutable. The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

GoogleCloudAiplatformV1beta1AutoscalingMetricSpec

The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.

Fields
metricName	string Required. The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
target	integer (int32 format) The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.

GoogleCloudAiplatformV1beta1AvroSource

The storage details for Avro input content.

Fields
gcsSource	object (GoogleCloudAiplatformV1beta1GcsSource) Required. Google Cloud Storage location.

GoogleCloudAiplatformV1beta1BatchCancelPipelineJobsRequest

Request message for PipelineService.BatchCancelPipelineJobs.

Fields
names[]	string Required. The names of the PipelineJobs to cancel. A maximum of 32 PipelineJobs can be cancelled in a batch. Format: projects/{project}/locations/{location}/pipelineJobs/{pipelineJob}

GoogleCloudAiplatformV1beta1BatchCancelPipelineJobsResponse

Response message for PipelineService.BatchCancelPipelineJobs.

Fields
pipelineJobs[]	object (GoogleCloudAiplatformV1beta1PipelineJob) PipelineJobs cancelled.

GoogleCloudAiplatformV1beta1BatchCreateFeaturesOperationMetadata

Details of operations that perform batch create Features.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Feature.

GoogleCloudAiplatformV1beta1BatchCreateFeaturesRequest

Request message for FeaturestoreService.BatchCreateFeatures.

Fields
requests[]	object (GoogleCloudAiplatformV1beta1CreateFeatureRequest) Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType. The parent field in each child request message can be omitted. If parent is set in a child request, then the value must match the parent value in this request message.

GoogleCloudAiplatformV1beta1BatchCreateFeaturesResponse

Response message for FeaturestoreService.BatchCreateFeatures.

Fields
features[]	object (GoogleCloudAiplatformV1beta1Feature) The Features created.

GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsRequest

Request message for TensorboardService.BatchCreateTensorboardRuns.

Fields
requests[]	object (GoogleCloudAiplatformV1beta1CreateTensorboardRunRequest) Required. The request message specifying the TensorboardRuns to create. A maximum of 1000 TensorboardRuns can be created in a batch.

GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsResponse

Response message for TensorboardService.BatchCreateTensorboardRuns.

Fields
tensorboardRuns[]	object (GoogleCloudAiplatformV1beta1TensorboardRun) The created TensorboardRuns.

GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesRequest

Request message for TensorboardService.BatchCreateTensorboardTimeSeries.

Fields
requests[]	object (GoogleCloudAiplatformV1beta1CreateTensorboardTimeSeriesRequest) Required. The request message specifying the TensorboardTimeSeries to create. A maximum of 1000 TensorboardTimeSeries can be created in a batch.

GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesResponse

Response message for TensorboardService.BatchCreateTensorboardTimeSeries.

Fields
tensorboardTimeSeries[]	object (GoogleCloudAiplatformV1beta1TensorboardTimeSeries) The created TensorboardTimeSeries.

GoogleCloudAiplatformV1beta1BatchDedicatedResources

A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.

Fields
machineSpec	object (GoogleCloudAiplatformV1beta1MachineSpec) Required. Immutable. The specification of a single machine.
maxReplicaCount	integer (int32 format) Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
startingReplicaCount	integer (int32 format) Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

GoogleCloudAiplatformV1beta1BatchDeletePipelineJobsRequest

Request message for PipelineService.BatchDeletePipelineJobs.

Fields
names[]	string Required. The names of the PipelineJobs to delete. A maximum of 32 PipelineJobs can be deleted in a batch. Format: projects/{project}/locations/{location}/pipelineJobs/{pipelineJob}

GoogleCloudAiplatformV1beta1BatchDeletePipelineJobsResponse

Response message for PipelineService.BatchDeletePipelineJobs.

Fields
pipelineJobs[]	object (GoogleCloudAiplatformV1beta1PipelineJob) PipelineJobs deleted.

GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsRequest

Request message for ModelService.BatchImportEvaluatedAnnotations

Fields
evaluatedAnnotations[]	object (GoogleCloudAiplatformV1beta1EvaluatedAnnotation) Required. Evaluated annotations resource to be imported.

GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsResponse

Response message for ModelService.BatchImportEvaluatedAnnotations

Fields
importedEvaluatedAnnotationsCount	integer (int32 format) Output only. Number of EvaluatedAnnotations imported.

GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesRequest

Request message for ModelService.BatchImportModelEvaluationSlices

Fields
modelEvaluationSlices[]	object (GoogleCloudAiplatformV1beta1ModelEvaluationSlice) Required. Model evaluation slice resource to be imported.

GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesResponse

Response message for ModelService.BatchImportModelEvaluationSlices

Fields
importedModelEvaluationSlices[]	string Output only. List of imported ModelEvaluationSlice.name.

GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadata

Runtime operation information for MigrationService.BatchMigrateResources.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.
partialResults[]	object (GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult) Partial results that reflect the latest migration operation progress.

GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult

Represents a partial result in batch migration operation for one MigrateResourceRequest.

Fields
dataset	string Migrated dataset resource name.
error	object (GoogleRpcStatus) The error result of the migration request in case of failure.
model	string Migrated model resource name.
request	object (GoogleCloudAiplatformV1beta1MigrateResourceRequest) It's the same as the value in MigrateResourceRequest.migrate_resource_requests.

GoogleCloudAiplatformV1beta1BatchMigrateResourcesRequest

Request message for MigrationService.BatchMigrateResources.

Fields
migrateResourceRequests[]	object (GoogleCloudAiplatformV1beta1MigrateResourceRequest) Required. The request messages specifying the resources to migrate. They must be in the same location as the destination. Up to 50 resources can be migrated in one batch.

GoogleCloudAiplatformV1beta1BatchMigrateResourcesResponse

Response message for MigrationService.BatchMigrateResources.

Fields
migrateResourceResponses[]	object (GoogleCloudAiplatformV1beta1MigrateResourceResponse) Successfully migrated resources.

GoogleCloudAiplatformV1beta1BatchPredictionJob

A job that uses a Model to produce predictions on multiple input instances. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

Fields
completionStats	object (GoogleCloudAiplatformV1beta1CompletionStats) Output only. Statistics on completed and failed prediction instances.
createTime	string (Timestamp format) Output only. Time when the BatchPredictionJob was created.
dedicatedResources	object (GoogleCloudAiplatformV1beta1BatchDedicatedResources) The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
disableContainerLogging	boolean For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
displayName	string Required. The user-defined name of this BatchPredictionJob.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
endTime	string (Timestamp format) Output only. Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error	object (GoogleRpcStatus) Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
explanationSpec	object (GoogleCloudAiplatformV1beta1ExplanationSpec) Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
generateExplanation	boolean Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
inputConfig	object (GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig) Required. Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
instanceConfig	object (GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig) Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
labels	map (key: string, value: string) The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
manualBatchTuningParameters	object (GoogleCloudAiplatformV1beta1ManualBatchTuningParameters) Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
model	string The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
modelMonitoringConfig	object (GoogleCloudAiplatformV1beta1ModelMonitoringConfig) Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
modelMonitoringStatsAnomalies[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomalies) Get batch prediction job monitoring statistics.
modelMonitoringStatus	object (GoogleRpcStatus) Output only. The running status of the model monitoring pipeline.
modelParameters	any The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
modelVersionId	string Output only. The version ID of the Model that produces the predictions via this job.
name	string Output only. Resource name of the BatchPredictionJob.
outputConfig	object (GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig) Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
outputInfo	object (GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfo) Output only. Information further describing the output of this job.
partialFailures[]	object (GoogleRpcStatus) Output only. Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
resourcesConsumed	object (GoogleCloudAiplatformV1beta1ResourcesConsumed) Output only. Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
serviceAccount	string The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
startTime	string (Timestamp format) Output only. Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
state	enum Output only. The detailed state of the job.
Enum type. Can be one of the following:
JOB_STATE_UNSPECIFIED	The job state is unspecified.
JOB_STATE_QUEUED	The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING	The service is preparing to run the job.
JOB_STATE_RUNNING	The job is in progress.
JOB_STATE_SUCCEEDED	The job completed successfully.
JOB_STATE_FAILED	The job failed.
JOB_STATE_CANCELLING	The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED	The job has been cancelled.
JOB_STATE_PAUSED	The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED	The job has expired.
JOB_STATE_UPDATING	The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED	The job is partially succeeded, some results may be missing due to errors.
unmanagedContainerModel	object (GoogleCloudAiplatformV1beta1UnmanagedContainerModel) Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
updateTime	string (Timestamp format) Output only. Time when the BatchPredictionJob was most recently updated.

GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig

Configures the input to BatchPredictionJob. See Model.supported_input_storage_formats for Model's supported input formats, and how instances should be expressed via any of them.

Fields
bigquerySource	object (GoogleCloudAiplatformV1beta1BigQuerySource) The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcsSource	object (GoogleCloudAiplatformV1beta1GcsSource) The Cloud Storage location for the input instances.
instancesFormat	string Required. The format in which instances are given, must be one of the Model's supported_input_storage_formats.

GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig

Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.

Fields
excludedFields[]	string Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, BigQuery or TfRecord.
includedFields[]	string Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, BigQuery or TfRecord.
instanceType	string The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
keyField	string The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig

Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.

Fields
bigqueryDestination	object (GoogleCloudAiplatformV1beta1BigQueryDestination) The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcsDestination	object (GoogleCloudAiplatformV1beta1GcsDestination) The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where ` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additionalerrors_0001.,errors_0002.,...,errors_N.files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additionalerrorfield which as value has google.rpc.Status containing onlycodeandmessage` fields.
predictionsFormat	string Required. The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.

GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfo

Further describes this job's output. Supplements output_config.

Fields
bigqueryOutputDataset	string Output only. The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
bigqueryOutputTable	string Output only. The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
gcsOutputDirectory	string Output only. The full path of the Cloud Storage directory created, into which the prediction output is written.

GoogleCloudAiplatformV1beta1BatchReadFeatureValuesOperationMetadata

Details of operations that batch reads Feature values.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Featurestore batch read Features values.

GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequest

Request message for FeaturestoreService.BatchReadFeatureValues.

Fields
bigqueryReadInstances	object (GoogleCloudAiplatformV1beta1BigQuerySource) Similar to csv_read_instances, but from BigQuery source.
csvReadInstances	object (GoogleCloudAiplatformV1beta1CsvSource) Each read instance consists of exactly one read timestamp and one or more entity IDs identifying entities of the corresponding EntityTypes whose Features are requested. Each output instance contains Feature values of requested entities concatenated together as of the read time. An example read instance may be foo_entity_id, bar_entity_id, 2020-01-01T10:00:00.123Z. An example output instance may be foo_entity_id, bar_entity_id, 2020-01-01T10:00:00.123Z, foo_entity_feature1_value, bar_entity_feature2_value. Timestamp in each read instance must be millisecond-aligned. csv_read_instances are read instances stored in a plain-text CSV file. The header should be: [ENTITY_TYPE_ID1], [ENTITY_TYPE_ID2], ..., timestamp The columns can be in any order. Values in the timestamp column must use the RFC 3339 format, e.g. 2012-07-30T10:43:17.123Z.
destination	object (GoogleCloudAiplatformV1beta1FeatureValueDestination) Required. Specifies output location and format.
entityTypeSpecs[]	object (GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestEntityTypeSpec) Required. Specifies EntityType grouping Features to read values of and settings.
passThroughFields[]	object (GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestPassThroughField) When not empty, the specified fields in the *_read_instances source will be joined as-is in the output, in addition to those fields from the Featurestore Entity. For BigQuery source, the type of the pass-through values will be automatically inferred. For CSV source, the pass-through values will be passed as opaque bytes.
startTime	string (Timestamp format) Optional. Excludes Feature values with feature generation timestamp before this timestamp. If not set, retrieve oldest values kept in Feature Store. Timestamp, if present, must not have higher than millisecond precision.

GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestEntityTypeSpec

Selects Features of an EntityType to read values of and specifies read settings.

Fields
entityTypeId	string Required. ID of the EntityType to select Features. The EntityType id is the entity_type_id specified during EntityType creation.
featureSelector	object (GoogleCloudAiplatformV1beta1FeatureSelector) Required. Selectors choosing which Feature values to read from the EntityType.
settings[]	object (GoogleCloudAiplatformV1beta1DestinationFeatureSetting) Per-Feature settings for the batch read.

GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestPassThroughField

Describe pass-through fields in read_instance source.

Fields
fieldName	string Required. The name of the field in the CSV header or the name of the column in BigQuery table. The naming restriction is the same as Feature.name.

GoogleCloudAiplatformV1beta1BatchReadTensorboardTimeSeriesDataResponse

Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.

Fields
timeSeriesData[]	object (GoogleCloudAiplatformV1beta1TimeSeriesData) The returned time series data.

GoogleCloudAiplatformV1beta1BigQueryDestination

The BigQuery location for the output content.

Fields
outputUri	string Required. BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.

GoogleCloudAiplatformV1beta1BigQuerySource

The BigQuery location for the input content.

Fields
inputUri	string Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

GoogleCloudAiplatformV1beta1BleuInput

Input for bleu metric.

Fields
instances[]	object (GoogleCloudAiplatformV1beta1BleuInstance) Required. Repeated bleu instances.
metricSpec	object (GoogleCloudAiplatformV1beta1BleuSpec) Required. Spec for bleu score metric.

GoogleCloudAiplatformV1beta1BleuInstance

Spec for bleu instance.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Required. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1BleuMetricValue

Bleu metric value for an instance.

Fields
score	number (float format) Output only. Bleu score.

GoogleCloudAiplatformV1beta1BleuResults

Results for bleu metric.

Fields
bleuMetricValues[]	object (GoogleCloudAiplatformV1beta1BleuMetricValue) Output only. Bleu metric values.

GoogleCloudAiplatformV1beta1Blob

Content blob. It's preferred to send as text directly rather than raw bytes.

Fields
data	string (bytes format) Required. Raw bytes.
mimeType	string Required. The IANA standard MIME type of the source data.

GoogleCloudAiplatformV1beta1BlurBaselineConfig

Config for blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

Fields
maxBlurSigma	number (float format) The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.

GoogleCloudAiplatformV1beta1BoolArray

A list of boolean values.

Fields
values[]	boolean A list of bool values.

GoogleCloudAiplatformV1beta1CacheConfig

Config of GenAI caching features. This is a singleton resource.

Fields
disableCache	boolean If set to true, disables GenAI caching. Otherwise caching is enabled.
name	string Identifier. Name of the cache config. Format: - projects/{project}/cacheConfig.

GoogleCloudAiplatformV1beta1Candidate

A response candidate generated from the model.

Fields
citationMetadata	object (GoogleCloudAiplatformV1beta1CitationMetadata) Output only. Source attribution of the generated content.
content	object (GoogleCloudAiplatformV1beta1Content) Output only. Content parts of the candidate.
finishMessage	string Output only. Describes the reason the mode stopped generating tokens in more detail. This is only filled when finish_reason is set.
finishReason	enum Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.
Enum type. Can be one of the following:
FINISH_REASON_UNSPECIFIED	The finish reason is unspecified.
STOP	Natural stop point of the model or provided stop sequence.
MAX_TOKENS	The maximum number of tokens as specified in the request was reached.
SAFETY	The token generation was stopped as the response was flagged for safety reasons. NOTE: When streaming the Candidate.content will be empty if content filters blocked the output.
RECITATION	The token generation was stopped as the response was flagged for unauthorized citations.
OTHER	All other reasons that stopped the token generation
BLOCKLIST	The token generation was stopped as the response was flagged for the terms which are included from the terminology blocklist.
PROHIBITED_CONTENT	The token generation was stopped as the response was flagged for the prohibited contents.
SPII	The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents.
groundingMetadata	object (GoogleCloudAiplatformV1beta1GroundingMetadata) Output only. Metadata specifies sources used to ground generated content.
index	integer (int32 format) Output only. Index of the candidate.
safetyRatings[]	object (GoogleCloudAiplatformV1beta1SafetyRating) Output only. List of ratings for the safety of a response candidate. There is at most one rating per category.

GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateMetatdata

This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for suggesting Trials.
study	string The name of the Study that the Trial belongs to.
trial	string The Trial name.

GoogleCloudAiplatformV1beta1CheckTrialEarlyStoppingStateResponse

Response message for VizierService.CheckTrialEarlyStoppingState.

Fields
shouldStop	boolean True if the Trial should stop.

GoogleCloudAiplatformV1beta1Citation

Source attributions for content.

Fields
endIndex	integer (int32 format) Output only. End index into the content.
license	string Output only. License of the attribution.
publicationDate	object (GoogleTypeDate) Output only. Publication date of the attribution.
startIndex	integer (int32 format) Output only. Start index into the content.
title	string Output only. Title of the attribution.
uri	string Output only. Url reference of the attribution.

GoogleCloudAiplatformV1beta1CitationMetadata

A collection of source attributions for a piece of content.

Fields
citations[]	object (GoogleCloudAiplatformV1beta1Citation) Output only. List of citations.

GoogleCloudAiplatformV1beta1CoherenceInput

Input for coherence metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1CoherenceInstance) Required. Coherence instance.
metricSpec	object (GoogleCloudAiplatformV1beta1CoherenceSpec) Required. Spec for coherence score metric.

GoogleCloudAiplatformV1beta1CoherenceInstance

Spec for coherence instance.

Fields
prediction	string Required. Output of the evaluated model.

GoogleCloudAiplatformV1beta1CoherenceResult

Spec for coherence result.

Fields
confidence	number (float format) Output only. Confidence for coherence score.
explanation	string Output only. Explanation for coherence score.
score	number (float format) Output only. Coherence score.

GoogleCloudAiplatformV1beta1CoherenceSpec

Spec for coherence score metric.

Fields
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1CompleteTrialRequest

Request message for VizierService.CompleteTrial.

Fields
finalMeasurement	object (GoogleCloudAiplatformV1beta1Measurement) 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
infeasibleReason	string Optional. A human readable reason why the trial was infeasible. This should only be provided if trial_infeasible is true.
trialInfeasible	boolean Optional. True if the Trial cannot be run with the given Parameter, and final_measurement will be ignored.

GoogleCloudAiplatformV1beta1CompletionStats

Success and error statistics of processing multiple entities (for example, DataItems or structured data rows) in batch.

Fields
failedCount	string (int64 format) Output only. The number of entities for which any error was encountered.
incompleteCount	string (int64 format) Output only. In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
successfulCount	string (int64 format) Output only. The number of entities that had been processed successfully.
successfulForecastPointCount	string (int64 format) Output only. The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.

GoogleCloudAiplatformV1beta1ComputeTokensRequest

Request message for ComputeTokens RPC call.

Fields
instances[]	any Required. The instances that are the input to token computing API call. Schema is identical to the prediction schema of the text model, even for the non-text models, like chat models, or Codey models.

GoogleCloudAiplatformV1beta1ComputeTokensResponse

Response message for ComputeTokens RPC call.

Fields
tokensInfo[]	object (GoogleCloudAiplatformV1beta1TokensInfo) Lists of tokens info from the input. A ComputeTokensRequest could have multiple instances with a prompt in each instance. We also need to return lists of tokens info for the request with multiple instances.

GoogleCloudAiplatformV1beta1ContainerRegistryDestination

The Container Registry location for the container image.

Fields
outputUri	string Required. Container Registry URI of a container image. Only Google Container Registry and Artifact Registry are supported now. Accepted forms: * Google Container Registry path. For example: gcr.io/projectId/imageName:tag. * Artifact Registry path. For example: us-central1-docker.pkg.dev/projectId/repoName/imageName:tag. If a tag is not specified, "latest" will be used as the default tag.

GoogleCloudAiplatformV1beta1ContainerSpec

The spec of a Container.

Fields
args[]	string The arguments to be passed when starting the container.
command[]	string The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
env[]	object (GoogleCloudAiplatformV1beta1EnvVar) Environment variables to be passed to the container. Maximum limit is 100.
imageUri	string Required. The URI of a container image in the Container Registry that is to be run on each worker replica.

GoogleCloudAiplatformV1beta1Content

The base structured datatype containing multi-part content of a message. A Content includes a role field designating the producer of the Content and a parts field containing multi-part data that contains the content of the message turn.

Fields
parts[]	object (GoogleCloudAiplatformV1beta1Part) Required. Ordered Parts that constitute a single message. Parts may have different IANA MIME types.
role	string Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.

GoogleCloudAiplatformV1beta1Context

Instance of a general context.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this Context was created.
description	string Description of the Context
displayName	string User provided display name of the Context. May be up to 128 Unicode characters.
etag	string An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your Contexts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Context (System labels are excluded).
metadata	map (key: string, value: any) Properties of the Context. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.
name	string Immutable. The resource name of the Context.
parentContexts[]	string Output only. A list of resource names of Contexts that are parents of this Context. A Context may have at most 10 parent_contexts.
schemaTitle	string The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
schemaVersion	string The version of the schema in schema_name to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
updateTime	string (Timestamp format) Output only. Timestamp when this Context was last updated.

GoogleCloudAiplatformV1beta1CopyModelOperationMetadata

Details of ModelService.CopyModel operation.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.

GoogleCloudAiplatformV1beta1CopyModelRequest

Request message for ModelService.CopyModel.

Fields
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key options. If this is set, then the Model copy will be encrypted with the provided encryption key.
modelId	string Optional. Copy source_model into a new Model with this ID. The ID will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
parentModel	string Optional. Specify this field to copy source_model into this existing Model as a new version. Format: projects/{project}/locations/{location}/models/{model}
sourceModel	string Required. The resource name of the Model to copy. That Model must be in the same Project. Format: projects/{project}/locations/{location}/models/{model}

GoogleCloudAiplatformV1beta1CopyModelResponse

Response message of ModelService.CopyModel operation.

Fields
model	string The name of the copied Model resource. Format: projects/{project}/locations/{location}/models/{model}
modelVersionId	string Output only. The version ID of the model that is copied.

GoogleCloudAiplatformV1beta1CountTokensRequest

Request message for PredictionService.CountTokens.

Fields
contents[]	object (GoogleCloudAiplatformV1beta1Content) Required. Input content.
instances[]	any Required. The instances that are the input to token counting call. Schema is identical to the prediction schema of the underlying model.
model	string Required. The name of the publisher model requested to serve the prediction. Format: projects/{project}/locations/{location}/publishers/*/models/*

GoogleCloudAiplatformV1beta1CountTokensResponse

Response message for PredictionService.CountTokens.

Fields
totalBillableCharacters	integer (int32 format) The total number of billable characters counted across all instances from the request.
totalTokens	integer (int32 format) The total number of tokens counted across all instances from the request.

GoogleCloudAiplatformV1beta1CreateDatasetOperationMetadata

Runtime operation information for DatasetService.CreateDataset.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1CreateDatasetVersionOperationMetadata

Runtime operation information for DatasetService.CreateDatasetVersion.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.

GoogleCloudAiplatformV1beta1CreateDeploymentResourcePoolOperationMetadata

Runtime operation information for CreateDeploymentResourcePool method.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1CreateDeploymentResourcePoolRequest

Request message for CreateDeploymentResourcePool method.

Fields
deploymentResourcePool	object (GoogleCloudAiplatformV1beta1DeploymentResourcePool) Required. The DeploymentResourcePool to create.
deploymentResourcePoolId	string Required. The ID to use for the DeploymentResourcePool, which will become the final component of the DeploymentResourcePool's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.

GoogleCloudAiplatformV1beta1CreateEndpointOperationMetadata

Runtime operation information for EndpointService.CreateEndpoint.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1CreateEntityTypeOperationMetadata

Details of operations that perform create EntityType.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for EntityType.

GoogleCloudAiplatformV1beta1CreateExtensionControllerOperationMetadata

Details of ExtensionControllerService.CreateExtensionController operation.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.

GoogleCloudAiplatformV1beta1CreateFeatureGroupOperationMetadata

Details of operations that perform create FeatureGroup.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for FeatureGroup.

GoogleCloudAiplatformV1beta1CreateFeatureOnlineStoreOperationMetadata

Details of operations that perform create FeatureOnlineStore.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for FeatureOnlineStore.

GoogleCloudAiplatformV1beta1CreateFeatureOperationMetadata

Details of operations that perform create Feature.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Feature.

GoogleCloudAiplatformV1beta1CreateFeatureRequest

Request message for FeaturestoreService.CreateFeature. Request message for FeatureRegistryService.CreateFeature.

Fields
feature	object (GoogleCloudAiplatformV1beta1Feature) Required. The Feature to create.
featureId	string Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.
parent	string Required. The resource name of the EntityType or FeatureGroup to create a Feature. Format for entity_type as parent: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} Format for feature_group as parent: projects/{project}/locations/{location}/featureGroups/{feature_group}

GoogleCloudAiplatformV1beta1CreateFeatureViewOperationMetadata

Details of operations that perform create FeatureView.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for FeatureView Create.

GoogleCloudAiplatformV1beta1CreateFeaturestoreOperationMetadata

Details of operations that perform create Featurestore.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Featurestore.

GoogleCloudAiplatformV1beta1CreateIndexEndpointOperationMetadata

Runtime operation information for IndexEndpointService.CreateIndexEndpoint.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1CreateIndexOperationMetadata

Runtime operation information for IndexService.CreateIndex.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.
nearestNeighborSearchOperationMetadata	object (GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadata) The operation metadata with regard to Matching Engine Index operation.

GoogleCloudAiplatformV1beta1CreateMetadataStoreOperationMetadata

Details of operations that perform MetadataService.CreateMetadataStore.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for creating a MetadataStore.

GoogleCloudAiplatformV1beta1CreateModelMonitorOperationMetadata

Runtime operation information for ModelMonitoringService.CreateModelMonitor.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1CreateModelMonitoringJobRequest

Request message for ModelMonitoringService.CreateModelMonitoringJob.

Fields
modelMonitoringJob	object (GoogleCloudAiplatformV1beta1ModelMonitoringJob) Required. The ModelMonitoringJob to create
modelMonitoringJobId	string Optional. The ID to use for the Model Monitoring Job, which will become the final component of the model monitoring job resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.
parent	string Required. The parent of the ModelMonitoringJob. Format: projects/{project}/locations/{location}/modelMoniitors/{model_monitor}

GoogleCloudAiplatformV1beta1CreateNotebookExecutionJobRequest

Request message for [NotebookService.CreateNotebookExecutionJob]

Fields
notebookExecutionJob	object (GoogleCloudAiplatformV1beta1NotebookExecutionJob) Required. The NotebookExecutionJob to create.
notebookExecutionJobId	string Optional. User specified ID for the NotebookExecutionJob.
parent	string Required. The resource name of the Location to create the NotebookExecutionJob. Format: projects/{project}/locations/{location}

GoogleCloudAiplatformV1beta1CreateNotebookRuntimeTemplateOperationMetadata

Metadata information for NotebookService.CreateNotebookRuntimeTemplate.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1CreatePersistentResourceOperationMetadata

Details of operations that perform create PersistentResource.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for PersistentResource.
progressMessage	string Progress Message for Create LRO

GoogleCloudAiplatformV1beta1CreatePipelineJobRequest

Request message for PipelineService.CreatePipelineJob.

Fields
parent	string Required. The resource name of the Location to create the PipelineJob in. Format: projects/{project}/locations/{location}
pipelineJob	object (GoogleCloudAiplatformV1beta1PipelineJob) Required. The PipelineJob to create.
pipelineJobId	string The ID to use for the PipelineJob, which will become the final component of the PipelineJob name. If not provided, an ID will be automatically generated. This value should be less than 128 characters, and valid characters are /a-z-/.

GoogleCloudAiplatformV1beta1CreateRegistryFeatureOperationMetadata

Details of operations that perform create FeatureGroup.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Feature.

GoogleCloudAiplatformV1beta1CreateSolverOperationMetadata

Runtime operation information for SolverService.CreateSolver.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The generic operation information.

GoogleCloudAiplatformV1beta1CreateSpecialistPoolOperationMetadata

Runtime operation information for SpecialistPoolService.CreateSpecialistPool.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1CreateTensorboardOperationMetadata

Details of operations that perform create Tensorboard.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Tensorboard.

GoogleCloudAiplatformV1beta1CreateTensorboardRunRequest

Request message for TensorboardService.CreateTensorboardRun.

Fields
parent	string Required. The resource name of the TensorboardExperiment to create the TensorboardRun in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}
tensorboardRun	object (GoogleCloudAiplatformV1beta1TensorboardRun) Required. The TensorboardRun to create.
tensorboardRunId	string Required. The ID to use for the Tensorboard run, which becomes the final component of the Tensorboard run's resource name. This value should be 1-128 characters, and valid characters are /a-z-/.

GoogleCloudAiplatformV1beta1CreateTensorboardTimeSeriesRequest

Request message for TensorboardService.CreateTensorboardTimeSeries.

Fields
parent	string Required. The resource name of the TensorboardRun to create the TensorboardTimeSeries in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}
tensorboardTimeSeries	object (GoogleCloudAiplatformV1beta1TensorboardTimeSeries) Required. The TensorboardTimeSeries to create.
tensorboardTimeSeriesId	string Optional. The user specified unique ID to use for the TensorboardTimeSeries, which becomes the final component of the TensorboardTimeSeries's resource name. This value should match "a-z0-9{0, 127}"

GoogleCloudAiplatformV1beta1CsvDestination

The storage details for CSV output content.

Fields
gcsDestination	object (GoogleCloudAiplatformV1beta1GcsDestination) Required. Google Cloud Storage location.

GoogleCloudAiplatformV1beta1CsvSource

The storage details for CSV input content.

Fields
gcsSource	object (GoogleCloudAiplatformV1beta1GcsSource) Required. Google Cloud Storage location.

GoogleCloudAiplatformV1beta1CustomJob

Represents a job that runs custom workloads such as a Docker container or a Python package. A CustomJob can have multiple worker pools and each worker pool can have its own machine and input spec. A CustomJob will be cleaned up once the job enters terminal state (failed or succeeded).

Fields
createTime	string (Timestamp format) Output only. Time when the CustomJob was created.
displayName	string Required. The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.
endTime	string (Timestamp format) Output only. Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error	object (GoogleRpcStatus) Output only. Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
jobSpec	object (GoogleCloudAiplatformV1beta1CustomJobSpec) Required. Job spec.
labels	map (key: string, value: string) The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
name	string Output only. Resource name of a CustomJob.
startTime	string (Timestamp format) Output only. Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.
state	enum Output only. The detailed state of the job.
Enum type. Can be one of the following:
JOB_STATE_UNSPECIFIED	The job state is unspecified.
JOB_STATE_QUEUED	The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING	The service is preparing to run the job.
JOB_STATE_RUNNING	The job is in progress.
JOB_STATE_SUCCEEDED	The job completed successfully.
JOB_STATE_FAILED	The job failed.
JOB_STATE_CANCELLING	The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED	The job has been cancelled.
JOB_STATE_PAUSED	The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED	The job has expired.
JOB_STATE_UPDATING	The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED	The job is partially succeeded, some results may be missing due to errors.
updateTime	string (Timestamp format) Output only. Time when the CustomJob was most recently updated.
webAccessUris	map (key: string, value: string) Output only. URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; 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.

GoogleCloudAiplatformV1beta1CustomJobSpec

Represents the spec of a CustomJob.

Fields
baseOutputDirectory	object (GoogleCloudAiplatformV1beta1GcsDestination) The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
enableDashboardAccess	boolean Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
enableWebAccess	boolean Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
experiment	string Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experimentRun	string Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
models[]	string Optional. The name of the Model resources for which to generate a mapping to artifact URIs. Applicable only to some of the Google-provided custom jobs. Format: projects/{project}/locations/{location}/models/{model} In order to retrieve a specific version of the model, also provide the version ID or version alias. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden If no version ID or alias is specified, the "default" version will be returned. The "default" version alias is created for the first version of the model, and can be moved to other versions later on. There will be exactly one default version.
network	string Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
persistentResourceId	string Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.
protectedArtifactLocationId	string The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
reservedIpRanges[]	string Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
scheduling	object (GoogleCloudAiplatformV1beta1Scheduling) Scheduling options for a CustomJob.
serviceAccount	string Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
tensorboard	string Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
workerPoolSpecs[]	object (GoogleCloudAiplatformV1beta1WorkerPoolSpec) Required. The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

GoogleCloudAiplatformV1beta1DataItem

A piece of data in a Dataset. Could be an image, a video, a document or plain text.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this DataItem was created.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) Optional. The labels with user-defined metadata to organize your DataItems. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one DataItem(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
name	string Output only. The resource name of the DataItem.
payload	any Required. The data that the DataItem represents (for example, an image or a text snippet). The schema of the payload is stored in the parent Dataset's metadata schema's dataItemSchemaUri field.
updateTime	string (Timestamp format) Output only. Timestamp when this DataItem was last updated.

GoogleCloudAiplatformV1beta1DataItemView

A container for a single DataItem and Annotations on it.

Fields
annotations[]	object (GoogleCloudAiplatformV1beta1Annotation) The Annotations on the DataItem. If too many Annotations should be returned for the DataItem, this field will be truncated per annotations_limit in request. If it was, then the has_truncated_annotations will be set to true.
dataItem	object (GoogleCloudAiplatformV1beta1DataItem) The DataItem.
hasTruncatedAnnotations	boolean True if and only if the Annotations field has been truncated. It happens if more Annotations for this DataItem met the request's annotation_filter than are allowed to be returned by annotations_limit. Note that if Annotations field is not being returned due to field mask, then this field will not be set to true no matter how many Annotations are there.

GoogleCloudAiplatformV1beta1DataLabelingJob

DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:

Fields
activeLearningConfig	object (GoogleCloudAiplatformV1beta1ActiveLearningConfig) Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
annotationLabels	map (key: string, value: string) Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
createTime	string (Timestamp format) Output only. Timestamp when this DataLabelingJob was created.
currentSpend	object (GoogleTypeMoney) Output only. Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
datasets[]	string Required. Dataset resource names. Right now we only support labeling from a single Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}
displayName	string Required. The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
error	object (GoogleRpcStatus) Output only. DataLabelingJob errors. It is only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
inputs	any Required. Input config parameters for the DataLabelingJob.
inputsSchemaUri	string Required. Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
instructionUri	string Required. The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
labelerCount	integer (int32 format) Required. Number of labelers to work on each DataItem.
labelingProgress	integer (int32 format) Output only. Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
name	string Output only. Resource name of the DataLabelingJob.
specialistPools[]	string The SpecialistPools' resource names associated with this job.
state	enum Output only. The detailed state of the job.
Enum type. Can be one of the following:
JOB_STATE_UNSPECIFIED	The job state is unspecified.
JOB_STATE_QUEUED	The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING	The service is preparing to run the job.
JOB_STATE_RUNNING	The job is in progress.
JOB_STATE_SUCCEEDED	The job completed successfully.
JOB_STATE_FAILED	The job failed.
JOB_STATE_CANCELLING	The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED	The job has been cancelled.
JOB_STATE_PAUSED	The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED	The job has expired.
JOB_STATE_UPDATING	The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED	The job is partially succeeded, some results may be missing due to errors.
updateTime	string (Timestamp format) Output only. Timestamp when this DataLabelingJob was updated most recently.

GoogleCloudAiplatformV1beta1Dataset

A collection of DataItems and Annotations on them.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this Dataset was created.
dataItemCount	string (int64 format) Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
description	string The description of the Dataset.
displayName	string Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your Datasets. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
metadata	any Required. Additional information about the Dataset.
metadataArtifact	string Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.
metadataSchemaUri	string Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
modelReference	string Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
name	string Output only. The resource name of the Dataset.
savedQueries[]	object (GoogleCloudAiplatformV1beta1SavedQuery) All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
updateTime	string (Timestamp format) Output only. Timestamp when this Dataset was last updated.

GoogleCloudAiplatformV1beta1DatasetVersion

Describes the dataset version.

Fields
bigQueryDatasetName	string Output only. Name of the associated BigQuery dataset.
createTime	string (Timestamp format) Output only. Timestamp when this DatasetVersion was created.
displayName	string The user-defined name of the DatasetVersion. The name can be up to 128 characters long and can consist of any UTF-8 characters.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
metadata	any Required. Output only. Additional information about the DatasetVersion.
modelReference	string Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions.
name	string Output only. The resource name of the DatasetVersion.
updateTime	string (Timestamp format) Output only. Timestamp when this DatasetVersion was last updated.

GoogleCloudAiplatformV1beta1DedicatedResources

A description of resources that are dedicated to a DeployedModel, and that need a higher degree of manual configuration.

Fields
autoscalingMetricSpecs[]	object (GoogleCloudAiplatformV1beta1AutoscalingMetricSpec) Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
machineSpec	object (GoogleCloudAiplatformV1beta1MachineSpec) Required. Immutable. The specification of a single machine used by the prediction.
maxReplicaCount	integer (int32 format) Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
minReplicaCount	integer (int32 format) Required. Immutable. The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.

GoogleCloudAiplatformV1beta1DeleteFeatureValuesOperationMetadata

Details of operations that delete Feature values.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Featurestore delete Features values.

GoogleCloudAiplatformV1beta1DeleteFeatureValuesRequest

Request message for FeaturestoreService.DeleteFeatureValues.

Fields
selectEntity	object (GoogleCloudAiplatformV1beta1DeleteFeatureValuesRequestSelectEntity) Select feature values to be deleted by specifying entities.
selectTimeRangeAndFeature	object (GoogleCloudAiplatformV1beta1DeleteFeatureValuesRequestSelectTimeRangeAndFeature) Select feature values to be deleted by specifying time range and features.

GoogleCloudAiplatformV1beta1DeleteFeatureValuesRequestSelectEntity

Message to select entity. If an entity id is selected, all the feature values corresponding to the entity id will be deleted, including the entityId.

Fields
entityIdSelector	object (GoogleCloudAiplatformV1beta1EntityIdSelector) Required. Selectors choosing feature values of which entity id to be deleted from the EntityType.

GoogleCloudAiplatformV1beta1DeleteFeatureValuesRequestSelectTimeRangeAndFeature

Message to select time range and feature. Values of the selected feature generated within an inclusive time range will be deleted. Using this option permanently deletes the feature values from the specified feature IDs within the specified time range. This might include data from the online storage. If you want to retain any deleted historical data in the online storage, you must re-ingest it.

Fields
featureSelector	object (GoogleCloudAiplatformV1beta1FeatureSelector) Required. Selectors choosing which feature values to be deleted from the EntityType.
skipOnlineStorageDelete	boolean If set, data will not be deleted from online storage. When time range is older than the data in online storage, setting this to be true will make the deletion have no impact on online serving.
timeRange	object (GoogleTypeInterval) Required. Select feature generated within a half-inclusive time range. The time range is lower inclusive and upper exclusive.

GoogleCloudAiplatformV1beta1DeleteFeatureValuesResponse

Response message for FeaturestoreService.DeleteFeatureValues.

Fields
selectEntity	object (GoogleCloudAiplatformV1beta1DeleteFeatureValuesResponseSelectEntity) Response for request specifying the entities to delete
selectTimeRangeAndFeature	object (GoogleCloudAiplatformV1beta1DeleteFeatureValuesResponseSelectTimeRangeAndFeature) Response for request specifying time range and feature

GoogleCloudAiplatformV1beta1DeleteFeatureValuesResponseSelectEntity

Response message if the request uses the SelectEntity option.

Fields
offlineStorageDeletedEntityRowCount	string (int64 format) The count of deleted entity rows in the offline storage. Each row corresponds to the combination of an entity ID and a timestamp. One entity ID can have multiple rows in the offline storage.
onlineStorageDeletedEntityCount	string (int64 format) The count of deleted entities in the online storage. Each entity ID corresponds to one entity.

GoogleCloudAiplatformV1beta1DeleteFeatureValuesResponseSelectTimeRangeAndFeature

Response message if the request uses the SelectTimeRangeAndFeature option.

Fields
impactedFeatureCount	string (int64 format) The count of the features or columns impacted. This is the same as the feature count in the request.
offlineStorageModifiedEntityRowCount	string (int64 format) The count of modified entity rows in the offline storage. Each row corresponds to the combination of an entity ID and a timestamp. One entity ID can have multiple rows in the offline storage. Within each row, only the features specified in the request are deleted.
onlineStorageModifiedEntityCount	string (int64 format) The count of modified entities in the online storage. Each entity ID corresponds to one entity. Within each entity, only the features specified in the request are deleted.

GoogleCloudAiplatformV1beta1DeleteMetadataStoreOperationMetadata

Details of operations that perform MetadataService.DeleteMetadataStore.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for deleting a MetadataStore.

GoogleCloudAiplatformV1beta1DeleteOperationMetadata

Details of operations that perform deletes of any entities.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.

GoogleCloudAiplatformV1beta1DeployIndexOperationMetadata

Runtime operation information for IndexEndpointService.DeployIndex.

Fields
deployedIndexId	string The unique index id specified by user
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1DeployIndexRequest

Request message for IndexEndpointService.DeployIndex.

Fields
deployedIndex	object (GoogleCloudAiplatformV1beta1DeployedIndex) Required. The DeployedIndex to be created within the IndexEndpoint.

GoogleCloudAiplatformV1beta1DeployIndexResponse

Response message for IndexEndpointService.DeployIndex.

Fields
deployedIndex	object (GoogleCloudAiplatformV1beta1DeployedIndex) The DeployedIndex that had been deployed in the IndexEndpoint.

GoogleCloudAiplatformV1beta1DeployModelOperationMetadata

Runtime operation information for EndpointService.DeployModel.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1DeployModelRequest

Request message for EndpointService.DeployModel.

Fields
deployedModel	object (GoogleCloudAiplatformV1beta1DeployedModel) Required. The DeployedModel to be created within the Endpoint. Note that Endpoint.traffic_split must be updated for the DeployedModel to start receiving traffic, either as part of this call, or via EndpointService.UpdateEndpoint.
trafficSplit	map (key: string, value: integer (int32 format)) A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If this field is non-empty, then the Endpoint's traffic_split will be overwritten with it. To refer to the ID of the just being deployed Model, a "0" should be used, and the actual ID of the new DeployedModel will be filled in its place by this method. The traffic percentage values must add up to 100. If this field is empty, then the Endpoint's traffic_split is not updated.

GoogleCloudAiplatformV1beta1DeployModelResponse

Response message for EndpointService.DeployModel.

Fields
deployedModel	object (GoogleCloudAiplatformV1beta1DeployedModel) The DeployedModel that had been deployed in the Endpoint.

GoogleCloudAiplatformV1beta1DeploySolverOperationMetadata

Runtime operation information for SolverService.DeploySolver.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The generic operation information.

GoogleCloudAiplatformV1beta1DeployedIndex

A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.

Fields
automaticResources	object (GoogleCloudAiplatformV1beta1AutomaticResources) Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Vertex AI, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
createTime	string (Timestamp format) Output only. Timestamp when the DeployedIndex was created.
dedicatedResources	object (GoogleCloudAiplatformV1beta1DedicatedResources) Optional. A description of resources that are dedicated to the DeployedIndex, and that need a higher degree of manual configuration. The field min_replica_count must be set to a value strictly greater than 0, or else validation will fail. We don't provide SLA when min_replica_count=1. If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000. Available machine types for SMALL shard: e2-standard-2 and all machine types available for MEDIUM and LARGE shard. Available machine types for MEDIUM shard: e2-standard-16 and all machine types available for LARGE shard. Available machine types for LARGE shard: e2-highmem-16, n2d-standard-32. n1-standard-16 and n1-standard-32 are still available, but we recommend e2-standard-16 and e2-highmem-16 for cost efficiency.
deployedIndexAuthConfig	object (GoogleCloudAiplatformV1beta1DeployedIndexAuthConfig) Optional. If set, the authentication is enabled for the private endpoint.
deploymentGroup	string Optional. The deployment group can be no longer than 64 characters (eg: 'test', 'prod'). If not set, we will use the 'default' deployment group. Creating deployment_groups with reserved_ip_ranges is a recommended practice when the peered network has multiple peering ranges. This creates your deployments from predictable IP spaces for easier traffic administration. Also, one deployment_group (except 'default') can only be used with the same reserved_ip_ranges which means if the deployment_group has been used with reserved_ip_ranges: [a, b, c], using it with [a, b] or [d, e] is disallowed. Note: we only support up to 5 deployment groups(not including 'default').
displayName	string The display name of the DeployedIndex. If not provided upon creation, the Index's display_name is used.
enableAccessLogging	boolean Optional. If true, private endpoint's access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each MatchRequest. Note that logs may incur a cost, especially if the deployed index receives a high queries per second rate (QPS). Estimate your costs before enabling this option.
id	string Required. The user specified ID of the DeployedIndex. The ID can be up to 128 characters long and must start with a letter and only contain letters, numbers, and underscores. The ID must be unique within the project it is created in.
index	string Required. The name of the Index this is the deployment of. We may refer to this Index as the DeployedIndex's "original" Index.
indexSyncTime	string (Timestamp format) Output only. The DeployedIndex may depend on various data on its original Index. Additionally when certain changes to the original Index are being done (e.g. when what the Index contains is being changed) the DeployedIndex may be asynchronously updated in the background to reflect these changes. If this timestamp's value is at least the Index.update_time of the original Index, it means that this DeployedIndex and the original Index are in sync. If this timestamp is older, then to see which updates this DeployedIndex already contains (and which it does not), one must list the operations that are running on the original Index. Only the successfully completed Operations with update_time equal or before this sync time are contained in this DeployedIndex.
privateEndpoints	object (GoogleCloudAiplatformV1beta1IndexPrivateEndpoints) Output only. Provides paths for users to send requests directly to the deployed index services running on Cloud via private services access. This field is populated if network is configured.
reservedIpRanges[]	string Optional. A list of reserved ip ranges under the VPC network that can be used for this DeployedIndex. If set, we will deploy the index within the provided ip ranges. Otherwise, the index might be deployed to any ip ranges under the provided VPC network. The value should be the name of the address (https://cloud.google.com/compute/docs/reference/rest/v1/addresses) Example: ['vertex-ai-ip-range']. For more information about subnets and network IP ranges, please see https://cloud.google.com/vpc/docs/subnets#manually_created_subnet_ip_ranges.

GoogleCloudAiplatformV1beta1DeployedIndexAuthConfig

Used to set up the auth on the DeployedIndex's private endpoint.

Fields
authProvider	object (GoogleCloudAiplatformV1beta1DeployedIndexAuthConfigAuthProvider) Defines the authentication provider that the DeployedIndex uses.

GoogleCloudAiplatformV1beta1DeployedIndexAuthConfigAuthProvider

Configuration for an authentication provider, including support for JSON Web Token (JWT).

Fields
allowedIssuers[]	string A list of allowed JWT issuers. Each entry must be a valid Google service account, in the following format: [e-mail]
audiences[]	string The list of JWT audiences. that are allowed to access. A JWT containing any of these audiences will be accepted.

GoogleCloudAiplatformV1beta1DeployedIndexRef

Points to a DeployedIndex.

Fields
deployedIndexId	string Immutable. The ID of the DeployedIndex in the above IndexEndpoint.
displayName	string Output only. The display name of the DeployedIndex.
indexEndpoint	string Immutable. A resource name of the IndexEndpoint.

GoogleCloudAiplatformV1beta1DeployedModel

A deployment of a Model. Endpoints contain one or more DeployedModels.

Fields
automaticResources	object (GoogleCloudAiplatformV1beta1AutomaticResources) A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
createTime	string (Timestamp format) Output only. Timestamp when the DeployedModel was created.
dedicatedResources	object (GoogleCloudAiplatformV1beta1DedicatedResources) A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
disableExplanations	boolean If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec.
displayName	string The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
enableAccessLogging	boolean If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.
enableContainerLogging	boolean If true, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging. Only supported for custom-trained Models and AutoML Tabular Models.
explanationSpec	object (GoogleCloudAiplatformV1beta1ExplanationSpec) Explanation configuration for this DeployedModel. When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
id	string Immutable. The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are /[0-9]/.
model	string Required. The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint. The resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.
modelVersionId	string Output only. The version ID of the model that is deployed.
privateEndpoints	object (GoogleCloudAiplatformV1beta1PrivateEndpoints) Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.
serviceAccount	string The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
sharedResources	string The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

GoogleCloudAiplatformV1beta1DeployedModelRef

Points to a DeployedModel.

Fields
deployedModelId	string Immutable. An ID of a DeployedModel in the above Endpoint.
endpoint	string Immutable. A resource name of an Endpoint.

GoogleCloudAiplatformV1beta1DeploymentResourcePool

A description of resources that can be shared by multiple DeployedModels, whose underlying specification consists of a DedicatedResources.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this DeploymentResourcePool was created.
dedicatedResources	object (GoogleCloudAiplatformV1beta1DedicatedResources) Required. The underlying DedicatedResources that the DeploymentResourcePool uses.
name	string Immutable. The resource name of the DeploymentResourcePool. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

GoogleCloudAiplatformV1beta1DestinationFeatureSetting

(No description provided)

Fields
destinationField	string Specify the field name in the export destination. If not specified, Feature ID is used.
featureId	string Required. The ID of the Feature to apply the setting to.

GoogleCloudAiplatformV1beta1DirectPredictRequest

Request message for PredictionService.DirectPredict.

Fields
inputs[]	object (GoogleCloudAiplatformV1beta1Tensor) The prediction input.
parameters	object (GoogleCloudAiplatformV1beta1Tensor) The parameters that govern the prediction.

GoogleCloudAiplatformV1beta1DirectPredictResponse

Response message for PredictionService.DirectPredict.

Fields
outputs[]	object (GoogleCloudAiplatformV1beta1Tensor) The prediction output.
parameters	object (GoogleCloudAiplatformV1beta1Tensor) The parameters that govern the prediction.

GoogleCloudAiplatformV1beta1DirectRawPredictRequest

Request message for PredictionService.DirectRawPredict.

Fields
input	string (bytes format) The prediction input.
methodName	string Fully qualified name of the API method being invoked to perform predictions. Format: /namespace.Service/Method/ Example: /tensorflow.serving.PredictionService/Predict

GoogleCloudAiplatformV1beta1DirectRawPredictResponse

Response message for PredictionService.DirectRawPredict.

Fields
output	string (bytes format) The prediction output.

GoogleCloudAiplatformV1beta1DiskSpec

Represents the spec of disk options.

Fields
bootDiskSizeGb	integer (int32 format) Size in GB of the boot disk (default is 100GB).
bootDiskType	string Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).

GoogleCloudAiplatformV1beta1DoubleArray

A list of double values.

Fields
values[]	number (double format) A list of double values.

GoogleCloudAiplatformV1beta1EncryptionSpec

Represents a customer-managed encryption key spec that can be applied to a top-level resource.

Fields
kmsKeyName	string Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

GoogleCloudAiplatformV1beta1Endpoint

Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this Endpoint was created.
deployedModels[]	object (GoogleCloudAiplatformV1beta1DeployedModel) Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
description	string The description of the Endpoint.
displayName	string Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
enablePrivateServiceConnect	boolean Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
modelDeploymentMonitoringJob	string Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}
name	string Output only. The resource name of the Endpoint.
network	string Optional. The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.
predictRequestResponseLoggingConfig	object (GoogleCloudAiplatformV1beta1PredictRequestResponseLoggingConfig) Configures the request-response logging for online prediction.
privateServiceConnectConfig	object (GoogleCloudAiplatformV1beta1PrivateServiceConnectConfig) Optional. Configuration for private service connect. network and private_service_connect_config are mutually exclusive.
trafficSplit	map (key: string, value: integer (int32 format)) A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment.
updateTime	string (Timestamp format) Output only. Timestamp when this Endpoint was last updated.

GoogleCloudAiplatformV1beta1EntityIdSelector

Selector for entityId. Getting ids from the given source.

Fields
csvSource	object (GoogleCloudAiplatformV1beta1CsvSource) Source of Csv
entityIdField	string Source column that holds entity IDs. If not provided, entity IDs are extracted from the column named entity_id.

GoogleCloudAiplatformV1beta1EntityType

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this EntityType was created.
description	string Optional. Description of the EntityType.
etag	string Optional. Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) Optional. The labels with user-defined metadata to organize your EntityTypes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one EntityType (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
monitoringConfig	object (GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfig) Optional. The default monitoring configuration for all Features with value type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 under this EntityType. If this is populated with [FeaturestoreMonitoringConfig.monitoring_interval] specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring is disabled.
name	string Immutable. Name of the EntityType. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} The last part entity_type is assigned by the client. The entity_type can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z and underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given a featurestore.
offlineStorageTtlDays	integer (int32 format) Optional. Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than offline_storage_ttl_days since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.
updateTime	string (Timestamp format) Output only. Timestamp when this EntityType was most recently updated.

GoogleCloudAiplatformV1beta1EnvVar

Represents an environment variable present in a Container or Python Module.

Fields
name	string Required. Name of the environment variable. Must be a valid C identifier.
value	string Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

GoogleCloudAiplatformV1beta1ErrorAnalysisAnnotation

Model error analysis for each annotation.

Fields
attributedItems[]	object (GoogleCloudAiplatformV1beta1ErrorAnalysisAnnotationAttributedItem) Attributed items for a given annotation, typically representing neighbors from the training sets constrained by the query type.
outlierScore	number (double format) The outlier score of this annotated item. Usually defined as the min of all distances from attributed items.
outlierThreshold	number (double format) The threshold used to determine if this annotation is an outlier or not.
queryType	enum The query type used for finding the attributed items.
Enum type. Can be one of the following:
QUERY_TYPE_UNSPECIFIED	Unspecified query type for model error analysis.
ALL_SIMILAR	Query similar samples across all classes in the dataset.
SAME_CLASS_SIMILAR	Query similar samples from the same class of the input sample.
SAME_CLASS_DISSIMILAR	Query dissimilar samples from the same class of the input sample.

GoogleCloudAiplatformV1beta1ErrorAnalysisAnnotationAttributedItem

Attributed items for a given annotation, typically representing neighbors from the training sets constrained by the query type.

Fields
annotationResourceName	string The unique ID for each annotation. Used by FE to allocate the annotation in DB.
distance	number (double format) The distance of this item to the annotation.

GoogleCloudAiplatformV1beta1EvaluateInstancesRequest

Request message for EvaluationService.EvaluateInstances.

Fields
bleuInput	object (GoogleCloudAiplatformV1beta1BleuInput) Instances and metric spec for bleu metric.
coherenceInput	object (GoogleCloudAiplatformV1beta1CoherenceInput) Input for coherence metric.
exactMatchInput	object (GoogleCloudAiplatformV1beta1ExactMatchInput) Auto metric instances. Instances and metric spec for exact match metric.
fluencyInput	object (GoogleCloudAiplatformV1beta1FluencyInput) LLM-based metric instance. General text generation metrics, applicable to other categories. Input for fluency metric.
fulfillmentInput	object (GoogleCloudAiplatformV1beta1FulfillmentInput) Input for fulfillment metric.
groundednessInput	object (GoogleCloudAiplatformV1beta1GroundednessInput) Input for groundedness metric.
pairwiseQuestionAnsweringQualityInput	object (GoogleCloudAiplatformV1beta1PairwiseQuestionAnsweringQualityInput) Input for pairwise question answering quality metric.
pairwiseSummarizationQualityInput	object (GoogleCloudAiplatformV1beta1PairwiseSummarizationQualityInput) Input for pairwise summarization quality metric.
questionAnsweringCorrectnessInput	object (GoogleCloudAiplatformV1beta1QuestionAnsweringCorrectnessInput) Input for question answering correctness metric.
questionAnsweringHelpfulnessInput	object (GoogleCloudAiplatformV1beta1QuestionAnsweringHelpfulnessInput) Input for question answering helpfulness metric.
questionAnsweringQualityInput	object (GoogleCloudAiplatformV1beta1QuestionAnsweringQualityInput) Input for question answering quality metric.
questionAnsweringRelevanceInput	object (GoogleCloudAiplatformV1beta1QuestionAnsweringRelevanceInput) Input for question answering relevance metric.
rougeInput	object (GoogleCloudAiplatformV1beta1RougeInput) Instances and metric spec for rouge metric.
safetyInput	object (GoogleCloudAiplatformV1beta1SafetyInput) Input for safety metric.
summarizationHelpfulnessInput	object (GoogleCloudAiplatformV1beta1SummarizationHelpfulnessInput) Input for summarization helpfulness metric.
summarizationQualityInput	object (GoogleCloudAiplatformV1beta1SummarizationQualityInput) Input for summarization quality metric.
summarizationVerbosityInput	object (GoogleCloudAiplatformV1beta1SummarizationVerbosityInput) Input for summarization verbosity metric.
toolCallValidInput	object (GoogleCloudAiplatformV1beta1ToolCallValidInput) Tool call metric instances. Input for tool call valid metric.
toolNameMatchInput	object (GoogleCloudAiplatformV1beta1ToolNameMatchInput) Input for tool name match metric.
toolParameterKeyMatchInput	object (GoogleCloudAiplatformV1beta1ToolParameterKeyMatchInput) Input for tool parameter key match metric.
toolParameterKvMatchInput	object (GoogleCloudAiplatformV1beta1ToolParameterKVMatchInput) Input for tool parameter key value match metric.

GoogleCloudAiplatformV1beta1EvaluateInstancesResponse

Response message for EvaluationService.EvaluateInstances.

Fields
bleuResults	object (GoogleCloudAiplatformV1beta1BleuResults) Results for bleu metric.
coherenceResult	object (GoogleCloudAiplatformV1beta1CoherenceResult) Result for coherence metric.
exactMatchResults	object (GoogleCloudAiplatformV1beta1ExactMatchResults) Auto metric evaluation results. Results for exact match metric.
fluencyResult	object (GoogleCloudAiplatformV1beta1FluencyResult) LLM-based metric evaluation result. General text generation metrics, applicable to other categories. Result for fluency metric.
fulfillmentResult	object (GoogleCloudAiplatformV1beta1FulfillmentResult) Result for fulfillment metric.
groundednessResult	object (GoogleCloudAiplatformV1beta1GroundednessResult) Result for groundedness metric.
pairwiseQuestionAnsweringQualityResult	object (GoogleCloudAiplatformV1beta1PairwiseQuestionAnsweringQualityResult) Result for pairwise question answering quality metric.
pairwiseSummarizationQualityResult	object (GoogleCloudAiplatformV1beta1PairwiseSummarizationQualityResult) Result for pairwise summarization quality metric.
questionAnsweringCorrectnessResult	object (GoogleCloudAiplatformV1beta1QuestionAnsweringCorrectnessResult) Result for question answering correctness metric.
questionAnsweringHelpfulnessResult	object (GoogleCloudAiplatformV1beta1QuestionAnsweringHelpfulnessResult) Result for question answering helpfulness metric.
questionAnsweringQualityResult	object (GoogleCloudAiplatformV1beta1QuestionAnsweringQualityResult) Question answering only metrics. Result for question answering quality metric.
questionAnsweringRelevanceResult	object (GoogleCloudAiplatformV1beta1QuestionAnsweringRelevanceResult) Result for question answering relevance metric.
rougeResults	object (GoogleCloudAiplatformV1beta1RougeResults) Results for rouge metric.
safetyResult	object (GoogleCloudAiplatformV1beta1SafetyResult) Result for safety metric.
summarizationHelpfulnessResult	object (GoogleCloudAiplatformV1beta1SummarizationHelpfulnessResult) Result for summarization helpfulness metric.
summarizationQualityResult	object (GoogleCloudAiplatformV1beta1SummarizationQualityResult) Summarization only metrics. Result for summarization quality metric.
summarizationVerbosityResult	object (GoogleCloudAiplatformV1beta1SummarizationVerbosityResult) Result for summarization verbosity metric.
toolCallValidResults	object (GoogleCloudAiplatformV1beta1ToolCallValidResults) Tool call metrics. Results for tool call valid metric.
toolNameMatchResults	object (GoogleCloudAiplatformV1beta1ToolNameMatchResults) Results for tool name match metric.
toolParameterKeyMatchResults	object (GoogleCloudAiplatformV1beta1ToolParameterKeyMatchResults) Results for tool parameter key match metric.
toolParameterKvMatchResults	object (GoogleCloudAiplatformV1beta1ToolParameterKVMatchResults) Results for tool parameter key value match metric.

GoogleCloudAiplatformV1beta1EvaluatedAnnotation

True positive, false positive, or false negative. EvaluatedAnnotation is only available under ModelEvaluationSlice with slice of annotationSpec dimension.

Fields
dataItemPayload	any Output only. The data item payload that the Model predicted this EvaluatedAnnotation on.
errorAnalysisAnnotations[]	object (GoogleCloudAiplatformV1beta1ErrorAnalysisAnnotation) Annotations of model error analysis results.
evaluatedDataItemViewId	string Output only. ID of the EvaluatedDataItemView under the same ancestor ModelEvaluation. The EvaluatedDataItemView consists of all ground truths and predictions on data_item_payload.
explanations[]	object (GoogleCloudAiplatformV1beta1EvaluatedAnnotationExplanation) Explanations of predictions. Each element of the explanations indicates the explanation for one explanation Method. The attributions list in the EvaluatedAnnotationExplanation.explanation object corresponds to the predictions list. For example, the second element in the attributions list explains the second element in the predictions list.
groundTruths[]	any Output only. The ground truth Annotations, i.e. the Annotations that exist in the test data the Model is evaluated on. For true positive, there is one and only one ground truth annotation, which matches the only prediction in predictions. For false positive, there are zero or more ground truth annotations that are similar to the only prediction in predictions, but not enough for a match. For false negative, there is one and only one ground truth annotation, which doesn't match any predictions created by the model. The schema of the ground truth is stored in ModelEvaluation.annotation_schema_uri
predictions[]	any Output only. The model predicted annotations. For true positive, there is one and only one prediction, which matches the only one ground truth annotation in ground_truths. For false positive, there is one and only one prediction, which doesn't match any ground truth annotation of the corresponding data_item_view_id. For false negative, there are zero or more predictions which are similar to the only ground truth annotation in ground_truths but not enough for a match. The schema of the prediction is stored in ModelEvaluation.annotation_schema_uri
type	enum Output only. Type of the EvaluatedAnnotation.
Enum type. Can be one of the following:
EVALUATED_ANNOTATION_TYPE_UNSPECIFIED	Invalid value.
TRUE_POSITIVE	The EvaluatedAnnotation is a true positive. It has a prediction created by the Model and a ground truth Annotation which the prediction matches.
FALSE_POSITIVE	The EvaluatedAnnotation is false positive. It has a prediction created by the Model which does not match any ground truth annotation.
FALSE_NEGATIVE	The EvaluatedAnnotation is false negative. It has a ground truth annotation which is not matched by any of the model created predictions.

GoogleCloudAiplatformV1beta1EvaluatedAnnotationExplanation

Explanation result of the prediction produced by the Model.

Fields
explanation	object (GoogleCloudAiplatformV1beta1Explanation) Explanation attribution response details.
explanationType	string Explanation type. For AutoML Image Classification models, possible values are: * image-integrated-gradients * image-xrai

GoogleCloudAiplatformV1beta1Event

An edge describing the relationship between an Artifact and an Execution in a lineage graph.

Fields
artifact	string Required. The relative resource name of the Artifact in the Event.
eventTime	string (Timestamp format) Output only. Time the Event occurred.
execution	string Output only. The relative resource name of the Execution in the Event.
labels	map (key: string, value: string) The labels with user-defined metadata to annotate Events. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Event (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
type	enum Required. The type of the Event.
Enum type. Can be one of the following:
TYPE_UNSPECIFIED	Unspecified whether input or output of the Execution.
INPUT	An input of the Execution.
OUTPUT	An output of the Execution.

GoogleCloudAiplatformV1beta1ExactMatchInput

Input for exact match metric.

Fields
instances[]	object (GoogleCloudAiplatformV1beta1ExactMatchInstance) Required. Repeated exact match instances.
metricSpec	object (GoogleCloudAiplatformV1beta1ExactMatchSpec) Required. Spec for exact match metric.

GoogleCloudAiplatformV1beta1ExactMatchInstance

Spec for exact match instance.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Required. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1ExactMatchMetricValue

Exact match metric value for an instance.

Fields
score	number (float format) Output only. Exact match score.

GoogleCloudAiplatformV1beta1ExactMatchResults

Results for exact match metric.

Fields
exactMatchMetricValues[]	object (GoogleCloudAiplatformV1beta1ExactMatchMetricValue) Output only. Exact match metric values.

GoogleCloudAiplatformV1beta1Examples

Example-based explainability that returns the nearest neighbors from the provided dataset.

Fields
exampleGcsSource	object (GoogleCloudAiplatformV1beta1ExamplesExampleGcsSource) The Cloud Storage input instances.
gcsSource	object (GoogleCloudAiplatformV1beta1GcsSource) The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
nearestNeighborSearchConfig	any The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighborCount	integer (int32 format) The number of neighbors to return when querying for examples.
presets	object (GoogleCloudAiplatformV1beta1Presets) Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

GoogleCloudAiplatformV1beta1ExamplesExampleGcsSource

The Cloud Storage input instances.

Fields
dataFormat	enum The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
Enum type. Can be one of the following:
DATA_FORMAT_UNSPECIFIED	Format unspecified, used when unset.
JSONL	Examples are stored in JSONL files.
gcsSource	object (GoogleCloudAiplatformV1beta1GcsSource) The Cloud Storage location for the input instances.

GoogleCloudAiplatformV1beta1ExamplesOverride

Overrides for example-based explanations.

Fields
crowdingCount	integer (int32 format) The number of neighbors to return that have the same crowding tag.
dataFormat	enum The format of the data being provided with each call.
Enum type. Can be one of the following:
DATA_FORMAT_UNSPECIFIED	Unspecified format. Must not be used.
INSTANCES	Provided data is a set of model inputs.
EMBEDDINGS	Provided data is a set of embeddings.
neighborCount	integer (int32 format) The number of neighbors to return.
restrictions[]	object (GoogleCloudAiplatformV1beta1ExamplesRestrictionsNamespace) Restrict the resulting nearest neighbors to respect these constraints.
returnEmbeddings	boolean If true, return the embeddings instead of neighbors.

GoogleCloudAiplatformV1beta1ExamplesRestrictionsNamespace

Restrictions namespace for example-based explanations overrides.

Fields
allow[]	string The list of allowed tags.
deny[]	string The list of deny tags.
namespaceName	string The namespace name.

GoogleCloudAiplatformV1beta1ExecuteExtensionRequest

Request message for ExtensionExecutionService.ExecuteExtension.

Fields
operationId	string Required. The desired ID of the operation to be executed in this extension as defined in ExtensionOperation.operation_id.
operationParams	map (key: string, value: any) Optional. Request parameters that will be used for executing this operation. The struct should be in a form of map with param name as the key and actual param value as the value. E.g. If this operation requires a param "name" to be set to "abc". you can set this to something like {"name": "abc"}.
runtimeAuthConfig	object (GoogleCloudAiplatformV1beta1AuthConfig) Optional. Auth config provided at runtime to override the default value in Extension.manifest.auth_config. The AuthConfig.auth_type should match the value in Extension.manifest.auth_config.

GoogleCloudAiplatformV1beta1ExecuteExtensionResponse

Response message for ExtensionExecutionService.ExecuteExtension.

Fields
content	string Response content from the extension. The content should be conformant to the response.content schema in the extension's manifest/OpenAPI spec.

GoogleCloudAiplatformV1beta1Execution

Instance of a general execution.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this Execution was created.
description	string Description of the Execution
displayName	string User provided display name of the Execution. May be up to 128 Unicode characters.
etag	string An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your Executions. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Execution (System labels are excluded).
metadata	map (key: string, value: any) Properties of the Execution. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.
name	string Output only. The resource name of the Execution.
schemaTitle	string The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
schemaVersion	string The version of the schema in schema_title to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
state	enum The state of this Execution. This is a property of the Execution, and does not imply or capture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines) and the system does not prescribe or check the validity of state transitions.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	Unspecified Execution state
NEW	The Execution is new
RUNNING	The Execution is running
COMPLETE	The Execution has finished running
FAILED	The Execution has failed
CACHED	The Execution completed through Cache hit.
CANCELLED	The Execution was cancelled.
updateTime	string (Timestamp format) Output only. Timestamp when this Execution was last updated.

GoogleCloudAiplatformV1beta1ExplainRequest

Request message for PredictionService.Explain.

Fields
concurrentExplanationSpecOverride	map (key: string, value: object (GoogleCloudAiplatformV1beta1ExplanationSpecOverride)) Optional. This field is the same as the one above, but supports multiple explanations to occur in parallel. The key can be any string. Each override will be run against the model, then its explanations will be grouped together. Note - these explanations are run In Addition to the default Explanation in the deployed model.
deployedModelId	string If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split.
explanationSpecOverride	object (GoogleCloudAiplatformV1beta1ExplanationSpecOverride) If specified, overrides the explanation_spec of the DeployedModel. Can be used for explaining prediction results with different configurations, such as: - Explaining top-5 predictions results as opposed to top-1; - Increasing path count or step count of the attribution methods to reduce approximate errors; - Using different baselines for explaining the prediction results.
instances[]	any Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.
parameters	any The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri.

GoogleCloudAiplatformV1beta1ExplainResponse

Response message for PredictionService.Explain.

Fields
concurrentExplanations	map (key: string, value: object (GoogleCloudAiplatformV1beta1ExplainResponseConcurrentExplanation)) This field stores the results of the explanations run in parallel with The default explanation strategy/method.
deployedModelId	string ID of the Endpoint's DeployedModel that served this explanation.
explanations[]	object (GoogleCloudAiplatformV1beta1Explanation) The explanations of the Model's PredictResponse.predictions. It has the same number of elements as instances to be explained.
predictions[]	any The predictions that are the output of the predictions call. Same as PredictResponse.predictions.

GoogleCloudAiplatformV1beta1ExplainResponseConcurrentExplanation

This message is a wrapper grouping Concurrent Explanations.

Fields
explanations[]	object (GoogleCloudAiplatformV1beta1Explanation) The explanations of the Model's PredictResponse.predictions. It has the same number of elements as instances to be explained.

GoogleCloudAiplatformV1beta1Explanation

Explanation of a prediction (provided in PredictResponse.predictions) produced by the Model on a given instance.

Fields
attributions[]	object (GoogleCloudAiplatformV1beta1Attribution) Output only. Feature attributions grouped by predicted outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. By default, we provide Shapley values for the predicted class. However, you can configure the explanation request to generate Shapley values for any other classes too. For example, if a model predicts a probability of 0.4 for approving a loan application, the model's decision is to reject the application since p(reject) = 0.6 > p(approve) = 0.4, and the default Shapley values would be computed for rejection decision and not approval, even though the latter might be the positive class. If users set ExplanationParameters.top_k, the attributions are sorted by instance_output_value in descending order. If ExplanationParameters.output_indices is specified, the attributions are stored by Attribution.output_index in the same order as they appear in the output_indices.
neighbors[]	object (GoogleCloudAiplatformV1beta1Neighbor) Output only. List of the nearest neighbors for example-based explanations. For models deployed with the examples explanations feature enabled, the attributions field is empty and instead the neighbors field is populated.

GoogleCloudAiplatformV1beta1ExplanationMetadata

Metadata describing the Model's input and output for explanation.

Fields
featureAttributionsSchemaUri	string Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
inputs	map (key: string, value: object (GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadata)) Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
latentSpaceSource	string Name of the source to generate embeddings for example based explanations.
outputs	map (key: string, value: object (GoogleCloudAiplatformV1beta1ExplanationMetadataOutputMetadata)) Required. Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.

GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadata

Metadata of the input of a feature. Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.

Fields
denseShapeTensorName	string Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
encodedBaselines[]	any A list of baselines for the encoded tensor. The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.
encodedTensorName	string Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
encoding	enum Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
Enum type. Can be one of the following:
ENCODING_UNSPECIFIED	Default value. This is the same as IDENTITY.
IDENTITY	The tensor represents one feature.
BAG_OF_FEATURES	The tensor represents a bag of features where each index maps to a feature. InputMetadata.index_feature_mapping must be provided for this encoding. For example: input = [27, 6.0, 150] index_feature_mapping = ["age", "height", "weight"]
BAG_OF_FEATURES_SPARSE	The tensor represents a bag of features where each index maps to a feature. Zero values in the tensor indicates feature being non-existent. InputMetadata.index_feature_mapping must be provided for this encoding. For example: input = [2, 0, 5, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"]
INDICATOR	The tensor is a list of binaries representing whether a feature exists or not (1 indicates existence). InputMetadata.index_feature_mapping must be provided for this encoding. For example: input = [1, 0, 1, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"]
COMBINED_EMBEDDING	The tensor is encoded into a 1-dimensional array represented by an encoded tensor. InputMetadata.encoded_tensor_name must be provided for this encoding. For example: input = ["This", "is", "a", "test", "."] encoded = [0.1, 0.2, 0.3, 0.4, 0.5]
CONCAT_EMBEDDING	Select this encoding when the input tensor is encoded into a 2-dimensional array represented by an encoded tensor. InputMetadata.encoded_tensor_name must be provided for this encoding. The first dimension of the encoded tensor's shape is the same as the input tensor's shape. For example: input = ["This", "is", "a", "test", "."] encoded = [[0.1, 0.2, 0.3, 0.4, 0.5], [0.2, 0.1, 0.4, 0.3, 0.5], [0.5, 0.1, 0.3, 0.5, 0.4], [0.5, 0.3, 0.1, 0.2, 0.4], [0.4, 0.3, 0.2, 0.5, 0.1]]
featureValueDomain	object (GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadataFeatureValueDomain) The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
groupName	string Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions, keyed by the group name.
indexFeatureMapping[]	string A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
indicesTensorName	string Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
inputBaselines[]	any Baseline inputs for this feature. If no baseline is specified, Vertex AI chooses the baseline for this feature. If multiple baselines are specified, Vertex AI returns the average attributions across them in Attribution.feature_attributions. For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor. For custom images, the element of the baselines must be in the same format as the feature's input in the instance[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.
inputTensorName	string Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.
modality	string Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
visualization	object (GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadataVisualization) Visualization configurations for image explanation.

GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadataFeatureValueDomain

Domain details of the input feature value. Provides numeric information about the feature, such as its range (min, max). If the feature has been pre-processed, for example with z-scoring, then it provides information about how to recover the original feature. For example, if the input feature is an image and it has been pre-processed to obtain 0-mean and stddev = 1 values, then original_mean, and original_stddev refer to the mean and stddev of the original feature (e.g. image tensor) from which input feature (with mean = 0 and stddev = 1) was obtained.

Fields
maxValue	number (float format) The maximum permissible value for this feature.
minValue	number (float format) The minimum permissible value for this feature.
originalMean	number (float format) If this input feature has been normalized to a mean value of 0, the original_mean specifies the mean value of the domain prior to normalization.
originalStddev	number (float format) If this input feature has been normalized to a standard deviation of 1.0, the original_stddev specifies the standard deviation of the domain prior to normalization.

GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadataVisualization

Visualization configurations for image explanation.

Fields
clipPercentLowerbound	number (float format) Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.
clipPercentUpperbound	number (float format) Excludes attributions above the specified percentile from the highlighted areas. Using the clip_percent_upperbound and clip_percent_lowerbound together can be useful for filtering out noise and making it easier to see areas of strong attribution. Defaults to 99.9.
colorMap	enum The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue.
Enum type. Can be one of the following:
COLOR_MAP_UNSPECIFIED	Should not be used.
PINK_GREEN	Positive: green. Negative: pink.
VIRIDIS	Viridis color map: A perceptually uniform color mapping which is easier to see by those with colorblindness and progresses from yellow to green to blue. Positive: yellow. Negative: blue.
RED	Positive: red. Negative: red.
GREEN	Positive: green. Negative: green.
RED_GREEN	Positive: green. Negative: red.
PINK_WHITE_GREEN	PiYG palette.
overlayType	enum How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE.
Enum type. Can be one of the following:
OVERLAY_TYPE_UNSPECIFIED	Default value. This is the same as NONE.
NONE	No overlay.
ORIGINAL	The attributions are shown on top of the original image.
GRAYSCALE	The attributions are shown on top of grayscaled version of the original image.
MASK_BLACK	The attributions are used as a mask to reveal predictive parts of the image and hide the un-predictive parts.
polarity	enum Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.
Enum type. Can be one of the following:
POLARITY_UNSPECIFIED	Default value. This is the same as POSITIVE.
POSITIVE	Highlights the pixels/outlines that were most influential to the model's prediction.
NEGATIVE	Setting polarity to negative highlights areas that does not lead to the models's current prediction.
BOTH	Shows both positive and negative attributions.
type	enum Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES.
Enum type. Can be one of the following:
TYPE_UNSPECIFIED	Should not be used.
PIXELS	Shows which pixel contributed to the image prediction.
OUTLINES	Shows which region contributed to the image prediction by outlining the region.

GoogleCloudAiplatformV1beta1ExplanationMetadataOutputMetadata

Metadata of the prediction output to be explained.

Fields
displayNameMappingKey	string Specify a field name in the prediction to look for the display name. Use this if the prediction contains the display names for the outputs. The display names in the prediction must have the same shape of the outputs, so that it can be located by Attribution.output_index for a specific output.
indexDisplayNameMapping	any Static mapping between the index and display name. Use this if the outputs are a deterministic n-dimensional array, e.g. a list of scores of all the classes in a pre-defined order for a multi-classification Model. It's not feasible if the outputs are non-deterministic, e.g. the Model produces top-k classes or sort the outputs by their values. The shape of the value must be an n-dimensional array of strings. The number of dimensions must match that of the outputs to be explained. The Attribution.output_display_name is populated by locating in the mapping with Attribution.output_index.
outputTensorName	string Name of the output tensor. Required and is only applicable to Vertex AI provided images for Tensorflow.

GoogleCloudAiplatformV1beta1ExplanationMetadataOverride

The ExplanationMetadata entries that can be overridden at online explanation time.

Fields
inputs	map (key: string, value: object (GoogleCloudAiplatformV1beta1ExplanationMetadataOverrideInputMetadataOverride)) Required. Overrides the input metadata of the features. The key is the name of the feature to be overridden. The keys specified here must exist in the input metadata to be overridden. If a feature is not specified here, the corresponding feature's input metadata is not overridden.

GoogleCloudAiplatformV1beta1ExplanationMetadataOverrideInputMetadataOverride

The input metadata entries to be overridden.

Fields
inputBaselines[]	any Baseline inputs for this feature. This overrides the input_baseline field of the ExplanationMetadata.InputMetadata object of the corresponding feature's input metadata. If it's not specified, the original baselines are not overridden.

GoogleCloudAiplatformV1beta1ExplanationParameters

Parameters to configure explaining for Model's predictions.

Fields
examples	object (GoogleCloudAiplatformV1beta1Examples) Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution	object (GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution) An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
outputIndices[]	any If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampledShapleyAttribution	object (GoogleCloudAiplatformV1beta1SampledShapleyAttribution) An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
topK	integer (int32 format) If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xraiAttribution	object (GoogleCloudAiplatformV1beta1XraiAttribution) An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.

GoogleCloudAiplatformV1beta1ExplanationSpec

Specification of Model explanation.

Fields
metadata	object (GoogleCloudAiplatformV1beta1ExplanationMetadata) Optional. Metadata describing the Model's input and output for explanation.
parameters	object (GoogleCloudAiplatformV1beta1ExplanationParameters) Required. Parameters that configure explaining of the Model's predictions.

GoogleCloudAiplatformV1beta1ExplanationSpecOverride

The ExplanationSpec entries that can be overridden at online explanation time.

Fields
examplesOverride	object (GoogleCloudAiplatformV1beta1ExamplesOverride) The example-based explanations parameter overrides.
metadata	object (GoogleCloudAiplatformV1beta1ExplanationMetadataOverride) The metadata to be overridden. If not specified, no metadata is overridden.
parameters	object (GoogleCloudAiplatformV1beta1ExplanationParameters) The parameters to be overridden. Note that the attribution method cannot be changed. If not specified, no parameter is overridden.

GoogleCloudAiplatformV1beta1ExportDataConfig

Describes what part of the Dataset is to be exported, the destination of the export and how to export.

Fields
annotationsFilter	string An expression for filtering what part of the Dataset is to be exported. Only Annotations that match this filter will be exported. The filter syntax is the same as in ListAnnotations.
fractionSplit	object (GoogleCloudAiplatformV1beta1ExportFractionSplit) Split based on fractions defining the size of each set.
gcsDestination	object (GoogleCloudAiplatformV1beta1GcsDestination) The Google Cloud Storage location where the output is to be written to. In the given directory a new directory will be created with name: export-data-- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory. Inside that directory, annotations with the same schema will be grouped into sub directories which are named with the corresponding annotations' schema title. Inside these sub directories, a schema.yaml will be created to describe the output format.

GoogleCloudAiplatformV1beta1ExportDataOperationMetadata

Runtime operation information for DatasetService.ExportData.

Fields
gcsOutputDirectory	string A Google Cloud Storage directory which path ends with '/'. The exported data is stored in the directory.
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.

GoogleCloudAiplatformV1beta1ExportDataRequest

Request message for DatasetService.ExportData.

Fields
exportConfig	object (GoogleCloudAiplatformV1beta1ExportDataConfig) Required. The desired output location.

GoogleCloudAiplatformV1beta1ExportDataResponse

Response message for DatasetService.ExportData.

Fields
exportedFiles[]	string All of the files that are exported in this export operation. For custom code training export, only three (training, validation and test) Cloud Storage paths in wildcard format are populated (for example, gs://.../training-*).

GoogleCloudAiplatformV1beta1ExportFeatureValuesOperationMetadata

Details of operations that exports Features values.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Featurestore export Feature values.

GoogleCloudAiplatformV1beta1ExportFeatureValuesRequest

Request message for FeaturestoreService.ExportFeatureValues.

Fields
destination	object (GoogleCloudAiplatformV1beta1FeatureValueDestination) Required. Specifies destination location and format.
featureSelector	object (GoogleCloudAiplatformV1beta1FeatureSelector) Required. Selects Features to export values of.
fullExport	object (GoogleCloudAiplatformV1beta1ExportFeatureValuesRequestFullExport) Exports all historical values of all entities of the EntityType within a time range
settings[]	object (GoogleCloudAiplatformV1beta1DestinationFeatureSetting) Per-Feature export settings.
snapshotExport	object (GoogleCloudAiplatformV1beta1ExportFeatureValuesRequestSnapshotExport) Exports the latest Feature values of all entities of the EntityType within a time range.

GoogleCloudAiplatformV1beta1ExportFeatureValuesRequestFullExport

Describes exporting all historical Feature values of all entities of the EntityType between [start_time, end_time].

Fields
endTime	string (Timestamp format) Exports Feature values as of this timestamp. If not set, retrieve values as of now. Timestamp, if present, must not have higher than millisecond precision.
startTime	string (Timestamp format) Excludes Feature values with feature generation timestamp before this timestamp. If not set, retrieve oldest values kept in Feature Store. Timestamp, if present, must not have higher than millisecond precision.

GoogleCloudAiplatformV1beta1ExportFeatureValuesRequestSnapshotExport

Describes exporting the latest Feature values of all entities of the EntityType between [start_time, snapshot_time].

Fields
snapshotTime	string (Timestamp format) Exports Feature values as of this timestamp. If not set, retrieve values as of now. Timestamp, if present, must not have higher than millisecond precision.
startTime	string (Timestamp format) Excludes Feature values with feature generation timestamp before this timestamp. If not set, retrieve oldest values kept in Feature Store. Timestamp, if present, must not have higher than millisecond precision.

GoogleCloudAiplatformV1beta1ExportFractionSplit

Assigns the input data to training, validation, and test sets as per the given fractions. Any of training_fraction, validation_fraction and test_fraction may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test.

Fields
testFraction	number (double format) The fraction of the input data that is to be used to evaluate the Model.
trainingFraction	number (double format) The fraction of the input data that is to be used to train the Model.
validationFraction	number (double format) The fraction of the input data that is to be used to validate the Model.

GoogleCloudAiplatformV1beta1ExportModelOperationMetadata

Details of ModelService.ExportModel operation.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.
outputInfo	object (GoogleCloudAiplatformV1beta1ExportModelOperationMetadataOutputInfo) Output only. Information further describing the output of this Model export.

GoogleCloudAiplatformV1beta1ExportModelOperationMetadataOutputInfo

Further describes the output of the ExportModel. Supplements ExportModelRequest.OutputConfig.

Fields
artifactOutputUri	string Output only. If the Model artifact is being exported to Google Cloud Storage this is the full path of the directory created, into which the Model files are being written to.
imageOutputUri	string Output only. If the Model image is being exported to Google Container Registry or Artifact Registry this is the full path of the image created.

GoogleCloudAiplatformV1beta1ExportModelRequest

Request message for ModelService.ExportModel.

Fields
outputConfig	object (GoogleCloudAiplatformV1beta1ExportModelRequestOutputConfig) Required. The desired output location and configuration.

GoogleCloudAiplatformV1beta1ExportModelRequestOutputConfig

Output configuration for the Model export.

Fields
artifactDestination	object (GoogleCloudAiplatformV1beta1GcsDestination) The Cloud Storage location where the Model artifact is to be written to. Under the directory given as the destination a new one with name "model-export--", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format, will be created. Inside, the Model and any of its supporting files will be written. This field should only be set when the exportableContent field of the [Model.supported_export_formats] object contains ARTIFACT.
exportFormatId	string The ID of the format in which the Model must be exported. Each Model lists the export formats it supports. If no value is provided here, then the first from the list of the Model's supported formats is used by default.
imageDestination	object (GoogleCloudAiplatformV1beta1ContainerRegistryDestination) The Google Container Registry or Artifact Registry uri where the Model container image will be copied to. This field should only be set when the exportableContent field of the [Model.supported_export_formats] object contains IMAGE.

GoogleCloudAiplatformV1beta1ExportTensorboardTimeSeriesDataRequest

Request message for TensorboardService.ExportTensorboardTimeSeriesData.

Fields
filter	string Exports the TensorboardTimeSeries' data that match the filter expression.
orderBy	string Field to use to sort the TensorboardTimeSeries' data. By default, TensorboardTimeSeries' data is returned in a pseudo random order.
pageSize	integer (int32 format) The maximum number of data points to return per page. The default page_size is 1000. Values must be between 1 and 10000. Values above 10000 are coerced to 10000.
pageToken	string A page token, received from a previous ExportTensorboardTimeSeriesData call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to ExportTensorboardTimeSeriesData must match the call that provided the page token.

GoogleCloudAiplatformV1beta1ExportTensorboardTimeSeriesDataResponse

Response message for TensorboardService.ExportTensorboardTimeSeriesData.

Fields
nextPageToken	string A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.
timeSeriesDataPoints[]	object (GoogleCloudAiplatformV1beta1TimeSeriesDataPoint) The returned time series data points.

GoogleCloudAiplatformV1beta1Extension

Extensions are tools for large language models to access external data, run computations, etc.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this Extension was created.
description	string Optional. The description of the Extension.
displayName	string Required. The display name of the Extension. The name can be up to 128 characters long and can consist of any UTF-8 characters.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
extensionOperations[]	object (GoogleCloudAiplatformV1beta1ExtensionOperation) Output only. Supported operations.
manifest	object (GoogleCloudAiplatformV1beta1ExtensionManifest) Required. Manifest of the Extension.
name	string Identifier. The resource name of the Extension.
privateServiceConnectConfig	object (GoogleCloudAiplatformV1beta1ExtensionPrivateServiceConnectConfig) Optional. The PrivateServiceConnect config for the extension. If specified, the service endpoints associated with the Extension should be registered with private network access in the provided Service Directory (https://cloud.google.com/service-directory/docs/configuring-private-network-access). If the service contains more than one endpoint with a network, the service will arbitrarilty choose one of the endpoints to use for extension execution.
runtimeConfig	object (GoogleCloudAiplatformV1beta1RuntimeConfig) Optional. Runtime config controlling the runtime behavior of this Extension.
toolUseExamples[]	object (GoogleCloudAiplatformV1beta1ToolUseExample) Optional. Examples to illustrate the usage of the extension as a tool.
updateTime	string (Timestamp format) Output only. Timestamp when this Extension was most recently updated.

GoogleCloudAiplatformV1beta1ExtensionManifest

Manifest spec of an Extension needed for runtime execution.

Fields
apiSpec	object (GoogleCloudAiplatformV1beta1ExtensionManifestApiSpec) Required. Immutable. The API specification shown to the LLM.
authConfig	object (GoogleCloudAiplatformV1beta1AuthConfig) Required. Immutable. Type of auth supported by this extension.
description	string Required. The natural language description shown to the LLM. It should describe the usage of the extension, and is essential for the LLM to perform reasoning.
name	string Required. Extension name shown to the LLM. The name can be up to 128 characters long.

GoogleCloudAiplatformV1beta1ExtensionManifestApiSpec

The API specification shown to the LLM.

Fields
openApiGcsUri	string Cloud Storage URI pointing to the OpenAPI spec.
openApiYaml	string The API spec in Open API standard and YAML format.

GoogleCloudAiplatformV1beta1ExtensionOperation

Operation of an extension.

Fields
functionDeclaration	object (GoogleCloudAiplatformV1beta1FunctionDeclaration) Output only. Structured representation of a function declaration as defined by the OpenAPI Spec.
operationId	string Operation ID that uniquely identifies the operations among the extension. See: "Operation Object" in https://swagger.io/specification/. This field is parsed from the OpenAPI spec. For HTTP extensions, if it does not exist in the spec, we will generate one from the HTTP method and path.

GoogleCloudAiplatformV1beta1ExtensionPrivateServiceConnectConfig

PrivateExtensionConfig configuration for the extension.

Fields

serviceDirectory

string Required. The Service Directory resource name in which the service endpoints associated to the extension are registered. Format: projects/{project_id}/locations/{location_id}/namespaces/{namespace_id}/services/{service_id} - The Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) should be granted servicedirectory.viewer and servicedirectory.pscAuthorizedService roles on the resource.

GoogleCloudAiplatformV1beta1Feature

Feature Metadata information. For example, color is a feature that describes an apple.

Fields
createTime	string (Timestamp format) Output only. Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.
description	string Description of the Feature.
disableMonitoring	boolean Optional. Only applicable for Vertex AI Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
etag	string Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
monitoringConfig	object (GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfig) Optional. Only applicable for Vertex AI Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with FeaturestoreMonitoringConfig.disabled = true, snapshot analysis monitoring is disabled; if FeaturestoreMonitoringConfig.monitoring_interval specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.
monitoringStats[]	object (GoogleCloudAiplatformV1beta1FeatureStatsAnomaly) Output only. Only applicable for Vertex AI Feature Store (Legacy). A list of historical SnapshotAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.
monitoringStatsAnomalies[]	object (GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomaly) Output only. Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
name	string Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
pointOfContact	string Entity responsible for maintaining this feature. Can be comma separated list of email addresses or URIs.
updateTime	string (Timestamp format) Output only. Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
valueType	enum Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value.
Enum type. Can be one of the following:
VALUE_TYPE_UNSPECIFIED	The value type is unspecified.
BOOL	Used for Feature that is a boolean.
BOOL_ARRAY	Used for Feature that is a list of boolean.
DOUBLE	Used for Feature that is double.
DOUBLE_ARRAY	Used for Feature that is a list of double.
INT64	Used for Feature that is INT64.
INT64_ARRAY	Used for Feature that is a list of INT64.
STRING	Used for Feature that is string.
STRING_ARRAY	Used for Feature that is a list of String.
BYTES	Used for Feature that is bytes.
versionColumnName	string Only applicable for Vertex AI Feature Store. The name of the BigQuery Table/View column hosting data for this version. If no value is provided, will use feature_id.

GoogleCloudAiplatformV1beta1FeatureGroup

Vertex AI Feature Group.

Fields
bigQuery	object (GoogleCloudAiplatformV1beta1FeatureGroupBigQuery) Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source. The BigQuery source table or view must have at least one entity ID column and a column named feature_timestamp.
createTime	string (Timestamp format) Output only. Timestamp when this FeatureGroup was created.
description	string Optional. Description of the FeatureGroup.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) Optional. The labels with user-defined metadata to organize your FeatureGroup. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureGroup(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
name	string Identifier. Name of the FeatureGroup. Format: projects/{project}/locations/{location}/featureGroups/{featureGroup}
updateTime	string (Timestamp format) Output only. Timestamp when this FeatureGroup was last updated.

GoogleCloudAiplatformV1beta1FeatureGroupBigQuery

Input source type for BigQuery Tables and Views.

Fields
bigQuerySource	object (GoogleCloudAiplatformV1beta1BigQuerySource) Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View.
entityIdColumns[]	string Optional. Columns to construct entity_id / row keys. If not provided defaults to entity_id.

GoogleCloudAiplatformV1beta1FeatureMonitoringStatsAnomaly

A list of historical SnapshotAnalysis or ImportFeaturesAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.

Fields
featureStatsAnomaly	object (GoogleCloudAiplatformV1beta1FeatureStatsAnomaly) Output only. The stats and anomalies generated at specific timestamp.
objective	enum Output only. The objective for each stats.
Enum type. Can be one of the following:
OBJECTIVE_UNSPECIFIED	If it's OBJECTIVE_UNSPECIFIED, monitoring_stats will be empty.
IMPORT_FEATURE_ANALYSIS	Stats are generated by Import Feature Analysis.
SNAPSHOT_ANALYSIS	Stats are generated by Snapshot Analysis.

GoogleCloudAiplatformV1beta1FeatureNoiseSigma

Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients.

Fields
noiseSigma[]	object (GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeature) Noise sigma per feature. No noise is added to features that are not set.

GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeature

Noise sigma for a single feature.

Fields
name	string The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma	number (float format) This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.

GoogleCloudAiplatformV1beta1FeatureOnlineStore

Vertex AI Feature Online Store provides a centralized repository for serving ML features and embedding indexes at low latency. The Feature Online Store is a top-level container.

Fields
bigtable	object (GoogleCloudAiplatformV1beta1FeatureOnlineStoreBigtable) Contains settings for the Cloud Bigtable instance that will be created to serve featureValues for all FeatureViews under this FeatureOnlineStore.
createTime	string (Timestamp format) Output only. Timestamp when this FeatureOnlineStore was created.
dedicatedServingEndpoint	object (GoogleCloudAiplatformV1beta1FeatureOnlineStoreDedicatedServingEndpoint) Optional. The dedicated serving endpoint for this FeatureOnlineStore, which is different from common Vertex service endpoint.
embeddingManagement	object (GoogleCloudAiplatformV1beta1FeatureOnlineStoreEmbeddingManagement) Optional. Deprecated: This field is no longer needed anymore and embedding management is automatically enabled when specifying Optimized storage type.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) Optional. The labels with user-defined metadata to organize your FeatureOnlineStore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
name	string Identifier. Name of the FeatureOnlineStore. Format: projects/{project}/locations/{location}/featureOnlineStores/{featureOnlineStore}
optimized	object (GoogleCloudAiplatformV1beta1FeatureOnlineStoreOptimized) Contains settings for the Optimized store that will be created to serve featureValues for all FeatureViews under this FeatureOnlineStore. When choose Optimized storage type, need to set PrivateServiceConnectConfig.enable_private_service_connect to use private endpoint. Otherwise will use public endpoint by default.
state	enum Output only. State of the featureOnlineStore.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	Default value. This value is unused.
STABLE	State when the featureOnlineStore configuration is not being updated and the fields reflect the current configuration of the featureOnlineStore. The featureOnlineStore is usable in this state.
UPDATING	The state of the featureOnlineStore configuration when it is being updated. During an update, the fields reflect either the original configuration or the updated configuration of the featureOnlineStore. The featureOnlineStore is still usable in this state.
updateTime	string (Timestamp format) Output only. Timestamp when this FeatureOnlineStore was last updated.

GoogleCloudAiplatformV1beta1FeatureOnlineStoreBigtable

(No description provided)

Fields
autoScaling	object (GoogleCloudAiplatformV1beta1FeatureOnlineStoreBigtableAutoScaling) Required. Autoscaling config applied to Bigtable Instance.

GoogleCloudAiplatformV1beta1FeatureOnlineStoreBigtableAutoScaling

(No description provided)

Fields
cpuUtilizationTarget	integer (int32 format) Optional. A percentage of the cluster's CPU capacity. Can be from 10% to 80%. When a cluster's CPU utilization exceeds the target that you have set, Bigtable immediately adds nodes to the cluster. When CPU utilization is substantially lower than the target, Bigtable removes nodes. If not set will default to 50%.
maxNodeCount	integer (int32 format) Required. The maximum number of nodes to scale up to. Must be greater than or equal to min_node_count, and less than or equal to 10 times of 'min_node_count'.
minNodeCount	integer (int32 format) Required. The minimum number of nodes to scale down to. Must be greater than or equal to 1.

GoogleCloudAiplatformV1beta1FeatureOnlineStoreDedicatedServingEndpoint

The dedicated serving endpoint for this FeatureOnlineStore. Only need to set when you choose Optimized storage type. Public endpoint is provisioned by default.

Fields
privateServiceConnectConfig	object (GoogleCloudAiplatformV1beta1PrivateServiceConnectConfig) Optional. Private service connect config. The private service connection is available only for Optimized storage type, not for embedding management now. If PrivateServiceConnectConfig.enable_private_service_connect set to true, customers will use private service connection to send request. Otherwise, the connection will set to public endpoint.
publicEndpointDomainName	string Output only. This field will be populated with the domain name to use for this FeatureOnlineStore
serviceAttachment	string Output only. The name of the service attachment resource. Populated if private service connect is enabled and after FeatureViewSync is created.

GoogleCloudAiplatformV1beta1FeatureOnlineStoreEmbeddingManagement

Deprecated: This sub message is no longer needed anymore and embedding management is automatically enabled when specifying Optimized storage type. Contains settings for embedding management.

Fields
enabled	boolean Optional. Immutable. Whether to enable embedding management in this FeatureOnlineStore. It's immutable after creation to ensure the FeatureOnlineStore availability.

GoogleCloudAiplatformV1beta1FeatureSelector

Selector for Features of an EntityType.

Fields
idMatcher	object (GoogleCloudAiplatformV1beta1IdMatcher) Required. Matches Features based on ID.

GoogleCloudAiplatformV1beta1FeatureStatsAnomaly

Stats and Anomaly generated at specific timestamp for specific Feature. The start_time and end_time are used to define the time range of the dataset that current stats belongs to, e.g. prediction traffic is bucketed into prediction datasets by time window. If the Dataset is not defined by time window, start_time = end_time. Timestamp of the stats and anomalies always refers to end_time. Raw stats and anomalies are stored in stats_uri or anomaly_uri in the tensorflow defined protos. Field data_stats contains almost identical information with the raw stats in Vertex AI defined proto, for UI to display.

Fields
anomalyDetectionThreshold	number (double format) This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
anomalyUri	string Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message tensorflow.metadata.v0.AnomalyInfo.
distributionDeviation	number (double format) Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
endTime	string (Timestamp format) The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
score	number (double format) Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
startTime	string (Timestamp format) The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
statsUri	string Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

GoogleCloudAiplatformV1beta1FeatureValue

Value for a feature.

Fields
boolArrayValue	object (GoogleCloudAiplatformV1beta1BoolArray) A list of bool type feature value.
boolValue	boolean Bool type feature value.
bytesValue	string (bytes format) Bytes feature value.
doubleArrayValue	object (GoogleCloudAiplatformV1beta1DoubleArray) A list of double type feature value.
doubleValue	number (double format) Double type feature value.
int64ArrayValue	object (GoogleCloudAiplatformV1beta1Int64Array) A list of int64 type feature value.
int64Value	string (int64 format) Int64 feature value.
metadata	object (GoogleCloudAiplatformV1beta1FeatureValueMetadata) Metadata of feature value.
stringArrayValue	object (GoogleCloudAiplatformV1beta1StringArray) A list of string type feature value.
stringValue	string String feature value.

GoogleCloudAiplatformV1beta1FeatureValueDestination

A destination location for Feature values and format.

Fields
bigqueryDestination	object (GoogleCloudAiplatformV1beta1BigQueryDestination) Output in BigQuery format. BigQueryDestination.output_uri in FeatureValueDestination.bigquery_destination must refer to a table.
csvDestination	object (GoogleCloudAiplatformV1beta1CsvDestination) Output in CSV format. Array Feature value types are not allowed in CSV format.
tfrecordDestination	object (GoogleCloudAiplatformV1beta1TFRecordDestination) Output in TFRecord format. Below are the mapping from Feature value type in Featurestore to Feature value type in TFRecord: Value type in Featurestore | Value type in TFRecord DOUBLE, DOUBLE_ARRAY | FLOAT_LIST INT64, INT64_ARRAY | INT64_LIST STRING, STRING_ARRAY, BYTES | BYTES_LIST true -> byte_string("true"), false -> byte_string("false") BOOL, BOOL_ARRAY (true, false) | BYTES_LIST

GoogleCloudAiplatformV1beta1FeatureValueList

Container for list of values.

Fields
values[]	object (GoogleCloudAiplatformV1beta1FeatureValue) A list of feature values. All of them should be the same data type.

GoogleCloudAiplatformV1beta1FeatureValueMetadata

Metadata of feature value.

Fields
generateTime	string (Timestamp format) Feature generation timestamp. Typically, it is provided by user at feature ingestion time. If not, feature store will use the system timestamp when the data is ingested into feature store. For streaming ingestion, the time, aligned by days, must be no older than five years (1825 days) and no later than one year (366 days) in the future.

GoogleCloudAiplatformV1beta1FeatureView

FeatureView is representation of values that the FeatureOnlineStore will serve based on its syncConfig.

Fields
bigQuerySource	object (GoogleCloudAiplatformV1beta1FeatureViewBigQuerySource) Optional. Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
createTime	string (Timestamp format) Output only. Timestamp when this FeatureView was created.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
featureRegistrySource	object (GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySource) Optional. Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
indexConfig	object (GoogleCloudAiplatformV1beta1FeatureViewIndexConfig) Optional. Configuration for index preparation for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
labels	map (key: string, value: string) Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
name	string Identifier. Name of the FeatureView. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}
serviceAccountEmail	string Output only. A Service Account unique to this FeatureView. The role bigquery.dataViewer should be granted to this service account to allow Vertex AI Feature Store to sync data to the online store.
serviceAgentType	enum Optional. Service agent type used during data sync. By default, the Vertex AI Service Agent is used. When using an IAM Policy to isolate this FeatureView within a project, a separate service account should be provisioned by setting this field to SERVICE_AGENT_TYPE_FEATURE_VIEW. This will generate a separate service account to access the BigQuery source table.
Enum type. Can be one of the following:
SERVICE_AGENT_TYPE_UNSPECIFIED	By default, the project-level Vertex AI Service Agent is enabled.
SERVICE_AGENT_TYPE_PROJECT	Indicates the project-level Vertex AI Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) will be used during sync jobs.
SERVICE_AGENT_TYPE_FEATURE_VIEW	Enable a FeatureView service account to be created by Vertex AI and output in the field service_account_email. This service account will be used to read from the source BigQuery table during sync.
syncConfig	object (GoogleCloudAiplatformV1beta1FeatureViewSyncConfig) Configures when data is to be synced/updated for this FeatureView. At the end of the sync the latest featureValues for each entityId of this FeatureView are made ready for online serving.
updateTime	string (Timestamp format) Output only. Timestamp when this FeatureView was last updated.
vectorSearchConfig	object (GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig) Optional. Deprecated: please use FeatureView.index_config instead.

GoogleCloudAiplatformV1beta1FeatureViewBigQuerySource

(No description provided)

Fields
entityIdColumns[]	string Required. Columns to construct entity_id / row keys.
uri	string Required. The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.

GoogleCloudAiplatformV1beta1FeatureViewDataKey

Lookup key for a feature view.

Fields
compositeKey	object (GoogleCloudAiplatformV1beta1FeatureViewDataKeyCompositeKey) The actual Entity ID will be composed from this struct. This should match with the way ID is defined in the FeatureView spec.
key	string String key to use for lookup.

GoogleCloudAiplatformV1beta1FeatureViewDataKeyCompositeKey

ID that is comprised from several parts (columns).

Fields
parts[]	string Parts to construct Entity ID. Should match with the same ID columns as defined in FeatureView in the same order.

GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySource

A Feature Registry source for features that need to be synced to Online Store.

Fields
featureGroups[]	object (GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceFeatureGroup) Required. List of features that need to be synced to Online Store.
projectNumber	string (int64 format) Optional. The project number of the parent project of the Feature Groups.

GoogleCloudAiplatformV1beta1FeatureViewFeatureRegistrySourceFeatureGroup

Features belonging to a single feature group that will be synced to Online Store.

Fields
featureGroupId	string Required. Identifier of the feature group.
featureIds[]	string Required. Identifiers of features under the feature group.

GoogleCloudAiplatformV1beta1FeatureViewIndexConfig

Configuration for vector indexing.

Fields
bruteForceConfig	object (GoogleCloudAiplatformV1beta1FeatureViewIndexConfigBruteForceConfig) Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
crowdingColumn	string Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it's guaranteed that no more than K entities of the same crowding attribute are returned in the response.
distanceMeasureType	enum Optional. The distance measure used in nearest neighbor search.
Enum type. Can be one of the following:
DISTANCE_MEASURE_TYPE_UNSPECIFIED	Should not be set.
SQUARED_L2_DISTANCE	Euclidean (L_2) Distance.
COSINE_DISTANCE	Cosine Distance. Defined as 1 - cosine similarity. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of COSINE distance. Our algorithms have been more optimized for DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is mathematically equivalent to COSINE distance and results in the same ranking.
DOT_PRODUCT_DISTANCE	Dot Product Distance. Defined as a negative of the dot product.
embeddingColumn	string Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
embeddingDimension	integer (int32 format) Optional. The number of dimensions of the input embedding.
filterColumns[]	string Optional. Columns of features that're used to filter vector search results.
treeAhConfig	object (GoogleCloudAiplatformV1beta1FeatureViewIndexConfigTreeAHConfig) Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

GoogleCloudAiplatformV1beta1FeatureViewIndexConfigTreeAHConfig

Configuration options for the tree-AH algorithm.

Fields
leafNodeEmbeddingCount	string (int64 format) Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.

GoogleCloudAiplatformV1beta1FeatureViewSync

FeatureViewSync is a representation of sync operation which copies data from data source to Feature View in Online Store.

Fields
createTime	string (Timestamp format) Output only. Time when this FeatureViewSync is created. Creation of a FeatureViewSync means that the job is pending / waiting for sufficient resources but may not have started the actual data transfer yet.
finalStatus	object (GoogleRpcStatus) Output only. Final status of the FeatureViewSync.
name	string Identifier. Name of the FeatureViewSync. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}/featureViewSyncs/{feature_view_sync}
runTime	object (GoogleTypeInterval) Output only. Time when this FeatureViewSync is finished.
syncSummary	object (GoogleCloudAiplatformV1beta1FeatureViewSyncSyncSummary) Output only. Summary of the sync job.

GoogleCloudAiplatformV1beta1FeatureViewSyncConfig

Configuration for Sync. Only one option is set.

Fields
cron	string Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * ", or "TZ=America/New_York 1 * * * ".

GoogleCloudAiplatformV1beta1FeatureViewSyncSyncSummary

Summary from the Sync job. For continuous syncs, the summary is updated periodically. For batch syncs, it gets updated on completion of the sync.

Fields
rowSynced	string (int64 format) Output only. Total number of rows synced.
totalSlot	string (int64 format) Output only. BigQuery slot milliseconds consumed for the sync job.

GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig

Deprecated. Use IndexConfig instead.

Fields
bruteForceConfig	object (GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfig) Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
crowdingColumn	string Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it's guaranteed that no more than K entities of the same crowding attribute are returned in the response.
distanceMeasureType	enum Optional. The distance measure used in nearest neighbor search.
Enum type. Can be one of the following:
DISTANCE_MEASURE_TYPE_UNSPECIFIED	Should not be set.
SQUARED_L2_DISTANCE	Euclidean (L_2) Distance.
COSINE_DISTANCE	Cosine Distance. Defined as 1 - cosine similarity. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of COSINE distance. Our algorithms have been more optimized for DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is mathematically equivalent to COSINE distance and results in the same ranking.
DOT_PRODUCT_DISTANCE	Dot Product Distance. Defined as a negative of the dot product.
embeddingColumn	string Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
embeddingDimension	integer (int32 format) Optional. The number of dimensions of the input embedding.
filterColumns[]	string Optional. Columns of features that're used to filter vector search results.
treeAhConfig	object (GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfig) Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfig

(No description provided)

Fields
leafNodeEmbeddingCount	string (int64 format) Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.

GoogleCloudAiplatformV1beta1Featurestore

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this Featurestore was created.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Optional. Customer-managed encryption key spec for data storage. If set, both of the online and offline data storage will be secured by this key.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) Optional. The labels with user-defined metadata to organize your Featurestore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Featurestore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
name	string Output only. Name of the Featurestore. Format: projects/{project}/locations/{location}/featurestores/{featurestore}
onlineServingConfig	object (GoogleCloudAiplatformV1beta1FeaturestoreOnlineServingConfig) Optional. Config for online storage resources. The field should not co-exist with the field of OnlineStoreReplicationConfig. If both of it and OnlineStoreReplicationConfig are unset, the feature store will not have an online store and cannot be used for online serving.
onlineStorageTtlDays	integer (int32 format) Optional. TTL in days for feature values that will be stored in online serving storage. The Feature Store online storage periodically removes obsolete feature values older than online_storage_ttl_days since the feature generation time. Note that online_storage_ttl_days should be less than or equal to offline_storage_ttl_days for each EntityType under a featurestore. If not set, default to 4000 days
state	enum Output only. State of the featurestore.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	Default value. This value is unused.
STABLE	State when the featurestore configuration is not being updated and the fields reflect the current configuration of the featurestore. The featurestore is usable in this state.
UPDATING	The state of the featurestore configuration when it is being updated. During an update, the fields reflect either the original configuration or the updated configuration of the featurestore. For example, online_serving_config.fixed_node_count can take minutes to update. While the update is in progress, the featurestore is in the UPDATING state, and the value of fixed_node_count can be the original value or the updated value, depending on the progress of the operation. Until the update completes, the actual number of nodes can still be the original value of fixed_node_count. The featurestore is still usable in this state.
updateTime	string (Timestamp format) Output only. Timestamp when this Featurestore was last updated.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfig

Configuration of how features in Featurestore are monitored.

Fields
categoricalThresholdConfig	object (GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig) Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
importFeaturesAnalysis	object (GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysis) The config for ImportFeatures Analysis Based Feature Monitoring.
numericalThresholdConfig	object (GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig) Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
snapshotAnalysis	object (GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysis) The config for Snapshot Analysis Based Feature Monitoring.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigImportFeaturesAnalysis

Configuration of the Featurestore's ImportFeature Analysis Based Monitoring. This type of analysis generates statistics for values of each Feature imported by every ImportFeatureValues operation.

Fields
anomalyDetectionBaseline	enum The baseline used to do anomaly detection for the statistics generated by import features analysis.
Enum type. Can be one of the following:
BASELINE_UNSPECIFIED	Should not be used.
LATEST_STATS	Choose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
MOST_RECENT_SNAPSHOT_STATS	Use the statistics generated by the most recent snapshot analysis if exists.
PREVIOUS_IMPORT_FEATURES_STATS	Use the statistics generated by the previous import features analysis if exists.
state	enum Whether to enable / disable / inherite default hebavior for import features analysis.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	Should not be used.
DEFAULT	The default behavior of whether to enable the monitoring. EntityType-level config: disabled. Feature-level config: inherited from the configuration of EntityType this Feature belongs to.
ENABLED	Explicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it. Feature-level config: enables import features analysis regardless of the EntityType-level config.
DISABLED	Explicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it. Feature-level config: disables import features analysis regardless of the EntityType-level config.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigSnapshotAnalysis

Configuration of the Featurestore's Snapshot Analysis Based Monitoring. This type of analysis generates statistics for each Feature based on a snapshot of the latest feature value of each entities every monitoring_interval.

Fields
disabled	boolean The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
monitoringInterval	string (Duration format) Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
monitoringIntervalDays	integer (int32 format) Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
stalenessDays	integer (int32 format) Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.

GoogleCloudAiplatformV1beta1FeaturestoreMonitoringConfigThresholdConfig

The config for Featurestore Monitoring threshold.

Fields
value	number (double format) Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

GoogleCloudAiplatformV1beta1FeaturestoreOnlineServingConfig

OnlineServingConfig specifies the details for provisioning online serving resources.

Fields
fixedNodeCount	integer (int32 format) The number of nodes for the online store. The number of nodes doesn't scale automatically, but you can manually update the number of nodes. If set to 0, the featurestore will not have an online store and cannot be used for online serving.
scaling	object (GoogleCloudAiplatformV1beta1FeaturestoreOnlineServingConfigScaling) Online serving scaling configuration. Only one of fixed_node_count and scaling can be set. Setting one will reset the other.

GoogleCloudAiplatformV1beta1FeaturestoreOnlineServingConfigScaling

Online serving scaling configuration. If min_node_count and max_node_count are set to the same value, the cluster will be configured with the fixed number of node (no auto-scaling).

Fields
cpuUtilizationTarget	integer (int32 format) Optional. The cpu utilization that the Autoscaler should be trying to achieve. This number is on a scale from 0 (no utilization) to 100 (total utilization), and is limited between 10 and 80. When a cluster's CPU utilization exceeds the target that you have set, Bigtable immediately adds nodes to the cluster. When CPU utilization is substantially lower than the target, Bigtable removes nodes. If not set or set to 0, default to 50.
maxNodeCount	integer (int32 format) The maximum number of nodes to scale up to. Must be greater than min_node_count, and less than or equal to 10 times of 'min_node_count'.
minNodeCount	integer (int32 format) Required. The minimum number of nodes to scale down to. Must be greater than or equal to 1.

GoogleCloudAiplatformV1beta1FetchFeatureValuesRequest

Request message for FeatureOnlineStoreService.FetchFeatureValues. All the features under the requested feature view will be returned.

Fields
dataFormat	enum Optional. Response data format. If not set, FeatureViewDataFormat.KEY_VALUE will be used.
Enum type. Can be one of the following:
FEATURE_VIEW_DATA_FORMAT_UNSPECIFIED	Not set. Will be treated as the KeyValue format.
KEY_VALUE	Return response data in key-value format.
PROTO_STRUCT	Return response data in proto Struct format.
dataKey	object (GoogleCloudAiplatformV1beta1FeatureViewDataKey) Optional. The request key to fetch feature values for.
format	enum Specify response data format. If not set, KeyValue format will be used. Deprecated. Use FetchFeatureValuesRequest.data_format.
Enum type. Can be one of the following:
FORMAT_UNSPECIFIED	Not set. Will be treated as the KeyValue format.
KEY_VALUE	Return response data in key-value format.
PROTO_STRUCT	Return response data in proto Struct format.
id	string Simple ID. The whole string will be used as is to identify Entity to fetch feature values for.

GoogleCloudAiplatformV1beta1FetchFeatureValuesResponse

Response message for FeatureOnlineStoreService.FetchFeatureValues

Fields
dataKey	object (GoogleCloudAiplatformV1beta1FeatureViewDataKey) The data key associated with this response. Will only be populated for FeatureOnlineStoreService.StreamingFetchFeatureValues RPCs.
keyValues	object (GoogleCloudAiplatformV1beta1FetchFeatureValuesResponseFeatureNameValuePairList) Feature values in KeyValue format.
protoStruct	map (key: string, value: any) Feature values in proto Struct format.

GoogleCloudAiplatformV1beta1FetchFeatureValuesResponseFeatureNameValuePairList

Response structure in the format of key (feature name) and (feature) value pair.

Fields
features[]	object (GoogleCloudAiplatformV1beta1FetchFeatureValuesResponseFeatureNameValuePairListFeatureNameValuePair) List of feature names and values.

GoogleCloudAiplatformV1beta1FetchFeatureValuesResponseFeatureNameValuePairListFeatureNameValuePair

Feature name & value pair.

Fields
name	string Feature short name.
value	object (GoogleCloudAiplatformV1beta1FeatureValue) Feature value.

GoogleCloudAiplatformV1beta1FileData

URI based data.

Fields
fileUri	string Required. URI.
mimeType	string Required. The IANA standard MIME type of the source data.

GoogleCloudAiplatformV1beta1FilterSplit

Assigns input data to training, validation, and test sets based on the given filters, data pieces not matched by any filter are ignored. Currently only supported for Datasets containing DataItems. If any of the filters in this message are to match nothing, then they can be set as '-' (the minus sign). Supported only for unstructured Datasets.

Fields
testFilter	string Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
trainingFilter	string Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
validationFilter	string Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.

GoogleCloudAiplatformV1beta1FindNeighborsRequest

The request message for MatchService.FindNeighbors.

Fields
deployedIndexId	string The ID of the DeployedIndex that will serve the request. This request is sent to a specific IndexEndpoint, as per the IndexEndpoint.network. That IndexEndpoint also has IndexEndpoint.deployed_indexes, and each such index has a DeployedIndex.id field. The value of the field below must equal one of the DeployedIndex.id fields of the IndexEndpoint that is being called for this request.
queries[]	object (GoogleCloudAiplatformV1beta1FindNeighborsRequestQuery) The list of queries.
returnFullDatapoint	boolean If set to true, the full datapoints (including all vector values and restricts) of the nearest neighbors are returned. Note that returning full datapoint will significantly increase the latency and cost of the query.

GoogleCloudAiplatformV1beta1FindNeighborsRequestQuery

A query to find a number of the nearest neighbors (most similar vectors) of a vector.

Fields
approximateNeighborCount	integer (int32 format) The number of neighbors to find via approximate search before exact reordering is performed. If not set, the default value from scam config is used; if set, this value must be > 0.
datapoint	object (GoogleCloudAiplatformV1beta1IndexDatapoint) Required. The datapoint/vector whose nearest neighbors should be searched for.
fractionLeafNodesToSearchOverride	number (double format) The fraction of the number of leaves to search, set at query time allows user to tune search performance. This value increase result in both search accuracy and latency increase. The value should be between 0.0 and 1.0. If not set or set to 0.0, query uses the default value specified in NearestNeighborSearchConfig.TreeAHConfig.fraction_leaf_nodes_to_search.
neighborCount	integer (int32 format) The number of nearest neighbors to be retrieved from database for each query. If not set, will use the default from the service configuration (https://cloud.google.com/vertex-ai/docs/matching-engine/configuring-indexes#nearest-neighbor-search-config).
perCrowdingAttributeNeighborCount	integer (int32 format) Crowding is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute. It's used for improving result diversity. This field is the maximum number of matches with the same crowding tag.
rrf	object (GoogleCloudAiplatformV1beta1FindNeighborsRequestQueryRRF) Optional. Represents RRF algorithm that combines search results.

GoogleCloudAiplatformV1beta1FindNeighborsRequestQueryRRF

Parameters for RRF algorithm that combines search results.

Fields
alpha	number (float format) Required. Users can provide an alpha value to give more weight to dense vs sparse results. For example, if the alpha is 0, we only return sparse and if the alpha is 1, we only return dense.

GoogleCloudAiplatformV1beta1FindNeighborsResponse

The response message for MatchService.FindNeighbors.

Fields
nearestNeighbors[]	object (GoogleCloudAiplatformV1beta1FindNeighborsResponseNearestNeighbors) The nearest neighbors of the query datapoints.

GoogleCloudAiplatformV1beta1FindNeighborsResponseNearestNeighbors

Nearest neighbors for one query.

Fields
id	string The ID of the query datapoint.
neighbors[]	object (GoogleCloudAiplatformV1beta1FindNeighborsResponseNeighbor) All its neighbors.

GoogleCloudAiplatformV1beta1FindNeighborsResponseNeighbor

A neighbor of the query vector.

Fields
datapoint	object (GoogleCloudAiplatformV1beta1IndexDatapoint) The datapoint of the neighbor. Note that full datapoints are returned only when "return_full_datapoint" is set to true. Otherwise, only the "datapoint_id" and "crowding_tag" fields are populated.
distance	number (double format) The distance between the neighbor and the dense embedding query.
sparseDistance	number (double format) The distance between the neighbor and the query sparse_embedding.

GoogleCloudAiplatformV1beta1FluencyInput

Input for fluency metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1FluencyInstance) Required. Fluency instance.
metricSpec	object (GoogleCloudAiplatformV1beta1FluencySpec) Required. Spec for fluency score metric.

GoogleCloudAiplatformV1beta1FluencyInstance

Spec for fluency instance.

Fields
prediction	string Required. Output of the evaluated model.

GoogleCloudAiplatformV1beta1FluencyResult

Spec for fluency result.

Fields
confidence	number (float format) Output only. Confidence for fluency score.
explanation	string Output only. Explanation for fluency score.
score	number (float format) Output only. Fluency score.

GoogleCloudAiplatformV1beta1FluencySpec

Spec for fluency score metric.

Fields
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1FractionSplit

Assigns the input data to training, validation, and test sets as per the given fractions. Any of training_fraction, validation_fraction and test_fraction may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test.

Fields
testFraction	number (double format) The fraction of the input data that is to be used to evaluate the Model.
trainingFraction	number (double format) The fraction of the input data that is to be used to train the Model.
validationFraction	number (double format) The fraction of the input data that is to be used to validate the Model.

GoogleCloudAiplatformV1beta1FulfillmentInput

Input for fulfillment metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1FulfillmentInstance) Required. Fulfillment instance.
metricSpec	object (GoogleCloudAiplatformV1beta1FulfillmentSpec) Required. Spec for fulfillment score metric.

GoogleCloudAiplatformV1beta1FulfillmentInstance

Spec for fulfillment instance.

Fields
instruction	string Required. Inference instruction prompt to compare prediction with.
prediction	string Required. Output of the evaluated model.

GoogleCloudAiplatformV1beta1FulfillmentResult

Spec for fulfillment result.

Fields
confidence	number (float format) Output only. Confidence for fulfillment score.
explanation	string Output only. Explanation for fulfillment score.
score	number (float format) Output only. Fulfillment score.

GoogleCloudAiplatformV1beta1FulfillmentSpec

Spec for fulfillment metric.

Fields
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1FunctionCall

A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values.

Fields
args	map (key: string, value: any) Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
name	string Required. The name of the function to call. Matches [FunctionDeclaration.name].

GoogleCloudAiplatformV1beta1FunctionCallingConfig

Function calling config.

Fields
allowedFunctionNames[]	string Optional. Function names to call. Only set when the Mode is ANY. Function names should match [FunctionDeclaration.name]. With mode set to ANY, model will predict a function call from the set of function names provided.
mode	enum Optional. Function calling mode.
Enum type. Can be one of the following:
MODE_UNSPECIFIED	Unspecified function calling mode. This value should not be used.
AUTO	Default model behavior, model decides to predict either a function call or a natural language repspose.
ANY	Model is constrained to always predicting a function call only. If "allowed_function_names" are set, the predicted function call will be limited to any one of "allowed_function_names", else the predicted function call will be any one of the provided "function_declarations".
NONE	Model will not predict any function call. Model behavior is same as when not passing any function declarations.

GoogleCloudAiplatformV1beta1FunctionDeclaration

Structured representation of a function declaration as defined by the OpenAPI 3.0 specification. Included in this declaration are the function name and parameters. This FunctionDeclaration is a representation of a block of code that can be used as a Tool by the model and executed by the client.

Fields
description	string Optional. Description and purpose of the function. Model uses it to decide how and whether to call the function.
name	string Required. The name of the function to call. Must start with a letter or an underscore. Must be a-z, A-Z, 0-9, or contain underscores, dots and dashes, with a maximum length of 64.
parameters	object (GoogleCloudAiplatformV1beta1Schema) Optional. Describes the parameters to this function in JSON Schema Object format. Reflects the Open API 3.03 Parameter Object. string Key: the name of the parameter. Parameter names are case sensitive. Schema Value: the Schema defining the type used for the parameter. For function with no parameters, this can be left unset. Parameter names must start with a letter or an underscore and must only contain chars a-z, A-Z, 0-9, or underscores with a maximum length of 64. Example with 1 required and 1 optional parameter: type: OBJECT properties: param1: type: STRING param2: type: INTEGER required: - param1
response	object (GoogleCloudAiplatformV1beta1Schema) Optional. Describes the output from this function in JSON Schema format. Reflects the Open API 3.03 Response Object. The Schema defines the type used for the response value of the function.

GoogleCloudAiplatformV1beta1FunctionResponse

The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction.

Fields
name	string Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
response	map (key: string, value: any) Required. The function response in JSON object format.

GoogleCloudAiplatformV1beta1GcsDestination

The Google Cloud Storage location where the output is to be written to.

Fields
outputUriPrefix	string Required. Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

GoogleCloudAiplatformV1beta1GcsSource

The Google Cloud Storage location for the input content.

Fields
uris[]	string Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.

GoogleCloudAiplatformV1beta1GenerateAccessTokenRequest

Request message for NotebookInternalService.GenerateAccessToken.

Fields
vmToken	string Required. The VM identity token (a JWT) for authenticating the VM. https://cloud.google.com/compute/docs/instances/verifying-instance-identity

GoogleCloudAiplatformV1beta1GenerateAccessTokenResponse

Response message for NotebookInternalService.GenerateToken.

Fields
accessToken	string Short-lived access token string which may be used to access Google APIs.
expiresIn	integer (int32 format) The time in seconds when the access token expires. Typically that's 3600.
scope	string Space-separated list of scopes contained in the returned token. https://cloud.google.com/docs/authentication/token-types#access-contents
tokenType	string Type of the returned access token (e.g. "Bearer"). It specifies how the token must be used. Bearer tokens may be used by any entity without proof of identity.

GoogleCloudAiplatformV1beta1GenerateContentRequest

Request message for [PredictionService.GenerateContent].

Fields
contents[]	object (GoogleCloudAiplatformV1beta1Content) Required. The content of the current conversation with the model. For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history + latest request.
generationConfig	object (GoogleCloudAiplatformV1beta1GenerationConfig) Optional. Generation config.
safetySettings[]	object (GoogleCloudAiplatformV1beta1SafetySetting) Optional. Per request settings for blocking unsafe content. Enforced on GenerateContentResponse.candidates.
systemInstruction	object (GoogleCloudAiplatformV1beta1Content) Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph.
toolConfig	object (GoogleCloudAiplatformV1beta1ToolConfig) Optional. Tool config. This config is shared for all tools provided in the request.
tools[]	object (GoogleCloudAiplatformV1beta1Tool) Optional. A list of Tools the model may use to generate the next response. A Tool is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.

GoogleCloudAiplatformV1beta1GenerateContentResponse

Response message for [PredictionService.GenerateContent].

Fields
candidates[]	object (GoogleCloudAiplatformV1beta1Candidate) Output only. Generated candidates.
promptFeedback	object (GoogleCloudAiplatformV1beta1GenerateContentResponsePromptFeedback) Output only. Content filter results for a prompt sent in the request. Note: Sent only in the first stream chunk. Only happens when no candidates were generated due to content violations.
usageMetadata	object (GoogleCloudAiplatformV1beta1GenerateContentResponseUsageMetadata) Usage metadata about the response(s).

GoogleCloudAiplatformV1beta1GenerateContentResponsePromptFeedback

Content filter results for a prompt sent in the request.

Fields
blockReason	enum Output only. Blocked reason.
Enum type. Can be one of the following:
BLOCKED_REASON_UNSPECIFIED	Unspecified blocked reason.
SAFETY	Candidates blocked due to safety.
OTHER	Candidates blocked due to other reason.
BLOCKLIST	Candidates blocked due to the terms which are included from the terminology blocklist.
PROHIBITED_CONTENT	Candidates blocked due to prohibited content.
blockReasonMessage	string Output only. A readable block reason message.
safetyRatings[]	object (GoogleCloudAiplatformV1beta1SafetyRating) Output only. Safety ratings.

GoogleCloudAiplatformV1beta1GenerateContentResponseUsageMetadata

Usage metadata about response(s).

Fields
candidatesTokenCount	integer (int32 format) Number of tokens in the response(s).
promptTokenCount	integer (int32 format) Number of tokens in the request.
totalTokenCount	integer (int32 format) (No description provided)

GoogleCloudAiplatformV1beta1GenerationConfig

Generation config.

Fields
candidateCount	integer (int32 format) Optional. Number of candidates to generate.
frequencyPenalty	number (float format) Optional. Frequency penalties.
maxOutputTokens	integer (int32 format) Optional. The maximum number of output tokens to generate per message.
presencePenalty	number (float format) Optional. Positive penalties.
responseMimeType	string Optional. Output response mimetype of the generated candidate text. Supported mimetype: - text/plain: (default) Text output. - application/json: JSON response in the candidates. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
responseStyle	enum Optional. Control Three levels of creativity in the model output. Default: RESPONSE_STYLE_BALANCED
Enum type. Can be one of the following:
RESPONSE_STYLE_UNSPECIFIED	response style unspecified.
RESPONSE_STYLE_PRECISE	Precise response.
RESPONSE_STYLE_BALANCED	Default response style.
RESPONSE_STYLE_CREATIVE	Creative response style.
stopSequences[]	string Optional. Stop sequences.
temperature	number (float format) Optional. Controls the randomness of predictions.
topK	number (float format) Optional. If specified, top-k sampling will be used.
topP	number (float format) Optional. If specified, nucleus sampling will be used.

GoogleCloudAiplatformV1beta1GenericOperationMetadata

Generic Metadata shared by all operations.

Fields
createTime	string (Timestamp format) Output only. Time when the operation was created.
partialFailures[]	object (GoogleRpcStatus) Output only. Partial failures encountered. E.g. single files that couldn't be read. This field should never exceed 20 entries. Status details field will contain standard Google Cloud error details.
updateTime	string (Timestamp format) Output only. Time when the operation was updated for the last time. If the operation has finished (successfully or not), this is the finish time.

GoogleCloudAiplatformV1beta1GenieSource

Contains information about the source of the models generated from Generative AI Studio.

Fields
baseModelUri	string Required. The public base model URI.

GoogleCloudAiplatformV1beta1GoogleDriveSource

The Google Drive location for the input content.

Fields
resourceIds[]	object (GoogleCloudAiplatformV1beta1GoogleDriveSourceResourceId) Required. Google Drive resource IDs.

GoogleCloudAiplatformV1beta1GoogleDriveSourceResourceId

The type and ID of the Google Drive resource.

Fields
resourceId	string Required. The ID of the Google Drive resource.
resourceType	enum Required. The type of the Google Drive resource.
Enum type. Can be one of the following:
RESOURCE_TYPE_UNSPECIFIED	Unspecified resource type.
RESOURCE_TYPE_FILE	File resource type.
RESOURCE_TYPE_FOLDER	Folder resource type.

GoogleCloudAiplatformV1beta1GroundednessInput

Input for groundedness metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1GroundednessInstance) Required. Groundedness instance.
metricSpec	object (GoogleCloudAiplatformV1beta1GroundednessSpec) Required. Spec for groundedness metric.

GoogleCloudAiplatformV1beta1GroundednessInstance

Spec for groundedness instance.

Fields
context	string Required. Background information provided in context used to compare against the prediction.
prediction	string Required. Output of the evaluated model.

GoogleCloudAiplatformV1beta1GroundednessResult

Spec for groundedness result.

Fields
confidence	number (float format) Output only. Confidence for groundedness score.
explanation	string Output only. Explanation for groundedness score.
score	number (float format) Output only. Groundedness score.

GoogleCloudAiplatformV1beta1GroundednessSpec

Spec for groundedness metric.

Fields
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1GroundingMetadata

Metadata returned to client when grounding is enabled.

Fields
retrievalQueries[]	string Optional. Queries executed by the retrieval tools.
searchEntryPoint	object (GoogleCloudAiplatformV1beta1SearchEntryPoint) Optional. Google search entry for the following-up web searches.
webSearchQueries[]	string Optional. Web search queries for the following-up web search.

GoogleCloudAiplatformV1beta1HyperparameterTuningJob

Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification.

Fields
createTime	string (Timestamp format) Output only. Time when the HyperparameterTuningJob was created.
displayName	string Required. The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
endTime	string (Timestamp format) Output only. Time when the HyperparameterTuningJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error	object (GoogleRpcStatus) Output only. Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
labels	map (key: string, value: string) The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
maxFailedTrialCount	integer (int32 format) The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
maxTrialCount	integer (int32 format) Required. The desired total number of Trials.
name	string Output only. Resource name of the HyperparameterTuningJob.
parallelTrialCount	integer (int32 format) Required. The desired number of Trials to run in parallel.
startTime	string (Timestamp format) Output only. Time when the HyperparameterTuningJob for the first time entered the JOB_STATE_RUNNING state.
state	enum Output only. The detailed state of the job.
Enum type. Can be one of the following:
JOB_STATE_UNSPECIFIED	The job state is unspecified.
JOB_STATE_QUEUED	The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING	The service is preparing to run the job.
JOB_STATE_RUNNING	The job is in progress.
JOB_STATE_SUCCEEDED	The job completed successfully.
JOB_STATE_FAILED	The job failed.
JOB_STATE_CANCELLING	The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED	The job has been cancelled.
JOB_STATE_PAUSED	The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED	The job has expired.
JOB_STATE_UPDATING	The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED	The job is partially succeeded, some results may be missing due to errors.
studySpec	object (GoogleCloudAiplatformV1beta1StudySpec) Required. Study configuration of the HyperparameterTuningJob.
trialJobSpec	object (GoogleCloudAiplatformV1beta1CustomJobSpec) Required. The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
trials[]	object (GoogleCloudAiplatformV1beta1Trial) Output only. Trials of the HyperparameterTuningJob.
updateTime	string (Timestamp format) Output only. Time when the HyperparameterTuningJob was most recently updated.

GoogleCloudAiplatformV1beta1IdMatcher

Matcher for Features of an EntityType by Feature ID.

Fields
ids[]	string Required. The following are accepted as ids: * A single-element list containing only *, which selects all Features in the target EntityType, or * A list containing only Feature IDs, which selects only Features with those IDs in the target EntityType.

GoogleCloudAiplatformV1beta1ImportDataConfig

Describes the location from where we import data into a Dataset, together with the labels that will be applied to the DataItems and the Annotations.

Fields
annotationLabels	map (key: string, value: string) Labels that will be applied to newly imported Annotations. If two Annotations are identical, one of them will be deduped. Two Annotations are considered identical if their payload, payload_schema_uri and all of their labels are the same. These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file.
dataItemLabels	map (key: string, value: string) Labels that will be applied to newly imported DataItems. If an identical DataItem as one being imported already exists in the Dataset, then these labels will be appended to these of the already existing one, and if labels with identical key is imported before, the old label value will be overwritten. If two DataItems are identical in the same import data operation, the labels will be combined and if key collision happens in this case, one of the values will be picked randomly. Two DataItems are considered identical if their content bytes are identical (e.g. image bytes or pdf bytes). These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file.
gcsSource	object (GoogleCloudAiplatformV1beta1GcsSource) The Google Cloud Storage location for the input content.
importSchemaUri	string Required. Points to a YAML file stored on Google Cloud Storage describing the import format. Validation will be done against the schema. The schema is defined as an OpenAPI 3.0.2 Schema Object.

GoogleCloudAiplatformV1beta1ImportDataOperationMetadata

Runtime operation information for DatasetService.ImportData.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.

GoogleCloudAiplatformV1beta1ImportDataRequest

Request message for DatasetService.ImportData.

Fields
importConfigs[]	object (GoogleCloudAiplatformV1beta1ImportDataConfig) Required. The desired input locations. The contents of all input locations will be imported in one batch.

GoogleCloudAiplatformV1beta1ImportExtensionOperationMetadata

Details of ExtensionRegistryService.ImportExtension operation.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.

GoogleCloudAiplatformV1beta1ImportFeatureValuesOperationMetadata

Details of operations that perform import Feature values.

Fields
blockingOperationIds[]	string (int64 format) List of ImportFeatureValues operations running under a single EntityType that are blocking this operation.
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Featurestore import Feature values.
importedEntityCount	string (int64 format) Number of entities that have been imported by the operation.
importedFeatureValueCount	string (int64 format) Number of Feature values that have been imported by the operation.
invalidRowCount	string (int64 format) The number of rows in input source that weren't imported due to either * Not having any featureValues. * Having a null entityId. * Having a null timestamp. * Not being parsable (applicable for CSV sources).
sourceUris[]	string The source URI from where Feature values are imported.
timestampOutsideRetentionRowsCount	string (int64 format) The number rows that weren't ingested due to having timestamps outside the retention boundary.

GoogleCloudAiplatformV1beta1ImportFeatureValuesRequest

Request message for FeaturestoreService.ImportFeatureValues.

Fields
avroSource	object (GoogleCloudAiplatformV1beta1AvroSource) (No description provided)
bigquerySource	object (GoogleCloudAiplatformV1beta1BigQuerySource) (No description provided)
csvSource	object (GoogleCloudAiplatformV1beta1CsvSource) (No description provided)
disableIngestionAnalysis	boolean If true, API doesn't start ingestion analysis pipeline.
disableOnlineServing	boolean If set, data will not be imported for online serving. This is typically used for backfilling, where Feature generation timestamps are not in the timestamp range needed for online serving.
entityIdField	string Source column that holds entity IDs. If not provided, entity IDs are extracted from the column named entity_id.
featureSpecs[]	object (GoogleCloudAiplatformV1beta1ImportFeatureValuesRequestFeatureSpec) Required. Specifications defining which Feature values to import from the entity. The request fails if no feature_specs are provided, and having multiple feature_specs for one Feature is not allowed.
featureTime	string (Timestamp format) Single Feature timestamp for all entities being imported. The timestamp must not have higher than millisecond precision.
featureTimeField	string Source column that holds the Feature timestamp for all Feature values in each entity.
workerCount	integer (int32 format) Specifies the number of workers that are used to write data to the Featurestore. Consider the online serving capacity that you require to achieve the desired import throughput without interfering with online serving. The value must be positive, and less than or equal to 100. If not set, defaults to using 1 worker. The low count ensures minimal impact on online serving performance.

GoogleCloudAiplatformV1beta1ImportFeatureValuesRequestFeatureSpec

Defines the Feature value(s) to import.

Fields
id	string Required. ID of the Feature to import values of. This Feature must exist in the target EntityType, or the request will fail.
sourceField	string Source column to get the Feature values from. If not set, uses the column with the same name as the Feature ID.

GoogleCloudAiplatformV1beta1ImportFeatureValuesResponse

Response message for FeaturestoreService.ImportFeatureValues.

Fields
importedEntityCount	string (int64 format) Number of entities that have been imported by the operation.
importedFeatureValueCount	string (int64 format) Number of Feature values that have been imported by the operation.
invalidRowCount	string (int64 format) The number of rows in input source that weren't imported due to either * Not having any featureValues. * Having a null entityId. * Having a null timestamp. * Not being parsable (applicable for CSV sources).
timestampOutsideRetentionRowsCount	string (int64 format) The number rows that weren't ingested due to having feature timestamps outside the retention boundary.

GoogleCloudAiplatformV1beta1ImportModelEvaluationRequest

Request message for ModelService.ImportModelEvaluation

Fields
modelEvaluation	object (GoogleCloudAiplatformV1beta1ModelEvaluation) Required. Model evaluation resource to be imported.

GoogleCloudAiplatformV1beta1ImportRagFilesConfig

Config for importing RagFiles.

Fields
gcsSource	object (GoogleCloudAiplatformV1beta1GcsSource) Google Cloud Storage location. Supports importing individual files as well as entire Google Cloud Storage directories. Sample formats: - gs://bucket_name/my_directory/object_name/my_file.txt - gs://bucket_name/my_directory
googleDriveSource	object (GoogleCloudAiplatformV1beta1GoogleDriveSource) Google Drive location. Supports importing individual files as well as Google Drive folders.
ragFileChunkingConfig	object (GoogleCloudAiplatformV1beta1RagFileChunkingConfig) Specifies the size and overlap of chunks after importing RagFiles.

GoogleCloudAiplatformV1beta1ImportRagFilesRequest

Request message for VertexRagDataService.ImportRagFiles.

Fields
importRagFilesConfig	object (GoogleCloudAiplatformV1beta1ImportRagFilesConfig) Required. The config for the RagFiles to be synced and imported into the RagCorpus. VertexRagDataService.ImportRagFiles.

GoogleCloudAiplatformV1beta1Index

A representation of a collection of database items organized in a way that allows for approximate nearest neighbor (a.k.a ANN) algorithms search.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this Index was created.
deployedIndexes[]	object (GoogleCloudAiplatformV1beta1DeployedIndexRef) Output only. The pointers to DeployedIndexes created from this Index. An Index can be only deleted if all its DeployedIndexes had been undeployed first.
description	string The description of the Index.
displayName	string Required. The display name of the Index. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Immutable. Customer-managed encryption key spec for an Index. If set, this Index and all sub-resources of this Index will be secured by this key.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
indexStats	object (GoogleCloudAiplatformV1beta1IndexStats) Output only. Stats of the index resource.
indexUpdateMethod	enum Immutable. The update method to use with this Index. If not set, BATCH_UPDATE will be used by default.
Enum type. Can be one of the following:
INDEX_UPDATE_METHOD_UNSPECIFIED	Should not be used.
BATCH_UPDATE	BatchUpdate: user can call UpdateIndex with files on Cloud Storage of Datapoints to update.
STREAM_UPDATE	StreamUpdate: user can call UpsertDatapoints/DeleteDatapoints to update the Index and the updates will be applied in corresponding DeployedIndexes in nearly real-time.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your Indexes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
metadata	any An additional information about the Index; the schema of the metadata can be found in metadata_schema.
metadataSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Index, that is specific to it. Unset if the Index does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
name	string Output only. The resource name of the Index.
updateTime	string (Timestamp format) Output only. Timestamp when this Index was most recently updated. This also includes any update to the contents of the Index. Note that Operations working on this Index may have their Operations.metadata.generic_metadata.update_time a little after the value of this timestamp, yet that does not mean their results are not already reflected in the Index. Result of any successfully completed Operation on the Index is reflected in it.

GoogleCloudAiplatformV1beta1IndexDatapoint

A datapoint of Index.

Fields
crowdingTag	object (GoogleCloudAiplatformV1beta1IndexDatapointCrowdingTag) Optional. CrowdingTag of the datapoint, the number of neighbors to return in each crowding can be configured during query.
datapointId	string Required. Unique identifier of the datapoint.
featureVector[]	number (float format) Required. Feature embedding vector for dense index. An array of numbers with the length of [NearestNeighborSearchConfig.dimensions].
numericRestricts[]	object (GoogleCloudAiplatformV1beta1IndexDatapointNumericRestriction) Optional. List of Restrict of the datapoint, used to perform "restricted searches" where boolean rule are used to filter the subset of the database eligible for matching. This uses numeric comparisons.
restricts[]	object (GoogleCloudAiplatformV1beta1IndexDatapointRestriction) Optional. List of Restrict of the datapoint, used to perform "restricted searches" where boolean rule are used to filter the subset of the database eligible for matching. This uses categorical tokens. See: https://cloud.google.com/vertex-ai/docs/matching-engine/filtering
sparseEmbedding	object (GoogleCloudAiplatformV1beta1IndexDatapointSparseEmbedding) Optional. Feature embedding vector for sparse index.

GoogleCloudAiplatformV1beta1IndexDatapointCrowdingTag

Crowding tag is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

Fields
crowdingAttribute	string The attribute value used for crowding. The maximum number of neighbors to return per crowding attribute value (per_crowding_attribute_num_neighbors) is configured per-query. This field is ignored if per_crowding_attribute_num_neighbors is larger than the total number of neighbors to return for a given query.

GoogleCloudAiplatformV1beta1IndexDatapointNumericRestriction

This field allows restricts to be based on numeric comparisons rather than categorical tokens.

Fields
namespace	string The namespace of this restriction. e.g.: cost.
op	enum This MUST be specified for queries and must NOT be specified for datapoints.
Enum type. Can be one of the following:
OPERATOR_UNSPECIFIED	Default value of the enum.
LESS	Datapoints are eligible iff their value is < the query's.
LESS_EQUAL	Datapoints are eligible iff their value is <= the query's.
EQUAL	Datapoints are eligible iff their value is == the query's.
GREATER_EQUAL	Datapoints are eligible iff their value is >= the query's.
GREATER	Datapoints are eligible iff their value is > the query's.
NOT_EQUAL	Datapoints are eligible iff their value is != the query's.
valueDouble	number (double format) Represents 64 bit float.
valueFloat	number (float format) Represents 32 bit float.
valueInt	string (int64 format) Represents 64 bit integer.

GoogleCloudAiplatformV1beta1IndexDatapointRestriction

Restriction of a datapoint which describe its attributes(tokens) from each of several attribute categories(namespaces).

Fields
allowList[]	string The attributes to allow in this namespace. e.g.: 'red'
denyList[]	string The attributes to deny in this namespace. e.g.: 'blue'
namespace	string The namespace of this restriction. e.g.: color.

GoogleCloudAiplatformV1beta1IndexDatapointSparseEmbedding

Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions.

Fields
dimensions[]	string (int64 format) Required. The list of indexes for the embedding values of the sparse vector.
values[]	number (float format) Required. The list of embedding values of the sparse vector.

GoogleCloudAiplatformV1beta1IndexEndpoint

Indexes are deployed into it. An IndexEndpoint can have multiple DeployedIndexes.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this IndexEndpoint was created.
deployedIndexes[]	object (GoogleCloudAiplatformV1beta1DeployedIndex) Output only. The indexes deployed in this endpoint.
description	string The description of the IndexEndpoint.
displayName	string Required. The display name of the IndexEndpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
enablePrivateServiceConnect	boolean Optional. Deprecated: If true, expose the IndexEndpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Immutable. Customer-managed encryption key spec for an IndexEndpoint. If set, this IndexEndpoint and all sub-resources of this IndexEndpoint will be secured by this key.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your IndexEndpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
name	string Output only. The resource name of the IndexEndpoint.
network	string Optional. The full name of the Google Compute Engine network to which the IndexEndpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. network and private_service_connect_config are mutually exclusive. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in '12345', and {network} is network name.
privateServiceConnectConfig	object (GoogleCloudAiplatformV1beta1PrivateServiceConnectConfig) Optional. Configuration for private service connect. network and private_service_connect_config are mutually exclusive.
publicEndpointDomainName	string Output only. If public_endpoint_enabled is true, this field will be populated with the domain name to use for this index endpoint.
publicEndpointEnabled	boolean Optional. If true, the deployed index will be accessible through public endpoint.
updateTime	string (Timestamp format) Output only. Timestamp when this IndexEndpoint was last updated. This timestamp is not updated when the endpoint's DeployedIndexes are updated, e.g. due to updates of the original Indexes they are the deployments of.

GoogleCloudAiplatformV1beta1IndexPrivateEndpoints

IndexPrivateEndpoints proto is used to provide paths for users to send requests via private endpoints (e.g. private service access, private service connect). To send request via private service access, use match_grpc_address. To send request via private service connect, use service_attachment.

Fields
matchGrpcAddress	string Output only. The ip address used to send match gRPC requests.
pscAutomatedEndpoints[]	object (GoogleCloudAiplatformV1beta1PscAutomatedEndpoints) Output only. PscAutomatedEndpoints is populated if private service connect is enabled if PscAutomatedConfig is set.
serviceAttachment	string Output only. The name of the service attachment resource. Populated if private service connect is enabled.

GoogleCloudAiplatformV1beta1IndexStats

Stats of the Index.

Fields
shardsCount	integer (int32 format) Output only. The number of shards in the Index.
sparseVectorsCount	string (int64 format) Output only. The number of sparse vectors in the Index.
vectorsCount	string (int64 format) Output only. The number of dense vectors in the Index.

GoogleCloudAiplatformV1beta1InputDataConfig

Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model.

Fields
annotationSchemaUri	string Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
annotationsFilter	string Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
bigqueryDestination	object (GoogleCloudAiplatformV1beta1BigQueryDestination) Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset.training" * AIPVALIDATION_DATA_URI = "bigquery_destination.dataset_.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
datasetId	string Required. The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
filterSplit	object (GoogleCloudAiplatformV1beta1FilterSplit) Split based on the provided filters for each set.
fractionSplit	object (GoogleCloudAiplatformV1beta1FractionSplit) Split based on fractions defining the size of each set.
gcsDestination	object (GoogleCloudAiplatformV1beta1GcsDestination) The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
persistMlUseAssignment	boolean Whether to persist the ML use assignment to data item system labels.
predefinedSplit	object (GoogleCloudAiplatformV1beta1PredefinedSplit) Supported only for tabular Datasets. Split based on a predefined key.
savedQueryId	string Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
stratifiedSplit	object (GoogleCloudAiplatformV1beta1StratifiedSplit) Supported only for tabular Datasets. Split based on the distribution of the specified column.
timestampSplit	object (GoogleCloudAiplatformV1beta1TimestampSplit) Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.

GoogleCloudAiplatformV1beta1Int64Array

A list of int64 values.

Fields
values[]	string (int64 format) A list of int64 values.

GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution

An attribution method that computes the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365

Fields
blurBaselineConfig	object (GoogleCloudAiplatformV1beta1BlurBaselineConfig) Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig	object (GoogleCloudAiplatformV1beta1SmoothGradConfig) Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount	integer (int32 format) Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.

GoogleCloudAiplatformV1beta1InternalOsServiceStateInstance

Request message for [InternalOsServiceStateInstance].

Fields
serviceName	enum Required. internal service name.
Enum type. Can be one of the following:
INTERNAL_OS_SERVICE_ENUM_UNSPECIFIED	Service name unknown.
DOCKER_SERVICE_STATE	Represents the internal os docker client.
CONTROL_PLANE_API_DNS_STATE	Represents reoslving DNS for the control plane api endpoint.
PROXY_REGISTRATION_DNS_STATE	Represents reoslving DNS for the proxy registration endpoint.
JUPYTER_STATE	Represents the jupyter endpoint.
JUPYTER_API_STATE	Represents the jupyter/api endpoint.
EUC_METADATA_API_STATE	Represents the EUC metadata server API endpoint.
EUC_AGENT_API_STATE	Represents the EUC agent server API endpoint.
IDLE_SHUTDOWN_AGENT_STATE	Represents the idle shutdown agent sidecar container.
PROXY_AGENT_STATE	Represents the proxy agent sidecar container.
serviceState	enum Required. internal service state.
Enum type. Can be one of the following:
UNKNOWN	Health status is unknown: not initialized or failed to retrieve.
HEALTHY	The resource is healthy.
UNHEALTHY	The resource is unhealthy.

GoogleCloudAiplatformV1beta1LargeModelReference

Contains information about the Large Model.

Fields
name	string Required. The unique name of the large Foundation or pre-built model. Like "chat-bison", "text-bison". Or model name with version ID, like "chat-bison@001", "text-bison@005", etc.

GoogleCloudAiplatformV1beta1LineageSubgraph

A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes.

Fields
artifacts[]	object (GoogleCloudAiplatformV1beta1Artifact) The Artifact nodes in the subgraph.
events[]	object (GoogleCloudAiplatformV1beta1Event) The Event edges between Artifacts and Executions in the subgraph.
executions[]	object (GoogleCloudAiplatformV1beta1Execution) The Execution nodes in the subgraph.

GoogleCloudAiplatformV1beta1ListAnnotationsResponse

Response message for DatasetService.ListAnnotations.

Fields
annotations[]	object (GoogleCloudAiplatformV1beta1Annotation) A list of Annotations that matches the specified filter in the request.
nextPageToken	string The standard List next-page token.

GoogleCloudAiplatformV1beta1ListArtifactsResponse

Response message for MetadataService.ListArtifacts.

Fields
artifacts[]	object (GoogleCloudAiplatformV1beta1Artifact) The Artifacts retrieved from the MetadataStore.
nextPageToken	string A token, which can be sent as ListArtifactsRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListBatchPredictionJobsResponse

Response message for JobService.ListBatchPredictionJobs

Fields
batchPredictionJobs[]	object (GoogleCloudAiplatformV1beta1BatchPredictionJob) List of BatchPredictionJobs in the requested page.
nextPageToken	string A token to retrieve the next page of results. Pass to ListBatchPredictionJobsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListContextsResponse

Response message for MetadataService.ListContexts.

Fields
contexts[]	object (GoogleCloudAiplatformV1beta1Context) The Contexts retrieved from the MetadataStore.
nextPageToken	string A token, which can be sent as ListContextsRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListCustomJobsResponse

Response message for JobService.ListCustomJobs

Fields
customJobs[]	object (GoogleCloudAiplatformV1beta1CustomJob) List of CustomJobs in the requested page.
nextPageToken	string A token to retrieve the next page of results. Pass to ListCustomJobsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListDataItemsResponse

Response message for DatasetService.ListDataItems.

Fields
dataItems[]	object (GoogleCloudAiplatformV1beta1DataItem) A list of DataItems that matches the specified filter in the request.
nextPageToken	string The standard List next-page token.

GoogleCloudAiplatformV1beta1ListDataLabelingJobsResponse

Response message for JobService.ListDataLabelingJobs.

Fields
dataLabelingJobs[]	object (GoogleCloudAiplatformV1beta1DataLabelingJob) A list of DataLabelingJobs that matches the specified filter in the request.
nextPageToken	string The standard List next-page token.

GoogleCloudAiplatformV1beta1ListDatasetVersionsResponse

Response message for DatasetService.ListDatasetVersions.

Fields
datasetVersions[]	object (GoogleCloudAiplatformV1beta1DatasetVersion) A list of DatasetVersions that matches the specified filter in the request.
nextPageToken	string The standard List next-page token.

GoogleCloudAiplatformV1beta1ListDatasetsResponse

Response message for DatasetService.ListDatasets.

Fields
datasets[]	object (GoogleCloudAiplatformV1beta1Dataset) A list of Datasets that matches the specified filter in the request.
nextPageToken	string The standard List next-page token.

GoogleCloudAiplatformV1beta1ListDeploymentResourcePoolsResponse

Response message for ListDeploymentResourcePools method.

Fields
deploymentResourcePools[]	object (GoogleCloudAiplatformV1beta1DeploymentResourcePool) The DeploymentResourcePools from the specified location.
nextPageToken	string A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListEndpointsResponse

Response message for EndpointService.ListEndpoints.

Fields
endpoints[]	object (GoogleCloudAiplatformV1beta1Endpoint) List of Endpoints in the requested page.
nextPageToken	string A token to retrieve the next page of results. Pass to ListEndpointsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListEntityTypesResponse

Response message for FeaturestoreService.ListEntityTypes.

Fields
entityTypes[]	object (GoogleCloudAiplatformV1beta1EntityType) The EntityTypes matching the request.
nextPageToken	string A token, which can be sent as ListEntityTypesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListExecutionsResponse

Response message for MetadataService.ListExecutions.

Fields
executions[]	object (GoogleCloudAiplatformV1beta1Execution) The Executions retrieved from the MetadataStore.
nextPageToken	string A token, which can be sent as ListExecutionsRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListExtensionsResponse

Response message for ExtensionRegistryService.ListExtensions

Fields
extensions[]	object (GoogleCloudAiplatformV1beta1Extension) List of Extension in the requested page.
nextPageToken	string A token to retrieve the next page of results. Pass to ListExtensionsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListFeatureGroupsResponse

Response message for FeatureRegistryService.ListFeatureGroups.

Fields
featureGroups[]	object (GoogleCloudAiplatformV1beta1FeatureGroup) The FeatureGroups matching the request.
nextPageToken	string A token, which can be sent as ListFeatureGroupsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListFeatureOnlineStoresResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureOnlineStores.

Fields
featureOnlineStores[]	object (GoogleCloudAiplatformV1beta1FeatureOnlineStore) The FeatureOnlineStores matching the request.
nextPageToken	string A token, which can be sent as ListFeatureOnlineStoresRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListFeatureViewSyncsResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureViewSyncs.

Fields
featureViewSyncs[]	object (GoogleCloudAiplatformV1beta1FeatureViewSync) The FeatureViewSyncs matching the request.
nextPageToken	string A token, which can be sent as ListFeatureViewSyncsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListFeatureViewsResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureViews.

Fields
featureViews[]	object (GoogleCloudAiplatformV1beta1FeatureView) The FeatureViews matching the request.
nextPageToken	string A token, which can be sent as ListFeatureViewsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListFeaturesResponse

Response message for FeaturestoreService.ListFeatures. Response message for FeatureRegistryService.ListFeatures.

Fields
features[]	object (GoogleCloudAiplatformV1beta1Feature) The Features matching the request.
nextPageToken	string A token, which can be sent as ListFeaturesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListFeaturestoresResponse

Response message for FeaturestoreService.ListFeaturestores.

Fields
featurestores[]	object (GoogleCloudAiplatformV1beta1Featurestore) The Featurestores matching the request.
nextPageToken	string A token, which can be sent as ListFeaturestoresRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListHyperparameterTuningJobsResponse

Response message for JobService.ListHyperparameterTuningJobs

Fields
hyperparameterTuningJobs[]	object (GoogleCloudAiplatformV1beta1HyperparameterTuningJob) List of HyperparameterTuningJobs in the requested page. HyperparameterTuningJob.trials of the jobs will be not be returned.
nextPageToken	string A token to retrieve the next page of results. Pass to ListHyperparameterTuningJobsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListIndexEndpointsResponse

Response message for IndexEndpointService.ListIndexEndpoints.

Fields
indexEndpoints[]	object (GoogleCloudAiplatformV1beta1IndexEndpoint) List of IndexEndpoints in the requested page.
nextPageToken	string A token to retrieve next page of results. Pass to ListIndexEndpointsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListIndexesResponse

Response message for IndexService.ListIndexes.

Fields
indexes[]	object (GoogleCloudAiplatformV1beta1Index) List of indexes in the requested page.
nextPageToken	string A token to retrieve next page of results. Pass to ListIndexesRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListMetadataSchemasResponse

Response message for MetadataService.ListMetadataSchemas.

Fields
metadataSchemas[]	object (GoogleCloudAiplatformV1beta1MetadataSchema) The MetadataSchemas found for the MetadataStore.
nextPageToken	string A token, which can be sent as ListMetadataSchemasRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListMetadataStoresResponse

Response message for MetadataService.ListMetadataStores.

Fields
metadataStores[]	object (GoogleCloudAiplatformV1beta1MetadataStore) The MetadataStores found for the Location.
nextPageToken	string A token, which can be sent as ListMetadataStoresRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages.

GoogleCloudAiplatformV1beta1ListModelDeploymentMonitoringJobsResponse

Response message for JobService.ListModelDeploymentMonitoringJobs.

Fields
modelDeploymentMonitoringJobs[]	object (GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJob) A list of ModelDeploymentMonitoringJobs that matches the specified filter in the request.
nextPageToken	string The standard List next-page token.

GoogleCloudAiplatformV1beta1ListModelEvaluationSlicesResponse

Response message for ModelService.ListModelEvaluationSlices.

Fields
modelEvaluationSlices[]	object (GoogleCloudAiplatformV1beta1ModelEvaluationSlice) List of ModelEvaluations in the requested page.
nextPageToken	string A token to retrieve next page of results. Pass to ListModelEvaluationSlicesRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListModelEvaluationsResponse

Response message for ModelService.ListModelEvaluations.

Fields
modelEvaluations[]	object (GoogleCloudAiplatformV1beta1ModelEvaluation) List of ModelEvaluations in the requested page.
nextPageToken	string A token to retrieve next page of results. Pass to ListModelEvaluationsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListModelMonitoringJobsResponse

Response message for ModelMonitoringService.ListModelMonitoringJobs.

Fields
modelMonitoringJobs[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringJob) A list of ModelMonitoringJobs that matches the specified filter in the request.
nextPageToken	string The standard List next-page token.

GoogleCloudAiplatformV1beta1ListModelMonitorsResponse

Response message for ModelMonitoringService.ListModelMonitors

Fields
modelMonitors[]	object (GoogleCloudAiplatformV1beta1ModelMonitor) List of ModelMonitor in the requested page.
nextPageToken	string A token to retrieve the next page of results. Pass to ListModelMonitorsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListModelVersionsResponse

Response message for ModelService.ListModelVersions

Fields
models[]	object (GoogleCloudAiplatformV1beta1Model) List of Model versions in the requested page. In the returned Model name field, version ID instead of regvision tag will be included.
nextPageToken	string A token to retrieve the next page of results. Pass to ListModelVersionsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListModelsResponse

Response message for ModelService.ListModels

Fields
models[]	object (GoogleCloudAiplatformV1beta1Model) List of Models in the requested page.
nextPageToken	string A token to retrieve next page of results. Pass to ListModelsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListNasJobsResponse

Response message for JobService.ListNasJobs

Fields
nasJobs[]	object (GoogleCloudAiplatformV1beta1NasJob) List of NasJobs in the requested page. NasJob.nas_job_output of the jobs will not be returned.
nextPageToken	string A token to retrieve the next page of results. Pass to ListNasJobsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListNasTrialDetailsResponse

Response message for JobService.ListNasTrialDetails

Fields
nasTrialDetails[]	object (GoogleCloudAiplatformV1beta1NasTrialDetail) List of top NasTrials in the requested page.
nextPageToken	string A token to retrieve the next page of results. Pass to ListNasTrialDetailsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1ListNotebookExecutionJobsResponse

Response message for [NotebookService.CreateNotebookExecutionJob]

Fields
nextPageToken	string A token to retrieve next page of results. Pass to ListNotebookExecutionJobs.page_token to obtain that page.
notebookExecutionJobs[]	object (GoogleCloudAiplatformV1beta1NotebookExecutionJob) List of NotebookExecutionJobs in the requested page.

GoogleCloudAiplatformV1beta1ListNotebookRuntimeTemplatesResponse

Response message for NotebookService.ListNotebookRuntimeTemplates.

Fields
nextPageToken	string A token to retrieve next page of results. Pass to ListNotebookRuntimeTemplatesRequest.page_token to obtain that page.
notebookRuntimeTemplates[]	object (GoogleCloudAiplatformV1beta1NotebookRuntimeTemplate) List of NotebookRuntimeTemplates in the requested page.

GoogleCloudAiplatformV1beta1ListNotebookRuntimesResponse

Response message for NotebookService.ListNotebookRuntimes.

Fields
nextPageToken	string A token to retrieve next page of results. Pass to ListNotebookRuntimesRequest.page_token to obtain that page.
notebookRuntimes[]	object (GoogleCloudAiplatformV1beta1NotebookRuntime) List of NotebookRuntimes in the requested page.

GoogleCloudAiplatformV1beta1ListOptimalTrialsResponse

Response message for VizierService.ListOptimalTrials.

Fields
optimalTrials[]	object (GoogleCloudAiplatformV1beta1Trial) 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

GoogleCloudAiplatformV1beta1ListPersistentResourcesResponse

Response message for PersistentResourceService.ListPersistentResources

Fields
nextPageToken	string A token to retrieve next page of results. Pass to ListPersistentResourcesRequest.page_token to obtain that page.
persistentResources[]	object (GoogleCloudAiplatformV1beta1PersistentResource) (No description provided)

GoogleCloudAiplatformV1beta1ListPipelineJobsResponse

Response message for PipelineService.ListPipelineJobs

Fields
nextPageToken	string A token to retrieve the next page of results. Pass to ListPipelineJobsRequest.page_token to obtain that page.
pipelineJobs[]	object (GoogleCloudAiplatformV1beta1PipelineJob) List of PipelineJobs in the requested page.

GoogleCloudAiplatformV1beta1ListPublisherModelsResponse

Response message for ModelGardenService.ListPublisherModels.

Fields
nextPageToken	string A token to retrieve next page of results. Pass to ListPublisherModels.page_token to obtain that page.
publisherModels[]	object (GoogleCloudAiplatformV1beta1PublisherModel) List of PublisherModels in the requested page.

GoogleCloudAiplatformV1beta1ListRagCorporaResponse

Response message for VertexRagDataService.ListRagCorpora.

Fields
nextPageToken	string A token to retrieve the next page of results. Pass to ListRagCorporaRequest.page_token to obtain that page.
ragCorpora[]	object (GoogleCloudAiplatformV1beta1RagCorpus) List of RagCorpora in the requested page.

GoogleCloudAiplatformV1beta1ListRagFilesResponse

Response message for VertexRagDataService.ListRagFiles.

Fields
nextPageToken	string A token to retrieve the next page of results. Pass to ListRagFilesRequest.page_token to obtain that page.
ragFiles[]	object (GoogleCloudAiplatformV1beta1RagFile) List of RagFiles in the requested page.

GoogleCloudAiplatformV1beta1ListReasoningEnginesResponse

Response message for ReasoningEngineService.ListReasoningEngines

Fields
nextPageToken	string A token to retrieve the next page of results. Pass to ListReasoningEnginesRequest.page_token to obtain that page.
reasoningEngines[]	object (GoogleCloudAiplatformV1beta1ReasoningEngine) List of ReasoningEngines in the requested page.

GoogleCloudAiplatformV1beta1ListSavedQueriesResponse

Response message for DatasetService.ListSavedQueries.

Fields
nextPageToken	string The standard List next-page token.
savedQueries[]	object (GoogleCloudAiplatformV1beta1SavedQuery) A list of SavedQueries that match the specified filter in the request.

GoogleCloudAiplatformV1beta1ListSchedulesResponse

Response message for ScheduleService.ListSchedules

Fields
nextPageToken	string A token to retrieve the next page of results. Pass to ListSchedulesRequest.page_token to obtain that page.
schedules[]	object (GoogleCloudAiplatformV1beta1Schedule) List of Schedules in the requested page.

GoogleCloudAiplatformV1beta1ListSpecialistPoolsResponse

Response message for SpecialistPoolService.ListSpecialistPools.

Fields
nextPageToken	string The standard List next-page token.
specialistPools[]	object (GoogleCloudAiplatformV1beta1SpecialistPool) A list of SpecialistPools that matches the specified filter in the request.

GoogleCloudAiplatformV1beta1ListStudiesResponse

Response message for VizierService.ListStudies.

Fields
nextPageToken	string Passes this token as the page_token field of the request for a subsequent call. If this field is omitted, there are no subsequent pages.
studies[]	object (GoogleCloudAiplatformV1beta1Study) The studies associated with the project.

GoogleCloudAiplatformV1beta1ListTensorboardExperimentsResponse

Response message for TensorboardService.ListTensorboardExperiments.

Fields
nextPageToken	string A token, which can be sent as ListTensorboardExperimentsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.
tensorboardExperiments[]	object (GoogleCloudAiplatformV1beta1TensorboardExperiment) The TensorboardExperiments mathching the request.

GoogleCloudAiplatformV1beta1ListTensorboardRunsResponse

Response message for TensorboardService.ListTensorboardRuns.

Fields
nextPageToken	string A token, which can be sent as ListTensorboardRunsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.
tensorboardRuns[]	object (GoogleCloudAiplatformV1beta1TensorboardRun) The TensorboardRuns mathching the request.

GoogleCloudAiplatformV1beta1ListTensorboardTimeSeriesResponse

Response message for TensorboardService.ListTensorboardTimeSeries.

Fields
nextPageToken	string A token, which can be sent as ListTensorboardTimeSeriesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.
tensorboardTimeSeries[]	object (GoogleCloudAiplatformV1beta1TensorboardTimeSeries) The TensorboardTimeSeries mathching the request.

GoogleCloudAiplatformV1beta1ListTensorboardsResponse

Response message for TensorboardService.ListTensorboards.

Fields
nextPageToken	string A token, which can be sent as ListTensorboardsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.
tensorboards[]	object (GoogleCloudAiplatformV1beta1Tensorboard) The Tensorboards mathching the request.

GoogleCloudAiplatformV1beta1ListTrainingPipelinesResponse

Response message for PipelineService.ListTrainingPipelines

Fields
nextPageToken	string A token to retrieve the next page of results. Pass to ListTrainingPipelinesRequest.page_token to obtain that page.
trainingPipelines[]	object (GoogleCloudAiplatformV1beta1TrainingPipeline) List of TrainingPipelines in the requested page.

GoogleCloudAiplatformV1beta1ListTrialsResponse

Response message for VizierService.ListTrials.

Fields
nextPageToken	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[]	object (GoogleCloudAiplatformV1beta1Trial) The Trials associated with the Study.

GoogleCloudAiplatformV1beta1ListTuningJobsResponse

Response message for GenAiTuningService.ListTuningJobs

Fields
nextPageToken	string A token to retrieve the next page of results. Pass to ListTuningJobsRequest.page_token to obtain that page.
tuningJobs[]	object (GoogleCloudAiplatformV1beta1TuningJob) List of TuningJobs in the requested page.

GoogleCloudAiplatformV1beta1LookupStudyRequest

Request message for VizierService.LookupStudy.

Fields
displayName	string Required. The user-defined display name of the Study

GoogleCloudAiplatformV1beta1MachineSpec

Specification of a single machine.

Fields
acceleratorCount	integer (int32 format) The number of accelerators to attach to the machine.
acceleratorType	enum Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
Enum type. Can be one of the following:
ACCELERATOR_TYPE_UNSPECIFIED	Unspecified accelerator type, which means no accelerator.
NVIDIA_TESLA_K80	Nvidia Tesla K80 GPU.
NVIDIA_TESLA_P100	Nvidia Tesla P100 GPU.
NVIDIA_TESLA_V100	Nvidia Tesla V100 GPU.
NVIDIA_TESLA_P4	Nvidia Tesla P4 GPU.
NVIDIA_TESLA_T4	Nvidia Tesla T4 GPU.
NVIDIA_TESLA_A100	Nvidia Tesla A100 GPU.
NVIDIA_A100_80GB	Nvidia A100 80GB GPU.
NVIDIA_L4	Nvidia L4 GPU.
NVIDIA_H100_80GB	Nvidia H100 80Gb GPU.
TPU_V2	TPU v2.
TPU_V3	TPU v3.
TPU_V4_POD	TPU v4.
TPU_V5_LITEPOD	TPU v5.
machineType	string Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpuTopology	string Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

GoogleCloudAiplatformV1beta1ManualBatchTuningParameters

Manual batch tuning parameters.

Fields

batchSize

integer (int32 format) Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.

GoogleCloudAiplatformV1beta1Measurement

A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.

Fields
elapsedDuration	string (Duration format) Output only. Time that the Trial has been running at the point of this Measurement.
metrics[]	object (GoogleCloudAiplatformV1beta1MeasurementMetric) Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
stepCount	string (int64 format) Output only. The number of steps the machine learning model has been trained for. Must be non-negative.

GoogleCloudAiplatformV1beta1MeasurementMetric

A message representing a metric in the measurement.

Fields
metricId	string Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
value	number (double format) Output only. The value for this metric.

GoogleCloudAiplatformV1beta1MergeVersionAliasesRequest

Request message for ModelService.MergeVersionAliases.

Fields

versionAliases[]

string Required. The set of version aliases to merge. The alias should be at most 128 characters, and match a-z{0,126}[a-z-0-9]. Add the - prefix to an alias means removing that alias from the version. - is NOT counted in the 128 characters. Example: -golden means removing the golden alias from the version. There is NO ordering in aliases, which means 1) The aliases returned from GetModel API might not have the exactly same order from this MergeVersionAliases API. 2) Adding and deleting the same alias in the request is not recommended, and the 2 operations will be cancelled out.

GoogleCloudAiplatformV1beta1MetadataSchema

Instance of a general MetadataSchema.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this MetadataSchema was created.
description	string Description of the Metadata Schema
name	string Output only. The resource name of the MetadataSchema.
schema	string Required. The raw YAML string representation of the MetadataSchema. The combination of [MetadataSchema.version] and the schema name given by title in [MetadataSchema.schema] must be unique within a MetadataStore. The schema is defined as an OpenAPI 3.0.2 MetadataSchema Object
schemaType	enum The type of the MetadataSchema. This is a property that identifies which metadata types will use the MetadataSchema.
Enum type. Can be one of the following:
METADATA_SCHEMA_TYPE_UNSPECIFIED	Unspecified type for the MetadataSchema.
ARTIFACT_TYPE	A type indicating that the MetadataSchema will be used by Artifacts.
EXECUTION_TYPE	A typee indicating that the MetadataSchema will be used by Executions.
CONTEXT_TYPE	A state indicating that the MetadataSchema will be used by Contexts.
schemaVersion	string The version of the MetadataSchema. The version's format must match the following regular expression: ^[0-9]+.+.+$, which would allow to order/compare different versions. Example: 1.0.0, 1.0.1, etc.

GoogleCloudAiplatformV1beta1MetadataStore

Instance of a metadata store. Contains a set of metadata that can be queried.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this MetadataStore was created.
description	string Description of the MetadataStore.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key spec for a Metadata Store. If set, this Metadata Store and all sub-resources of this Metadata Store are secured using this key.
name	string Output only. The resource name of the MetadataStore instance.
state	object (GoogleCloudAiplatformV1beta1MetadataStoreMetadataStoreState) Output only. State information of the MetadataStore.
updateTime	string (Timestamp format) Output only. Timestamp when this MetadataStore was last updated.

GoogleCloudAiplatformV1beta1MetadataStoreMetadataStoreState

Represents state information for a MetadataStore.

Fields
diskUtilizationBytes	string (int64 format) The disk utilization of the MetadataStore in bytes.

GoogleCloudAiplatformV1beta1MigratableResource

Represents one resource that exists in automl.googleapis.com, datalabeling.googleapis.com or ml.googleapis.com.

Fields
automlDataset	object (GoogleCloudAiplatformV1beta1MigratableResourceAutomlDataset) Output only. Represents one Dataset in automl.googleapis.com.
automlModel	object (GoogleCloudAiplatformV1beta1MigratableResourceAutomlModel) Output only. Represents one Model in automl.googleapis.com.
dataLabelingDataset	object (GoogleCloudAiplatformV1beta1MigratableResourceDataLabelingDataset) Output only. Represents one Dataset in datalabeling.googleapis.com.
lastMigrateTime	string (Timestamp format) Output only. Timestamp when the last migration attempt on this MigratableResource started. Will not be set if there's no migration attempt on this MigratableResource.
lastUpdateTime	string (Timestamp format) Output only. Timestamp when this MigratableResource was last updated.
mlEngineModelVersion	object (GoogleCloudAiplatformV1beta1MigratableResourceMlEngineModelVersion) Output only. Represents one Version in ml.googleapis.com.

GoogleCloudAiplatformV1beta1MigratableResourceAutomlDataset

Represents one Dataset in automl.googleapis.com.

Fields
dataset	string Full resource name of automl Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}.
datasetDisplayName	string The Dataset's display name in automl.googleapis.com.

GoogleCloudAiplatformV1beta1MigratableResourceAutomlModel

Represents one Model in automl.googleapis.com.

Fields
model	string Full resource name of automl Model. Format: projects/{project}/locations/{location}/models/{model}.
modelDisplayName	string The Model's display name in automl.googleapis.com.

GoogleCloudAiplatformV1beta1MigratableResourceDataLabelingDataset

Represents one Dataset in datalabeling.googleapis.com.

Fields
dataLabelingAnnotatedDatasets[]	object (GoogleCloudAiplatformV1beta1MigratableResourceDataLabelingDatasetDataLabelingAnnotatedDataset) The migratable AnnotatedDataset in datalabeling.googleapis.com belongs to the data labeling Dataset.
dataset	string Full resource name of data labeling Dataset. Format: projects/{project}/datasets/{dataset}.
datasetDisplayName	string The Dataset's display name in datalabeling.googleapis.com.

GoogleCloudAiplatformV1beta1MigratableResourceDataLabelingDatasetDataLabelingAnnotatedDataset

Represents one AnnotatedDataset in datalabeling.googleapis.com.

Fields
annotatedDataset	string Full resource name of data labeling AnnotatedDataset. Format: projects/{project}/datasets/{dataset}/annotatedDatasets/{annotated_dataset}.
annotatedDatasetDisplayName	string The AnnotatedDataset's display name in datalabeling.googleapis.com.

GoogleCloudAiplatformV1beta1MigratableResourceMlEngineModelVersion

Represents one model Version in ml.googleapis.com.

Fields
endpoint	string The ml.googleapis.com endpoint that this model Version currently lives in. Example values: * ml.googleapis.com * us-centrall-ml.googleapis.com * europe-west4-ml.googleapis.com * asia-east1-ml.googleapis.com
version	string Full resource name of ml engine model Version. Format: projects/{project}/models/{model}/versions/{version}.

GoogleCloudAiplatformV1beta1MigrateResourceRequest

Config of migrating one resource from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

Fields
migrateAutomlDatasetConfig	object (GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateAutomlDatasetConfig) Config for migrating Dataset in automl.googleapis.com to Vertex AI's Dataset.
migrateAutomlModelConfig	object (GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateAutomlModelConfig) Config for migrating Model in automl.googleapis.com to Vertex AI's Model.
migrateDataLabelingDatasetConfig	object (GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateDataLabelingDatasetConfig) Config for migrating Dataset in datalabeling.googleapis.com to Vertex AI's Dataset.
migrateMlEngineModelVersionConfig	object (GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateMlEngineModelVersionConfig) Config for migrating Version in ml.googleapis.com to Vertex AI's Model.

GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateAutomlDatasetConfig

Config for migrating Dataset in automl.googleapis.com to Vertex AI's Dataset.

Fields
dataset	string Required. Full resource name of automl Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}.
datasetDisplayName	string Required. Display name of the Dataset in Vertex AI. System will pick a display name if unspecified.

GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateAutomlModelConfig

Config for migrating Model in automl.googleapis.com to Vertex AI's Model.

Fields
model	string Required. Full resource name of automl Model. Format: projects/{project}/locations/{location}/models/{model}.
modelDisplayName	string Optional. Display name of the model in Vertex AI. System will pick a display name if unspecified.

GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateDataLabelingDatasetConfig

Config for migrating Dataset in datalabeling.googleapis.com to Vertex AI's Dataset.

Fields
dataset	string Required. Full resource name of data labeling Dataset. Format: projects/{project}/datasets/{dataset}.
datasetDisplayName	string Optional. Display name of the Dataset in Vertex AI. System will pick a display name if unspecified.
migrateDataLabelingAnnotatedDatasetConfigs[]	object (GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateDataLabelingDatasetConfigMigrateDataLabelingAnnotatedDatasetConfig) Optional. Configs for migrating AnnotatedDataset in datalabeling.googleapis.com to Vertex AI's SavedQuery. The specified AnnotatedDatasets have to belong to the datalabeling Dataset.

GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateDataLabelingDatasetConfigMigrateDataLabelingAnnotatedDatasetConfig

Config for migrating AnnotatedDataset in datalabeling.googleapis.com to Vertex AI's SavedQuery.

Fields
annotatedDataset	string Required. Full resource name of data labeling AnnotatedDataset. Format: projects/{project}/datasets/{dataset}/annotatedDatasets/{annotated_dataset}.

GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateMlEngineModelVersionConfig

Config for migrating version in ml.googleapis.com to Vertex AI's Model.

Fields
endpoint	string Required. The ml.googleapis.com endpoint that this model version should be migrated from. Example values: * ml.googleapis.com * us-centrall-ml.googleapis.com * europe-west4-ml.googleapis.com * asia-east1-ml.googleapis.com
modelDisplayName	string Required. Display name of the model in Vertex AI. System will pick a display name if unspecified.
modelVersion	string Required. Full resource name of ml engine model version. Format: projects/{project}/models/{model}/versions/{version}.

GoogleCloudAiplatformV1beta1MigrateResourceResponse

Describes a successfully migrated resource.

Fields
dataset	string Migrated Dataset's resource name.
migratableResource	object (GoogleCloudAiplatformV1beta1MigratableResource) Before migration, the identifier in ml.googleapis.com, automl.googleapis.com or datalabeling.googleapis.com.
model	string Migrated Model's resource name.

GoogleCloudAiplatformV1beta1Model

A trained machine learning Model.

Fields
artifactUri	string Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.
baseModelSource	object (GoogleCloudAiplatformV1beta1ModelBaseModelSource) Optional. User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.
containerSpec	object (GoogleCloudAiplatformV1beta1ModelContainerSpec) Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.
createTime	string (Timestamp format) Output only. Timestamp when this Model was uploaded into Vertex AI.
deployedModels[]	object (GoogleCloudAiplatformV1beta1DeployedModelRef) Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
description	string The description of the Model.
displayName	string Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
explanationSpec	object (GoogleCloudAiplatformV1beta1ExplanationSpec) The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
metadata	any Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
metadataArtifact	string Output only. The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.
metadataSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
modelSourceInfo	object (GoogleCloudAiplatformV1beta1ModelSourceInfo) Output only. Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or saved and tuned from Genie or Model Garden.
name	string The resource name of the Model.
originalModelInfo	object (GoogleCloudAiplatformV1beta1ModelOriginalModelInfo) Output only. If this Model is a copy of another Model, this contains info about the original.
predictSchemata	object (GoogleCloudAiplatformV1beta1PredictSchemata) The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
supportedDeploymentResourcesTypes[]	string Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.
supportedExportFormats[]	object (GoogleCloudAiplatformV1beta1ModelExportFormat) Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.
supportedInputStorageFormats[]	string Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: * jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. * csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. * tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. * tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. * bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. * file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
supportedOutputStorageFormats[]	string Output only. The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: * jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination. * csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. * bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
trainingPipeline	string Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.
updateTime	string (Timestamp format) Output only. Timestamp when this Model was most recently updated.
versionAliases[]	string User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
versionCreateTime	string (Timestamp format) Output only. Timestamp when this version was created.
versionDescription	string The description of this version.
versionId	string Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.
versionUpdateTime	string (Timestamp format) Output only. Timestamp when this version was most recently updated.

GoogleCloudAiplatformV1beta1ModelBaseModelSource

User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

Fields
genieSource	object (GoogleCloudAiplatformV1beta1GenieSource) Information about the base model of Genie models.
modelGardenSource	object (GoogleCloudAiplatformV1beta1ModelGardenSource) Source information of Model Garden models.

GoogleCloudAiplatformV1beta1ModelContainerSpec

Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification.

Fields
args[]	string Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command[]	string Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deploymentTimeout	string (Duration format) Immutable. Deployment timeout. Limit for deployment timeout is 2 hours.
env[]	object (GoogleCloudAiplatformV1beta1EnvVar) Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
grpcPorts[]	object (GoogleCloudAiplatformV1beta1Port) Immutable. List of ports to expose from the container. Vertex AI sends gRPC prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, gRPC requests to the container will be disabled. Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
healthProbe	object (GoogleCloudAiplatformV1beta1Probe) Immutable. Specification for Kubernetes readiness probe.
healthRoute	string Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
imageUri	string Required. Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
ports[]	object (GoogleCloudAiplatformV1beta1Port) Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute	string Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
sharedMemorySizeMb	string (int64 format) Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.
startupProbe	object (GoogleCloudAiplatformV1beta1Probe) Immutable. Specification for Kubernetes startup probe.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTable

ModelDeploymentMonitoringBigQueryTable specifies the BigQuery table name as well as some information of the logs stored in this table.

Fields
bigqueryTablePath	string The created BigQuery table to store logs. Customer could do their own query & analysis. Format: bq://.model_deployment_monitoring_._
logSource	enum The source of log.
Enum type. Can be one of the following:
LOG_SOURCE_UNSPECIFIED	Unspecified source.
TRAINING	Logs coming from Training dataset.
SERVING	Logs coming from Serving traffic.
logType	enum The type of log.
Enum type. Can be one of the following:
LOG_TYPE_UNSPECIFIED	Unspecified type.
PREDICT	Predict logs.
EXPLAIN	Explain logs.
requestResponseLoggingSchemaVersion	string Output only. The schema version of the request/response logging BigQuery table. Default to v1 if unset.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJob

Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

Fields
analysisInstanceSchemaUri	string YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
bigqueryTables[]	object (GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTable) Output only. The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response
createTime	string (Timestamp format) Output only. Timestamp when this ModelDeploymentMonitoringJob was created.
displayName	string Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
enableMonitoringPipelineLogs	boolean If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.
endpoint	string Required. Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
error	object (GoogleRpcStatus) Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
latestMonitoringPipelineMetadata	object (GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadata) Output only. Latest triggered monitoring pipeline metadata.
logTtl	string (Duration format) The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.
loggingSamplingStrategy	object (GoogleCloudAiplatformV1beta1SamplingStrategy) Required. Sample Strategy for logging.
modelDeploymentMonitoringObjectiveConfigs[]	object (GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfig) Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
modelDeploymentMonitoringScheduleConfig	object (GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfig) Required. Schedule config for running the monitoring job.
modelMonitoringAlertConfig	object (GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig) Alert config for model monitoring.
name	string Output only. Resource name of a ModelDeploymentMonitoringJob.
nextScheduleTime	string (Timestamp format) Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.
predictInstanceSchemaUri	string YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.
samplePredictInstance	any Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
scheduleState	enum Output only. Schedule state when the monitoring job is in Running state.
Enum type. Can be one of the following:
MONITORING_SCHEDULE_STATE_UNSPECIFIED	Unspecified state.
PENDING	The pipeline is picked up and wait to run.
OFFLINE	The pipeline is offline and will be scheduled for next run.
RUNNING	The pipeline is running.
state	enum Output only. The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.
Enum type. Can be one of the following:
JOB_STATE_UNSPECIFIED	The job state is unspecified.
JOB_STATE_QUEUED	The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING	The service is preparing to run the job.
JOB_STATE_RUNNING	The job is in progress.
JOB_STATE_SUCCEEDED	The job completed successfully.
JOB_STATE_FAILED	The job failed.
JOB_STATE_CANCELLING	The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED	The job has been cancelled.
JOB_STATE_PAUSED	The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED	The job has expired.
JOB_STATE_UPDATING	The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED	The job is partially succeeded, some results may be missing due to errors.
statsAnomaliesBaseDirectory	object (GoogleCloudAiplatformV1beta1GcsDestination) Stats anomalies base folder path.
updateTime	string (Timestamp format) Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadata

All metadata of most recent monitoring pipelines.

Fields
runTime	string (Timestamp format) The time that most recent monitoring pipelines that is related to this run.
status	object (GoogleRpcStatus) The status of the most recent monitoring pipeline.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfig

ModelDeploymentMonitoringObjectiveConfig contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig.

Fields
deployedModelId	string The DeployedModel ID of the objective config.
objectiveConfig	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig) The objective config of for the modelmonitoring job of this deployed model.

GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfig

The config for scheduling monitoring job.

Fields
monitorInterval	string (Duration format) Required. The model monitoring job scheduling interval. It will be rounded up to next full hour. This defines how often the monitoring jobs are triggered.
monitorWindow	string (Duration format) The time window of the prediction data being included in each prediction dataset. This window specifies how long the data should be collected from historical model results for each run. If not set, ModelDeploymentMonitoringScheduleConfig.monitor_interval will be used. e.g. If currently the cutoff time is 2022-01-08 14:30:00 and the monitor_window is set to be 3600, then data from 2022-01-08 13:30:00 to 2022-01-08 14:30:00 will be retrieved and aggregated to calculate the monitoring statistics.

GoogleCloudAiplatformV1beta1ModelEvaluation

A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data.

Fields
biasConfigs	object (GoogleCloudAiplatformV1beta1ModelEvaluationBiasConfig) Specify the configuration for bias detection.
createTime	string (Timestamp format) Output only. Timestamp when this ModelEvaluation was created.
displayName	string The display name of the ModelEvaluation.
explanationSpecs[]	object (GoogleCloudAiplatformV1beta1ModelEvaluationModelEvaluationExplanationSpec) Describes the values of ExplanationSpec that are used for explaining the predicted values on the evaluated data.
metadata	any The metadata of the ModelEvaluation. For the ModelEvaluation uploaded from Managed Pipeline, metadata contains a structured value with keys of "pipeline_job_id", "evaluation_dataset_type", "evaluation_dataset_path", "row_based_metrics_path".
metrics	any Evaluation metrics of the Model. The schema of the metrics is stored in metrics_schema_uri
metricsSchemaUri	string Points to a YAML file stored on Google Cloud Storage describing the metrics of this ModelEvaluation. The schema is defined as an OpenAPI 3.0.2 Schema Object.
modelExplanation	object (GoogleCloudAiplatformV1beta1ModelExplanation) Aggregated explanation metrics for the Model's prediction output over the data this ModelEvaluation uses. This field is populated only if the Model is evaluated with explanations, and only for AutoML tabular Models.
name	string Output only. The resource name of the ModelEvaluation.
sliceDimensions[]	string All possible dimensions of ModelEvaluationSlices. The dimensions can be used as the filter of the ModelService.ListModelEvaluationSlices request, in the form of slice.dimension =.

GoogleCloudAiplatformV1beta1ModelEvaluationBiasConfig

Configuration for bias detection.

Fields
biasSlices	object (GoogleCloudAiplatformV1beta1ModelEvaluationSliceSliceSliceSpec) Specification for how the data should be sliced for bias. It contains a list of slices, with limitation of two slices. The first slice of data will be the slice_a. The second slice in the list (slice_b) will be compared against the first slice. If only a single slice is provided, then slice_a will be compared against "not slice_a". Below are examples with feature "education" with value "low", "medium", "high" in the dataset: Example 1: bias_slices = [{'education': 'low'}] A single slice provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'medium' or 'high'. Example 2: bias_slices = [{'education': 'low'}, {'education': 'high'}] Two slices provided. In this case, slice_a is the collection of data with 'education' equals 'low', and slice_b is the collection of data with 'education' equals 'high'.
labels[]	string Positive labels selection on the target field.

GoogleCloudAiplatformV1beta1ModelEvaluationModelEvaluationExplanationSpec

(No description provided)

Fields
explanationSpec	object (GoogleCloudAiplatformV1beta1ExplanationSpec) Explanation spec details.
explanationType	string Explanation type. For AutoML Image Classification models, possible values are: * image-integrated-gradients * image-xrai

GoogleCloudAiplatformV1beta1ModelEvaluationSlice

A collection of metrics calculated by comparing Model's predictions on a slice of the test data against ground truth annotations.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this ModelEvaluationSlice was created.
metrics	any Output only. Sliced evaluation metrics of the Model. The schema of the metrics is stored in metrics_schema_uri
metricsSchemaUri	string Output only. Points to a YAML file stored on Google Cloud Storage describing the metrics of this ModelEvaluationSlice. The schema is defined as an OpenAPI 3.0.2 Schema Object.
modelExplanation	object (GoogleCloudAiplatformV1beta1ModelExplanation) Output only. Aggregated explanation metrics for the Model's prediction output over the data this ModelEvaluation uses. This field is populated only if the Model is evaluated with explanations, and only for tabular Models.
name	string Output only. The resource name of the ModelEvaluationSlice.
slice	object (GoogleCloudAiplatformV1beta1ModelEvaluationSliceSlice) Output only. The slice of the test data that is used to evaluate the Model.

GoogleCloudAiplatformV1beta1ModelEvaluationSliceSlice

Definition of a slice.

Fields
dimension	string Output only. The dimension of the slice. Well-known dimensions are: * annotationSpec: This slice is on the test data that has either ground truth or prediction with AnnotationSpec.display_name equals to value. * slice: This slice is a user customized slice defined by its SliceSpec.
sliceSpec	object (GoogleCloudAiplatformV1beta1ModelEvaluationSliceSliceSliceSpec) Output only. Specification for how the data was sliced.
value	string Output only. The value of the dimension in this slice.

GoogleCloudAiplatformV1beta1ModelEvaluationSliceSliceSliceSpec

Specification for how the data should be sliced.

Fields
configs	map (key: string, value: object (GoogleCloudAiplatformV1beta1ModelEvaluationSliceSliceSliceSpecSliceConfig)) Mapping configuration for this SliceSpec. The key is the name of the feature. By default, the key will be prefixed by "instance" as a dictionary prefix for Vertex Batch Predictions output format.

GoogleCloudAiplatformV1beta1ModelEvaluationSliceSliceSliceSpecRange

A range of values for slice(s). low is inclusive, high is exclusive.

Fields
high	number (float format) Exclusive high value for the range.
low	number (float format) Inclusive low value for the range.

GoogleCloudAiplatformV1beta1ModelEvaluationSliceSliceSliceSpecSliceConfig

Specification message containing the config for this SliceSpec. When kind is selected as value and/or range, only a single slice will be computed. When all_values is present, a separate slice will be computed for each possible label/value for the corresponding key in config. Examples, with feature zip_code with values 12345, 23334, 88888 and feature country with values "US", "Canada", "Mexico" in the dataset: Example 1: { "zip_code": { "value": { "float_value": 12345.0 } } } A single slice for any data with zip_code 12345 in the dataset. Example 2: { "zip_code": { "range": { "low": 12345, "high": 20000 } } } A single slice containing data where the zip_codes between 12345 and 20000 For this example, data with the zip_code of 12345 will be in this slice. Example 3: { "zip_code": { "range": { "low": 10000, "high": 20000 } }, "country": { "value": { "string_value": "US" } } } A single slice containing data where the zip_codes between 10000 and 20000 has the country "US". For this example, data with the zip_code of 12345 and country "US" will be in this slice. Example 4: { "country": {"all_values": { "value": true } } } Three slices are computed, one for each unique country in the dataset. Example 5: { "country": { "all_values": { "value": true } }, "zip_code": { "value": { "float_value": 12345.0 } } } Three slices are computed, one for each unique country in the dataset where the zip_code is also 12345. For this example, data with zip_code 12345 and country "US" will be in one slice, zip_code 12345 and country "Canada" in another slice, and zip_code 12345 and country "Mexico" in another slice, totaling 3 slices.

Fields
allValues	boolean If all_values is set to true, then all possible labels of the keyed feature will have another slice computed. Example: {"all_values":{"value":true}}
range	object (GoogleCloudAiplatformV1beta1ModelEvaluationSliceSliceSliceSpecRange) A range of values for a numerical feature. Example: {"range":{"low":10000.0,"high":50000.0}} will capture 12345 and 23334 in the slice.
value	object (GoogleCloudAiplatformV1beta1ModelEvaluationSliceSliceSliceSpecValue) A unique specific value for a given feature. Example: { "value": { "string_value": "12345" } }

GoogleCloudAiplatformV1beta1ModelEvaluationSliceSliceSliceSpecValue

Single value that supports strings and floats.

Fields
floatValue	number (float format) Float type.
stringValue	string String type.

GoogleCloudAiplatformV1beta1ModelExplanation

Aggregated explanation metrics for a Model over a set of instances.

Fields

meanAttributions[]

object (GoogleCloudAiplatformV1beta1Attribution) Output only. Aggregated attributions explaining the Model's prediction outputs over the set of instances. The attributions are grouped by outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. The baselineOutputValue, instanceOutputValue and featureAttributions fields are averaged over the test data. NOTE: Currently AutoML tabular classification Models produce only one attribution, which averages attributions over all the classes it predicts. Attribution.approximation_error is not populated.

GoogleCloudAiplatformV1beta1ModelExportFormat

Represents export format supported by the Model. All formats export to Google Cloud Storage.

Fields
exportableContents[]	string Output only. The content of this Model that may be exported.
id	string Output only. The ID of the export format. The possible format IDs are: * tflite Used for Android mobile devices. * edgetpu-tflite Used for Edge TPU devices. * tf-saved-model A tensorflow model in SavedModel format. * tf-js A TensorFlow.js model that can be used in the browser and in Node.js using JavaScript. * core-ml Used for iOS mobile devices. * custom-trained A Model that was uploaded or trained by custom code.

GoogleCloudAiplatformV1beta1ModelGardenSource

Contains information about the source of the models generated from Model Garden.

Fields
publicModelName	string Required. The model garden source model resource name.

GoogleCloudAiplatformV1beta1ModelMonitor

Vertex AI Model Monitoring Service serves as a central hub for the analysis and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this ModelMonitor was created.
displayName	string The display name of the ModelMonitor. The name can be up to 128 characters long and can consist of any UTF-8.
explanationSpec	object (GoogleCloudAiplatformV1beta1ExplanationSpec) Optional model explanation spec. It is used for feature attribution monitoring.
modelMonitoringSchema	object (GoogleCloudAiplatformV1beta1ModelMonitoringSchema) Monitoring Schema is to specify the model's features, prediction outputs and ground truth properties. It is used to extract pertinent data from the dataset and to process features based on their properties. Make sure that the schema aligns with your dataset, if it does not, we will be unable to extract data from the dataset. It is required for most models, but optional for Vertex AI AutoML Tables unless the schem information is not available.
modelMonitoringTarget	object (GoogleCloudAiplatformV1beta1ModelMonitorModelMonitoringTarget) The entity that is subject to analysis. Currently only models in Vertex AI Model Registry are supported. If you want to analyze the model which is outside the Vertex AI, you could register a model in Vertex AI Model Registry using just a display name.
name	string Immutable. Resource name of the ModelMonitor. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}.
notificationSpec	object (GoogleCloudAiplatformV1beta1ModelMonitoringNotificationSpec) Optional default notification spec, it can be overridden in the ModelMonitoringJob notification spec.
outputSpec	object (GoogleCloudAiplatformV1beta1ModelMonitoringOutputSpec) Optional default monitoring metrics/logs export spec, it can be overridden in the ModelMonitoringJob output spec. If not specified, a default Google Cloud Storage bucket will be created under your project.
tabularObjective	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveSpecTabularObjective) Optional default tabular model monitoring objective.
trainingDataset	object (GoogleCloudAiplatformV1beta1ModelMonitoringInput) Optional training dataset used to train the model. It can serve as a reference dataset to identify changes in production.
updateTime	string (Timestamp format) Output only. Timestamp when this ModelMonitor was updated most recently.

GoogleCloudAiplatformV1beta1ModelMonitorModelMonitoringTarget

The monitoring target refers to the entity that is subject to analysis. e.g. Vertex AI Model version.

Fields
vertexModel	object (GoogleCloudAiplatformV1beta1ModelMonitorModelMonitoringTargetVertexModelSource) Model in Vertex AI Model Registry.

GoogleCloudAiplatformV1beta1ModelMonitorModelMonitoringTargetVertexModelSource

Model in Vertex AI Model Registry.

Fields
model	string Model resource name. Format: projects/{project}/locations/{location}/models/{model}.
modelVersionId	string Model version id.

GoogleCloudAiplatformV1beta1ModelMonitoringAlert

Represents a single monitoring alert. This is currently used in the SearchModelMonitoringAlerts api, thus the alert wrapped in this message belongs to the resource asked in the request.

Fields
alertTime	string (Timestamp format) Alert creation time.
anomaly	object (GoogleCloudAiplatformV1beta1ModelMonitoringAnomaly) Anomaly details.
objectiveType	string One of the supported monitoring objectives: raw-feature-drift prediction-output-drift feature-attribution
statsName	string The stats name.

GoogleCloudAiplatformV1beta1ModelMonitoringAlertCondition

Monitoring alert triggered condition.

Fields
threshold	number (double format) A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig

The alert config for model monitoring.

Fields
emailAlertConfig	object (GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig) Email alert config.
enableLogging	boolean Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notificationChannels[]	string Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig

The config for email alert.

Fields
userEmails[]	string The email addresses to send the alert.

GoogleCloudAiplatformV1beta1ModelMonitoringAnomaly

Represents a single model monitoring anomaly.

Fields
algorithm	string Algorithm used to calculated the metrics, eg: jensen_shannon_divergence, l_infinity.
modelMonitoringJob	string Model monitoring job resource name.
tabularAnomaly	object (GoogleCloudAiplatformV1beta1ModelMonitoringAnomalyTabularAnomaly) Tabular anomaly.

GoogleCloudAiplatformV1beta1ModelMonitoringAnomalyTabularAnomaly

Tabular anomaly details.

Fields
anomaly	any Anomaly body.
anomalyUri	string Additional anomaly information. e.g. Google Cloud Storage uri.
condition	object (GoogleCloudAiplatformV1beta1ModelMonitoringAlertCondition) The alert condition associated with this anomaly.
summary	string Overview of this anomaly.
triggerTime	string (Timestamp format) The time the anomaly was triggered.

GoogleCloudAiplatformV1beta1ModelMonitoringConfig

The model monitoring configuration used for Batch Prediction Job.

Fields
alertConfig	object (GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig) Model monitoring alert config.
analysisInstanceSchemaUri	string YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
objectiveConfigs[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig) Model monitoring objective config.
statsAnomaliesBaseDirectory	object (GoogleCloudAiplatformV1beta1GcsDestination) A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.

GoogleCloudAiplatformV1beta1ModelMonitoringInput

Model monitoring data input spec.

Fields
batchPredictionOutput	object (GoogleCloudAiplatformV1beta1ModelMonitoringInputBatchPredictionOutput) Vertex AI Batch prediction Job.
columnizedDataset	object (GoogleCloudAiplatformV1beta1ModelMonitoringInputModelMonitoringDataset) Columnized dataset.
timeInterval	object (GoogleTypeInterval) The time interval (pair of start_time and end_time) for which results should be returned.
timeOffset	object (GoogleCloudAiplatformV1beta1ModelMonitoringInputTimeOffset) The time offset setting for which results should be returned.
vertexEndpointLogs	object (GoogleCloudAiplatformV1beta1ModelMonitoringInputVertexEndpointLogs) Vertex AI Endpoint request & response logging.

GoogleCloudAiplatformV1beta1ModelMonitoringInputBatchPredictionOutput

Data from Vertex AI Batch prediction job output.

Fields
batchPredictionJob	string Vertex AI Batch prediction job resource name. The job must match the model version specified in [ModelMonitor].[model_monitoring_target].

GoogleCloudAiplatformV1beta1ModelMonitoringInputModelMonitoringDataset

Input dataset spec.

Fields
bigquerySource	object (GoogleCloudAiplatformV1beta1ModelMonitoringInputModelMonitoringDatasetModelMonitoringBigQuerySource) BigQuery data source.
gcsSource	object (GoogleCloudAiplatformV1beta1ModelMonitoringInputModelMonitoringDatasetModelMonitoringGcsSource) Google Cloud Storage data source.
timestampField	string The timestamp field. Usually for serving data.
vertexDataset	string Resource name of the Vertex AI managed dataset.

GoogleCloudAiplatformV1beta1ModelMonitoringInputModelMonitoringDatasetModelMonitoringBigQuerySource

Dataset spec for data sotred in BigQuery.

Fields
query	string Standard SQL to be used instead of the table_uri.
tableUri	string BigQuery URI to a table, up to 2000 characters long. All the columns in the table will be selected. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

GoogleCloudAiplatformV1beta1ModelMonitoringInputModelMonitoringDatasetModelMonitoringGcsSource

Dataset spec for data stored in Google Cloud Storage.

Fields
format	enum Data format of the dataset.
Enum type. Can be one of the following:
DATA_FORMAT_UNSPECIFIED	Data format unspecified, used when this field is unset.
CSV	CSV files.
TF_RECORD	TfRecord files
JSONL	JsonL files.
gcsUri	string Google Cloud Storage URI to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.

GoogleCloudAiplatformV1beta1ModelMonitoringInputTimeOffset

Time offset setting.

Fields
offset	string [offset] is the time difference from the cut-off time. For scheduled jobs, the cut-off time is the scheduled time. For non-scheduled jobs, it's the time when the job was created. Currently we support the following format: 'w|W': Week, 'd|D': Day, 'h|H': Hour E.g. '1h' stands for 1 hour, '2d' stands for 2 days.
window	string [window] refers to the scope of data selected for analysis. It allows you to specify the quantity of data you wish to examine. Currently we support the following format: 'w|W': Week, 'd|D': Day, 'h|H': Hour E.g. '1h' stands for 1 hour, '2d' stands for 2 days.

GoogleCloudAiplatformV1beta1ModelMonitoringInputVertexEndpointLogs

Data from Vertex AI Endpoint request response logging.

Fields
endpoints[]	string List of endpoint resource names. The endpoints must enable the logging with the [Endpoint].[request_response_logging_config], and must contain the deployed model corresponding to the model version specified in [ModelMonitor].[model_monitoring_target].

GoogleCloudAiplatformV1beta1ModelMonitoringJob

Represents a model monitoring job that analyze dataset using different monitoring algorithm.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this ModelMonitoringJob was created.
displayName	string The display name of the ModelMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8.
jobExecutionDetail	object (GoogleCloudAiplatformV1beta1ModelMonitoringJobExecutionDetail) Output only. Execution results for all the monitoring objectives.
modelMonitoringSpec	object (GoogleCloudAiplatformV1beta1ModelMonitoringSpec) Monitoring monitoring job spec. It outlines the specifications for monitoring objectives, notifications, and result exports. If left blank, the default monitoring specifications from the top-level resource 'ModelMonitor' will be applied. If provided, we will use the specification defined here rather than the default one.
name	string Output only. Resource name of a ModelMonitoringJob. Format: projects/{project_id}/locations/{location_id}/modelMonitors/{model_monitor_id}/modelMonitoringJobs/{model_monitoring_job_id}
schedule	string Output only. Schedule resource name. It will only appear when this job is triggered by a schedule.
scheduleTime	string (Timestamp format) Output only. Timestamp when this ModelMonitoringJob was scheduled. It will only appear when this job is triggered by a schedule.
state	enum Output only. The state of the monitoring job. * When the job is still creating, the state will be 'JOB_STATE_PENDING'. * Once the job is successfully created, the state will be 'JOB_STATE_RUNNING'. * Once the job is finished, the state will be one of 'JOB_STATE_FAILED', 'JOB_STATE_SUCCEEDED', 'JOB_STATE_PARTIALLY_SUCCEEDED'.
Enum type. Can be one of the following:
JOB_STATE_UNSPECIFIED	The job state is unspecified.
JOB_STATE_QUEUED	The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING	The service is preparing to run the job.
JOB_STATE_RUNNING	The job is in progress.
JOB_STATE_SUCCEEDED	The job completed successfully.
JOB_STATE_FAILED	The job failed.
JOB_STATE_CANCELLING	The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED	The job has been cancelled.
JOB_STATE_PAUSED	The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED	The job has expired.
JOB_STATE_UPDATING	The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED	The job is partially succeeded, some results may be missing due to errors.
updateTime	string (Timestamp format) Output only. Timestamp when this ModelMonitoringJob was updated most recently.

GoogleCloudAiplatformV1beta1ModelMonitoringJobExecutionDetail

Represent the execution details of the job.

Fields
baselineDatasets[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringJobExecutionDetailProcessedDataset) Processed baseline datasets.
error	object (GoogleRpcStatus) Additional job error status.
objectiveStatus	map (key: string, value: object (GoogleRpcStatus)) Status of data processing for each monitoring objective. Key is the objective.
targetDatasets[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringJobExecutionDetailProcessedDataset) Processed target datasets.

GoogleCloudAiplatformV1beta1ModelMonitoringJobExecutionDetailProcessedDataset

Processed dataset information.

Fields
location	string Actual data location of the processed dataset.
timeRange	object (GoogleTypeInterval) Dataset time range information if any.

GoogleCloudAiplatformV1beta1ModelMonitoringNotificationSpec

Notification spec(email, notification channel) for model monitoring statistics/alerts.

Fields
emailConfig	object (GoogleCloudAiplatformV1beta1ModelMonitoringNotificationSpecEmailConfig) Email alert config.
enableCloudLogging	boolean Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notificationChannelConfigs[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringNotificationSpecNotificationChannelConfig) Notification channel config.

GoogleCloudAiplatformV1beta1ModelMonitoringNotificationSpecEmailConfig

The config for email alerts.

Fields
userEmails[]	string The email addresses to send the alerts.

GoogleCloudAiplatformV1beta1ModelMonitoringNotificationSpecNotificationChannelConfig

Google Cloud Notification Channel config.

Fields
notificationChannel	string Resource names of the NotificationChannels. Must be of the format projects//notificationChannels/

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig

The objective configuration for model monitoring, including the information needed to detect anomalies for one particular model.

Fields
explanationConfig	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfig) The config for integrating with Vertex Explainable AI.
predictionDriftDetectionConfig	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfig) The config for drift of prediction data.
trainingDataset	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDataset) Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
trainingPredictionSkewDetectionConfig	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfig) The config for skew between training data and prediction data.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfig

The config for integrating with Vertex Explainable AI. Only applicable if the Model has explanation_spec populated.

Fields
enableFeatureAttributes	boolean If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanationBaseline	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline) Predictions generated by the BatchPredictionJob using baseline dataset.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline

Output from BatchPredictionJob for Model Monitoring baseline dataset, which can be used to generate baseline attribution scores.

Fields
bigquery	object (GoogleCloudAiplatformV1beta1BigQueryDestination) BigQuery location for BatchExplain output.
gcs	object (GoogleCloudAiplatformV1beta1GcsDestination) Cloud Storage location for BatchExplain output.
predictionFormat	enum The storage format of the predictions generated BatchPrediction job.
Enum type. Can be one of the following:
PREDICTION_FORMAT_UNSPECIFIED	Should not be set.
JSONL	Predictions are in JSONL files.
BIGQUERY	Predictions are in BigQuery.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfig

The config for Prediction data drift detection.

Fields
attributionScoreDriftThresholds	map (key: string, value: object (GoogleCloudAiplatformV1beta1ThresholdConfig)) Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
defaultDriftThreshold	object (GoogleCloudAiplatformV1beta1ThresholdConfig) Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
driftThresholds	map (key: string, value: object (GoogleCloudAiplatformV1beta1ThresholdConfig)) Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDataset

Training Dataset information.

Fields
bigquerySource	object (GoogleCloudAiplatformV1beta1BigQuerySource) The BigQuery table of the unmanaged Dataset used to train this Model.
dataFormat	string Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset	string The resource name of the Dataset used to train this Model.
gcsSource	object (GoogleCloudAiplatformV1beta1GcsSource) The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
loggingSamplingStrategy	object (GoogleCloudAiplatformV1beta1SamplingStrategy) Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
targetField	string The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfig

The config for Training & Prediction data skew detection. It specifies the training dataset sources and the skew detection parameters.

Fields
attributionScoreSkewThresholds	map (key: string, value: object (GoogleCloudAiplatformV1beta1ThresholdConfig)) Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
defaultSkewThreshold	object (GoogleCloudAiplatformV1beta1ThresholdConfig) Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skewThresholds	map (key: string, value: object (GoogleCloudAiplatformV1beta1ThresholdConfig)) Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveSpec

Monitoring objectives spec.

Fields
baselineDataset	object (GoogleCloudAiplatformV1beta1ModelMonitoringInput) Baseline dataset. It could be the training dataset or production serving dataset from a previous period.
explanationSpec	object (GoogleCloudAiplatformV1beta1ExplanationSpec) The explanation spec. This spec is required when the objectives spec includes feature attribution objectives.
tabularObjective	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveSpecTabularObjective) Tabular monitoring objective.
targetDataset	object (GoogleCloudAiplatformV1beta1ModelMonitoringInput) Target dataset.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveSpecDataDriftSpec

Data drift monitoring spec. Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period.

Fields
categoricalMetricType	string Supported metrics type: * l_infinity * jensen_shannon_divergence
defaultCategoricalAlertCondition	object (GoogleCloudAiplatformV1beta1ModelMonitoringAlertCondition) Default alert condition for all the categorical features.
defaultNumericAlertCondition	object (GoogleCloudAiplatformV1beta1ModelMonitoringAlertCondition) Default alert condition for all the numeric features.
featureAlertConditions	map (key: string, value: object (GoogleCloudAiplatformV1beta1ModelMonitoringAlertCondition)) Per feature alert condition will override default alert condition.
features[]	string Feature names / Prediction output names interested in monitoring. These should be a subset of the input feature names or prediction output names specified in the monitoring schema. If the field is not specified all features / prediction outputs outlied in the monitoring schema will be used.
numericMetricType	string Supported metrics type: * jensen_shannon_divergence

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveSpecFeatureAttributionSpec

Feature attribution monitoring spec.

Fields
batchExplanationDedicatedResources	object (GoogleCloudAiplatformV1beta1BatchDedicatedResources) The config of resources used by the Model Monitoring during the batch explanation for non-AutoML models. If not set, n1-standard-2 machine type will be used by default.
defaultAlertCondition	object (GoogleCloudAiplatformV1beta1ModelMonitoringAlertCondition) Default alert condition for all the features.
featureAlertConditions	map (key: string, value: object (GoogleCloudAiplatformV1beta1ModelMonitoringAlertCondition)) Per feature alert condition will override default alert condition.
features[]	string Feature names interested in monitoring. These should be a subset of the input feature names specified in the monitoring schema. If the field is not specified all features outlied in the monitoring schema will be used.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveSpecTabularObjective

Tabular monitoring objective.

Fields
featureAttributionSpec	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveSpecFeatureAttributionSpec) Feature attribution monitoring spec.
featureDriftSpec	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveSpecDataDriftSpec) Input feature distribution drift monitoring spec.
predictionOutputDriftSpec	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveSpecDataDriftSpec) Prediction output distribution drift monitoring spec.

GoogleCloudAiplatformV1beta1ModelMonitoringOutputSpec

Specification for the export destination of monitoring results, including metrics, logs, etc.

Fields
gcsBaseDirectory	object (GoogleCloudAiplatformV1beta1GcsDestination) Google Cloud Storage base folder path for metrics, error logs, etc.

GoogleCloudAiplatformV1beta1ModelMonitoringSchema

The Model Monitoring Schema definition.

Fields
featureFields[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringSchemaFieldSchema) Feature names of the model. Vertex AI will try to match the features from your dataset as follows: * For 'csv' files, the header names are required, and we will extract the corresponding feature values when the header names align with the feature names. * For 'jsonl' files, we will extract the corresponding feature values if the key names match the feature names. Note: Nested features are not supported, so please ensure your features are flattened. Ensure the feature values are scalar or an array of scalars. * For 'bigquery' dataset, we will extract the corresponding feature values if the column names match the feature names. Note: The column type can be a scalar or an array of scalars. STRUCT or JSON types are not supported. You may use SQL queries to select or aggregate the relevant features from your original table. However, ensure that the 'schema' of the query results meets our requirements. * For the Vertex AI Endpoint Request Response Logging table or Vertex AI Batch Prediction Job results. If the instance_type is an array, ensure that the sequence in feature_fields matches the order of features in the prediction instance. We will match the feature with the array in the order specified in [feature_fields].
groundTruthFields[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringSchemaFieldSchema) Target /ground truth names of the model.
predictionFields[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringSchemaFieldSchema) Prediction output names of the model. The requirements are the same as the feature_fields. For AutoML Tables, the prediction output name presented in schema will be: predicted_{target_column}, the target_column is the one you specified when you train the model. For Prediction output drift analysis: * AutoML Classification, the distribution of the argmax label will be analyzed. * AutoML Regression, the distribution of the value will be analyzed.

GoogleCloudAiplatformV1beta1ModelMonitoringSchemaFieldSchema

Schema field definition.

Fields
dataType	string Supported data types are: float integer boolean string categorical
name	string Field name.
repeated	boolean Describes if the schema field is an array of given data type.

GoogleCloudAiplatformV1beta1ModelMonitoringSpec

Monitoring monitoring job spec. It outlines the specifications for monitoring objectives, notifications, and result exports.

Fields
notificationSpec	object (GoogleCloudAiplatformV1beta1ModelMonitoringNotificationSpec) The model monitoring notification spec.
objectiveSpec	object (GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveSpec) The monitoring objective spec.
outputSpec	object (GoogleCloudAiplatformV1beta1ModelMonitoringOutputSpec) The Output destination spec for metrics, error logs, etc.

GoogleCloudAiplatformV1beta1ModelMonitoringStats

Represents the collection of statistics for a metric.

Fields
tabularStats	object (GoogleCloudAiplatformV1beta1ModelMonitoringTabularStats) Generated tabular statistics.

GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomalies

Statistics and anomalies generated by Model Monitoring.

Fields
anomalyCount	integer (int32 format) Number of anomalies within all stats.
deployedModelId	string Deployed Model ID.
featureStats[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomalies) A list of historical Stats and Anomalies generated for all Features.
objective	enum Model Monitoring Objective those stats and anomalies belonging to.
Enum type. Can be one of the following:
MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED	Default value, should not be set.
RAW_FEATURE_SKEW	Raw feature values' stats to detect skew between Training-Prediction datasets.
RAW_FEATURE_DRIFT	Raw feature values' stats to detect drift between Serving-Prediction datasets.
FEATURE_ATTRIBUTION_SKEW	Feature attribution scores to detect skew between Training-Prediction datasets.
FEATURE_ATTRIBUTION_DRIFT	Feature attribution scores to detect skew between Prediction datasets collected within different time windows.

GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomalies

Historical Stats (and Anomalies) for a specific Feature.

Fields
featureDisplayName	string Display Name of the Feature.
predictionStats[]	object (GoogleCloudAiplatformV1beta1FeatureStatsAnomaly) A list of historical stats generated by different time window's Prediction Dataset.
threshold	object (GoogleCloudAiplatformV1beta1ThresholdConfig) Threshold for anomaly detection.
trainingStats	object (GoogleCloudAiplatformV1beta1FeatureStatsAnomaly) Stats calculated for the Training Dataset.

GoogleCloudAiplatformV1beta1ModelMonitoringStatsDataPoint

Represents a single statistics data point.

Fields
algorithm	string Algorithm used to calculated the metrics, eg: jensen_shannon_divergence, l_infinity.
baselineStats	object (GoogleCloudAiplatformV1beta1ModelMonitoringStatsDataPointTypedValue) Statistics from baseline dataset.
createTime	string (Timestamp format) Statistics create time.
currentStats	object (GoogleCloudAiplatformV1beta1ModelMonitoringStatsDataPointTypedValue) Statistics from current dataset.
hasAnomaly	boolean Indicate if the statistics has anomaly.
modelMonitoringJob	string Model monitoring job resource name.
schedule	string Schedule resource name.
thresholdValue	number (double format) Threshold value.

GoogleCloudAiplatformV1beta1ModelMonitoringStatsDataPointTypedValue

Typed value of the statistics.

Fields
distributionValue	object (GoogleCloudAiplatformV1beta1ModelMonitoringStatsDataPointTypedValueDistributionDataValue) Distribution.
doubleValue	number (double format) Double.

GoogleCloudAiplatformV1beta1ModelMonitoringStatsDataPointTypedValueDistributionDataValue

Summary statistics for a population of values.

Fields
distribution	any tensorflow.metadata.v0.DatasetFeatureStatistics format.
distributionDeviation	number (double format) Distribution distance deviation from the current dataset's statistics to baseline dataset's statistics. * For categorical feature, the distribution distance is calculated by L-inifinity norm or Jensen–Shannon divergence. * For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.

GoogleCloudAiplatformV1beta1ModelMonitoringTabularStats

A collection of data points that describes the time-varying values of a tabular metric.

Fields
dataPoints[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringStatsDataPoint) The data points of this time series. When listing time series, points are returned in reverse time order.
objectiveType	string One of the supported monitoring objectives: raw-feature-drift prediction-output-drift feature-attribution
statsName	string The stats name.

GoogleCloudAiplatformV1beta1ModelOriginalModelInfo

Contains information about the original Model if this Model is a copy.

Fields
model	string Output only. The resource name of the Model this Model is a copy of, including the revision. Format: projects/{project}/locations/{location}/models/{model_id}@{version_id}

GoogleCloudAiplatformV1beta1ModelSourceInfo

Detail description of the source information of the model.

Fields
copy	boolean If this Model is copy of another Model. If true then source_type pertains to the original.
sourceType	enum Type of the model source.
Enum type. Can be one of the following:
MODEL_SOURCE_TYPE_UNSPECIFIED	Should not be used.
AUTOML	The Model is uploaded by automl training pipeline.
CUSTOM	The Model is uploaded by user or custom training pipeline.
BQML	The Model is registered and sync'ed from BigQuery ML.
MODEL_GARDEN	The Model is saved or tuned from Model Garden.
GENIE	The Model is saved or tuned from Genie.
CUSTOM_TEXT_EMBEDDING	The Model is uploaded by text embedding finetuning pipeline.
MARKETPLACE	The Model is saved or tuned from Marketplace.

GoogleCloudAiplatformV1beta1MutateDeployedIndexOperationMetadata

Runtime operation information for IndexEndpointService.MutateDeployedIndex.

Fields
deployedIndexId	string The unique index id specified by user
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1MutateDeployedIndexResponse

Response message for IndexEndpointService.MutateDeployedIndex.

Fields
deployedIndex	object (GoogleCloudAiplatformV1beta1DeployedIndex) The DeployedIndex that had been updated in the IndexEndpoint.

GoogleCloudAiplatformV1beta1MutateDeployedModelOperationMetadata

Runtime operation information for EndpointService.MutateDeployedModel.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1MutateDeployedModelRequest

Request message for EndpointService.MutateDeployedModel.

Fields
deployedModel	object (GoogleCloudAiplatformV1beta1DeployedModel) Required. The DeployedModel to be mutated within the Endpoint. Only the following fields can be mutated: * min_replica_count in either DedicatedResources or AutomaticResources * max_replica_count in either DedicatedResources or AutomaticResources * autoscaling_metric_specs * disable_container_logging (v1 only) * enable_container_logging (v1beta1 only)
updateMask	string (FieldMask format) Required. The update mask applies to the resource. See google.protobuf.FieldMask.

GoogleCloudAiplatformV1beta1MutateDeployedModelResponse

Response message for EndpointService.MutateDeployedModel.

Fields
deployedModel	object (GoogleCloudAiplatformV1beta1DeployedModel) The DeployedModel that's being mutated.

GoogleCloudAiplatformV1beta1NasJob

Represents a Neural Architecture Search (NAS) job.

Fields
createTime	string (Timestamp format) Output only. Time when the NasJob was created.
displayName	string Required. The display name of the NasJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
enableRestrictedImageTraining	boolean Optional. Enable a separation of Custom model training and restricted image training for tenant project.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key options for a NasJob. If this is set, then all resources created by the NasJob will be encrypted with the provided encryption key.
endTime	string (Timestamp format) Output only. Time when the NasJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error	object (GoogleRpcStatus) Output only. Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
labels	map (key: string, value: string) The labels with user-defined metadata to organize NasJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
name	string Output only. Resource name of the NasJob.
nasJobOutput	object (GoogleCloudAiplatformV1beta1NasJobOutput) Output only. Output of the NasJob.
nasJobSpec	object (GoogleCloudAiplatformV1beta1NasJobSpec) Required. The specification of a NasJob.
startTime	string (Timestamp format) Output only. Time when the NasJob for the first time entered the JOB_STATE_RUNNING state.
state	enum Output only. The detailed state of the job.
Enum type. Can be one of the following:
JOB_STATE_UNSPECIFIED	The job state is unspecified.
JOB_STATE_QUEUED	The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING	The service is preparing to run the job.
JOB_STATE_RUNNING	The job is in progress.
JOB_STATE_SUCCEEDED	The job completed successfully.
JOB_STATE_FAILED	The job failed.
JOB_STATE_CANCELLING	The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED	The job has been cancelled.
JOB_STATE_PAUSED	The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED	The job has expired.
JOB_STATE_UPDATING	The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED	The job is partially succeeded, some results may be missing due to errors.
updateTime	string (Timestamp format) Output only. Time when the NasJob was most recently updated.

GoogleCloudAiplatformV1beta1NasJobOutput

Represents a uCAIP NasJob output.

Fields
multiTrialJobOutput	object (GoogleCloudAiplatformV1beta1NasJobOutputMultiTrialJobOutput) Output only. The output of this multi-trial Neural Architecture Search (NAS) job.

GoogleCloudAiplatformV1beta1NasJobOutputMultiTrialJobOutput

The output of a multi-trial Neural Architecture Search (NAS) jobs.

Fields
searchTrials[]	object (GoogleCloudAiplatformV1beta1NasTrial) Output only. List of NasTrials that were started as part of search stage.
trainTrials[]	object (GoogleCloudAiplatformV1beta1NasTrial) Output only. List of NasTrials that were started as part of train stage.

GoogleCloudAiplatformV1beta1NasJobSpec

Represents the spec of a NasJob.

Fields
multiTrialAlgorithmSpec	object (GoogleCloudAiplatformV1beta1NasJobSpecMultiTrialAlgorithmSpec) The spec of multi-trial algorithms.
resumeNasJobId	string The ID of the existing NasJob in the same Project and Location which will be used to resume search. search_space_spec and nas_algorithm_spec are obtained from previous NasJob hence should not provide them again for this NasJob.
searchSpaceSpec	string It defines the search space for Neural Architecture Search (NAS).

GoogleCloudAiplatformV1beta1NasJobSpecMultiTrialAlgorithmSpec

The spec of multi-trial Neural Architecture Search (NAS).

Fields
metric	object (GoogleCloudAiplatformV1beta1NasJobSpecMultiTrialAlgorithmSpecMetricSpec) Metric specs for the NAS job. Validation for this field is done at multi_trial_algorithm_spec field.
multiTrialAlgorithm	enum The multi-trial Neural Architecture Search (NAS) algorithm type. Defaults to REINFORCEMENT_LEARNING.
Enum type. Can be one of the following:
MULTI_TRIAL_ALGORITHM_UNSPECIFIED	Defaults to REINFORCEMENT_LEARNING.
REINFORCEMENT_LEARNING	The Reinforcement Learning Algorithm for Multi-trial Neural Architecture Search (NAS).
GRID_SEARCH	The Grid Search Algorithm for Multi-trial Neural Architecture Search (NAS).
searchTrialSpec	object (GoogleCloudAiplatformV1beta1NasJobSpecMultiTrialAlgorithmSpecSearchTrialSpec) Required. Spec for search trials.
trainTrialSpec	object (GoogleCloudAiplatformV1beta1NasJobSpecMultiTrialAlgorithmSpecTrainTrialSpec) Spec for train trials. Top N [TrainTrialSpec.max_parallel_trial_count] search trials will be trained for every M [TrainTrialSpec.frequency] trials searched.

GoogleCloudAiplatformV1beta1NasJobSpecMultiTrialAlgorithmSpecMetricSpec

Represents a metric to optimize.

Fields
goal	enum Required. The optimization goal of the metric.
Enum type. Can be one of the following:
GOAL_TYPE_UNSPECIFIED	Goal Type will default to maximize.
MAXIMIZE	Maximize the goal metric.
MINIMIZE	Minimize the goal metric.
metricId	string Required. The ID of the metric. Must not contain whitespaces.

GoogleCloudAiplatformV1beta1NasJobSpecMultiTrialAlgorithmSpecSearchTrialSpec

Represent spec for search trials.

Fields
maxFailedTrialCount	integer (int32 format) The number of failed trials that need to be seen before failing the NasJob. If set to 0, Vertex AI decides how many trials must fail before the whole job fails.
maxParallelTrialCount	integer (int32 format) Required. The maximum number of trials to run in parallel.
maxTrialCount	integer (int32 format) Required. The maximum number of Neural Architecture Search (NAS) trials to run.
searchTrialJobSpec	object (GoogleCloudAiplatformV1beta1CustomJobSpec) Required. The spec of a search trial job. The same spec applies to all search trials.

GoogleCloudAiplatformV1beta1NasJobSpecMultiTrialAlgorithmSpecTrainTrialSpec

Represent spec for train trials.

Fields
frequency	integer (int32 format) Required. Frequency of search trials to start train stage. Top N [TrainTrialSpec.max_parallel_trial_count] search trials will be trained for every M [TrainTrialSpec.frequency] trials searched.
maxParallelTrialCount	integer (int32 format) Required. The maximum number of trials to run in parallel.
trainTrialJobSpec	object (GoogleCloudAiplatformV1beta1CustomJobSpec) Required. The spec of a train trial job. The same spec applies to all train trials.

GoogleCloudAiplatformV1beta1NasTrial

Represents a uCAIP NasJob trial.

Fields
endTime	string (Timestamp format) Output only. Time when the NasTrial's status changed to SUCCEEDED or INFEASIBLE.
finalMeasurement	object (GoogleCloudAiplatformV1beta1Measurement) Output only. The final measurement containing the objective value.
id	string Output only. The identifier of the NasTrial assigned by the service.
startTime	string (Timestamp format) Output only. Time when the NasTrial was started.
state	enum Output only. The detailed state of the NasTrial.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	The NasTrial state is unspecified.
REQUESTED	Indicates that a specific NasTrial has been requested, but it has not yet been suggested by the service.
ACTIVE	Indicates that the NasTrial has been suggested.
STOPPING	Indicates that the NasTrial should stop according to the service.
SUCCEEDED	Indicates that the NasTrial is completed successfully.
INFEASIBLE	Indicates that the NasTrial should not be attempted again. The service will set a NasTrial to INFEASIBLE when it's done but missing the final_measurement.

GoogleCloudAiplatformV1beta1NasTrialDetail

Represents a NasTrial details along with its parameters. If there is a corresponding train NasTrial, the train NasTrial is also returned.

Fields
name	string Output only. Resource name of the NasTrialDetail.
parameters	string The parameters for the NasJob NasTrial.
searchTrial	object (GoogleCloudAiplatformV1beta1NasTrial) The requested search NasTrial.
trainTrial	object (GoogleCloudAiplatformV1beta1NasTrial) The train NasTrial corresponding to search_trial. Only populated if search_trial is used for training.

GoogleCloudAiplatformV1beta1NearestNeighborQuery

A query to find a number of similar entities.

Fields
embedding	object (GoogleCloudAiplatformV1beta1NearestNeighborQueryEmbedding) Optional. The embedding vector that be used for similar search.
entityId	string Optional. The entity id whose similar entities should be searched for. If embedding is set, search will use embedding instead of entity_id.
neighborCount	integer (int32 format) Optional. The number of similar entities to be retrieved from feature view for each query.
parameters	object (GoogleCloudAiplatformV1beta1NearestNeighborQueryParameters) Optional. Parameters that can be set to tune query on the fly.
perCrowdingAttributeNeighborCount	integer (int32 format) Optional. Crowding is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than sper_crowding_attribute_neighbor_count of the k neighbors returned have the same value of crowding_attribute. It's used for improving result diversity.
stringFilters[]	object (GoogleCloudAiplatformV1beta1NearestNeighborQueryStringFilter) Optional. The list of string filters.

GoogleCloudAiplatformV1beta1NearestNeighborQueryEmbedding

The embedding vector.

Fields
value[]	number (float format) Optional. Individual value in the embedding.

GoogleCloudAiplatformV1beta1NearestNeighborQueryParameters

Parameters that can be overrided in each query to tune query latency and recall.

Fields
approximateNeighborCandidates	integer (int32 format) Optional. The number of neighbors to find via approximate search before exact reordering is performed; if set, this value must be > neighbor_count.
leafNodesSearchFraction	number (double format) Optional. The fraction of the number of leaves to search, set at query time allows user to tune search performance. This value increase result in both search accuracy and latency increase. The value should be between 0.0 and 1.0.

GoogleCloudAiplatformV1beta1NearestNeighborQueryStringFilter

String filter is used to search a subset of the entities by using boolean rules on string columns. For example: if a query specifies string filter with 'name = color, allow_tokens = {red, blue}, deny_tokens = {purple}',' then that query will match entities that are red or blue, but if those points are also purple, then they will be excluded even if they are red/blue. Only string filter is supported for now, numeric filter will be supported in the near future.

Fields
allowTokens[]	string Optional. The allowed tokens.
denyTokens[]	string Optional. The denied tokens.
name	string Required. Column names in BigQuery that used as filters.

GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadata

Runtime operation metadata with regard to Matching Engine Index.

Fields
contentValidationStats[]	object (GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadataContentValidationStats) The validation stats of the content (per file) to be inserted or updated on the Matching Engine Index resource. Populated if contentsDeltaUri is provided as part of Index.metadata. Please note that, currently for those files that are broken or has unsupported file format, we will not have the stats for those files.
dataBytesCount	string (int64 format) The ingested data size in bytes.

GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadataContentValidationStats

(No description provided)

Fields
invalidRecordCount	string (int64 format) Number of records in this file we skipped due to validate errors.
invalidSparseRecordCount	string (int64 format) Number of sparse records in this file we skipped due to validate errors.
partialErrors[]	object (GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadataRecordError) The detail information of the partial failures encountered for those invalid records that couldn't be parsed. Up to 50 partial errors will be reported.
sourceGcsUri	string Cloud Storage URI pointing to the original file in user's bucket.
validRecordCount	string (int64 format) Number of records in this file that were successfully processed.
validSparseRecordCount	string (int64 format) Number of sparse records in this file that were successfully processed.

GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadataRecordError

(No description provided)

Fields
embeddingId	string Empty if the embedding id is failed to parse.
errorMessage	string A human-readable message that is shown to the user to help them fix the error. Note that this message may change from time to time, your code should check against error_type as the source of truth.
errorType	enum The error type of this record.
Enum type. Can be one of the following:
ERROR_TYPE_UNSPECIFIED	Default, shall not be used.
EMPTY_LINE	The record is empty.
INVALID_JSON_SYNTAX	Invalid json format.
INVALID_CSV_SYNTAX	Invalid csv format.
INVALID_AVRO_SYNTAX	Invalid avro format.
INVALID_EMBEDDING_ID	The embedding id is not valid.
EMBEDDING_SIZE_MISMATCH	The size of the dense embedding vectors does not match with the specified dimension.
NAMESPACE_MISSING	The namespace field is missing.
PARSING_ERROR	Generic catch-all error. Only used for validation failure where the root cause cannot be easily retrieved programmatically.
DUPLICATE_NAMESPACE	There are multiple restricts with the same namespace value.
OP_IN_DATAPOINT	Numeric restrict has operator specified in datapoint.
MULTIPLE_VALUES	Numeric restrict has multiple values specified.
INVALID_NUMERIC_VALUE	Numeric restrict has invalid numeric value specified.
INVALID_ENCODING	File is not in UTF_8 format.
INVALID_SPARSE_DIMENSIONS	Error parsing sparse dimensions field.
INVALID_TOKEN_VALUE	Token restrict value is invalid.
INVALID_SPARSE_EMBEDDING	Invalid sparse embedding.
rawRecord	string The original content of this record.
sourceGcsUri	string Cloud Storage URI pointing to the original file in user's bucket.

GoogleCloudAiplatformV1beta1NearestNeighbors

Nearest neighbors for one query.

Fields
neighbors[]	object (GoogleCloudAiplatformV1beta1NearestNeighborsNeighbor) All its neighbors.

GoogleCloudAiplatformV1beta1NearestNeighborsNeighbor

A neighbor of the query vector.

Fields
distance	number (double format) The distance between the neighbor and the query vector.
entityId	string The id of the similar entity.
entityKeyValues	object (GoogleCloudAiplatformV1beta1FetchFeatureValuesResponse) The attributes of the neighbor, e.g. filters, crowding and metadata Note that full entities are returned only when "return_full_entity" is set to true. Otherwise, only the "entity_id" and "distance" fields are populated.

GoogleCloudAiplatformV1beta1Neighbor

Neighbors for example-based explanations.

Fields
neighborDistance	number (double format) Output only. The neighbor distance.
neighborId	string Output only. The neighbor id.

GoogleCloudAiplatformV1beta1NetworkSpec

Network spec.

Fields
enableInternetAccess	boolean Whether to enable public internet access. Default false.
network	string The full name of the Google Compute Engine network
subnetwork	string The name of the subnet that this instance is in. Format: projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}

GoogleCloudAiplatformV1beta1NfsMount

Represents a mount configuration for Network File System (NFS) to mount.

Fields
mountPoint	string Required. Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
path	string Required. Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path
server	string Required. IP address of the NFS server.

GoogleCloudAiplatformV1beta1NotebookEucConfig

The euc configuration of NotebookRuntimeTemplate.

Fields
bypassActasCheck	boolean Output only. Whether ActAs check is bypassed for service account attached to the VM. If false, we need ActAs check for the default Compute Engine Service account. When a Runtime is created, a VM is allocated using Default Compute Engine Service Account. Any user requesting to use this Runtime requires Service Account User (ActAs) permission over this SA. If true, Runtime owner is using EUC and does not require the above permission as VM no longer use default Compute Engine SA, but a P4SA.
eucDisabled	boolean Input only. Whether EUC is disabled in this NotebookRuntimeTemplate. In proto3, the default value of a boolean is false. In this way, by default EUC will be enabled for NotebookRuntimeTemplate.

GoogleCloudAiplatformV1beta1NotebookExecutionJob

NotebookExecutionJob represents an instance of a notebook execution.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this NotebookExecutionJob was created.
customEnvironmentSpec	object (GoogleCloudAiplatformV1beta1NotebookExecutionJobCustomEnvironmentSpec) The custom compute configuration for an execution job.
dataformRepositorySource	object (GoogleCloudAiplatformV1beta1NotebookExecutionJobDataformRepositorySource) The Dataform Repository pointing to a single file notebook repository.
directNotebookSource	object (GoogleCloudAiplatformV1beta1NotebookExecutionJobDirectNotebookSource) The contents of an input notebook file.
displayName	string The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
executionTimeout	string (Duration format) Max running time of the execution job in seconds (default 86400s / 24 hrs).
executionUser	string The user email to run the execution as. Only supported by Colab runtimes.
gcsNotebookSource	object (GoogleCloudAiplatformV1beta1NotebookExecutionJobGcsNotebookSource) The Cloud Storage url pointing to the ipynb file. Format: gs://bucket/notebook_file.ipynb
gcsOutputUri	string The Cloud Storage location to upload the result to. Format: gs://bucket-name
jobState	enum Output only. The state of the NotebookExecutionJob.
Enum type. Can be one of the following:
JOB_STATE_UNSPECIFIED	The job state is unspecified.
JOB_STATE_QUEUED	The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING	The service is preparing to run the job.
JOB_STATE_RUNNING	The job is in progress.
JOB_STATE_SUCCEEDED	The job completed successfully.
JOB_STATE_FAILED	The job failed.
JOB_STATE_CANCELLING	The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED	The job has been cancelled.
JOB_STATE_PAUSED	The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED	The job has expired.
JOB_STATE_UPDATING	The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED	The job is partially succeeded, some results may be missing due to errors.
labels	map (key: string, value: string) The labels with user-defined metadata to organize NotebookExecutionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
name	string Output only. The resource name of this NotebookExecutionJob. Format: projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}
notebookRuntimeTemplateResourceName	string The NotebookRuntimeTemplate to source compute configuration from.
scheduleResourceName	string Output only. The Schedule resource name if this job is triggered by one. Format: projects/{project_id}/locations/{location}/schedules/{schedule_id}
serviceAccount	string The service account to run the execution as.
status	object (GoogleRpcStatus) Output only. Populated when the NotebookExecutionJob is completed. When there is an error during notebook execution, the error details are populated.
updateTime	string (Timestamp format) Output only. Timestamp when this NotebookExecutionJob was most recently updated.

GoogleCloudAiplatformV1beta1NotebookExecutionJobCustomEnvironmentSpec

Compute configuration to use for an execution job.

Fields
machineSpec	object (GoogleCloudAiplatformV1beta1MachineSpec) The specification of a single machine for the execution job.
networkSpec	object (GoogleCloudAiplatformV1beta1NetworkSpec) The network configuration to use for the execution job.
persistentDiskSpec	object (GoogleCloudAiplatformV1beta1PersistentDiskSpec) The specification of a persistent disk to attach for the execution job.

GoogleCloudAiplatformV1beta1NotebookExecutionJobDataformRepositorySource

The Dataform Repository containing the input notebook.

Fields
commitSha	string The commit SHA to read repository with. If unset, the file will be read at HEAD.
dataformRepositoryResourceName	string The resource name of the Dataform Repository. Format: projects/{project_id}/locations/{location}/repositories/{repository_id}

GoogleCloudAiplatformV1beta1NotebookExecutionJobDirectNotebookSource

The content of the input notebook in ipynb format.

Fields
content	string (bytes format) The base64-encoded contents of the input notebook file.

GoogleCloudAiplatformV1beta1NotebookExecutionJobGcsNotebookSource

The Cloud Storage uri for the input notebook.

Fields
generation	string The version of the Cloud Storage object to read. If unset, the current version of the object is read. See https://cloud.google.com/storage/docs/metadata#generation-number.
uri	string The Cloud Storage uri pointing to the ipynb file. Format: gs://bucket/notebook_file.ipynb

GoogleCloudAiplatformV1beta1NotebookIdleShutdownConfig

The idle shutdown configuration of NotebookRuntimeTemplate, which contains the idle_timeout as required field.

Fields
idleShutdownDisabled	boolean Whether Idle Shutdown is disabled in this NotebookRuntimeTemplate.
idleTimeout	string (Duration format) Required. Duration is accurate to the second. In Notebook, Idle Timeout is accurate to minute so the range of idle_timeout (second) is: 10 * 60 ~ 1440 * 60.

GoogleCloudAiplatformV1beta1NotebookReservationAffinity

Notebook Reservation Affinity for consuming Zonal reservation.

Fields
consumeReservationType	enum Required. Specifies the type of reservation from which this instance can consume resources: RESERVATION_ANY (default), RESERVATION_SPECIFIC, or RESERVATION_NONE. See Consuming reserved instances for examples.
Enum type. Can be one of the following:
RESERVATION_AFFINITY_TYPE_UNSPECIFIED	Default type.
RESERVATION_NONE	Do not consume from any allocated capacity.
RESERVATION_ANY	Consume any reservation available.
RESERVATION_SPECIFIC	Must consume from a specific reservation. Must specify key value fields for specifying the reservations.
key	string Optional. Corresponds to the label key of a reservation resource. To target a RESERVATION_SPECIFIC by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.
values[]	string Optional. Corresponds to the label values of a reservation resource. This must be the full path name of Reservation.

GoogleCloudAiplatformV1beta1NotebookRuntime

A runtime is a virtual machine allocated to a particular user for a particular Notebook file on temporary basis with lifetime limited to 24 hours.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this NotebookRuntime was created.
description	string The description of the NotebookRuntime.
displayName	string Required. The display name of the NotebookRuntime. The name can be up to 128 characters long and can consist of any UTF-8 characters.
expirationTime	string (Timestamp format) Output only. Timestamp when this NotebookRuntime will be expired: 1. System Predefined NotebookRuntime: 24 hours after creation. After expiration, system predifined runtime will be deleted. 2. User created NotebookRuntime: 6 months after last upgrade. After expiration, user created runtime will be stopped and allowed for upgrade.
healthState	enum Output only. The health state of the NotebookRuntime.
Enum type. Can be one of the following:
HEALTH_STATE_UNSPECIFIED	Unspecified health state.
HEALTHY	NotebookRuntime is in healthy state. Applies to ACTIVE state.
UNHEALTHY	NotebookRuntime is in unhealthy state. Applies to ACTIVE state.
isUpgradable	boolean Output only. Whether NotebookRuntime is upgradable.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your NotebookRuntime. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one NotebookRuntime (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for NotebookRuntime: * "aiplatform.googleapis.com/notebook_runtime_gce_instance_id": output only, its value is the Compute Engine instance id. * "aiplatform.googleapis.com/colab_enterprise_entry_service": its value is either "bigquery" or "vertex"; if absent, it should be "vertex". This is to describe the entry service, either BigQuery or Vertex.
name	string Output only. The resource name of the NotebookRuntime.
networkTags[]	string Optional. The Compute Engine tags to add to runtime (see Tagging instances).
notebookRuntimeTemplateRef	object (GoogleCloudAiplatformV1beta1NotebookRuntimeTemplateRef) Output only. The pointer to NotebookRuntimeTemplate this NotebookRuntime is created from.
notebookRuntimeType	enum Output only. The type of the notebook runtime.
Enum type. Can be one of the following:
NOTEBOOK_RUNTIME_TYPE_UNSPECIFIED	Unspecified notebook runtime type, NotebookRuntimeType will default to USER_DEFINED.
USER_DEFINED	runtime or template with coustomized configurations from user.
ONE_CLICK	runtime or template with system defined configurations.
proxyUri	string Output only. The proxy endpoint used to access the NotebookRuntime.
reservationAffinity	object (GoogleCloudAiplatformV1beta1NotebookReservationAffinity) Output only. Reservation Affinity of the notebook runtime.
runtimeState	enum Output only. The runtime (instance) state of the NotebookRuntime.
Enum type. Can be one of the following:
RUNTIME_STATE_UNSPECIFIED	Unspecified runtime state.
RUNNING	NotebookRuntime is in running state.
BEING_STARTED	NotebookRuntime is in starting state.
BEING_STOPPED	NotebookRuntime is in stopping state.
STOPPED	NotebookRuntime is in stopped state.
BEING_UPGRADED	NotebookRuntime is in upgrading state. It is in the middle of upgrading process.
ERROR	NotebookRuntime was unable to start/stop properly.
INVALID	NotebookRuntime is in invalid state. Cannot be recovered.
runtimeUser	string Required. The user email of the NotebookRuntime.
satisfiesPzi	boolean Output only. Reserved for future use.
satisfiesPzs	boolean Output only. Reserved for future use.
serviceAccount	string Output only. The service account that the NotebookRuntime workload runs as.
updateTime	string (Timestamp format) Output only. Timestamp when this NotebookRuntime was most recently updated.
version	string Output only. The VM os image version of NotebookRuntime.

GoogleCloudAiplatformV1beta1NotebookRuntimeTemplate

A template that specifies runtime configurations such as machine type, runtime version, network configurations, etc. Multiple runtimes can be created from a runtime template.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this NotebookRuntimeTemplate was created.
dataPersistentDiskSpec	object (GoogleCloudAiplatformV1beta1PersistentDiskSpec) Optional. The specification of persistent disk attached to the runtime as data disk storage.
description	string The description of the NotebookRuntimeTemplate.
displayName	string Required. The display name of the NotebookRuntimeTemplate. The name can be up to 128 characters long and can consist of any UTF-8 characters.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
eucConfig	object (GoogleCloudAiplatformV1beta1NotebookEucConfig) EUC configuration of the NotebookRuntimeTemplate.
idleShutdownConfig	object (GoogleCloudAiplatformV1beta1NotebookIdleShutdownConfig) The idle shutdown configuration of NotebookRuntimeTemplate. This config will only be set when idle shutdown is enabled.
isDefault	boolean Output only. The default template to use if not specified.
labels	map (key: string, value: string) The labels with user-defined metadata to organize the NotebookRuntimeTemplates. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
machineSpec	object (GoogleCloudAiplatformV1beta1MachineSpec) Optional. Immutable. The specification of a single machine for the template.
name	string The resource name of the NotebookRuntimeTemplate.
networkSpec	object (GoogleCloudAiplatformV1beta1NetworkSpec) Optional. Network spec.
networkTags[]	string Optional. The Compute Engine tags to add to runtime (see Tagging instances).
notebookRuntimeType	enum Optional. Immutable. The type of the notebook runtime template.
Enum type. Can be one of the following:
NOTEBOOK_RUNTIME_TYPE_UNSPECIFIED	Unspecified notebook runtime type, NotebookRuntimeType will default to USER_DEFINED.
USER_DEFINED	runtime or template with coustomized configurations from user.
ONE_CLICK	runtime or template with system defined configurations.
reservationAffinity	object (GoogleCloudAiplatformV1beta1NotebookReservationAffinity) Optional. Reservation Affinity of the notebook runtime template.
serviceAccount	string The service account that the runtime workload runs as. You can use any service account within the same project, but you must have the service account user permission to use the instance. If not specified, the Compute Engine default service account is used.
shieldedVmConfig	object (GoogleCloudAiplatformV1beta1ShieldedVmConfig) Optional. Immutable. Runtime Shielded VM spec.
updateTime	string (Timestamp format) Output only. Timestamp when this NotebookRuntimeTemplate was most recently updated.

GoogleCloudAiplatformV1beta1NotebookRuntimeTemplateRef

Points to a NotebookRuntimeTemplateRef.

Fields
notebookRuntimeTemplate	string Immutable. A resource name of the NotebookRuntimeTemplate.

GoogleCloudAiplatformV1beta1PairwiseQuestionAnsweringQualityInput

Input for pairwise question answering quality metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1PairwiseQuestionAnsweringQualityInstance) Required. Pairwise question answering quality instance.
metricSpec	object (GoogleCloudAiplatformV1beta1PairwiseQuestionAnsweringQualitySpec) Required. Spec for pairwise question answering quality score metric.

GoogleCloudAiplatformV1beta1PairwiseQuestionAnsweringQualityInstance

Spec for pairwise question answering quality instance.

Fields
baselinePrediction	string Required. Output of the baseline model.
context	string Required. Text to answer the question.
instruction	string Required. Question Answering prompt for LLM.
prediction	string Required. Output of the candidate model.
reference	string Optional. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1PairwiseQuestionAnsweringQualityResult

Spec for pairwise question answering quality result.

Fields
confidence	number (float format) Output only. Confidence for question answering quality score.
explanation	string Output only. Explanation for question answering quality score.
pairwiseChoice	enum Output only. Pairwise question answering prediction choice.
Enum type. Can be one of the following:
PAIRWISE_CHOICE_UNSPECIFIED	Unspecified prediction choice.
BASELINE	Baseline prediction wins
CANDIDATE	Candidate prediction wins
TIE	Winner cannot be determined

GoogleCloudAiplatformV1beta1PairwiseQuestionAnsweringQualitySpec

Spec for pairwise question answering quality score metric.

Fields
useReference	boolean Optional. Whether to use instance.reference to compute question answering quality.
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1PairwiseSummarizationQualityInput

Input for pairwise summarization quality metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1PairwiseSummarizationQualityInstance) Required. Pairwise summarization quality instance.
metricSpec	object (GoogleCloudAiplatformV1beta1PairwiseSummarizationQualitySpec) Required. Spec for pairwise summarization quality score metric.

GoogleCloudAiplatformV1beta1PairwiseSummarizationQualityInstance

Spec for pairwise summarization quality instance.

Fields
baselinePrediction	string Required. Output of the baseline model.
context	string Required. Text to be summarized.
instruction	string Required. Summarization prompt for LLM.
prediction	string Required. Output of the candidate model.
reference	string Optional. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1PairwiseSummarizationQualityResult

Spec for pairwise summarization quality result.

Fields
confidence	number (float format) Output only. Confidence for summarization quality score.
explanation	string Output only. Explanation for summarization quality score.
pairwiseChoice	enum Output only. Pairwise summarization prediction choice.
Enum type. Can be one of the following:
PAIRWISE_CHOICE_UNSPECIFIED	Unspecified prediction choice.
BASELINE	Baseline prediction wins
CANDIDATE	Candidate prediction wins
TIE	Winner cannot be determined

GoogleCloudAiplatformV1beta1PairwiseSummarizationQualitySpec

Spec for pairwise summarization quality score metric.

Fields
useReference	boolean Optional. Whether to use instance.reference to compute pairwise summarization quality.
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1Part

A datatype containing media that is part of a multi-part Content message. A Part consists of data which has an associated datatype. A Part can only contain one of the accepted types in Part.data. A Part must have a fixed IANA MIME type identifying the type and subtype of the media if inline_data or file_data field is filled with raw bytes.

Fields
fileData	object (GoogleCloudAiplatformV1beta1FileData) Optional. URI based data.
functionCall	object (GoogleCloudAiplatformV1beta1FunctionCall) Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
functionResponse	object (GoogleCloudAiplatformV1beta1FunctionResponse) Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.
inlineData	object (GoogleCloudAiplatformV1beta1Blob) Optional. Inlined bytes data.
text	string Optional. Text part (can be code).
videoMetadata	object (GoogleCloudAiplatformV1beta1VideoMetadata) Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.

GoogleCloudAiplatformV1beta1PersistentDiskSpec

Represents the spec of persistent disk options.

Fields
diskSizeGb	string (int64 format) Size in GB of the disk (default is 100GB).
diskType	string Type of the disk (default is "pd-standard"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) "pd-standard" (Persistent Disk Hard Disk Drive) "pd-balanced" (Balanced Persistent Disk) "pd-extreme" (Extreme Persistent Disk)

GoogleCloudAiplatformV1beta1PersistentResource

Represents long-lasting resources that are dedicated to users to runs custom workloads. A PersistentResource can have multiple node pools and each node pool can have its own machine spec.

Fields
createTime	string (Timestamp format) Output only. Time when the PersistentResource was created.
displayName	string Optional. The display name of the PersistentResource. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Optional. Customer-managed encryption key spec for a PersistentResource. If set, this PersistentResource and all sub-resources of this PersistentResource will be secured by this key.
error	object (GoogleRpcStatus) Output only. Only populated when persistent resource's state is STOPPING or ERROR.
labels	map (key: string, value: string) Optional. The labels with user-defined metadata to organize PersistentResource. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
name	string Immutable. Resource name of a PersistentResource.
network	string Optional. The full name of the Compute Engine network to peered with Vertex AI to host the persistent resources. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the resources aren't peered with any network.
reservedIpRanges[]	string Optional. A list of names for the reserved IP ranges under the VPC network that can be used for this persistent resource. If set, we will deploy the persistent resource within the provided IP ranges. Otherwise, the persistent resource is deployed to any IP ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
resourcePools[]	object (GoogleCloudAiplatformV1beta1ResourcePool) Required. The spec of the pools of different resources.
resourceRuntime	object (GoogleCloudAiplatformV1beta1ResourceRuntime) Output only. Runtime information of the Persistent Resource.
resourceRuntimeSpec	object (GoogleCloudAiplatformV1beta1ResourceRuntimeSpec) Optional. Persistent Resource runtime spec. For example, used for Ray cluster configuration.
startTime	string (Timestamp format) Output only. Time when the PersistentResource for the first time entered the RUNNING state.
state	enum Output only. The detailed state of a Study.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	Not set.
PROVISIONING	The PROVISIONING state indicates the persistent resources is being created.
RUNNING	The RUNNING state indicates the persistent resource is healthy and fully usable.
STOPPING	The STOPPING state indicates the persistent resource is being deleted.
ERROR	The ERROR state indicates the persistent resource may be unusable. Details can be found in the error field.
REBOOTING	The REBOOTING state indicates the persistent resource is being rebooted (PR is not available right now but is expected to be ready again later).
UPDATING	The UPDATING state indicates the persistent resource is being updated.
updateTime	string (Timestamp format) Output only. Time when the PersistentResource was most recently updated.

GoogleCloudAiplatformV1beta1PipelineJob

An instance of a machine learning PipelineJob.

Fields
createTime	string (Timestamp format) Output only. Pipeline creation time.
displayName	string The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key spec for a pipelineJob. If set, this PipelineJob and all of its sub-resources will be secured by this key.
endTime	string (Timestamp format) Output only. Pipeline end time.
error	object (GoogleRpcStatus) Output only. The error that occurred during pipeline execution. Only populated when the pipeline's state is FAILED or CANCELLED.
jobDetail	object (GoogleCloudAiplatformV1beta1PipelineJobDetail) Output only. The details of pipeline run. Not available in the list view.
labels	map (key: string, value: string) The labels with user-defined metadata to organize PipelineJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Note there is some reserved label key for Vertex AI Pipelines. - vertex-ai-pipelines-run-billing-id, user set value will get overrided.
name	string Output only. The resource name of the PipelineJob.
network	string The full name of the Compute Engine network to which the Pipeline Job's workload should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. Private services access must already be configured for the network. Pipeline job will apply the network configuration to the Google Cloud resources being launched, if applied, such as Vertex AI Training or Dataflow job. If left unspecified, the workload is not peered with any network.
pipelineSpec	map (key: string, value: any) The spec of the pipeline.
preflightValidations	boolean Optional. Whether to do component level validations before job creation.
reservedIpRanges[]	string A list of names for the reserved ip ranges under the VPC network that can be used for this Pipeline Job's workload. If set, we will deploy the Pipeline Job's workload within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
runtimeConfig	object (GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfig) Runtime config of the pipeline.
scheduleName	string Output only. The schedule resource name. Only returned if the Pipeline is created by Schedule API.
serviceAccount	string The service account that the pipeline workload runs as. If not specified, the Compute Engine default service account in the project will be used. See https://cloud.google.com/compute/docs/access/service-accounts#default_service_account Users starting the pipeline must have the iam.serviceAccounts.actAs permission on this service account.
startTime	string (Timestamp format) Output only. Pipeline start time.
state	enum Output only. The detailed state of the job.
Enum type. Can be one of the following:
PIPELINE_STATE_UNSPECIFIED	The pipeline state is unspecified.
PIPELINE_STATE_QUEUED	The pipeline has been created or resumed, and processing has not yet begun.
PIPELINE_STATE_PENDING	The service is preparing to run the pipeline.
PIPELINE_STATE_RUNNING	The pipeline is in progress.
PIPELINE_STATE_SUCCEEDED	The pipeline completed successfully.
PIPELINE_STATE_FAILED	The pipeline failed.
PIPELINE_STATE_CANCELLING	The pipeline is being cancelled. From this state, the pipeline may only go to either PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
PIPELINE_STATE_CANCELLED	The pipeline has been cancelled.
PIPELINE_STATE_PAUSED	The pipeline has been stopped, and can be resumed.
templateMetadata	object (GoogleCloudAiplatformV1beta1PipelineTemplateMetadata) Output only. Pipeline template metadata. Will fill up fields if PipelineJob.template_uri is from supported template registry.
templateUri	string A template uri from where the PipelineJob.pipeline_spec, if empty, will be downloaded. Currently, only uri from Vertex Template Registry & Gallery is supported. Reference to https://cloud.google.com/vertex-ai/docs/pipelines/create-pipeline-template.
updateTime	string (Timestamp format) Output only. Timestamp when this PipelineJob was most recently updated.

GoogleCloudAiplatformV1beta1PipelineJobDetail

The runtime detail of PipelineJob.

Fields
pipelineContext	object (GoogleCloudAiplatformV1beta1Context) Output only. The context of the pipeline.
pipelineRunContext	object (GoogleCloudAiplatformV1beta1Context) Output only. The context of the current pipeline run.
taskDetails[]	object (GoogleCloudAiplatformV1beta1PipelineTaskDetail) Output only. The runtime details of the tasks under the pipeline.

GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfig

The runtime config of a PipelineJob.

Fields
failurePolicy	enum Represents the failure policy of a pipeline. Currently, the default of a pipeline is that the pipeline will continue to run until no more tasks can be executed, also known as PIPELINE_FAILURE_POLICY_FAIL_SLOW. However, if a pipeline is set to PIPELINE_FAILURE_POLICY_FAIL_FAST, it will stop scheduling any new tasks when a task has failed. Any scheduled tasks will continue to completion.
Enum type. Can be one of the following:
PIPELINE_FAILURE_POLICY_UNSPECIFIED	Default value, and follows fail slow behavior.
PIPELINE_FAILURE_POLICY_FAIL_SLOW	Indicates that the pipeline should continue to run until all possible tasks have been scheduled and completed.
PIPELINE_FAILURE_POLICY_FAIL_FAST	Indicates that the pipeline should stop scheduling new tasks after a task has failed.
gcsOutputDirectory	string Required. A path in a Cloud Storage bucket, which will be treated as the root output directory of the pipeline. It is used by the system to generate the paths of output artifacts. The artifact paths are generated with a sub-path pattern {job_id}/{task_id}/{output_key} under the specified output directory. The service account specified in this pipeline must have the storage.objects.get and storage.objects.create permissions for this bucket.
inputArtifacts	map (key: string, value: object (GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfigInputArtifact)) The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact.
parameterValues	map (key: string, value: any) The runtime parameters of the PipelineJob. The parameters will be passed into PipelineJob.pipeline_spec to replace the placeholders at runtime. This field is used by pipelines built using PipelineJob.pipeline_spec.schema_version 2.1.0, such as pipelines built using Kubeflow Pipelines SDK 1.9 or higher and the v2 DSL.
parameters	map (key: string, value: object (GoogleCloudAiplatformV1beta1Value)) Deprecated. Use RuntimeConfig.parameter_values instead. The runtime parameters of the PipelineJob. The parameters will be passed into PipelineJob.pipeline_spec to replace the placeholders at runtime. This field is used by pipelines built using PipelineJob.pipeline_spec.schema_version 2.0.0 or lower, such as pipelines built using Kubeflow Pipelines SDK 1.8 or lower.

GoogleCloudAiplatformV1beta1PipelineJobRuntimeConfigInputArtifact

The type of an input artifact.

Fields
artifactId	string Artifact resource id from MLMD. Which is the last portion of an artifact resource name: projects/{project}/locations/{location}/metadataStores/default/artifacts/{artifact_id}. The artifact must stay within the same project, location and default metadatastore as the pipeline.

GoogleCloudAiplatformV1beta1PipelineTaskDetail

The runtime detail of a task execution.

Fields
createTime	string (Timestamp format) Output only. Task create time.
endTime	string (Timestamp format) Output only. Task end time.
error	object (GoogleRpcStatus) Output only. The error that occurred during task execution. Only populated when the task's state is FAILED or CANCELLED.
execution	object (GoogleCloudAiplatformV1beta1Execution) Output only. The execution metadata of the task.
executorDetail	object (GoogleCloudAiplatformV1beta1PipelineTaskExecutorDetail) Output only. The detailed execution info.
inputs	map (key: string, value: object (GoogleCloudAiplatformV1beta1PipelineTaskDetailArtifactList)) Output only. The runtime input artifacts of the task.
outputs	map (key: string, value: object (GoogleCloudAiplatformV1beta1PipelineTaskDetailArtifactList)) Output only. The runtime output artifacts of the task.
parentTaskId	string (int64 format) Output only. The id of the parent task if the task is within a component scope. Empty if the task is at the root level.
pipelineTaskStatus[]	object (GoogleCloudAiplatformV1beta1PipelineTaskDetailPipelineTaskStatus) Output only. A list of task status. This field keeps a record of task status evolving over time.
startTime	string (Timestamp format) Output only. Task start time.
state	enum Output only. State of the task.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	Unspecified.
PENDING	Specifies pending state for the task.
RUNNING	Specifies task is being executed.
SUCCEEDED	Specifies task completed successfully.
CANCEL_PENDING	Specifies Task cancel is in pending state.
CANCELLING	Specifies task is being cancelled.
CANCELLED	Specifies task was cancelled.
FAILED	Specifies task failed.
SKIPPED	Specifies task was skipped due to cache hit.
NOT_TRIGGERED	Specifies that the task was not triggered because the task's trigger policy is not satisfied. The trigger policy is specified in the condition field of PipelineJob.pipeline_spec.
taskId	string (int64 format) Output only. The system generated ID of the task.
taskName	string Output only. The user specified name of the task that is defined in pipeline_spec.

GoogleCloudAiplatformV1beta1PipelineTaskDetailArtifactList

A list of artifact metadata.

Fields
artifacts[]	object (GoogleCloudAiplatformV1beta1Artifact) Output only. A list of artifact metadata.

GoogleCloudAiplatformV1beta1PipelineTaskDetailPipelineTaskStatus

A single record of the task status.

Fields
error	object (GoogleRpcStatus) Output only. The error that occurred during the state. May be set when the state is any of the non-final state (PENDING/RUNNING/CANCELLING) or FAILED state. If the state is FAILED, the error here is final and not going to be retried. If the state is a non-final state, the error indicates a system-error being retried.
state	enum Output only. The state of the task.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	Unspecified.
PENDING	Specifies pending state for the task.
RUNNING	Specifies task is being executed.
SUCCEEDED	Specifies task completed successfully.
CANCEL_PENDING	Specifies Task cancel is in pending state.
CANCELLING	Specifies task is being cancelled.
CANCELLED	Specifies task was cancelled.
FAILED	Specifies task failed.
SKIPPED	Specifies task was skipped due to cache hit.
NOT_TRIGGERED	Specifies that the task was not triggered because the task's trigger policy is not satisfied. The trigger policy is specified in the condition field of PipelineJob.pipeline_spec.
updateTime	string (Timestamp format) Output only. Update time of this status.

GoogleCloudAiplatformV1beta1PipelineTaskExecutorDetail

The runtime detail of a pipeline executor.

Fields
containerDetail	object (GoogleCloudAiplatformV1beta1PipelineTaskExecutorDetailContainerDetail) Output only. The detailed info for a container executor.
customJobDetail	object (GoogleCloudAiplatformV1beta1PipelineTaskExecutorDetailCustomJobDetail) Output only. The detailed info for a custom job executor.

GoogleCloudAiplatformV1beta1PipelineTaskExecutorDetailContainerDetail

The detail of a container execution. It contains the job names of the lifecycle of a container execution.

Fields
failedMainJobs[]	string Output only. The names of the previously failed CustomJob for the main container executions. The list includes the all attempts in chronological order.
failedPreCachingCheckJobs[]	string Output only. The names of the previously failed CustomJob for the pre-caching-check container executions. This job will be available if the PipelineJob.pipeline_spec specifies the pre_caching_check hook in the lifecycle events. The list includes the all attempts in chronological order.
mainJob	string Output only. The name of the CustomJob for the main container execution.
preCachingCheckJob	string Output only. The name of the CustomJob for the pre-caching-check container execution. This job will be available if the PipelineJob.pipeline_spec specifies the pre_caching_check hook in the lifecycle events.

GoogleCloudAiplatformV1beta1PipelineTaskExecutorDetailCustomJobDetail

The detailed info for a custom job executor.

Fields
failedJobs[]	string Output only. The names of the previously failed CustomJob. The list includes the all attempts in chronological order.
job	string Output only. The name of the CustomJob.

GoogleCloudAiplatformV1beta1PipelineTemplateMetadata

Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry.

Fields
version	string The version_name in artifact registry. Will always be presented in output if the PipelineJob.template_uri is from supported template registry. Format is "sha256:abcdef123456...".

GoogleCloudAiplatformV1beta1Port

Represents a network port in a container.

Fields
containerPort	integer (int32 format) The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

GoogleCloudAiplatformV1beta1PredefinedSplit

Assigns input data to training, validation, and test sets based on the value of a provided key. Supported only for tabular Datasets.

Fields
key	string Required. The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

GoogleCloudAiplatformV1beta1PredictRequest

Request message for PredictionService.Predict.

Fields
instances[]	any Required. The instances that are the input to the prediction call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the prediction call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.
parameters	any The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri.

GoogleCloudAiplatformV1beta1PredictRequestResponseLoggingConfig

Configuration for logging request-response to a BigQuery table.

Fields
bigqueryDestination	object (GoogleCloudAiplatformV1beta1BigQueryDestination) BigQuery table for logging. If only given a project, a new dataset will be created with name logging__ where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores). If no table name is given, a new table will be created with name request_response_logging
enabled	boolean If logging is enabled or not.
samplingRate	number (double format) Percentage of requests to be logged, expressed as a fraction in range(0,1].

GoogleCloudAiplatformV1beta1PredictResponse

Response message for PredictionService.Predict.

Fields
deployedModelId	string ID of the Endpoint's DeployedModel that served this prediction.
metadata	any Output only. Request-level metadata returned by the model. The metadata type will be dependent upon the model implementation.
model	string Output only. The resource name of the Model which is deployed as the DeployedModel that this prediction hits.
modelDisplayName	string Output only. The display name of the Model which is deployed as the DeployedModel that this prediction hits.
modelVersionId	string Output only. The version ID of the Model which is deployed as the DeployedModel that this prediction hits.
predictions[]	any The predictions that are the output of the predictions call. The schema of any single prediction may be specified via Endpoint's DeployedModels' Model's PredictSchemata's prediction_schema_uri.

GoogleCloudAiplatformV1beta1PredictSchemata

Contains the schemata used in Model's predictions and explanations via PredictionService.Predict, PredictionService.Explain and BatchPredictionJob.

Fields
instanceSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
parametersSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
predictionSchemaUri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

GoogleCloudAiplatformV1beta1Presets

Preset configuration for example-based explanations

Fields
modality	enum The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
Enum type. Can be one of the following:
MODALITY_UNSPECIFIED	Should not be set. Added as a recommended best practice for enums
IMAGE	IMAGE modality
TEXT	TEXT modality
TABULAR	TABULAR modality
query	enum Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
Enum type. Can be one of the following:
PRECISE	More precise neighbors as a trade-off against slower response.
FAST	Faster response as a trade-off against less precise neighbors.

GoogleCloudAiplatformV1beta1PrivateEndpoints

PrivateEndpoints proto is used to provide paths for users to send requests privately. To send request via private service access, use predict_http_uri, explain_http_uri or health_http_uri. To send request via private service connect, use service_attachment.

Fields
explainHttpUri	string Output only. Http(s) path to send explain requests.
healthHttpUri	string Output only. Http(s) path to send health check requests.
predictHttpUri	string Output only. Http(s) path to send prediction requests.
serviceAttachment	string Output only. The name of the service attachment resource. Populated if private service connect is enabled.

GoogleCloudAiplatformV1beta1PrivateServiceConnectConfig

Represents configuration for private service connect.

Fields
enablePrivateServiceConnect	boolean Required. If true, expose the IndexEndpoint via private service connect.
projectAllowlist[]	string A list of Projects from which the forwarding rule will target the service attachment.

GoogleCloudAiplatformV1beta1Probe

Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic.

Fields
exec	object (GoogleCloudAiplatformV1beta1ProbeExecAction) Exec specifies the action to take.
periodSeconds	integer (int32 format) How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeoutSeconds	integer (int32 format) Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.

GoogleCloudAiplatformV1beta1ProbeExecAction

ExecAction specifies a command to execute.

Fields

command[]

string Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.

GoogleCloudAiplatformV1beta1PscAutomatedEndpoints

PscAutomatedEndpoints defines the output of the forwarding rule automatically created by each PscAutomationConfig.

Fields
matchAddress	string Ip Address created by the automated forwarding rule.
network	string Corresponding network in pscAutomationConfigs.
projectId	string Corresponding project_id in pscAutomationConfigs

GoogleCloudAiplatformV1beta1PublisherModel

A Model Garden Publisher Model.

Fields
frameworks[]	string Optional. Additional information about the model's Frameworks.
launchStage	enum Optional. Indicates the launch stage of the model.
Enum type. Can be one of the following:
LAUNCH_STAGE_UNSPECIFIED	The model launch stage is unspecified.
EXPERIMENTAL	Used to indicate the PublisherModel is at Experimental launch stage, available to a small set of customers.
PRIVATE_PREVIEW	Used to indicate the PublisherModel is at Private Preview launch stage, only available to a small set of customers, although a larger set of customers than an Experimental launch. Previews are the first launch stage used to get feedback from customers.
PUBLIC_PREVIEW	Used to indicate the PublisherModel is at Public Preview launch stage, available to all customers, although not supported for production workloads.
GA	Used to indicate the PublisherModel is at GA launch stage, available to all customers and ready for production workload.
name	string Output only. The resource name of the PublisherModel.
openSourceCategory	enum Required. Indicates the open source category of the publisher model.
Enum type. Can be one of the following:
OPEN_SOURCE_CATEGORY_UNSPECIFIED	The open source category is unspecified, which should not be used.
PROPRIETARY	Used to indicate the PublisherModel is not open sourced.
GOOGLE_OWNED_OSS_WITH_GOOGLE_CHECKPOINT	Used to indicate the PublisherModel is a Google-owned open source model w/ Google checkpoint.
THIRD_PARTY_OWNED_OSS_WITH_GOOGLE_CHECKPOINT	Used to indicate the PublisherModel is a 3p-owned open source model w/ Google checkpoint.
GOOGLE_OWNED_OSS	Used to indicate the PublisherModel is a Google-owned pure open source model.
THIRD_PARTY_OWNED_OSS	Used to indicate the PublisherModel is a 3p-owned pure open source model.
parent	object (GoogleCloudAiplatformV1beta1PublisherModelParent) Optional. The parent that this model was customized from. E.g., Vision API, Natural Language API, LaMDA, T5, etc. Foundation models don't have parents.
predictSchemata	object (GoogleCloudAiplatformV1beta1PredictSchemata) Optional. The schemata that describes formats of the PublisherModel's predictions and explanations as given and returned via PredictionService.Predict.
publisherModelTemplate	string Optional. Output only. Immutable. Used to indicate this model has a publisher model and provide the template of the publisher model resource name.
supportedActions	object (GoogleCloudAiplatformV1beta1PublisherModelCallToAction) Optional. Supported call-to-action options.
versionId	string Output only. Immutable. The version ID of the PublisherModel. A new version is committed when a new model version is uploaded under an existing model id. It is an auto-incrementing decimal number in string representation.
versionState	enum Optional. Indicates the state of the model version.
Enum type. Can be one of the following:
VERSION_STATE_UNSPECIFIED	The version state is unspecified.
VERSION_STATE_STABLE	Used to indicate the version is stable.
VERSION_STATE_UNSTABLE	Used to indicate the version is unstable.

GoogleCloudAiplatformV1beta1PublisherModelCallToAction

Actions could take on this Publisher Model.

Fields
createApplication	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences) Optional. Create application using the PublisherModel.
deploy	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionDeploy) Optional. Deploy the PublisherModel to Vertex Endpoint.
deployGke	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionDeployGke) Optional. Deploy PublisherModel to Google Kubernetes Engine.
fineTune	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences) Optional. Fine tune the PublisherModel with the third-party model tuning UI.
openEvaluationPipeline	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences) Optional. Open evaluation pipeline of the PublisherModel.
openFineTuningPipeline	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences) Optional. Open fine-tuning pipeline of the PublisherModel.
openFineTuningPipelines	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionOpenFineTuningPipelines) Optional. Open fine-tuning pipelines of the PublisherModel.
openGenerationAiStudio	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences) Optional. Open in Generation AI Studio.
openGenie	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences) Optional. Open Genie / Playground.
openNotebook	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences) Optional. Open notebook of the PublisherModel.
openNotebooks	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionOpenNotebooks) Optional. Open notebooks of the PublisherModel.
openPromptTuningPipeline	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences) Optional. Open prompt-tuning pipeline of the PublisherModel.
requestAccess	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences) Optional. Request for access.
viewRestApi	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionViewRestApi) Optional. To view Rest API docs.

GoogleCloudAiplatformV1beta1PublisherModelCallToActionDeploy

Model metadata that is needed for UploadModel or DeployModel/CreateEndpoint requests.

Fields
artifactUri	string Optional. The path to the directory containing the Model artifact and any of its supporting files.
automaticResources	object (GoogleCloudAiplatformV1beta1AutomaticResources) A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
containerSpec	object (GoogleCloudAiplatformV1beta1ModelContainerSpec) Optional. The specification of the container that is to be used when deploying this Model in Vertex AI. Not present for Large Models.
dedicatedResources	object (GoogleCloudAiplatformV1beta1DedicatedResources) A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
deployTaskName	string Optional. The name of the deploy task (e.g., "text to image generation").
largeModelReference	object (GoogleCloudAiplatformV1beta1LargeModelReference) Optional. Large model reference. When this is set, model_artifact_spec is not needed.
modelDisplayName	string Optional. Default model display name.
publicArtifactUri	string Optional. The signed URI for ephemeral Cloud Storage access to model artifact.
sharedResources	string The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
title	string Required. The title of the regional resource reference.

GoogleCloudAiplatformV1beta1PublisherModelCallToActionDeployGke

Configurations for PublisherModel GKE deployment

Fields
gkeYamlConfigs[]	string Optional. GKE deployment configuration in yaml format.

GoogleCloudAiplatformV1beta1PublisherModelCallToActionOpenFineTuningPipelines

Open fine tuning pipelines.

Fields
fineTuningPipelines[]	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences) Required. Regional resource references to fine tuning pipelines.

GoogleCloudAiplatformV1beta1PublisherModelCallToActionOpenNotebooks

Open notebooks.

Fields
notebooks[]	object (GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences) Required. Regional resource references to notebooks.

GoogleCloudAiplatformV1beta1PublisherModelCallToActionRegionalResourceReferences

The regional resource name or the URI. Key is region, e.g., us-central1, europe-west2, global, etc..

Fields
references	map (key: string, value: object (GoogleCloudAiplatformV1beta1PublisherModelResourceReference)) Required.
resourceDescription	string Optional. Description of the resource.
resourceTitle	string Optional. Title of the resource.
resourceUseCase	string Optional. Use case (CUJ) of the resource.
title	string Required.

GoogleCloudAiplatformV1beta1PublisherModelCallToActionViewRestApi

Rest API docs.

Fields
documentations[]	object (GoogleCloudAiplatformV1beta1PublisherModelDocumentation) Required.
title	string Required. The title of the view rest API.

GoogleCloudAiplatformV1beta1PublisherModelDocumentation

A named piece of documentation.

Fields
content	string Required. Content of this piece of document (in Markdown format).
title	string Required. E.g., OVERVIEW, USE CASES, DOCUMENTATION, SDK & SAMPLES, JAVA, NODE.JS, etc..

GoogleCloudAiplatformV1beta1PublisherModelParent

The information about the parent of a model.

Fields
displayName	string Required. The display name of the parent. E.g., LaMDA, T5, Vision API, Natural Language API.
reference	object (GoogleCloudAiplatformV1beta1PublisherModelResourceReference) Optional. The Google Cloud resource name or the URI reference.

GoogleCloudAiplatformV1beta1PublisherModelResourceReference

Reference to a resource.

Fields
description	string Description of the resource.
resourceName	string The resource name of the Google Cloud resource.
uri	string The URI of the resource.
useCase	string Use case (CUJ) of the resource.

GoogleCloudAiplatformV1beta1PurgeArtifactsMetadata

Details of operations that perform MetadataService.PurgeArtifacts.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for purging Artifacts.

GoogleCloudAiplatformV1beta1PurgeArtifactsRequest

Request message for MetadataService.PurgeArtifacts.

Fields
filter	string Required. A required filter matching the Artifacts to be purged. E.g., update_time <= 2020-11-19T11:30:00-04:00.
force	boolean Optional. Flag to indicate to actually perform the purge. If force is set to false, the method will return a sample of Artifact names that would be deleted.

GoogleCloudAiplatformV1beta1PurgeArtifactsResponse

Response message for MetadataService.PurgeArtifacts.

Fields
purgeCount	string (int64 format) The number of Artifacts that this request deleted (or, if force is false, the number of Artifacts that will be deleted). This can be an estimate.
purgeSample[]	string A sample of the Artifact names that will be deleted. Only populated if force is set to false. The maximum number of samples is 100 (it is possible to return fewer).

GoogleCloudAiplatformV1beta1PurgeContextsMetadata

Details of operations that perform MetadataService.PurgeContexts.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for purging Contexts.

GoogleCloudAiplatformV1beta1PurgeContextsRequest

Request message for MetadataService.PurgeContexts.

Fields
filter	string Required. A required filter matching the Contexts to be purged. E.g., update_time <= 2020-11-19T11:30:00-04:00.
force	boolean Optional. Flag to indicate to actually perform the purge. If force is set to false, the method will return a sample of Context names that would be deleted.

GoogleCloudAiplatformV1beta1PurgeContextsResponse

Response message for MetadataService.PurgeContexts.

Fields
purgeCount	string (int64 format) The number of Contexts that this request deleted (or, if force is false, the number of Contexts that will be deleted). This can be an estimate.
purgeSample[]	string A sample of the Context names that will be deleted. Only populated if force is set to false. The maximum number of samples is 100 (it is possible to return fewer).

GoogleCloudAiplatformV1beta1PurgeExecutionsMetadata

Details of operations that perform MetadataService.PurgeExecutions.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for purging Executions.

GoogleCloudAiplatformV1beta1PurgeExecutionsRequest

Request message for MetadataService.PurgeExecutions.

Fields
filter	string Required. A required filter matching the Executions to be purged. E.g., update_time <= 2020-11-19T11:30:00-04:00.
force	boolean Optional. Flag to indicate to actually perform the purge. If force is set to false, the method will return a sample of Execution names that would be deleted.

GoogleCloudAiplatformV1beta1PurgeExecutionsResponse

Response message for MetadataService.PurgeExecutions.

Fields
purgeCount	string (int64 format) The number of Executions that this request deleted (or, if force is false, the number of Executions that will be deleted). This can be an estimate.
purgeSample[]	string A sample of the Execution names that will be deleted. Only populated if force is set to false. The maximum number of samples is 100 (it is possible to return fewer).

GoogleCloudAiplatformV1beta1PythonPackageSpec

The spec of a Python packaged code.

Fields
args[]	string Command line arguments to be passed to the Python task.
env[]	object (GoogleCloudAiplatformV1beta1EnvVar) Environment variables to be passed to the python module. Maximum limit is 100.
executorImageUri	string Required. The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
packageUris[]	string Required. The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
pythonModule	string Required. The Python module name to run after installing the packages.

GoogleCloudAiplatformV1beta1QueryDeployedModelsResponse

Response message for QueryDeployedModels method.

Fields
deployedModelRefs[]	object (GoogleCloudAiplatformV1beta1DeployedModelRef) References to the DeployedModels that share the specified deploymentResourcePool.
deployedModels[]	object (GoogleCloudAiplatformV1beta1DeployedModel) DEPRECATED Use deployed_model_refs instead.
nextPageToken	string A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.
totalDeployedModelCount	integer (int32 format) The total number of DeployedModels on this DeploymentResourcePool.
totalEndpointCount	integer (int32 format) The total number of Endpoints that have DeployedModels on this DeploymentResourcePool.

GoogleCloudAiplatformV1beta1QueryExtensionRequest

Request message for ExtensionExecutionService.QueryExtension.

Fields
contents[]	object (GoogleCloudAiplatformV1beta1Content) Required. The content of the current conversation with the model. For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history + latest request.

GoogleCloudAiplatformV1beta1QueryExtensionResponse

Response message for ExtensionExecutionService.QueryExtension.

Fields
failureMessage	string Failure message if any.
steps[]	object (GoogleCloudAiplatformV1beta1Content) Steps of extension or LLM interaction, can contain function call, function response, or text response. The last step contains the final response to the query.

GoogleCloudAiplatformV1beta1QueryReasoningEngineRequest

Request message for ReasoningEngineExecutionService.Query.

Fields
input	map (key: string, value: any) Optional. Input content provided by users in JSON object format. Examples include text query, function calling parameters, media bytes, etc.

GoogleCloudAiplatformV1beta1QueryReasoningEngineResponse

Response message for ReasoningEngineExecutionService.Query

Fields
output	any Response provided by users in JSON object format.

GoogleCloudAiplatformV1beta1QuestionAnsweringCorrectnessInput

Input for question answering correctness metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1QuestionAnsweringCorrectnessInstance) Required. Question answering correctness instance.
metricSpec	object (GoogleCloudAiplatformV1beta1QuestionAnsweringCorrectnessSpec) Required. Spec for question answering correctness score metric.

GoogleCloudAiplatformV1beta1QuestionAnsweringCorrectnessInstance

Spec for question answering correctness instance.

Fields
context	string Optional. Text provided as context to answer the question.
instruction	string Required. The question asked and other instruction in the inference prompt.
prediction	string Required. Output of the evaluated model.
reference	string Optional. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1QuestionAnsweringCorrectnessResult

Spec for question answering correctness result.

Fields
confidence	number (float format) Output only. Confidence for question answering correctness score.
explanation	string Output only. Explanation for question answering correctness score.
score	number (float format) Output only. Question Answering Correctness score.

GoogleCloudAiplatformV1beta1QuestionAnsweringCorrectnessSpec

Spec for question answering correctness metric.

Fields
useReference	boolean Optional. Whether to use instance.reference to compute question answering correctness.
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1QuestionAnsweringHelpfulnessInput

Input for question answering helpfulness metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1QuestionAnsweringHelpfulnessInstance) Required. Question answering helpfulness instance.
metricSpec	object (GoogleCloudAiplatformV1beta1QuestionAnsweringHelpfulnessSpec) Required. Spec for question answering helpfulness score metric.

GoogleCloudAiplatformV1beta1QuestionAnsweringHelpfulnessInstance

Spec for question answering helpfulness instance.

Fields
context	string Optional. Text provided as context to answer the question.
instruction	string Required. The question asked and other instruction in the inference prompt.
prediction	string Required. Output of the evaluated model.
reference	string Optional. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1QuestionAnsweringHelpfulnessResult

Spec for question answering helpfulness result.

Fields
confidence	number (float format) Output only. Confidence for question answering helpfulness score.
explanation	string Output only. Explanation for question answering helpfulness score.
score	number (float format) Output only. Question Answering Helpfulness score.

GoogleCloudAiplatformV1beta1QuestionAnsweringHelpfulnessSpec

Spec for question answering helpfulness metric.

Fields
useReference	boolean Optional. Whether to use instance.reference to compute question answering helpfulness.
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1QuestionAnsweringQualityInput

Input for question answering quality metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1QuestionAnsweringQualityInstance) Required. Question answering quality instance.
metricSpec	object (GoogleCloudAiplatformV1beta1QuestionAnsweringQualitySpec) Required. Spec for question answering quality score metric.

GoogleCloudAiplatformV1beta1QuestionAnsweringQualityInstance

Spec for question answering quality instance.

Fields
context	string Required. Text to answer the question.
instruction	string Required. Question Answering prompt for LLM.
prediction	string Required. Output of the evaluated model.
reference	string Optional. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1QuestionAnsweringQualityResult

Spec for question answering quality result.

Fields
confidence	number (float format) Output only. Confidence for question answering quality score.
explanation	string Output only. Explanation for question answering quality score.
score	number (float format) Output only. Question Answering Quality score.

GoogleCloudAiplatformV1beta1QuestionAnsweringQualitySpec

Spec for question answering quality score metric.

Fields
useReference	boolean Optional. Whether to use instance.reference to compute question answering quality.
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1QuestionAnsweringRelevanceInput

Input for question answering relevance metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1QuestionAnsweringRelevanceInstance) Required. Question answering relevance instance.
metricSpec	object (GoogleCloudAiplatformV1beta1QuestionAnsweringRelevanceSpec) Required. Spec for question answering relevance score metric.

GoogleCloudAiplatformV1beta1QuestionAnsweringRelevanceInstance

Spec for question answering relevance instance.

Fields
context	string Optional. Text provided as context to answer the question.
instruction	string Required. The question asked and other instruction in the inference prompt.
prediction	string Required. Output of the evaluated model.
reference	string Optional. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1QuestionAnsweringRelevanceResult

Spec for question answering relevance result.

Fields
confidence	number (float format) Output only. Confidence for question answering relevance score.
explanation	string Output only. Explanation for question answering relevance score.
score	number (float format) Output only. Question Answering Relevance score.

GoogleCloudAiplatformV1beta1QuestionAnsweringRelevanceSpec

Spec for question answering relevance metric.

Fields
useReference	boolean Optional. Whether to use instance.reference to compute question answering relevance.
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1RagContexts

Relevant contexts for one query.

Fields
contexts[]	object (GoogleCloudAiplatformV1beta1RagContextsContext) All its contexts.

GoogleCloudAiplatformV1beta1RagContextsContext

A context of the query.

Fields
distance	number (double format) The distance between the query vector and the context text vector.
sourceUri	string For vertex RagStore, if the file is imported from Cloud Storage or Google Drive, source_uri will be original file URI in Cloud Storage or Google Drive; if file is uploaded, source_uri will be file display name.
text	string The text chunk.

GoogleCloudAiplatformV1beta1RagCorpus

A RagCorpus is a RagFile container and a project can have multiple RagCorpora.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this RagCorpus was created.
description	string Optional. The description of the RagCorpus.
displayName	string Required. The display name of the RagCorpus. The name can be up to 128 characters long and can consist of any UTF-8 characters.
name	string Output only. The resource name of the RagCorpus.
updateTime	string (Timestamp format) Output only. Timestamp when this RagCorpus was last updated.

GoogleCloudAiplatformV1beta1RagFile

A RagFile contains user data for chunking, embedding and indexing.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this RagFile was created.
description	string Optional. The description of the RagFile.
directUploadSource	object (GoogleCloudAiplatformV1beta1DirectUploadSource) Output only. The RagFile is encapsulated and uploaded in the UploadRagFile request.
displayName	string Required. The display name of the RagFile. The name can be up to 128 characters long and can consist of any UTF-8 characters.
gcsSource	object (GoogleCloudAiplatformV1beta1GcsSource) Output only. Google Cloud Storage location of the RagFile. It does not support wildcards in the Cloud Storage uri for now.
googleDriveSource	object (GoogleCloudAiplatformV1beta1GoogleDriveSource) Output only. Google Drive location. Supports importing individual files as well as Google Drive folders.
name	string Output only. The resource name of the RagFile.
ragFileType	enum Output only. The type of the RagFile.
Enum type. Can be one of the following:
RAG_FILE_TYPE_UNSPECIFIED	RagFile type is unspecified.
RAG_FILE_TYPE_TXT	RagFile type is TXT.
RAG_FILE_TYPE_PDF	RagFile type is PDF.
sizeBytes	string (int64 format) Output only. The size of the RagFile in bytes.
updateTime	string (Timestamp format) Output only. Timestamp when this RagFile was last updated.

GoogleCloudAiplatformV1beta1RagFileChunkingConfig

Specifies the size and overlap of chunks for RagFiles.

Fields
chunkOverlap	integer (int32 format) The overlap between chunks.
chunkSize	integer (int32 format) The size of the chunks.

GoogleCloudAiplatformV1beta1RagQuery

A query to retrieve relevant contexts.

Fields
similarityTopK	integer (int32 format) Optional. The number of contexts to retrieve.
text	string Optional. The query in text format to get relevant contexts.

GoogleCloudAiplatformV1beta1RawPredictRequest

Request message for PredictionService.RawPredict.

Fields

httpBody

object (GoogleApiHttpBody) The prediction input. Supports HTTP headers and arbitrary data payload. A DeployedModel may have an upper limit on the number of instances it supports per request. When this limit it is exceeded for an AutoML model, the RawPredict method returns an error. When this limit is exceeded for a custom-trained model, the behavior varies depending on the model. You can specify the schema for each instance in the predict_schemata.instance_schema_uri field when you create a Model. This schema applies when you deploy the Model as a DeployedModel to an Endpoint and use the RawPredict method.

GoogleCloudAiplatformV1beta1RayMetricSpec

Configuration for the Ray metrics.

Fields
disabled	boolean Optional. Flag to disable the Ray metrics collection.

GoogleCloudAiplatformV1beta1RaySpec

Configuration information for the Ray cluster. For experimental launch, Ray cluster creation and Persistent cluster creation are 1:1 mapping: We will provision all the nodes within the Persistent cluster as Ray nodes.

Fields
headNodeResourcePoolId	string Optional. This will be used to indicate which resource pool will serve as the Ray head node(the first node within that pool). Will use the machine from the first workerpool as the head node by default if this field isn't set.
imageUri	string Optional. Default image for user to choose a preferred ML framework (for example, TensorFlow or Pytorch) by choosing from Vertex prebuilt images. Either this or the resource_pool_images is required. Use this field if you need all the resource pools to have the same Ray image. Otherwise, use the {@code resource_pool_images} field.
rayMetricSpec	object (GoogleCloudAiplatformV1beta1RayMetricSpec) Optional. Ray metrics configurations.
resourcePoolImages	map (key: string, value: string) Optional. Required if image_uri isn't set. A map of resource_pool_id to prebuild Ray image if user need to use different images for different head/worker pools. This map needs to cover all the resource pool ids. Example: { "ray_head_node_pool": "head image" "ray_worker_node_pool1": "worker image" "ray_worker_node_pool2": "another worker image" }

GoogleCloudAiplatformV1beta1ReadFeatureValuesRequest

Request message for FeaturestoreOnlineServingService.ReadFeatureValues.

Fields
entityId	string Required. ID for a specific entity. For example, for a machine learning model predicting user clicks on a website, an entity ID could be user_123.
featureSelector	object (GoogleCloudAiplatformV1beta1FeatureSelector) Required. Selector choosing Features of the target EntityType.

GoogleCloudAiplatformV1beta1ReadFeatureValuesResponse

Response message for FeaturestoreOnlineServingService.ReadFeatureValues.

Fields
entityView	object (GoogleCloudAiplatformV1beta1ReadFeatureValuesResponseEntityView) Entity view with Feature values. This may be the entity in the Featurestore if values for all Features were requested, or a projection of the entity in the Featurestore if values for only some Features were requested.
header	object (GoogleCloudAiplatformV1beta1ReadFeatureValuesResponseHeader) Response header.

GoogleCloudAiplatformV1beta1ReadFeatureValuesResponseEntityView

Entity view with Feature values.

Fields
data[]	object (GoogleCloudAiplatformV1beta1ReadFeatureValuesResponseEntityViewData) Each piece of data holds the k requested values for one requested Feature. If no values for the requested Feature exist, the corresponding cell will be empty. This has the same size and is in the same order as the features from the header ReadFeatureValuesResponse.header.
entityId	string ID of the requested entity.

GoogleCloudAiplatformV1beta1ReadFeatureValuesResponseEntityViewData

Container to hold value(s), successive in time, for one Feature from the request.

Fields
value	object (GoogleCloudAiplatformV1beta1FeatureValue) Feature value if a single value is requested.
values	object (GoogleCloudAiplatformV1beta1FeatureValueList) Feature values list if values, successive in time, are requested. If the requested number of values is greater than the number of existing Feature values, nonexistent values are omitted instead of being returned as empty.

GoogleCloudAiplatformV1beta1ReadFeatureValuesResponseFeatureDescriptor

Metadata for requested Features.

Fields
id	string Feature ID.

GoogleCloudAiplatformV1beta1ReadFeatureValuesResponseHeader

Response header with metadata for the requested ReadFeatureValuesRequest.entity_type and Features.

Fields
entityType	string The resource name of the EntityType from the ReadFeatureValuesRequest. Value format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}.
featureDescriptors[]	object (GoogleCloudAiplatformV1beta1ReadFeatureValuesResponseFeatureDescriptor) List of Feature metadata corresponding to each piece of ReadFeatureValuesResponse.EntityView.data.

GoogleCloudAiplatformV1beta1ReadIndexDatapointsRequest

The request message for MatchService.ReadIndexDatapoints.

Fields
deployedIndexId	string The ID of the DeployedIndex that will serve the request.
ids[]	string IDs of the datapoints to be searched for.

GoogleCloudAiplatformV1beta1ReadIndexDatapointsResponse

The response message for MatchService.ReadIndexDatapoints.

Fields
datapoints[]	object (GoogleCloudAiplatformV1beta1IndexDatapoint) The result list of datapoints.

GoogleCloudAiplatformV1beta1ReadTensorboardBlobDataResponse

Response message for TensorboardService.ReadTensorboardBlobData.

Fields
blobs[]	object (GoogleCloudAiplatformV1beta1TensorboardBlob) Blob messages containing blob bytes.

GoogleCloudAiplatformV1beta1ReadTensorboardSizeResponse

Response message for TensorboardService.ReadTensorboardSize.

Fields
storageSizeByte	string (int64 format) Payload storage size for the TensorBoard

GoogleCloudAiplatformV1beta1ReadTensorboardTimeSeriesDataResponse

Response message for TensorboardService.ReadTensorboardTimeSeriesData.

Fields
timeSeriesData	object (GoogleCloudAiplatformV1beta1TimeSeriesData) The returned time series data.

GoogleCloudAiplatformV1beta1ReadTensorboardUsageResponse

Response message for TensorboardService.ReadTensorboardUsage.

Fields
monthlyUsageData	map (key: string, value: object (GoogleCloudAiplatformV1beta1ReadTensorboardUsageResponsePerMonthUsageData)) Maps year-month (YYYYMM) string to per month usage data.

GoogleCloudAiplatformV1beta1ReadTensorboardUsageResponsePerMonthUsageData

Per month usage data

Fields
userUsageData[]	object (GoogleCloudAiplatformV1beta1ReadTensorboardUsageResponsePerUserUsageData) Usage data for each user in the given month.

GoogleCloudAiplatformV1beta1ReadTensorboardUsageResponsePerUserUsageData

Per user usage data.

Fields
username	string User's username
viewCount	string (int64 format) Number of times the user has read data within the Tensorboard.

GoogleCloudAiplatformV1beta1ReasoningEngine

ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this ReasoningEngine was created.
description	string Optional. The description of the ReasoningEngine.
displayName	string Required. The display name of the ReasoningEngine.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
name	string Identifier. The resource name of the ReasoningEngine.
spec	object (GoogleCloudAiplatformV1beta1ReasoningEngineSpec) Required. Configurations of the ReasoningEngine
updateTime	string (Timestamp format) Output only. Timestamp when this ReasoningEngine was most recently updated.

GoogleCloudAiplatformV1beta1ReasoningEngineSpec

ReasoningEngine configurations

Fields
classMethods[]	object Optional. Declarations for object class methods.
packageSpec	object (GoogleCloudAiplatformV1beta1ReasoningEngineSpecPackageSpec) Required. User provided package spec of the ReasoningEngine.

GoogleCloudAiplatformV1beta1ReasoningEngineSpecPackageSpec

User provided package spec like pickled object and package requirements.

Fields
dependencyFilesGcsUri	string Optional. The Cloud Storage URI of the dependency files in tar.gz format.
pickleObjectGcsUri	string Optional. The Cloud Storage URI of the pickled python object.
pythonVersion	string Optional. The Python version. Currently support 3.8, 3.9, 3.10, 3.11. If not specified, default value is 3.10.
requirementsGcsUri	string Optional. The Cloud Storage URI of the requirements.txt file

GoogleCloudAiplatformV1beta1RebootPersistentResourceOperationMetadata

Details of operations that perform reboot PersistentResource.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for PersistentResource.
progressMessage	string Progress Message for Reboot LRO

GoogleCloudAiplatformV1beta1RemoveContextChildrenRequest

Request message for MetadataService.DeleteContextChildrenRequest.

Fields
childContexts[]	string The resource names of the child Contexts.

GoogleCloudAiplatformV1beta1RemoveDatapointsRequest

Request message for IndexService.RemoveDatapoints

Fields
datapointIds[]	string A list of datapoint ids to be deleted.

GoogleCloudAiplatformV1beta1ReportExecutionEventRequest

Request message for NotebookInternalService.ReportExecutionEvent.

Fields
eventType	enum Required. The type of the event.
Enum type. Can be one of the following:
EVENT_TYPE_UNSPECIFIED	Unspecified.
ACTIVE	Notebook execution process has started. Expect this message within expected time to provision compute.
DONE	Notebook execution process is completed. Expect this message within timeout.
FAILED	Notebook execution process has failed. Expect this message within timeout.
status	object (GoogleRpcStatus) Optional. The error details of the event.
vmToken	string Required. The VM identity token (a JWT) for authenticating the VM. https://cloud.google.com/compute/docs/instances/verifying-instance-identity

GoogleCloudAiplatformV1beta1ReportRuntimeEventRequest

Request message for NotebookInternalService.ReportRuntimeEvent.

Fields
eventDetails	map (key: string, value: string) Optional. The details of the request for debug.
eventType	enum Required. The type of the event.
Enum type. Can be one of the following:
EVENT_TYPE_UNSPECIFIED	Unspecified.
HEARTBEAT	Used for readiness reporting.
IDLE	Used for idle reporting.
internalOsServiceStateInstance[]	object (GoogleCloudAiplatformV1beta1InternalOsServiceStateInstance) The details of the internal os service states.
internalOsServiceStateInstances[]	object (GoogleCloudAiplatformV1beta1InternalOsServiceStateInstance) Optional. The details of the internal os service states.
vmToken	string Required. The VM identity token (a JWT) for authenticating the VM. https://cloud.google.com/compute/docs/instances/verifying-instance-identity

GoogleCloudAiplatformV1beta1ReportRuntimeEventResponse

Response message for NotebookInternalService.ReportRuntimeEvent.

Fields
idleShutdownMessage	string If the idle shutdown is blocked by CP, CP will send the block message. Otherwise, this field is not set.

GoogleCloudAiplatformV1beta1ResourcePool

Represents the spec of a group of resources of the same type, for example machine type, disk, and accelerators, in a PersistentResource.

Fields
autoscalingSpec	object (GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpec) Optional. Optional spec to configure GKE autoscaling
diskSpec	object (GoogleCloudAiplatformV1beta1DiskSpec) Optional. Disk spec for the machine in this node pool.
id	string Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it's generated automatically.
machineSpec	object (GoogleCloudAiplatformV1beta1MachineSpec) Required. Immutable. The specification of a single machine.
replicaCount	string (int64 format) Optional. The total number of machines to use for this resource pool.
usedReplicaCount	string (int64 format) Output only. The number of machines currently in use by training jobs for this resource pool. Will replace idle_replica_count.

GoogleCloudAiplatformV1beta1ResourcePoolAutoscalingSpec

The min/max number of replicas allowed if enabling autoscaling

Fields
maxReplicaCount	string (int64 format) Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error
minReplicaCount	string (int64 format) Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error

GoogleCloudAiplatformV1beta1ResourceRuntime

Persistent Cluster runtime information as output

Fields
accessUris	map (key: string, value: string) Output only. URIs for user to connect to the Cluster. Example: { "RAY_HEAD_NODE_INTERNAL_IP": "head-node-IP:10001" "RAY_DASHBOARD_URI": "ray-dashboard-address:8888" }
notebookRuntimeTemplate	string Output only. The resource name of NotebookRuntimeTemplate for the RoV Persistent Cluster The NotebokRuntimeTemplate is created in the same VPC (if set), and with the same Ray and Python version as the Persistent Cluster. Example: "projects/1000/locations/us-central1/notebookRuntimeTemplates/abc123"

GoogleCloudAiplatformV1beta1ResourceRuntimeSpec

Configuration for the runtime on a PersistentResource instance, including but not limited to: * Service accounts used to run the workloads. * Whether to make it a dedicated Ray Cluster.

Fields
raySpec	object (GoogleCloudAiplatformV1beta1RaySpec) Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.
serviceAccountSpec	object (GoogleCloudAiplatformV1beta1ServiceAccountSpec) Optional. Configure the use of workload identity on the PersistentResource

GoogleCloudAiplatformV1beta1ResourcesConsumed

Statistics information about resource consumption.

Fields
replicaHours	number (double format) Output only. The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.

GoogleCloudAiplatformV1beta1RestoreDatasetVersionOperationMetadata

Runtime operation information for DatasetService.RestoreDatasetVersion.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.

GoogleCloudAiplatformV1beta1ResumeScheduleRequest

Request message for ScheduleService.ResumeSchedule.

Fields
catchUp	boolean Optional. Whether to backfill missed runs when the schedule is resumed from PAUSED state. If set to true, all missed runs will be scheduled. New runs will be scheduled after the backfill is complete. This will also update Schedule.catch_up field. Default to false.

GoogleCloudAiplatformV1beta1Retrieval

Defines a retrieval tool that model can call to access external knowledge.

Fields
disableAttribution	boolean Optional. Disable using the result from this tool in detecting grounding attribution. This does not affect how the result is given to the model for generation.
vertexAiSearch	object (GoogleCloudAiplatformV1beta1VertexAISearch) Set to use data source powered by Vertex AI Search.
vertexRagStore	object (GoogleCloudAiplatformV1beta1VertexRagStore) Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.

GoogleCloudAiplatformV1beta1RetrieveContextsRequest

Request message for VertexRagService.RetrieveContexts.

Fields
query	object (GoogleCloudAiplatformV1beta1RagQuery) Required. Single RAG retrieve query.
vertexRagStore	object (GoogleCloudAiplatformV1beta1RetrieveContextsRequestVertexRagStore) The data source for Vertex RagStore.

GoogleCloudAiplatformV1beta1RetrieveContextsRequestVertexRagStore

The data source for Vertex RagStore.

Fields
ragCorpora[]	string Optional. Deprecated. Please use rag_resources to specify the data source.
ragResources[]	object (GoogleCloudAiplatformV1beta1RetrieveContextsRequestVertexRagStoreRagResource) Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.
vectorDistanceThreshold	number (double format) Optional. Only return contexts with vector distance smaller than the threshold.

GoogleCloudAiplatformV1beta1RetrieveContextsRequestVertexRagStoreRagResource

The definition of the Rag resource.

Fields
ragCorpus	string Optional. RagCorpora resource name. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}
ragFileIds[]	string Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field.

GoogleCloudAiplatformV1beta1RetrieveContextsResponse

Response message for VertexRagService.RetrieveContexts.

Fields
contexts	object (GoogleCloudAiplatformV1beta1RagContexts) The contexts of the query.

GoogleCloudAiplatformV1beta1RougeInput

Input for rouge metric.

Fields
instances[]	object (GoogleCloudAiplatformV1beta1RougeInstance) Required. Repeated rouge instances.
metricSpec	object (GoogleCloudAiplatformV1beta1RougeSpec) Required. Spec for rouge score metric.

GoogleCloudAiplatformV1beta1RougeInstance

Spec for rouge instance.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Required. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1RougeMetricValue

Rouge metric value for an instance.

Fields
score	number (float format) Output only. Rouge score.

GoogleCloudAiplatformV1beta1RougeResults

Results for rouge metric.

Fields
rougeMetricValues[]	object (GoogleCloudAiplatformV1beta1RougeMetricValue) Output only. Rouge metric values.

GoogleCloudAiplatformV1beta1RougeSpec

Spec for rouge score metric - calculates the recall of n-grams in prediction as compared to reference - returns a score ranging between 0 and 1.

Fields
rougeType	string Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.
splitSummaries	boolean Optional. Whether to split summaries while using rougeLsum.
useStemmer	boolean Optional. Whether to use stemmer to compute rouge score.

GoogleCloudAiplatformV1beta1RuntimeConfig

Runtime configuration to run the extension.

Fields
codeInterpreterRuntimeConfig	object (GoogleCloudAiplatformV1beta1RuntimeConfigCodeInterpreterRuntimeConfig) Code execution runtime configurations for code interpreter extension.
defaultParams	map (key: string, value: any) Optional. Default parameters that will be set for all the execution of this extension. If specified, the parameter values can be overridden by values in [[ExecuteExtensionRequest.operation_params]] at request time. The struct should be in a form of map with param name as the key and actual param value as the value. E.g. If this operation requires a param "name" to be set to "abc". you can set this to something like {"name": "abc"}.
vertexAiSearchRuntimeConfig	object (GoogleCloudAiplatformV1beta1RuntimeConfigVertexAISearchRuntimeConfig) Runtime configuration for Vertext AI Search extension.

GoogleCloudAiplatformV1beta1RuntimeConfigCodeInterpreterRuntimeConfig

(No description provided)

Fields
fileInputGcsBucket	string Optional. The Cloud Storage bucket for file input of this Extension. If specified, support input from the Cloud Storage bucket. Vertex Extension Custom Code Service Agent should be granted file reader to this bucket. If not specified, the extension will only accept file contents from request body and reject Cloud Storage file inputs.
fileOutputGcsBucket	string Optional. The Cloud Storage bucket for file output of this Extension. If specified, write all output files to the Cloud Storage bucket. Vertex Extension Custom Code Service Agent should be granted file writer to this bucket. If not specified, the file content will be output in response body.

GoogleCloudAiplatformV1beta1RuntimeConfigVertexAISearchRuntimeConfig

(No description provided)

Fields
appId	string Vertex AI Search App ID. This is used to construct the search request. By setting this app_id, API will construct the serving config which is required to call search API for the user. The app_id and serving_config_name cannot both be empty at the same time.
servingConfigName	string [Deprecated] Please use app_id instead. Vertex AI Search serving config name. Format: projects/{project}/locations/{location}/collections/{collection}/engines/{engine}/servingConfigs/{serving_config}

GoogleCloudAiplatformV1beta1SafetyInput

Input for safety metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1SafetyInstance) Required. Safety instance.
metricSpec	object (GoogleCloudAiplatformV1beta1SafetySpec) Required. Spec for safety metric.

GoogleCloudAiplatformV1beta1SafetyInstance

Spec for safety instance.

Fields
prediction	string Required. Output of the evaluated model.

GoogleCloudAiplatformV1beta1SafetyRating

Safety rating corresponding to the generated content.

Fields
blocked	boolean Output only. Indicates whether the content was filtered out because of this rating.
category	enum Output only. Harm category.
Enum type. Can be one of the following:
HARM_CATEGORY_UNSPECIFIED	The harm category is unspecified.
HARM_CATEGORY_HATE_SPEECH	The harm category is hate speech.
HARM_CATEGORY_DANGEROUS_CONTENT	The harm category is dangerous content.
HARM_CATEGORY_HARASSMENT	The harm category is harassment.
HARM_CATEGORY_SEXUALLY_EXPLICIT	The harm category is sexually explicit content.
probability	enum Output only. Harm probability levels in the content.
Enum type. Can be one of the following:
HARM_PROBABILITY_UNSPECIFIED	Harm probability unspecified.
NEGLIGIBLE	Negligible level of harm.
LOW	Low level of harm.
MEDIUM	Medium level of harm.
HIGH	High level of harm.
probabilityScore	number (float format) Output only. Harm probability score.
severity	enum Output only. Harm severity levels in the content.
Enum type. Can be one of the following:
HARM_SEVERITY_UNSPECIFIED	Harm severity unspecified.
HARM_SEVERITY_NEGLIGIBLE	Negligible level of harm severity.
HARM_SEVERITY_LOW	Low level of harm severity.
HARM_SEVERITY_MEDIUM	Medium level of harm severity.
HARM_SEVERITY_HIGH	High level of harm severity.
severityScore	number (float format) Output only. Harm severity score.

GoogleCloudAiplatformV1beta1SafetyResult

Spec for safety result.

Fields
confidence	number (float format) Output only. Confidence for safety score.
explanation	string Output only. Explanation for safety score.
score	number (float format) Output only. Safety score.

GoogleCloudAiplatformV1beta1SafetySetting

Safety settings.

Fields
category	enum Required. Harm category.
Enum type. Can be one of the following:
HARM_CATEGORY_UNSPECIFIED	The harm category is unspecified.
HARM_CATEGORY_HATE_SPEECH	The harm category is hate speech.
HARM_CATEGORY_DANGEROUS_CONTENT	The harm category is dangerous content.
HARM_CATEGORY_HARASSMENT	The harm category is harassment.
HARM_CATEGORY_SEXUALLY_EXPLICIT	The harm category is sexually explicit content.
method	enum Optional. Specify if the threshold is used for probability or severity score. If not specified, the threshold is used for probability score.
Enum type. Can be one of the following:
HARM_BLOCK_METHOD_UNSPECIFIED	The harm block method is unspecified.
SEVERITY	The harm block method uses both probability and severity scores.
PROBABILITY	The harm block method uses the probability score.
threshold	enum Required. The harm block threshold.
Enum type. Can be one of the following:
HARM_BLOCK_THRESHOLD_UNSPECIFIED	Unspecified harm block threshold.
BLOCK_LOW_AND_ABOVE	Block low threshold and above (i.e. block more).
BLOCK_MEDIUM_AND_ABOVE	Block medium threshold and above.
BLOCK_ONLY_HIGH	Block only high threshold (i.e. block less).
BLOCK_NONE	Block none.

GoogleCloudAiplatformV1beta1SafetySpec

Spec for safety metric.

Fields
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1SampleConfig

Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.

Fields
followingBatchSamplePercentage	integer (int32 format) The percentage of data needed to be labeled in each following batch (except the first batch).
initialBatchSamplePercentage	integer (int32 format) The percentage of data needed to be labeled in the first batch.
sampleStrategy	enum Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
Enum type. Can be one of the following:
SAMPLE_STRATEGY_UNSPECIFIED	Default will be treated as UNCERTAINTY.
UNCERTAINTY	Sample the most uncertain data to label.

GoogleCloudAiplatformV1beta1SampledShapleyAttribution

An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.

Fields
pathCount	integer (int32 format) Required. The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

GoogleCloudAiplatformV1beta1SamplingStrategy

Sampling Strategy for logging, can be for both training and prediction dataset.

Fields
randomSampleConfig	object (GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig) Random sample config. Will support more sampling strategies later.

GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig

Requests are randomly selected.

Fields
sampleRate	number (double format) Sample rate (0, 1]

GoogleCloudAiplatformV1beta1SavedQuery

A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.

Fields
annotationFilter	string Output only. Filters on the Annotations in the dataset.
annotationSpecCount	integer (int32 format) Output only. Number of AnnotationSpecs in the context of the SavedQuery.
createTime	string (Timestamp format) Output only. Timestamp when this SavedQuery was created.
displayName	string Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters.
etag	string Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens.
metadata	any Some additional information about the SavedQuery.
name	string Output only. Resource name of the SavedQuery.
problemType	string Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING
supportAutomlTraining	boolean Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.
updateTime	string (Timestamp format) Output only. Timestamp when SavedQuery was last updated.

GoogleCloudAiplatformV1beta1Scalar

One point viewable on a scalar metric plot.

Fields
value	number (double format) Value of the point at this step / timestamp.

GoogleCloudAiplatformV1beta1Schedule

An instance of a Schedule periodically schedules runs to make API calls based on user specified time specification and API request type.

Fields
allowQueueing	boolean Optional. Whether new scheduled runs can be queued when max_concurrent_runs limit is reached. If set to true, new runs will be queued instead of skipped. Default to false.
catchUp	boolean Output only. Whether to backfill missed runs when the schedule is resumed from PAUSED state. If set to true, all missed runs will be scheduled. New runs will be scheduled after the backfill is complete. Default to false.
createModelMonitoringJobRequest	object (GoogleCloudAiplatformV1beta1CreateModelMonitoringJobRequest) Request for ModelMonitoringService.CreateModelMonitoringJob.
createNotebookExecutionJobRequest	object (GoogleCloudAiplatformV1beta1CreateNotebookExecutionJobRequest) Request for NotebookService.CreateNotebookExecutionJob.
createPipelineJobRequest	object (GoogleCloudAiplatformV1beta1CreatePipelineJobRequest) Request for PipelineService.CreatePipelineJob. CreatePipelineJobRequest.parent field is required (format: projects/{project}/locations/{location}).
createTime	string (Timestamp format) Output only. Timestamp when this Schedule was created.
cron	string Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * ", or "TZ=America/New_York 1 * * * ".
displayName	string Required. User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.
endTime	string (Timestamp format) Optional. Timestamp after which no new runs can be scheduled. If specified, The schedule will be completed when either end_time is reached or when scheduled_run_count >= max_run_count. If not specified, new runs will keep getting scheduled until this Schedule is paused or deleted. Already scheduled runs will be allowed to complete. Unset if not specified.
lastPauseTime	string (Timestamp format) Output only. Timestamp when this Schedule was last paused. Unset if never paused.
lastResumeTime	string (Timestamp format) Output only. Timestamp when this Schedule was last resumed. Unset if never resumed from pause.
lastScheduledRunResponse	object (GoogleCloudAiplatformV1beta1ScheduleRunResponse) Output only. Response of the last scheduled run. This is the response for starting the scheduled requests and not the execution of the operations/jobs created by the requests (if applicable). Unset if no run has been scheduled yet.
maxConcurrentRunCount	string (int64 format) Required. Maximum number of runs that can be started concurrently for this Schedule. This is the limit for starting the scheduled requests and not the execution of the operations/jobs created by the requests (if applicable).
maxRunCount	string (int64 format) Optional. Maximum run count of the schedule. If specified, The schedule will be completed when either started_run_count >= max_run_count or when end_time is reached. If not specified, new runs will keep getting scheduled until this Schedule is paused or deleted. Already scheduled runs will be allowed to complete. Unset if not specified.
name	string Immutable. The resource name of the Schedule.
nextRunTime	string (Timestamp format) Output only. Timestamp when this Schedule should schedule the next run. Having a next_run_time in the past means the runs are being started behind schedule.
startTime	string (Timestamp format) Optional. Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.
startedRunCount	string (int64 format) Output only. The number of runs started by this schedule.
state	enum Output only. The state of this Schedule.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	Unspecified.
ACTIVE	The Schedule is active. Runs are being scheduled on the user-specified timespec.
PAUSED	The schedule is paused. No new runs will be created until the schedule is resumed. Already started runs will be allowed to complete.
COMPLETED	The Schedule is completed. No new runs will be scheduled. Already started runs will be allowed to complete. Schedules in completed state cannot be paused or resumed.
updateTime	string (Timestamp format) Output only. Timestamp when this Schedule was updated.

GoogleCloudAiplatformV1beta1ScheduleRunResponse

Status of a scheduled run.

Fields
runResponse	string The response of the scheduled run.
scheduledRunTime	string (Timestamp format) The scheduled run time based on the user-specified schedule.

GoogleCloudAiplatformV1beta1Scheduling

All parameters related to queuing and scheduling of custom jobs.

Fields
disableRetries	boolean Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.
restartJobOnWorkerRestart	boolean Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
timeout	string (Duration format) The maximum job running time. The default is 7 days.

GoogleCloudAiplatformV1beta1Schema

Schema is used to define the format of input/output data. Represents a select subset of an OpenAPI 3.0 schema object. More fields may be added in the future as needed.

Fields
default	any Optional. Default value of the data.
description	string Optional. The description of the data.
enum[]	string Optional. Possible values of the element of Type.STRING with enum format. For example we can define an Enum Direction as : {type:STRING, format:enum, enum:["EAST", NORTH", "SOUTH", "WEST"]}
example	any Optional. Example of the object. Will only populated when the object is the root.
format	string Optional. The format of the data. Supported formats: for NUMBER type: "float", "double" for INTEGER type: "int32", "int64" for STRING type: "email", "byte", etc
items	object (GoogleCloudAiplatformV1beta1Schema) Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
maxItems	string (int64 format) Optional. Maximum number of the elements for Type.ARRAY.
maxLength	string (int64 format) Optional. Maximum length of the Type.STRING
maxProperties	string (int64 format) Optional. Maximum number of the properties for Type.OBJECT.
maximum	number (double format) Optional. Maximum value of the Type.INTEGER and Type.NUMBER
minItems	string (int64 format) Optional. Minimum number of the elements for Type.ARRAY.
minLength	string (int64 format) Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
minProperties	string (int64 format) Optional. Minimum number of the properties for Type.OBJECT.
minimum	number (double format) Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
nullable	boolean Optional. Indicates if the value may be null.
pattern	string Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
properties	map (key: string, value: object (GoogleCloudAiplatformV1beta1Schema)) Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
required[]	string Optional. Required properties of Type.OBJECT.
title	string Optional. The title of the Schema.
type	enum Optional. The type of the data.
Enum type. Can be one of the following:
TYPE_UNSPECIFIED	Not specified, should not be used.
STRING	OpenAPI string type
NUMBER	OpenAPI number type
INTEGER	OpenAPI integer type
BOOLEAN	OpenAPI boolean type
ARRAY	OpenAPI array type
OBJECT	OpenAPI object type

GoogleCloudAiplatformV1beta1SchemaAnnotationSpecColor

An entry of mapping between color and AnnotationSpec. The mapping is used in segmentation mask.

Fields
color	object (GoogleTypeColor) The color of the AnnotationSpec in a segmentation mask.
displayName	string The display name of the AnnotationSpec represented by the color in the segmentation mask.
id	string The ID of the AnnotationSpec represented by the color in the segmentation mask.

GoogleCloudAiplatformV1beta1SchemaImageBoundingBoxAnnotation

Annotation details specific to image object detection.

Fields
annotationSpecId	string The resource Id of the AnnotationSpec that this Annotation pertains to.
displayName	string The display name of the AnnotationSpec that this Annotation pertains to.
xMax	number (double format) The rightmost coordinate of the bounding box.
xMin	number (double format) The leftmost coordinate of the bounding box.
yMax	number (double format) The bottommost coordinate of the bounding box.
yMin	number (double format) The topmost coordinate of the bounding box.

GoogleCloudAiplatformV1beta1SchemaImageClassificationAnnotation

Annotation details specific to image classification.

Fields
annotationSpecId	string The resource Id of the AnnotationSpec that this Annotation pertains to.
displayName	string The display name of the AnnotationSpec that this Annotation pertains to.

GoogleCloudAiplatformV1beta1SchemaImageDataItem

Payload of Image DataItem.

Fields
gcsUri	string Required. Google Cloud Storage URI points to the original image in user's bucket. The image is up to 30MB in size.
mimeType	string Output only. The mime type of the content of the image. Only the images in below listed mime types are supported. - image/jpeg - image/gif - image/png - image/webp - image/bmp - image/tiff - image/vnd.microsoft.icon

GoogleCloudAiplatformV1beta1SchemaImageDatasetMetadata

The metadata of Datasets that contain Image DataItems.

Fields
dataItemSchemaUri	string Points to a YAML file stored on Google Cloud Storage describing payload of the Image DataItems that belong to this Dataset.
gcsBucket	string Google Cloud Storage Bucket name that contains the blob data of this Dataset.

GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotation

Annotation details specific to image segmentation.

Fields
maskAnnotation	object (GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationMaskAnnotation) Mask based segmentation annotation. Only one mask annotation can exist for one image.
polygonAnnotation	object (GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationPolygonAnnotation) Polygon annotation.
polylineAnnotation	object (GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationPolylineAnnotation) Polyline annotation.

GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationMaskAnnotation

The mask based segmentation annotation.

Fields
annotationSpecColors[]	object (GoogleCloudAiplatformV1beta1SchemaAnnotationSpecColor) The mapping between color and AnnotationSpec for this Annotation.
maskGcsUri	string Google Cloud Storage URI that points to the mask image. The image must be in PNG format. It must have the same size as the DataItem's image. Each pixel in the image mask represents the AnnotationSpec which the pixel in the image DataItem belong to. Each color is mapped to one AnnotationSpec based on annotation_spec_colors.

GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationPolygonAnnotation

Represents a polygon in image.

Fields
annotationSpecId	string The resource Id of the AnnotationSpec that this Annotation pertains to.
displayName	string The display name of the AnnotationSpec that this Annotation pertains to.
vertexes[]	object (GoogleCloudAiplatformV1beta1SchemaVertex) The vertexes are connected one by one and the last vertex is connected to the first one to represent a polygon.

GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationPolylineAnnotation

Represents a polyline in image.

Fields
annotationSpecId	string The resource Id of the AnnotationSpec that this Annotation pertains to.
displayName	string The display name of the AnnotationSpec that this Annotation pertains to.
vertexes[]	object (GoogleCloudAiplatformV1beta1SchemaVertex) The vertexes are connected one by one and the last vertex in not connected to the first one.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsBoundingBoxMetrics

Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.

Fields
confidenceMetrics[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsBoundingBoxMetricsConfidenceMetrics) Metrics for each label-match confidence_threshold from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is derived from them.
iouThreshold	number (float format) The intersection-over-union threshold value used to compute this metrics entry.
meanAveragePrecision	number (float format) The mean average precision, most often close to auPrc.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsBoundingBoxMetricsConfidenceMetrics

Metrics for a single confidence threshold.

Fields
confidenceThreshold	number (float format) The confidence threshold value used to compute the metrics.
f1Score	number (float format) The harmonic mean of recall and precision.
precision	number (float format) Precision under the given confidence threshold.
recall	number (float format) Recall under the given confidence threshold.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetrics

Metrics for classification evaluation results.

Fields
auPrc	number (float format) The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.
auRoc	number (float format) The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.
confidenceMetrics[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics) Metrics for each confidenceThreshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and positionThreshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of positionThreshold, but from these no aggregated metrics are computed.
confusionMatrix	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsConfusionMatrix) Confusion matrix of the evaluation.
logLoss	number (float format) The Log Loss metric.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics

(No description provided)

Fields
confidenceThreshold	number (float format) Metrics are computed with an assumption that the Model never returns predictions with score lower than this value.
confusionMatrix	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsConfusionMatrix) Confusion matrix of the evaluation for this confidence_threshold.
f1Score	number (float format) The harmonic mean of recall and precision. For summary metrics, it computes the micro-averaged F1 score.
f1ScoreAt1	number (float format) The harmonic mean of recallAt1 and precisionAt1.
f1ScoreMacro	number (float format) Macro-averaged F1 Score.
f1ScoreMicro	number (float format) Micro-averaged F1 Score.
falseNegativeCount	string (int64 format) The number of ground truth labels that are not matched by a Model created label.
falsePositiveCount	string (int64 format) The number of Model created labels that do not match a ground truth label.
falsePositiveRate	number (float format) False Positive Rate for the given confidence threshold.
falsePositiveRateAt1	number (float format) The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem.
maxPredictions	integer (int32 format) Metrics are computed with an assumption that the Model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidenceThreshold.
precision	number (float format) Precision for the given confidence threshold.
precisionAt1	number (float format) The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem.
recall	number (float format) Recall (True Positive Rate) for the given confidence threshold.
recallAt1	number (float format) The Recall (True Positive Rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem.
trueNegativeCount	string (int64 format) The number of labels that were not created by the Model, but if they would, they would not match a ground truth label.
truePositiveCount	string (int64 format) The number of Model created labels that match a ground truth label.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsConfusionMatrix

(No description provided)

Fields
annotationSpecs[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsConfusionMatrixAnnotationSpecRef) AnnotationSpecs used in the confusion matrix. For AutoML Text Extraction, a special negative AnnotationSpec with empty id and displayName of "NULL" will be added as the last element.
rows[]	array Rows in the confusion matrix. The number of rows is equal to the size of annotationSpecs. rowsi is the number of DataItems that have ground truth of the annotationSpecs[i] and are predicted as annotationSpecs[j] by the Model being evaluated. For Text Extraction, when annotationSpecs[i] is the last element in annotationSpecs, i.e. the special negative AnnotationSpec, rowsi is the number of predicted entities of annoatationSpec[j] that are not labeled as any of the ground truth AnnotationSpec. When annotationSpecs[j] is the special negative AnnotationSpec, rowsi is the number of entities have ground truth of annotationSpec[i] that are not predicted as an entity by the Model. The value of the last cell, i.e. rowi where i == j and annotationSpec[i] is the special negative AnnotationSpec, is always 0.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsConfusionMatrixAnnotationSpecRef

(No description provided)

Fields
displayName	string Display name of the AnnotationSpec.
id	string ID of the AnnotationSpec.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsForecastingEvaluationMetrics

Metrics for forecasting evaluation results.

Fields
meanAbsoluteError	number (float format) Mean Absolute Error (MAE).
meanAbsolutePercentageError	number (float format) Mean absolute percentage error. Infinity when there are zeros in the ground truth.
quantileMetrics[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsForecastingEvaluationMetricsQuantileMetricsEntry) The quantile metrics entries for each quantile.
rSquared	number (float format) Coefficient of determination as Pearson correlation coefficient. Undefined when ground truth or predictions are constant or near constant.
rootMeanSquaredError	number (float format) Root Mean Squared Error (RMSE).
rootMeanSquaredLogError	number (float format) Root mean squared log error. Undefined when there are negative ground truth values or predictions.
rootMeanSquaredPercentageError	number (float format) Root Mean Square Percentage Error. Square root of MSPE. Undefined/imaginary when MSPE is negative.
weightedAbsolutePercentageError	number (float format) Weighted Absolute Percentage Error. Does not use weights, this is just what the metric is called. Undefined if actual values sum to zero. Will be very large if actual values sum to a very small number.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsForecastingEvaluationMetricsQuantileMetricsEntry

Entry for the Quantiles loss type optimization objective.

Fields
observedQuantile	number (double format) This is a custom metric that calculates the percentage of true values that were less than the predicted value for that quantile. Only populated when optimization_objective is minimize-quantile-loss and each entry corresponds to an entry in quantiles The percent value can be used to compare with the quantile value, which is the target value.
quantile	number (double format) The quantile for this entry.
scaledPinballLoss	number (float format) The scaled pinball loss of this quantile.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsGeneralTextGenerationEvaluationMetrics

(No description provided)

Fields
bleu	number (float format) BLEU (bilingual evaluation understudy) scores based on sacrebleu implementation.
rougeLSum	number (float format) ROUGE-L (Longest Common Subsequence) scoring at summary level.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsImageObjectDetectionEvaluationMetrics

Metrics for image object detection evaluation results.

Fields
boundingBoxMeanAveragePrecision	number (float format) The single metric for bounding boxes evaluation: the meanAveragePrecision averaged over all boundingBoxMetricsEntries.
boundingBoxMetrics[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsBoundingBoxMetrics) The bounding boxes match metrics for each intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair.
evaluatedBoundingBoxCount	integer (int32 format) The total number of bounding boxes (i.e. summed over all images) the ground truth used to create this evaluation had.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsImageSegmentationEvaluationMetrics

Metrics for image segmentation evaluation results.

Fields
confidenceMetricsEntries[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsImageSegmentationEvaluationMetricsConfidenceMetricsEntry) Metrics for each confidenceThreshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 Precision-recall curve can be derived from it.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsImageSegmentationEvaluationMetricsConfidenceMetricsEntry

(No description provided)

Fields
confidenceThreshold	number (float format) Metrics are computed with an assumption that the model never returns predictions with score lower than this value.
confusionMatrix	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsConfusionMatrix) Confusion matrix for the given confidence threshold.
diceScoreCoefficient	number (float format) DSC or the F1 score, The harmonic mean of recall and precision.
iouScore	number (float format) The intersection-over-union score. The measure of overlap of the annotation's category mask with ground truth category mask on the DataItem.
precision	number (float format) Precision for the given confidence threshold.
recall	number (float format) Recall (True Positive Rate) for the given confidence threshold.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsPairwiseTextGenerationEvaluationMetrics

Metrics for general pairwise text generation evaluation results.

Fields
accuracy	number (float format) Fraction of cases where the autorater agreed with the human raters.
baselineModelWinRate	number (float format) Percentage of time the autorater decided the baseline model had the better response.
cohensKappa	number (float format) A measurement of agreement between the autorater and human raters that takes the likelihood of random agreement into account.
f1Score	number (float format) Harmonic mean of precision and recall.
falseNegativeCount	string (int64 format) Number of examples where the autorater chose the baseline model, but humans preferred the model.
falsePositiveCount	string (int64 format) Number of examples where the autorater chose the model, but humans preferred the baseline model.
humanPreferenceBaselineModelWinRate	number (float format) Percentage of time humans decided the baseline model had the better response.
humanPreferenceModelWinRate	number (float format) Percentage of time humans decided the model had the better response.
modelWinRate	number (float format) Percentage of time the autorater decided the model had the better response.
precision	number (float format) Fraction of cases where the autorater and humans thought the model had a better response out of all cases where the autorater thought the model had a better response. True positive divided by all positive.
recall	number (float format) Fraction of cases where the autorater and humans thought the model had a better response out of all cases where the humans thought the model had a better response.
trueNegativeCount	string (int64 format) Number of examples where both the autorater and humans decided that the model had the worse response.
truePositiveCount	string (int64 format) Number of examples where both the autorater and humans decided that the model had the better response.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsQuestionAnsweringEvaluationMetrics

(No description provided)

Fields
exactMatch	number (float format) The rate at which the input predicted strings exactly match their references.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsRegressionEvaluationMetrics

Metrics for regression evaluation results.

Fields
meanAbsoluteError	number (float format) Mean Absolute Error (MAE).
meanAbsolutePercentageError	number (float format) Mean absolute percentage error. Infinity when there are zeros in the ground truth.
rSquared	number (float format) Coefficient of determination as Pearson correlation coefficient. Undefined when ground truth or predictions are constant or near constant.
rootMeanSquaredError	number (float format) Root Mean Squared Error (RMSE).
rootMeanSquaredLogError	number (float format) Root mean squared log error. Undefined when there are negative ground truth values or predictions.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsSummarizationEvaluationMetrics

(No description provided)

Fields
rougeLSum	number (float format) ROUGE-L (Longest Common Subsequence) scoring at summary level.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsTextExtractionEvaluationMetrics

Metrics for text extraction evaluation results.

Fields
confidenceMetrics[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsTextExtractionEvaluationMetricsConfidenceMetrics) Metrics that have confidence thresholds. Precision-recall curve can be derived from them.
confusionMatrix	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsConfusionMatrix) Confusion matrix of the evaluation. Only set for Models where number of AnnotationSpecs is no more than 10. Only set for ModelEvaluations, not for ModelEvaluationSlices.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsTextExtractionEvaluationMetricsConfidenceMetrics

(No description provided)

Fields
confidenceThreshold	number (float format) Metrics are computed with an assumption that the Model never returns predictions with score lower than this value.
f1Score	number (float format) The harmonic mean of recall and precision.
precision	number (float format) Precision for the given confidence threshold.
recall	number (float format) Recall (True Positive Rate) for the given confidence threshold.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsTextSentimentEvaluationMetrics

Model evaluation metrics for text sentiment problems.

Fields
confusionMatrix	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsConfusionMatrix) Confusion matrix of the evaluation. Only set for ModelEvaluations, not for ModelEvaluationSlices.
f1Score	number (float format) The harmonic mean of recall and precision.
linearKappa	number (float format) Linear weighted kappa. Only set for ModelEvaluations, not for ModelEvaluationSlices.
meanAbsoluteError	number (float format) Mean absolute error. Only set for ModelEvaluations, not for ModelEvaluationSlices.
meanSquaredError	number (float format) Mean squared error. Only set for ModelEvaluations, not for ModelEvaluationSlices.
precision	number (float format) Precision.
quadraticKappa	number (float format) Quadratic weighted kappa. Only set for ModelEvaluations, not for ModelEvaluationSlices.
recall	number (float format) Recall.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsTrackMetrics

UNIMPLEMENTED. Track matching model metrics for a single track match threshold and multiple label match confidence thresholds.

Fields
confidenceMetrics[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsTrackMetricsConfidenceMetrics) Metrics for each label-match confidenceThreshold from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is derived from them.
iouThreshold	number (float format) The intersection-over-union threshold value between bounding boxes across frames used to compute this metric entry.
meanBoundingBoxIou	number (float format) The mean bounding box iou over all confidence thresholds.
meanMismatchRate	number (float format) The mean mismatch rate over all confidence thresholds.
meanTrackingAveragePrecision	number (float format) The mean average precision over all confidence thresholds.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsTrackMetricsConfidenceMetrics

Metrics for a single confidence threshold.

Fields
boundingBoxIou	number (float format) Bounding box intersection-over-union precision. Measures how well the bounding boxes overlap between each other (e.g. complete overlap or just barely above iou_threshold).
confidenceThreshold	number (float format) The confidence threshold value used to compute the metrics.
mismatchRate	number (float format) Mismatch rate, which measures the tracking consistency, i.e. correctness of instance ID continuity.
trackingPrecision	number (float format) Tracking precision.
trackingRecall	number (float format) Tracking recall.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsVideoActionMetrics

The Evaluation metrics given a specific precision_window_length.

Fields
confidenceMetrics[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsVideoActionMetricsConfidenceMetrics) Metrics for each label-match confidence_threshold from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99.
meanAveragePrecision	number (float format) The mean average precision.
precisionWindowLength	string (Duration format) This VideoActionMetrics is calculated based on this prediction window length. If the predicted action's timestamp is inside the time window whose center is the ground truth action's timestamp with this specific length, the prediction result is treated as a true positive.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsVideoActionMetricsConfidenceMetrics

Metrics for a single confidence threshold.

Fields
confidenceThreshold	number (float format) Output only. The confidence threshold value used to compute the metrics.
f1Score	number (float format) Output only. The harmonic mean of recall and precision.
precision	number (float format) Output only. Precision for the given confidence threshold.
recall	number (float format) Output only. Recall for the given confidence threshold.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsVideoActionRecognitionMetrics

Model evaluation metrics for video action recognition.

Fields
evaluatedActionCount	integer (int32 format) The number of ground truth actions used to create this evaluation.
videoActionMetrics[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsVideoActionMetrics) The metric entries for precision window lengths: 1s,2s,3s.

GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsVideoObjectTrackingMetrics

Model evaluation metrics for video object tracking problems. Evaluates prediction quality of both labeled bounding boxes and labeled tracks (i.e. series of bounding boxes sharing same label and instance ID).

Fields
boundingBoxMeanAveragePrecision	number (float format) The single metric for bounding boxes evaluation: the meanAveragePrecision averaged over all boundingBoxMetrics.
boundingBoxMetrics[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsBoundingBoxMetrics) The bounding boxes match metrics for each intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair.
evaluatedBoundingBoxCount	integer (int32 format) UNIMPLEMENTED. The total number of bounding boxes (i.e. summed over all frames) the ground truth used to create this evaluation had.
evaluatedFrameCount	integer (int32 format) UNIMPLEMENTED. The number of video frames used to create this evaluation.
evaluatedTrackCount	integer (int32 format) UNIMPLEMENTED. The total number of tracks (i.e. as seen across all frames) the ground truth used to create this evaluation had.
trackMeanAveragePrecision	number (float format) UNIMPLEMENTED. The single metric for tracks accuracy evaluation: the meanAveragePrecision averaged over all trackMetrics.
trackMeanBoundingBoxIou	number (float format) UNIMPLEMENTED. The single metric for tracks bounding box iou evaluation: the meanBoundingBoxIou averaged over all trackMetrics.
trackMeanMismatchRate	number (float format) UNIMPLEMENTED. The single metric for tracking consistency evaluation: the meanMismatchRate averaged over all trackMetrics.
trackMetrics[]	object (GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsTrackMetrics) UNIMPLEMENTED. The tracks match metrics for each intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair.

GoogleCloudAiplatformV1beta1SchemaPredictInstanceImageClassificationPredictionInstance

Prediction input format for Image Classification.

Fields
content	string The image bytes or Cloud Storage URI to make the prediction on.
mimeType	string The MIME type of the content of the image. Only the images in below listed MIME types are supported. - image/jpeg - image/gif - image/png - image/webp - image/bmp - image/tiff - image/vnd.microsoft.icon

GoogleCloudAiplatformV1beta1SchemaPredictInstanceImageObjectDetectionPredictionInstance

Prediction input format for Image Object Detection.

Fields
content	string The image bytes or Cloud Storage URI to make the prediction on.
mimeType	string The MIME type of the content of the image. Only the images in below listed MIME types are supported. - image/jpeg - image/gif - image/png - image/webp - image/bmp - image/tiff - image/vnd.microsoft.icon

GoogleCloudAiplatformV1beta1SchemaPredictInstanceImageSegmentationPredictionInstance

Prediction input format for Image Segmentation.

Fields
content	string The image bytes to make the predictions on.
mimeType	string The MIME type of the content of the image. Only the images in below listed MIME types are supported. - image/jpeg - image/png

GoogleCloudAiplatformV1beta1SchemaPredictInstanceTextClassificationPredictionInstance

Prediction input format for Text Classification.

Fields
content	string The text snippet to make the predictions on.
mimeType	string The MIME type of the text snippet. The supported MIME types are listed below. - text/plain

GoogleCloudAiplatformV1beta1SchemaPredictInstanceTextExtractionPredictionInstance

Prediction input format for Text Extraction.

Fields
content	string The text snippet to make the predictions on.
key	string This field is only used for batch prediction. If a key is provided, the batch prediction result will by mapped to this key. If omitted, then the batch prediction result will contain the entire input instance. Vertex AI will not check if keys in the request are duplicates, so it is up to the caller to ensure the keys are unique.
mimeType	string The MIME type of the text snippet. The supported MIME types are listed below. - text/plain

GoogleCloudAiplatformV1beta1SchemaPredictInstanceTextSentimentPredictionInstance

Prediction input format for Text Sentiment.

Fields
content	string The text snippet to make the predictions on.
mimeType	string The MIME type of the text snippet. The supported MIME types are listed below. - text/plain

GoogleCloudAiplatformV1beta1SchemaPredictInstanceVideoActionRecognitionPredictionInstance

Prediction input format for Video Action Recognition.

Fields
content	string The Google Cloud Storage location of the video on which to perform the prediction.
mimeType	string The MIME type of the content of the video. Only the following are supported: video/mp4 video/avi video/quicktime
timeSegmentEnd	string The end, exclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision, and "inf" or "Infinity" is allowed, which means the end of the video.
timeSegmentStart	string The beginning, inclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision.

GoogleCloudAiplatformV1beta1SchemaPredictInstanceVideoClassificationPredictionInstance

Prediction input format for Video Classification.

Fields
content	string The Google Cloud Storage location of the video on which to perform the prediction.
mimeType	string The MIME type of the content of the video. Only the following are supported: video/mp4 video/avi video/quicktime
timeSegmentEnd	string The end, exclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision, and "inf" or "Infinity" is allowed, which means the end of the video.
timeSegmentStart	string The beginning, inclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision.

GoogleCloudAiplatformV1beta1SchemaPredictInstanceVideoObjectTrackingPredictionInstance

Prediction input format for Video Object Tracking.

Fields
content	string The Google Cloud Storage location of the video on which to perform the prediction.
mimeType	string The MIME type of the content of the video. Only the following are supported: video/mp4 video/avi video/quicktime
timeSegmentEnd	string The end, exclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision, and "inf" or "Infinity" is allowed, which means the end of the video.
timeSegmentStart	string The beginning, inclusive, of the video's time segment on which to perform the prediction. Expressed as a number of seconds as measured from the start of the video, with "s" appended at the end. Fractions are allowed, up to a microsecond precision.

GoogleCloudAiplatformV1beta1SchemaPredictParamsImageClassificationPredictionParams

Prediction model parameters for Image Classification.

Fields
confidenceThreshold	number (float format) The Model only returns predictions with at least this confidence score. Default value is 0.0
maxPredictions	integer (int32 format) The Model only returns up to that many top, by confidence score, predictions per instance. If this number is very high, the Model may return fewer predictions. Default value is 10.

GoogleCloudAiplatformV1beta1SchemaPredictParamsImageObjectDetectionPredictionParams

Prediction model parameters for Image Object Detection.

Fields
confidenceThreshold	number (float format) The Model only returns predictions with at least this confidence score. Default value is 0.0
maxPredictions	integer (int32 format) The Model only returns up to that many top, by confidence score, predictions per instance. Note that number of returned predictions is also limited by metadata's predictionsLimit. Default value is 10.

GoogleCloudAiplatformV1beta1SchemaPredictParamsImageSegmentationPredictionParams

Prediction model parameters for Image Segmentation.

Fields
confidenceThreshold	number (float format) When the model predicts category of pixels of the image, it will only provide predictions for pixels that it is at least this much confident about. All other pixels will be classified as background. Default value is 0.5.

GoogleCloudAiplatformV1beta1SchemaPredictParamsVideoActionRecognitionPredictionParams

Prediction model parameters for Video Action Recognition.

Fields
confidenceThreshold	number (float format) The Model only returns predictions with at least this confidence score. Default value is 0.0
maxPredictions	integer (int32 format) The model only returns up to that many top, by confidence score, predictions per frame of the video. If this number is very high, the Model may return fewer predictions per frame. Default value is 50.

GoogleCloudAiplatformV1beta1SchemaPredictParamsVideoClassificationPredictionParams

Prediction model parameters for Video Classification.

Fields
confidenceThreshold	number (float format) The Model only returns predictions with at least this confidence score. Default value is 0.0
maxPredictions	integer (int32 format) The Model only returns up to that many top, by confidence score, predictions per instance. If this number is very high, the Model may return fewer predictions. Default value is 10,000.
oneSecIntervalClassification	boolean Set to true to request classification for a video at one-second intervals. Vertex AI returns labels and their confidence scores for each second of the entire time segment of the video that user specified in the input WARNING: Model evaluation is not done for this classification type, the quality of it depends on the training data, but there are no metrics provided to describe that quality. Default value is false
segmentClassification	boolean Set to true to request segment-level classification. Vertex AI returns labels and their confidence scores for the entire time segment of the video that user specified in the input instance. Default value is true
shotClassification	boolean Set to true to request shot-level classification. Vertex AI determines the boundaries for each camera shot in the entire time segment of the video that user specified in the input instance. Vertex AI then returns labels and their confidence scores for each detected shot, along with the start and end time of the shot. WARNING: Model evaluation is not done for this classification type, the quality of it depends on the training data, but there are no metrics provided to describe that quality. Default value is false

GoogleCloudAiplatformV1beta1SchemaPredictParamsVideoObjectTrackingPredictionParams

Prediction model parameters for Video Object Tracking.

Fields
confidenceThreshold	number (float format) The Model only returns predictions with at least this confidence score. Default value is 0.0
maxPredictions	integer (int32 format) The model only returns up to that many top, by confidence score, predictions per frame of the video. If this number is very high, the Model may return fewer predictions per frame. Default value is 50.
minBoundingBoxSize	number (float format) Only bounding boxes with shortest edge at least that long as a relative value of video frame size are returned. Default value is 0.0.

GoogleCloudAiplatformV1beta1SchemaPredictPredictionClassificationPredictionResult

Prediction output format for Image and Text Classification.

Fields
confidences[]	number (float format) The Model's confidences in correctness of the predicted IDs, higher value means higher confidence. Order matches the Ids.
displayNames[]	string The display names of the AnnotationSpecs that had been identified, order matches the IDs.
ids[]	string (int64 format) The resource IDs of the AnnotationSpecs that had been identified.

GoogleCloudAiplatformV1beta1SchemaPredictPredictionImageObjectDetectionPredictionResult

Prediction output format for Image Object Detection.

Fields
bboxes[]	array Bounding boxes, i.e. the rectangles over the image, that pinpoint the found AnnotationSpecs. Given in order that matches the IDs. Each bounding box is an array of 4 numbers xMin, xMax, yMin, and yMax, which represent the extremal coordinates of the box. They are relative to the image size, and the point 0,0 is in the top left of the image.
confidences[]	number (float format) The Model's confidences in correctness of the predicted IDs, higher value means higher confidence. Order matches the Ids.
displayNames[]	string The display names of the AnnotationSpecs that had been identified, order matches the IDs.
ids[]	string (int64 format) The resource IDs of the AnnotationSpecs that had been identified, ordered by the confidence score descendingly.

GoogleCloudAiplatformV1beta1SchemaPredictPredictionImageSegmentationPredictionResult

Prediction output format for Image Segmentation.

Fields
categoryMask	string A PNG image where each pixel in the mask represents the category in which the pixel in the original image was predicted to belong to. The size of this image will be the same as the original image. The mapping between the AnntoationSpec and the color can be found in model's metadata. The model will choose the most likely category and if none of the categories reach the confidence threshold, the pixel will be marked as background.
confidenceMask	string A one channel image which is encoded as an 8bit lossless PNG. The size of the image will be the same as the original image. For a specific pixel, darker color means less confidence in correctness of the cateogry in the categoryMask for the corresponding pixel. Black means no confidence and white means complete confidence.

GoogleCloudAiplatformV1beta1SchemaPredictPredictionTabularClassificationPredictionResult

Prediction output format for Tabular Classification.

Fields
classes[]	string The name of the classes being classified, contains all possible values of the target column.
scores[]	number (float format) The model's confidence in each class being correct, higher value means higher confidence. The N-th score corresponds to the N-th class in classes.

GoogleCloudAiplatformV1beta1SchemaPredictPredictionTabularRegressionPredictionResult

Prediction output format for Tabular Regression.

Fields
lowerBound	number (float format) The lower bound of the prediction interval.
quantilePredictions[]	number (float format) Quantile predictions, in 1-1 correspondence with quantile_values.
quantileValues[]	number (float format) Quantile values.
upperBound	number (float format) The upper bound of the prediction interval.
value	number (float format) The regression value.

GoogleCloudAiplatformV1beta1SchemaPredictPredictionTextExtractionPredictionResult

Prediction output format for Text Extraction.

Fields
confidences[]	number (float format) The Model's confidences in correctness of the predicted IDs, higher value means higher confidence. Order matches the Ids.
displayNames[]	string The display names of the AnnotationSpecs that had been identified, order matches the IDs.
ids[]	string (int64 format) The resource IDs of the AnnotationSpecs that had been identified, ordered by the confidence score descendingly.
textSegmentEndOffsets[]	string (int64 format) The end offsets, inclusive, of the text segment in which the AnnotationSpec has been identified. Expressed as a zero-based number of characters as measured from the start of the text snippet.
textSegmentStartOffsets[]	string (int64 format) The start offsets, inclusive, of the text segment in which the AnnotationSpec has been identified. Expressed as a zero-based number of characters as measured from the start of the text snippet.

GoogleCloudAiplatformV1beta1SchemaPredictPredictionTextSentimentPredictionResult

Prediction output format for Text Sentiment

Fields
sentiment	integer (int32 format) The integer sentiment labels between 0 (inclusive) and sentimentMax label (inclusive), while 0 maps to the least positive sentiment and sentimentMax maps to the most positive one. The higher the score is, the more positive the sentiment in the text snippet is. Note: sentimentMax is an integer value between 1 (inclusive) and 10 (inclusive).

GoogleCloudAiplatformV1beta1SchemaPredictPredictionTftFeatureImportance

(No description provided)

Fields
attributeColumns[]	string (No description provided)
attributeWeights[]	number (float format) (No description provided)
contextColumns[]	string (No description provided)
contextWeights[]	number (float format) TFT feature importance values. Each pair for {context/horizon/attribute} should have the same shape since the weight corresponds to the column names.
horizonColumns[]	string (No description provided)
horizonWeights[]	number (float format) (No description provided)

GoogleCloudAiplatformV1beta1SchemaPredictPredictionTimeSeriesForecastingPredictionResult

Prediction output format for Time Series Forecasting.

Fields
quantilePredictions[]	number (float format) Quantile predictions, in 1-1 correspondence with quantile_values.
quantileValues[]	number (float format) Quantile values.
tftFeatureImportance	object (GoogleCloudAiplatformV1beta1SchemaPredictPredictionTftFeatureImportance) Only use these if TFt is enabled.
value	number (float format) The regression value.

GoogleCloudAiplatformV1beta1SchemaPredictPredictionVideoActionRecognitionPredictionResult

Prediction output format for Video Action Recognition.

Fields
confidence	number (float format) The Model's confidence in correction of this prediction, higher value means higher confidence.
displayName	string The display name of the AnnotationSpec that had been identified.
id	string The resource ID of the AnnotationSpec that had been identified.
timeSegmentEnd	string (Duration format) The end, exclusive, of the video's time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end.
timeSegmentStart	string (Duration format) The beginning, inclusive, of the video's time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end.

GoogleCloudAiplatformV1beta1SchemaPredictPredictionVideoClassificationPredictionResult

Prediction output format for Video Classification.

Fields
confidence	number (float format) The Model's confidence in correction of this prediction, higher value means higher confidence.
displayName	string The display name of the AnnotationSpec that had been identified.
id	string The resource ID of the AnnotationSpec that had been identified.
timeSegmentEnd	string (Duration format) The end, exclusive, of the video's time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end. Note that for 'segment-classification' prediction type, this equals the original 'timeSegmentEnd' from the input instance, for other types it is the end of a shot or a 1 second interval respectively.
timeSegmentStart	string (Duration format) The beginning, inclusive, of the video's time segment in which the AnnotationSpec has been identified. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end. Note that for 'segment-classification' prediction type, this equals the original 'timeSegmentStart' from the input instance, for other types it is the start of a shot or a 1 second interval respectively.
type	string The type of the prediction. The requested types can be configured via parameters. This will be one of - segment-classification - shot-classification - one-sec-interval-classification

GoogleCloudAiplatformV1beta1SchemaPredictPredictionVideoObjectTrackingPredictionResult

Prediction output format for Video Object Tracking.

Fields
confidence	number (float format) The Model's confidence in correction of this prediction, higher value means higher confidence.
displayName	string The display name of the AnnotationSpec that had been identified.
frames[]	object (GoogleCloudAiplatformV1beta1SchemaPredictPredictionVideoObjectTrackingPredictionResultFrame) All of the frames of the video in which a single object instance has been detected. The bounding boxes in the frames identify the same object.
id	string The resource ID of the AnnotationSpec that had been identified.
timeSegmentEnd	string (Duration format) The end, inclusive, of the video's time segment in which the object instance has been detected. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end.
timeSegmentStart	string (Duration format) The beginning, inclusive, of the video's time segment in which the object instance has been detected. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end.

GoogleCloudAiplatformV1beta1SchemaPredictPredictionVideoObjectTrackingPredictionResultFrame

The fields xMin, xMax, yMin, and yMax refer to a bounding box, i.e. the rectangle over the video frame pinpointing the found AnnotationSpec. The coordinates are relative to the frame size, and the point 0,0 is in the top left of the frame.

Fields
timeOffset	string (Duration format) A time (frame) of a video in which the object has been detected. Expressed as a number of seconds as measured from the start of the video, with fractions up to a microsecond precision, and with "s" appended at the end.
xMax	number (float format) The rightmost coordinate of the bounding box.
xMin	number (float format) The leftmost coordinate of the bounding box.
yMax	number (float format) The bottommost coordinate of the bounding box.
yMin	number (float format) The topmost coordinate of the bounding box.

GoogleCloudAiplatformV1beta1SchemaPredictionResult

Represents a line of JSONL in the batch prediction output file.

Fields
error	object (GoogleCloudAiplatformV1beta1SchemaPredictionResultError) The error result. Do not set prediction if this is set.
instance	map (key: string, value: any) User's input instance. Struct is used here instead of Any so that JsonFormat does not append an extra "@type" field when we convert the proto to JSON.
key	string Optional user-provided key from the input instance.
prediction	any The prediction result. Value is used here instead of Any so that JsonFormat does not append an extra "@type" field when we convert the proto to JSON and so we can represent array of objects. Do not set error if this is set.

GoogleCloudAiplatformV1beta1SchemaPredictionResultError

(No description provided)

Fields
message	string Error message with additional details.
status	enum Error status. This will be serialized into the enum name e.g. "NOT_FOUND".
Enum type. Can be one of the following:
OK	Not an error; returned on success. HTTP Mapping: 200 OK
CANCELLED	The operation was cancelled, typically by the caller. HTTP Mapping: 499 Client Closed Request
UNKNOWN	Unknown error. For example, this error may be returned when a Status value received from another address space belongs to an error space that is not known in this address space. Also errors raised by APIs that do not return enough error information may be converted to this error. HTTP Mapping: 500 Internal Server Error
INVALID_ARGUMENT	The client specified an invalid argument. Note that this differs from FAILED_PRECONDITION. INVALID_ARGUMENT indicates arguments that are problematic regardless of the state of the system (e.g., a malformed file name). HTTP Mapping: 400 Bad Request
DEADLINE_EXCEEDED	The deadline expired before the operation could complete. For operations that change the state of the system, this error may be returned even if the operation has completed successfully. For example, a successful response from a server could have been delayed long enough for the deadline to expire. HTTP Mapping: 504 Gateway Timeout
NOT_FOUND	Some requested entity (e.g., file or directory) was not found. Note to server developers: if a request is denied for an entire class of users, such as gradual feature rollout or undocumented allowlist, NOT_FOUND may be used. If a request is denied for some users within a class of users, such as user-based access control, PERMISSION_DENIED must be used. HTTP Mapping: 404 Not Found
ALREADY_EXISTS	The entity that a client attempted to create (e.g., file or directory) already exists. HTTP Mapping: 409 Conflict
PERMISSION_DENIED	The caller does not have permission to execute the specified operation. PERMISSION_DENIED must not be used for rejections caused by exhausting some resource (use RESOURCE_EXHAUSTED instead for those errors). PERMISSION_DENIED must not be used if the caller can not be identified (use UNAUTHENTICATED instead for those errors). This error code does not imply the request is valid or the requested entity exists or satisfies other pre-conditions. HTTP Mapping: 403 Forbidden
UNAUTHENTICATED	The request does not have valid authentication credentials for the operation. HTTP Mapping: 401 Unauthorized
RESOURCE_EXHAUSTED	Some resource has been exhausted, perhaps a per-user quota, or perhaps the entire file system is out of space. HTTP Mapping: 429 Too Many Requests
FAILED_PRECONDITION	The operation was rejected because the system is not in a state required for the operation's execution. For example, the directory to be deleted is non-empty, an rmdir operation is applied to a non-directory, etc. Service implementors can use the following guidelines to decide between FAILED_PRECONDITION, ABORTED, and UNAVAILABLE: (a) Use UNAVAILABLE if the client can retry just the failing call. (b) Use ABORTED if the client should retry at a higher level. For example, when a client-specified test-and-set fails, indicating the client should restart a read-modify-write sequence. (c) Use FAILED_PRECONDITION if the client should not retry until the system state has been explicitly fixed. For example, if an "rmdir" fails because the directory is non-empty, FAILED_PRECONDITION should be returned since the client should not retry unless the files are deleted from the directory. HTTP Mapping: 400 Bad Request
ABORTED	The operation was aborted, typically due to a concurrency issue such as a sequencer check failure or transaction abort. See the guidelines above for deciding between FAILED_PRECONDITION, ABORTED, and UNAVAILABLE. HTTP Mapping: 409 Conflict
OUT_OF_RANGE	The operation was attempted past the valid range. E.g., seeking or reading past end-of-file. Unlike INVALID_ARGUMENT, this error indicates a problem that may be fixed if the system state changes. For example, a 32-bit file system will generate INVALID_ARGUMENT if asked to read at an offset that is not in the range [0,2^32-1], but it will generate OUT_OF_RANGE if asked to read from an offset past the current file size. There is a fair bit of overlap between FAILED_PRECONDITION and OUT_OF_RANGE. We recommend using OUT_OF_RANGE (the more specific error) when it applies so that callers who are iterating through a space can easily look for an OUT_OF_RANGE error to detect when they are done. HTTP Mapping: 400 Bad Request
UNIMPLEMENTED	The operation is not implemented or is not supported/enabled in this service. HTTP Mapping: 501 Not Implemented
INTERNAL	Internal errors. This means that some invariants expected by the underlying system have been broken. This error code is reserved for serious errors. HTTP Mapping: 500 Internal Server Error
UNAVAILABLE	The service is currently unavailable. This is most likely a transient condition, which can be corrected by retrying with a backoff. Note that it is not always safe to retry non-idempotent operations. See the guidelines above for deciding between FAILED_PRECONDITION, ABORTED, and UNAVAILABLE. HTTP Mapping: 503 Service Unavailable
DATA_LOSS	Unrecoverable data loss or corruption. HTTP Mapping: 500 Internal Server Error

GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadata

The metadata of Datasets that contain tables data.

Fields
inputConfig	object (GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataInputConfig) (No description provided)

GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataBigQuerySource

(No description provided)

Fields
uri	string The URI of a BigQuery table. e.g. bq://projectId.bqDatasetId.bqTableId

GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataGcsSource

(No description provided)

Fields
uri[]	string Cloud Storage URI of one or more files. Only CSV files are supported. The first line of the CSV file is used as the header. If there are multiple files, the header is the first line of the lexicographically first file, the other files must either contain the exact same header or omit the header.

GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataInputConfig

The tables Dataset's data source. The Dataset doesn't store the data directly, but only pointer(s) to its data.

Fields
bigquerySource	object (GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataBigQuerySource) (No description provided)
gcsSource	object (GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataGcsSource) (No description provided)

GoogleCloudAiplatformV1beta1SchemaTextClassificationAnnotation

Annotation details specific to text classification.

Fields
annotationSpecId	string The resource Id of the AnnotationSpec that this Annotation pertains to.
displayName	string The display name of the AnnotationSpec that this Annotation pertains to.

GoogleCloudAiplatformV1beta1SchemaTextDataItem

Payload of Text DataItem.

Fields
gcsUri	string Output only. Google Cloud Storage URI points to the original text in user's bucket. The text file is up to 10MB in size.

GoogleCloudAiplatformV1beta1SchemaTextDatasetMetadata

The metadata of Datasets that contain Text DataItems.

Fields
dataItemSchemaUri	string Points to a YAML file stored on Google Cloud Storage describing payload of the Text DataItems that belong to this Dataset.
gcsBucket	string Google Cloud Storage Bucket name that contains the blob data of this Dataset.

GoogleCloudAiplatformV1beta1SchemaTextExtractionAnnotation

Annotation details specific to text extraction.

Fields
annotationSpecId	string The resource Id of the AnnotationSpec that this Annotation pertains to.
displayName	string The display name of the AnnotationSpec that this Annotation pertains to.
textSegment	object (GoogleCloudAiplatformV1beta1SchemaTextSegment) The segment of the text content.

GoogleCloudAiplatformV1beta1SchemaTextSegment

The text segment inside of DataItem.

Fields
content	string The text content in the segment for output only.
endOffset	string (uint64 format) Zero-based character index of the first character past the end of the text segment (counting character from the beginning of the text). The character at the end_offset is NOT included in the text segment.
startOffset	string (uint64 format) Zero-based character index of the first character of the text segment (counting characters from the beginning of the text).

GoogleCloudAiplatformV1beta1SchemaTextSentimentAnnotation

Annotation details specific to text sentiment.

Fields
annotationSpecId	string The resource Id of the AnnotationSpec that this Annotation pertains to.
displayName	string The display name of the AnnotationSpec that this Annotation pertains to.
sentiment	integer (int32 format) The sentiment score for text.
sentimentMax	integer (int32 format) The sentiment max score for text.

GoogleCloudAiplatformV1beta1SchemaTextSentimentSavedQueryMetadata

The metadata of SavedQuery contains TextSentiment Annotations.

Fields
sentimentMax	integer (int32 format) The maximum sentiment of sentiment Anntoation in this SavedQuery.

GoogleCloudAiplatformV1beta1SchemaTimeSegment

A time period inside of a DataItem that has a time dimension (e.g. video).

Fields
endTimeOffset	string (Duration format) End of the time segment (exclusive), represented as the duration since the start of the DataItem.
startTimeOffset	string (Duration format) Start of the time segment (inclusive), represented as the duration since the start of the DataItem.

GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadata

The metadata of Datasets that contain time series data.

Fields
inputConfig	object (GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataInputConfig) (No description provided)
timeColumn	string The column name of the time column that identifies time order in the time series.
timeSeriesIdentifierColumn	string The column name of the time series identifier column that identifies the time series.

GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataBigQuerySource

(No description provided)

Fields
uri	string The URI of a BigQuery table.

GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataGcsSource

(No description provided)

Fields
uri[]	string Cloud Storage URI of one or more files. Only CSV files are supported. The first line of the CSV file is used as the header. If there are multiple files, the header is the first line of the lexicographically first file, the other files must either contain the exact same header or omit the header.

GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataInputConfig

The time series Dataset's data source. The Dataset doesn't store the data directly, but only pointer(s) to its data.

Fields
bigquerySource	object (GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataBigQuerySource) (No description provided)
gcsSource	object (GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataGcsSource) (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecasting

A TrainingJob that trains and uploads an AutoML Forecasting Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputs) The input parameters of this TrainingJob.
metadata	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingMetadata) The metadata information.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputs

(No description provided)

Fields
additionalExperiments[]	string Additional experiment flags for the time series forcasting training.
availableAtForecastColumns[]	string Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.
contextWindow	string (int64 format) The amount of time into the past training and prediction data is used for model training and prediction respectively. Expressed in number of units defined by the data_granularity field.
dataGranularity	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsGranularity) Expected difference in time granularity between rows in the data.
enableProbabilisticInference	boolean If probabilistic inference is enabled, the model will fit a distribution that captures the uncertainty of a prediction. At inference time, the predictive distribution is used to make a point prediction that minimizes the optimization objective. For example, the mean of a predictive distribution is the point prediction that minimizes RMSE loss. If quantiles are specified, then the quantiles of the distribution are also returned. The optimization objective cannot be minimize-quantile-loss.
exportEvaluatedDataItemsConfig	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig) Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
forecastHorizon	string (int64 format) The amount of time into the future for which forecasted values for the target are returned. Expressed in number of units defined by the data_granularity field.
hierarchyConfig	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHierarchyConfig) Configuration that defines the hierarchical relationship of time series and parameters for hierarchical forecasting strategies.
holidayRegions[]	string The geographical region based on which the holiday effect is applied in modeling by adding holiday categorical array feature that include all holidays matching the date. This option only allowed when data_granularity is day. By default, holiday effect modeling is disabled. To turn it on, specify the holiday region using this option.
optimizationObjective	string Objective function the model is optimizing towards. The training process creates a model that optimizes the value of the objective function over the validation set. The supported optimization objectives: * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). * "minimize-mae" - Minimize mean-absolute error (MAE). * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE). * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE). * "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and mean-absolute-error (MAE). * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles defined in quantiles. * "minimize-mape" - Minimize the mean absolute percentage error.
quantiles[]	number (double format) Quantiles to use for minimize-quantile-loss optimization_objective, or for probabilistic inference. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Required if the value of optimization_objective is minimize-quantile-loss. Represents the percent quantiles to use for that objective. Quantiles must be unique.
targetColumn	string The name of the column that the Model is to predict values for. This column must be unavailable at forecast.
timeColumn	string The name of the column that identifies time order in the time series. This column must be available at forecast.
timeSeriesAttributeColumns[]	string Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.
timeSeriesIdentifierColumn	string The name of the column that identifies the time series.
trainBudgetMilliNodeHours	string (int64 format) Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
transformations[]	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation) Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
unavailableAtForecastColumns[]	string Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.
validationOptions	string Validation options for the data validation component. The available options are: * "fail-pipeline" - default, will validate against the validation and fail the pipeline if it fails. * "ignore-validation" - ignore the results of the validation and continue
weightColumn	string Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
windowConfig	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionWindowConfig) Config containing strategy for generating sliding windows.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsGranularity

A duration of time expressed in time granularity units.

Fields
quantity	string (int64 format) The number of granularity_units between data points in the training data. If granularity_unit is minute, can be 1, 5, 10, 15, or 30. For all other values of granularity_unit, must be 1.
unit	string The time granularity unit of this time period. The supported units are: * "minute" * "hour" * "day" * "week" * "month" * "year"

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation

(No description provided)

Fields
auto	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationAutoTransformation) (No description provided)
categorical	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalTransformation) (No description provided)
numeric	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericTransformation) (No description provided)
text	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextTransformation) (No description provided)
timestamp	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTimestampTransformation) (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationAutoTransformation

Training pipeline will infer the proper transformation based on the statistic of dataset.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalTransformation

Training pipeline will perform following transformation functions. * The categorical string as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index. * Categories that appear less than 5 times in the training dataset are treated as the "unknown" category. The "unknown" category gets its own special lookup index and resulting embedding.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericTransformation

Training pipeline will perform following transformation functions. * The value converted to float32. * The z_score of the value. * log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. * z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. * A boolean value that indicates whether the value is valid.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextTransformation

Training pipeline will perform following transformation functions. * The text as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTimestampTransformation

Training pipeline will perform following transformation functions. * Apply the transformation functions for Numerical columns. * Determine the year, month, day,and weekday. Treat each value from the timestamp as a Categorical column. * Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.

Fields
columnName	string (No description provided)
timeFormat	string The format in which that time field is expressed. The time_format must either be one of: * unix-seconds * unix-milliseconds * unix-microseconds * unix-nanoseconds (for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written in strftime syntax. If time_format is not set, then the default format is RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingMetadata

Model metadata specific to AutoML Forecasting.

Fields
evaluatedDataItemsBigqueryUri	string BigQuery destination uri for exported evaluated examples.
trainCostMilliNodeHours	string (int64 format) Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClassification

A TrainingJob that trains and uploads an AutoML Image Classification Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClassificationInputs) The input parameters of this TrainingJob.
metadata	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClassificationMetadata) The metadata information.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClassificationInputs

(No description provided)

Fields
baseModelId	string The ID of the base model. If it is specified, the new model will be trained based on the base model. Otherwise, the new model will be trained from scratch. The base model must be in the same Project and Location as the new Model to train, and have the same modelType.
budgetMilliNodeHours	string (int64 format) The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. For modelType cloud(default), the budget must be between 8,000 and 800,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time, considering 8 nodes are used. For model types mobile-tf-low-latency-1, mobile-tf-versatile-1, mobile-tf-high-accuracy-1, the training budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24,000 which represents one day in wall time on a single node that is used.
disableEarlyStopping	boolean Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Classification might stop training before the entire training budget has been used.
modelType	enum (No description provided)
Enum type. Can be one of the following:
MODEL_TYPE_UNSPECIFIED	Should not be set.
CLOUD	A Model best tailored to be used within Google Cloud, and which cannot be exported. Default.
CLOUD_1	A model type best tailored to be used within Google Cloud, which cannot be exported externally. Compared to the CLOUD model above, it is expected to have higher prediction accuracy.
MOBILE_TF_LOW_LATENCY_1	A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models.
MOBILE_TF_VERSATILE_1	A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device with afterwards.
MOBILE_TF_HIGH_ACCURACY_1	A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other mobile models.
EFFICIENTNET	EfficientNet model for Model Garden training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.
MAXVIT	MaxViT model for Model Garden training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.
VIT	ViT model for Model Garden training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.
COCA	CoCa model for Model Garden training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.
multiLabel	boolean If false, a single-label (multi-class) Model will be trained (i.e. assuming that for each image just up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e. assuming that for each image multiple annotations may be applicable).
tunableParameter	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutomlImageTrainingTunableParameter) Trainer type for Vision TrainRequest.
uptrainBaseModelId	string The ID of base model for upTraining. If it is specified, the new model will be upTrained based on the base model for upTraining. Otherwise, the new model will be trained from scratch. The base model for upTraining must be in the same Project and Location as the new Model to train, and have the same modelType.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClassificationMetadata

(No description provided)

Fields
costMilliNodeHours	string (int64 format) The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
successfulStopReason	enum For successful job completions, this is the reason why the job has finished.
Enum type. Can be one of the following:
SUCCESSFUL_STOP_REASON_UNSPECIFIED	Should not be set.
BUDGET_REACHED	The inputs.budgetMilliNodeHours had been reached.
MODEL_CONVERGED	Further training of the Model ceased to increase its quality, since it already has converged.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObjectDetection

A TrainingJob that trains and uploads an AutoML Image Object Detection Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionInputs) The input parameters of this TrainingJob.
metadata	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionMetadata) The metadata information

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionInputs

(No description provided)

Fields
budgetMilliNodeHours	string (int64 format) The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. For modelType cloud(default), the budget must be between 20,000 and 900,000 milli node hours, inclusive. The default value is 216,000 which represents one day in wall time, considering 9 nodes are used. For model types mobile-tf-low-latency-1, mobile-tf-versatile-1, mobile-tf-high-accuracy-1 the training budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24,000 which represents one day in wall time on a single node that is used.
disableEarlyStopping	boolean Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Object Detection might stop training before the entire training budget has been used.
modelType	enum (No description provided)
Enum type. Can be one of the following:
MODEL_TYPE_UNSPECIFIED	Should not be set.
CLOUD_HIGH_ACCURACY_1	A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to have a higher latency, but should also have a higher prediction quality than other cloud models.
CLOUD_LOW_LATENCY_1	A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to have a low latency, but may have lower prediction quality than other cloud models.
CLOUD_1	A model best tailored to be used within Google Cloud, and which cannot be exported. Compared to the CLOUD_HIGH_ACCURACY_1 and CLOUD_LOW_LATENCY_1 models above, it is expected to have higher prediction quality and lower latency.
MOBILE_TF_LOW_LATENCY_1	A model that, in addition to being available within Google Cloud can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models.
MOBILE_TF_VERSATILE_1	A model that, in addition to being available within Google Cloud can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards.
MOBILE_TF_HIGH_ACCURACY_1	A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other mobile models.
CLOUD_STREAMING_1	A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to best support predictions in streaming with lower latency and lower prediction quality than other cloud models.
SPINENET	SpineNet for Model Garden training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.
YOLO	YOLO for Model Garden training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.
tunableParameter	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutomlImageTrainingTunableParameter) Trainer type for Vision TrainRequest.
uptrainBaseModelId	string The ID of base model for upTraining. If it is specified, the new model will be upTrained based on the base model for upTraining. Otherwise, the new model will be trained from scratch. The base model for upTraining must be in the same Project and Location as the new Model to train, and have the same modelType.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionMetadata

(No description provided)

Fields
costMilliNodeHours	string (int64 format) The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
successfulStopReason	enum For successful job completions, this is the reason why the job has finished.
Enum type. Can be one of the following:
SUCCESSFUL_STOP_REASON_UNSPECIFIED	Should not be set.
BUDGET_REACHED	The inputs.budgetMilliNodeHours had been reached.
MODEL_CONVERGED	Further training of the Model ceased to increase its quality, since it already has converged.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegmentation

A TrainingJob that trains and uploads an AutoML Image Segmentation Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegmentationInputs) The input parameters of this TrainingJob.
metadata	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegmentationMetadata) The metadata information.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegmentationInputs

(No description provided)

Fields
baseModelId	string The ID of the base model. If it is specified, the new model will be trained based on the base model. Otherwise, the new model will be trained from scratch. The base model must be in the same Project and Location as the new Model to train, and have the same modelType.
budgetMilliNodeHours	string (int64 format) The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. Or actual_wall_clock_hours = train_budget_milli_node_hours / (number_of_nodes_involved * 1000) For modelType cloud-high-accuracy-1(default), the budget must be between 20,000 and 2,000,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time (1000 milli * 24 hours * 8 nodes).
modelType	enum (No description provided)
Enum type. Can be one of the following:
MODEL_TYPE_UNSPECIFIED	Should not be set.
CLOUD_HIGH_ACCURACY_1	A model to be used via prediction calls to uCAIP API. Expected to have a higher latency, but should also have a higher prediction quality than other models.
CLOUD_LOW_ACCURACY_1	A model to be used via prediction calls to uCAIP API. Expected to have a lower latency but relatively lower prediction quality.
MOBILE_TF_LOW_LATENCY_1	A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow model and used on a mobile or edge device afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegmentationMetadata

(No description provided)

Fields
costMilliNodeHours	string (int64 format) The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
successfulStopReason	enum For successful job completions, this is the reason why the job has finished.
Enum type. Can be one of the following:
SUCCESSFUL_STOP_REASON_UNSPECIFIED	Should not be set.
BUDGET_REACHED	The inputs.budgetMilliNodeHours had been reached.
MODEL_CONVERGED	Further training of the Model ceased to increase its quality, since it already has converged.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTables

A TrainingJob that trains and uploads an AutoML Tables Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputs) The input parameters of this TrainingJob.
metadata	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesMetadata) The metadata information.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputs

(No description provided)

Fields
additionalExperiments[]	string Additional experiment flags for the Tables training pipeline.
disableEarlyStopping	boolean Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.
exportEvaluatedDataItemsConfig	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig) Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
optimizationObjective	string Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set. The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used. classification (binary): "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC) curve. "minimize-log-loss" - Minimize log loss. "maximize-au-prc" - Maximize the area under the precision-recall curve. "maximize-precision-at-recall" - Maximize precision for a specified recall value. "maximize-recall-at-precision" - Maximize recall for a specified precision value. classification (multi-class): "minimize-log-loss" (default) - Minimize log loss. regression: "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
optimizationObjectivePrecisionValue	number (float format) Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
optimizationObjectiveRecallValue	number (float format) Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
predictionType	string The type of prediction the Model is to produce. "classification" - Predict one out of multiple target values is picked for each row. "regression" - Predict a value based on its relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings.
targetColumn	string The column name of the target column that the model is to predict.
trainBudgetMilliNodeHours	string (int64 format) Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
transformations[]	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation) Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
weightColumnName	string Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation

(No description provided)

Fields
auto	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationAutoTransformation) (No description provided)
categorical	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalTransformation) (No description provided)
numeric	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericTransformation) (No description provided)
repeatedCategorical	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalArrayTransformation) (No description provided)
repeatedNumeric	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericArrayTransformation) (No description provided)
repeatedText	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextArrayTransformation) (No description provided)
text	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextTransformation) (No description provided)
timestamp	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTimestampTransformation) (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationAutoTransformation

Training pipeline will infer the proper transformation based on the statistic of dataset.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalArrayTransformation

Treats the column as categorical array and performs following transformation functions. * For each element in the array, convert the category name to a dictionary lookup index and generate an embedding for each index. Combine the embedding of all elements into a single embedding using the mean. * Empty arrays treated as an embedding of zeroes.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalTransformation

Training pipeline will perform following transformation functions. * The categorical string as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index. * Categories that appear less than 5 times in the training dataset are treated as the "unknown" category. The "unknown" category gets its own special lookup index and resulting embedding.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericArrayTransformation

Treats the column as numerical array and performs following transformation functions. * All transformations for Numerical types applied to the average of the all elements. * The average of empty arrays is treated as zero.

Fields
columnName	string (No description provided)
invalidValuesAllowed	boolean If invalid values is allowed, the training pipeline will create a boolean feature that indicated whether the value is valid. Otherwise, the training pipeline will discard the input row from trainining data.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericTransformation

Training pipeline will perform following transformation functions. * The value converted to float32. * The z_score of the value. * log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. * z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. * A boolean value that indicates whether the value is valid.

Fields
columnName	string (No description provided)
invalidValuesAllowed	boolean If invalid values is allowed, the training pipeline will create a boolean feature that indicated whether the value is valid. Otherwise, the training pipeline will discard the input row from trainining data.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextArrayTransformation

Treats the column as text array and performs following transformation functions. * Concatenate all text values in the array into a single text value using a space (" ") as a delimiter, and then treat the result as a single text value. Apply the transformations for Text columns. * Empty arrays treated as an empty text.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextTransformation

Training pipeline will perform following transformation functions. * The text as is--no change to case, punctuation, spelling, tense, and so on. * Tokenize text to words. Convert each words to a dictionary lookup index and generate an embedding for each index. Combine the embedding of all elements into a single embedding using the mean. * Tokenization is based on unicode script boundaries. * Missing values get their own lookup index and resulting embedding. * Stop-words receive no special treatment and are not removed.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTimestampTransformation

Training pipeline will perform following transformation functions. * Apply the transformation functions for Numerical columns. * Determine the year, month, day,and weekday. Treat each value from the * timestamp as a Categorical column. * Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.

Fields
columnName	string (No description provided)
invalidValuesAllowed	boolean If invalid values is allowed, the training pipeline will create a boolean feature that indicated whether the value is valid. Otherwise, the training pipeline will discard the input row from trainining data.
timeFormat	string The format in which that time field is expressed. The time_format must either be one of: * unix-seconds * unix-milliseconds * unix-microseconds * unix-nanoseconds (for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written in strftime syntax. If time_format is not set, then the default format is RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesMetadata

Model metadata specific to AutoML Tables.

Fields
evaluatedDataItemsBigqueryUri	string BigQuery destination uri for exported evaluated examples.
trainCostMilliNodeHours	string (int64 format) Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextClassification

A TrainingJob that trains and uploads an AutoML Text Classification Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextClassificationInputs) The input parameters of this TrainingJob.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextClassificationInputs

(No description provided)

Fields
multiLabel	boolean (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextExtraction

A TrainingJob that trains and uploads an AutoML Text Extraction Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextExtractionInputs) The input parameters of this TrainingJob.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextSentiment

A TrainingJob that trains and uploads an AutoML Text Sentiment Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextSentimentInputs) The input parameters of this TrainingJob.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextSentimentInputs

(No description provided)

Fields

sentimentMax

integer (int32 format) A sentiment is expressed as an integer ordinal, where higher value means a more positive sentiment. The range of sentiments that will be used is between 0 and sentimentMax (inclusive on both ends), and all the values in the range must be represented in the dataset before a model can be created. Only the Annotations with this sentimentMax will be used for training. sentimentMax value must be between 1 and 10 (inclusive).

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoActionRecognition

A TrainingJob that trains and uploads an AutoML Video Action Recognition Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoActionRecognitionInputs) The input parameters of this TrainingJob.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoActionRecognitionInputs

(No description provided)

Fields
modelType	enum (No description provided)
Enum type. Can be one of the following:
MODEL_TYPE_UNSPECIFIED	Should not be set.
CLOUD	A model best tailored to be used within Google Cloud, and which c annot be exported. Default.
MOBILE_VERSATILE_1	A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards.
MOBILE_JETSON_VERSATILE_1	A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) to a Jetson device afterwards.
MOBILE_CORAL_VERSATILE_1	A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a Coral device afterwards.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoClassification

A TrainingJob that trains and uploads an AutoML Video Classification Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoClassificationInputs) The input parameters of this TrainingJob.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoClassificationInputs

(No description provided)

Fields
modelType	enum (No description provided)
Enum type. Can be one of the following:
MODEL_TYPE_UNSPECIFIED	Should not be set.
CLOUD	A model best tailored to be used within Google Cloud, and which cannot be exported. Default.
MOBILE_VERSATILE_1	A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards.
MOBILE_JETSON_VERSATILE_1	A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) to a Jetson device afterwards.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoObjectTracking

A TrainingJob that trains and uploads an AutoML Video ObjectTracking Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoObjectTrackingInputs) The input parameters of this TrainingJob.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoObjectTrackingInputs

(No description provided)

Fields
modelType	enum (No description provided)
Enum type. Can be one of the following:
MODEL_TYPE_UNSPECIFIED	Should not be set.
CLOUD	A model best tailored to be used within Google Cloud, and which c annot be exported. Default.
MOBILE_VERSATILE_1	A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards.
MOBILE_CORAL_VERSATILE_1	A versatile model that is meant to be exported (see ModelService.ExportModel) and used on a Google Coral device.
MOBILE_CORAL_LOW_LATENCY_1	A model that trades off quality for low latency, to be exported (see ModelService.ExportModel) and used on a Google Coral device.
MOBILE_JETSON_VERSATILE_1	A versatile model that is meant to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device.
MOBILE_JETSON_LOW_LATENCY_1	A model that trades off quality for low latency, to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutomlImageTrainingTunableParameter

A wrapper class which contains the tunable parameters in an AutoML Image training job.

Fields
checkpointName	string Optional. An unique name of pretrained model checkpoint provided in model garden, it will be mapped to a GCS location internally.
datasetConfig	map (key: string, value: string) Customizable dataset settings, used in the model_garden_trainer.
studySpec	object (GoogleCloudAiplatformV1beta1StudySpec) Optioinal. StudySpec of hyperparameter tuning job. Required for model_garden_trainer.
trainerConfig	map (key: string, value: string) Customizable trainer settings, used in the model_garden_trainer.
trainerType	enum (No description provided)
Enum type. Can be one of the following:
TRAINER_TYPE_UNSPECIFIED	Default value.
AUTOML_TRAINER	(No description provided)
MODEL_GARDEN_TRAINER	(No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionCustomJobMetadata

(No description provided)

Fields
backingCustomJob	string The resource name of the CustomJob that has been created to carry out this custom task.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionCustomTask

A TrainingJob that trains a custom code Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1CustomJobSpec) The input parameters of this CustomTask.
metadata	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionCustomJobMetadata) The metadata information.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig

Configuration for exporting test set predictions to a BigQuery table.

Fields
destinationBigqueryUri	string URI of desired destination BigQuery table. Expected format: bq://{project_id}:{dataset_id}:{table} If not specified, then results are exported to the following auto-created BigQuery table: {project_id}:export_evaluated_examples_{model_name}_{yyyy_MM_dd'T'HH_mm_ss_SSS'Z'}.evaluated_examples
overrideExistingTable	boolean If true and an export destination is specified, then the contents of the destination are overwritten. Otherwise, if the export destination already exists, then the export operation fails.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHierarchyConfig

Configuration that defines the hierarchical relationship of time series and parameters for hierarchical forecasting strategies.

Fields
groupColumns[]	string A list of time series attribute column names that define the time series hierarchy. Only one level of hierarchy is supported, ex. 'region' for a hierarchy of stores or 'department' for a hierarchy of products. If multiple columns are specified, time series will be grouped by their combined values, ex. ('blue', 'large') for 'color' and 'size', up to 5 columns are accepted. If no group columns are specified, all time series are considered to be part of the same group.
groupTemporalTotalWeight	number (double format) The weight of the loss for predictions aggregated over both the horizon and time series in the same hierarchy group.
groupTotalWeight	number (double format) The weight of the loss for predictions aggregated over time series in the same group.
temporalTotalWeight	number (double format) The weight of the loss for predictions aggregated over the horizon for a single time series.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterTuningJobMetadata

(No description provided)

Fields
backingHyperparameterTuningJob	string The resource name of the HyperparameterTuningJob that has been created to carry out this HyperparameterTuning task.
bestTrialBackingCustomJob	string The resource name of the CustomJob that has been created to run the best Trial of this HyperparameterTuning task.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterTuningJobSpec

(No description provided)

Fields
maxFailedTrialCount	integer (int32 format) The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
maxTrialCount	integer (int32 format) The desired total number of Trials.
parallelTrialCount	integer (int32 format) The desired number of Trials to run in parallel.
studySpec	object (GoogleCloudAiplatformV1beta1StudySpec) Study configuration of the HyperparameterTuningJob.
trialJobSpec	object (GoogleCloudAiplatformV1beta1CustomJobSpec) The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterTuningTask

A TrainingJob that tunes Hypererparameters of a custom code Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterTuningJobSpec) The input parameters of this HyperparameterTuningTask.
metadata	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterTuningJobMetadata) The metadata information.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecasting

A TrainingJob that trains and uploads an AutoML Forecasting Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputs) The input parameters of this TrainingJob.
metadata	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingMetadata) The metadata information.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputs

(No description provided)

Fields
additionalExperiments[]	string Additional experiment flags for the time series forcasting training.
availableAtForecastColumns[]	string Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.
contextWindow	string (int64 format) The amount of time into the past training and prediction data is used for model training and prediction respectively. Expressed in number of units defined by the data_granularity field.
dataGranularity	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsGranularity) Expected difference in time granularity between rows in the data.
exportEvaluatedDataItemsConfig	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig) Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
forecastHorizon	string (int64 format) The amount of time into the future for which forecasted values for the target are returned. Expressed in number of units defined by the data_granularity field.
hierarchyConfig	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHierarchyConfig) Configuration that defines the hierarchical relationship of time series and parameters for hierarchical forecasting strategies.
holidayRegions[]	string The geographical region based on which the holiday effect is applied in modeling by adding holiday categorical array feature that include all holidays matching the date. This option only allowed when data_granularity is day. By default, holiday effect modeling is disabled. To turn it on, specify the holiday region using this option.
optimizationObjective	string Objective function the model is optimizing towards. The training process creates a model that optimizes the value of the objective function over the validation set. The supported optimization objectives: * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). * "minimize-mae" - Minimize mean-absolute error (MAE). * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE). * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE). * "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and mean-absolute-error (MAE). * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles defined in quantiles. * "minimize-mape" - Minimize the mean absolute percentage error.
quantiles[]	number (double format) Quantiles to use for minimize-quantile-loss optimization_objective. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Required if the value of optimization_objective is minimize-quantile-loss. Represents the percent quantiles to use for that objective. Quantiles must be unique.
targetColumn	string The name of the column that the Model is to predict values for. This column must be unavailable at forecast.
timeColumn	string The name of the column that identifies time order in the time series. This column must be available at forecast.
timeSeriesAttributeColumns[]	string Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.
timeSeriesIdentifierColumn	string The name of the column that identifies the time series.
trainBudgetMilliNodeHours	string (int64 format) Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
transformations[]	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformation) Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
unavailableAtForecastColumns[]	string Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.
validationOptions	string Validation options for the data validation component. The available options are: * "fail-pipeline" - default, will validate against the validation and fail the pipeline if it fails. * "ignore-validation" - ignore the results of the validation and continue
weightColumn	string Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1. This column must be available at forecast.
windowConfig	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionWindowConfig) Config containing strategy for generating sliding windows.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsGranularity

A duration of time expressed in time granularity units.

Fields
quantity	string (int64 format) The number of granularity_units between data points in the training data. If granularity_unit is minute, can be 1, 5, 10, 15, or 30. For all other values of granularity_unit, must be 1.
unit	string The time granularity unit of this time period. The supported units are: * "minute" * "hour" * "day" * "week" * "month" * "year"

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformation

(No description provided)

Fields
auto	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationAutoTransformation) (No description provided)
categorical	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationCategoricalTransformation) (No description provided)
numeric	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationNumericTransformation) (No description provided)
text	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationTextTransformation) (No description provided)
timestamp	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationTimestampTransformation) (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationAutoTransformation

Training pipeline will infer the proper transformation based on the statistic of dataset.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationCategoricalTransformation

Training pipeline will perform following transformation functions. * The categorical string as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index. * Categories that appear less than 5 times in the training dataset are treated as the "unknown" category. The "unknown" category gets its own special lookup index and resulting embedding.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationNumericTransformation

Training pipeline will perform following transformation functions. * The value converted to float32. * The z_score of the value. * log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. * z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationTextTransformation

Training pipeline will perform following transformation functions. * The text as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingInputsTransformationTimestampTransformation

Training pipeline will perform following transformation functions. * Apply the transformation functions for Numerical columns. * Determine the year, month, day,and weekday. Treat each value from the timestamp as a Categorical column. * Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.

Fields
columnName	string (No description provided)
timeFormat	string The format in which that time field is expressed. The time_format must either be one of: * unix-seconds * unix-milliseconds * unix-microseconds * unix-nanoseconds (for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written in strftime syntax. If time_format is not set, then the default format is RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionSeq2SeqPlusForecastingMetadata

Model metadata specific to Seq2Seq Plus Forecasting.

Fields
evaluatedDataItemsBigqueryUri	string BigQuery destination uri for exported evaluated examples.
trainCostMilliNodeHours	string (int64 format) Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecasting

A TrainingJob that trains and uploads an AutoML Forecasting Model.

Fields
inputs	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputs) The input parameters of this TrainingJob.
metadata	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingMetadata) The metadata information.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputs

(No description provided)

Fields
additionalExperiments[]	string Additional experiment flags for the time series forcasting training.
availableAtForecastColumns[]	string Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.
contextWindow	string (int64 format) The amount of time into the past training and prediction data is used for model training and prediction respectively. Expressed in number of units defined by the data_granularity field.
dataGranularity	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsGranularity) Expected difference in time granularity between rows in the data.
exportEvaluatedDataItemsConfig	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig) Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
forecastHorizon	string (int64 format) The amount of time into the future for which forecasted values for the target are returned. Expressed in number of units defined by the data_granularity field.
hierarchyConfig	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHierarchyConfig) Configuration that defines the hierarchical relationship of time series and parameters for hierarchical forecasting strategies.
holidayRegions[]	string The geographical region based on which the holiday effect is applied in modeling by adding holiday categorical array feature that include all holidays matching the date. This option only allowed when data_granularity is day. By default, holiday effect modeling is disabled. To turn it on, specify the holiday region using this option.
optimizationObjective	string Objective function the model is optimizing towards. The training process creates a model that optimizes the value of the objective function over the validation set. The supported optimization objectives: * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). * "minimize-mae" - Minimize mean-absolute error (MAE). * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE). * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE). * "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and mean-absolute-error (MAE). * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles defined in quantiles. * "minimize-mape" - Minimize the mean absolute percentage error.
quantiles[]	number (double format) Quantiles to use for minimize-quantile-loss optimization_objective. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Required if the value of optimization_objective is minimize-quantile-loss. Represents the percent quantiles to use for that objective. Quantiles must be unique.
targetColumn	string The name of the column that the Model is to predict values for. This column must be unavailable at forecast.
timeColumn	string The name of the column that identifies time order in the time series. This column must be available at forecast.
timeSeriesAttributeColumns[]	string Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.
timeSeriesIdentifierColumn	string The name of the column that identifies the time series.
trainBudgetMilliNodeHours	string (int64 format) Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won't be attempted and will error. The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
transformations[]	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformation) Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter.
unavailableAtForecastColumns[]	string Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.
validationOptions	string Validation options for the data validation component. The available options are: * "fail-pipeline" - default, will validate against the validation and fail the pipeline if it fails. * "ignore-validation" - ignore the results of the validation and continue
weightColumn	string Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1. This column must be available at forecast.
windowConfig	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionWindowConfig) Config containing strategy for generating sliding windows.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsGranularity

A duration of time expressed in time granularity units.

Fields
quantity	string (int64 format) The number of granularity_units between data points in the training data. If granularity_unit is minute, can be 1, 5, 10, 15, or 30. For all other values of granularity_unit, must be 1.
unit	string The time granularity unit of this time period. The supported units are: * "minute" * "hour" * "day" * "week" * "month" * "year"

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformation

(No description provided)

Fields
auto	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformationAutoTransformation) (No description provided)
categorical	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformationCategoricalTransformation) (No description provided)
numeric	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformationNumericTransformation) (No description provided)
text	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformationTextTransformation) (No description provided)
timestamp	object (GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformationTimestampTransformation) (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformationAutoTransformation

Training pipeline will infer the proper transformation based on the statistic of dataset.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformationCategoricalTransformation

Training pipeline will perform following transformation functions. * The categorical string as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index. * Categories that appear less than 5 times in the training dataset are treated as the "unknown" category. The "unknown" category gets its own special lookup index and resulting embedding.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformationNumericTransformation

Training pipeline will perform following transformation functions. * The value converted to float32. * The z_score of the value. * log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value. * z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformationTextTransformation

Training pipeline will perform following transformation functions. * The text as is--no change to case, punctuation, spelling, tense, and so on. * Convert the category name to a dictionary lookup index and generate an embedding for each index.

Fields
columnName	string (No description provided)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingInputsTransformationTimestampTransformation

Training pipeline will perform following transformation functions. * Apply the transformation functions for Numerical columns. * Determine the year, month, day,and weekday. Treat each value from the timestamp as a Categorical column. * Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.

Fields
columnName	string (No description provided)
timeFormat	string The format in which that time field is expressed. The time_format must either be one of: * unix-seconds * unix-milliseconds * unix-microseconds * unix-nanoseconds (for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written in strftime syntax. If time_format is not set, then the default format is RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z)

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionTftForecastingMetadata

Model metadata specific to TFT Forecasting.

Fields
evaluatedDataItemsBigqueryUri	string BigQuery destination uri for exported evaluated examples.
trainCostMilliNodeHours	string (int64 format) Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionWindowConfig

Config that contains the strategy used to generate sliding windows in time series training. A window is a series of rows that comprise the context up to the time of prediction, and the horizon following. The corresponding row for each window marks the start of the forecast horizon. Each window is used as an input example for training/evaluation.

Fields
column	string Name of the column that should be used to generate sliding windows. The column should contain either booleans or string booleans; if the value of the row is True, generate a sliding window with the horizon starting at that row. The column will not be used as a feature in training.
maxCount	string (int64 format) Maximum number of windows that should be generated across all time series.
strideLength	string (int64 format) Stride length used to generate input examples. Within one time series, every {$STRIDE_LENGTH} rows will be used to generate a sliding window.

GoogleCloudAiplatformV1beta1SchemaVertex

A vertex represents a 2D point in the image. NOTE: the normalized vertex coordinates are relative to the original image and range from 0 to 1.

Fields
x	number (double format) X coordinate.
y	number (double format) Y coordinate.

GoogleCloudAiplatformV1beta1SchemaVideoActionRecognitionAnnotation

Annotation details specific to video action recognition.

Fields
annotationSpecId	string The resource Id of the AnnotationSpec that this Annotation pertains to.
displayName	string The display name of the AnnotationSpec that this Annotation pertains to.
timeSegment	object (GoogleCloudAiplatformV1beta1SchemaTimeSegment) This Annotation applies to the time period represented by the TimeSegment. If it's not set, the Annotation applies to the whole video.

GoogleCloudAiplatformV1beta1SchemaVideoClassificationAnnotation

Annotation details specific to video classification.

Fields
annotationSpecId	string The resource Id of the AnnotationSpec that this Annotation pertains to.
displayName	string The display name of the AnnotationSpec that this Annotation pertains to.
timeSegment	object (GoogleCloudAiplatformV1beta1SchemaTimeSegment) This Annotation applies to the time period represented by the TimeSegment. If it's not set, the Annotation applies to the whole video.

GoogleCloudAiplatformV1beta1SchemaVideoDataItem

Payload of Video DataItem.

Fields
gcsUri	string Required. Google Cloud Storage URI points to the original video in user's bucket. The video is up to 50 GB in size and up to 3 hour in duration.
mimeType	string Output only. The mime type of the content of the video. Only the videos in below listed mime types are supported. Supported mime_type: - video/mp4 - video/avi - video/quicktime

GoogleCloudAiplatformV1beta1SchemaVideoDatasetMetadata

The metadata of Datasets that contain Video DataItems.

Fields
dataItemSchemaUri	string Points to a YAML file stored on Google Cloud Storage describing payload of the Video DataItems that belong to this Dataset.
gcsBucket	string Google Cloud Storage Bucket name that contains the blob data of this Dataset.

GoogleCloudAiplatformV1beta1SchemaVideoObjectTrackingAnnotation

Annotation details specific to video object tracking.

Fields
annotationSpecId	string The resource Id of the AnnotationSpec that this Annotation pertains to.
displayName	string The display name of the AnnotationSpec that this Annotation pertains to.
instanceId	string (int64 format) The instance of the object, expressed as a positive integer. Used to track the same object across different frames.
timeOffset	string (Duration format) A time (frame) of a video to which this annotation pertains. Represented as the duration since the video's start.
xMax	number (double format) The rightmost coordinate of the bounding box.
xMin	number (double format) The leftmost coordinate of the bounding box.
yMax	number (double format) The bottommost coordinate of the bounding box.
yMin	number (double format) The topmost coordinate of the bounding box.

GoogleCloudAiplatformV1beta1SchemaVisualInspectionClassificationLabelSavedQueryMetadata

(No description provided)

Fields
multiLabel	boolean Whether or not the classification label is multi_label.

GoogleCloudAiplatformV1beta1SearchDataItemsResponse

Response message for DatasetService.SearchDataItems.

Fields
dataItemViews[]	object (GoogleCloudAiplatformV1beta1DataItemView) The DataItemViews read.
nextPageToken	string A token to retrieve next page of results. Pass to SearchDataItemsRequest.page_token to obtain that page.

GoogleCloudAiplatformV1beta1SearchEntryPoint

Google search entry point.

Fields
renderedContent	string Optional. Web content snippet that can be embedded in a web page or an app webview.
sdkBlob	string (bytes format) Optional. Base64 encoded JSON representing array of tuple.

GoogleCloudAiplatformV1beta1SearchFeaturesResponse

Response message for FeaturestoreService.SearchFeatures.

Fields
features[]	object (GoogleCloudAiplatformV1beta1Feature) The Features matching the request. Fields returned: * name * description * labels * create_time * update_time
nextPageToken	string A token, which can be sent as SearchFeaturesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

GoogleCloudAiplatformV1beta1SearchMigratableResourcesRequest

Request message for MigrationService.SearchMigratableResources.

Fields
filter	string A filter for your search. You can use the following types of filters: * Resource type filters. The following strings filter for a specific type of MigratableResource: * ml_engine_model_version:* * automl_model:* * automl_dataset:* * data_labeling_dataset:* * "Migrated or not" filters. The following strings filter for resources that either have or have not already been migrated: * last_migrate_time:* filters for migrated resources. * NOT last_migrate_time:* filters for not yet migrated resources.
pageSize	integer (int32 format) The standard page size. The default and maximum value is 100.
pageToken	string The standard page token.

GoogleCloudAiplatformV1beta1SearchMigratableResourcesResponse

Response message for MigrationService.SearchMigratableResources.

Fields
migratableResources[]	object (GoogleCloudAiplatformV1beta1MigratableResource) All migratable resources that can be migrated to the location specified in the request.
nextPageToken	string The standard next-page token. The migratable_resources may not fill page_size in SearchMigratableResourcesRequest even when there are subsequent pages.

GoogleCloudAiplatformV1beta1SearchModelDeploymentMonitoringStatsAnomaliesRequest

Request message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

Fields
deployedModelId	string Required. The DeployedModel ID of the [ModelDeploymentMonitoringObjectiveConfig.deployed_model_id].
endTime	string (Timestamp format) The latest timestamp of stats being generated. If not set, indicates feching stats till the latest possible one.
featureDisplayName	string The feature display name. If specified, only return the stats belonging to this feature. Format: ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.feature_display_name, example: "user_destination".
objectives[]	object (GoogleCloudAiplatformV1beta1SearchModelDeploymentMonitoringStatsAnomaliesRequestStatsAnomaliesObjective) Required. Objectives of the stats to retrieve.
pageSize	integer (int32 format) The standard list page size.
pageToken	string A page token received from a previous JobService.SearchModelDeploymentMonitoringStatsAnomalies call.
startTime	string (Timestamp format) The earliest timestamp of stats being generated. If not set, indicates fetching stats till the earliest possible one.

GoogleCloudAiplatformV1beta1SearchModelDeploymentMonitoringStatsAnomaliesRequestStatsAnomaliesObjective

Stats requested for specific objective.

Fields
topFeatureCount	integer (int32 format) If set, all attribution scores between SearchModelDeploymentMonitoringStatsAnomaliesRequest.start_time and SearchModelDeploymentMonitoringStatsAnomaliesRequest.end_time are fetched, and page token doesn't take effect in this case. Only used to retrieve attribution score for the top Features which has the highest attribution score in the latest monitoring run.
type	enum (No description provided)
Enum type. Can be one of the following:
MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED	Default value, should not be set.
RAW_FEATURE_SKEW	Raw feature values' stats to detect skew between Training-Prediction datasets.
RAW_FEATURE_DRIFT	Raw feature values' stats to detect drift between Serving-Prediction datasets.
FEATURE_ATTRIBUTION_SKEW	Feature attribution scores to detect skew between Training-Prediction datasets.
FEATURE_ATTRIBUTION_DRIFT	Feature attribution scores to detect skew between Prediction datasets collected within different time windows.

GoogleCloudAiplatformV1beta1SearchModelDeploymentMonitoringStatsAnomaliesResponse

Response message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

Fields
monitoringStats[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomalies) Stats retrieved for requested objectives. There are at most 1000 ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.prediction_stats in the response.
nextPageToken	string The page token that can be used by the next JobService.SearchModelDeploymentMonitoringStatsAnomalies call.

GoogleCloudAiplatformV1beta1SearchModelMonitoringAlertsRequest

Request message for ModelMonitoringService.SearchModelMonitoringAlerts.

Fields
alertTimeInterval	object (GoogleTypeInterval) If non-empty, returns the alerts in this time interval.
modelMonitoringJob	string If non-empty, returns the alerts of this model monitoring job.
objectiveType	string If non-empty, returns the alerts of this objective type. Supported monitoring objectives: raw-feature-drift prediction-output-drift feature-attribution
pageSize	integer (int32 format) The standard list page size.
pageToken	string A page token received from a previous ModelMonitoringService.SearchModelMonitoringAlerts call.
statsName	string If non-empty, returns the alerts of this stats_name.

GoogleCloudAiplatformV1beta1SearchModelMonitoringAlertsResponse

Response message for ModelMonitoringService.SearchModelMonitoringAlerts.

Fields
modelMonitoringAlerts[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringAlert) Alerts retrieved for the requested objectives. Sorted by alert time descendingly.
nextPageToken	string The page token that can be used by the next ModelMonitoringService.SearchModelMonitoringAlerts call.
totalNumberAlerts	string (int64 format) The total number of alerts retrieved by the requested objectives.

GoogleCloudAiplatformV1beta1SearchModelMonitoringStatsFilter

Filter for searching ModelMonitoringStats.

Fields
tabularStatsFilter	object (GoogleCloudAiplatformV1beta1SearchModelMonitoringStatsFilterTabularStatsFilter) Tabular statistics filter.

GoogleCloudAiplatformV1beta1SearchModelMonitoringStatsFilterTabularStatsFilter

Tabular statistics filter.

Fields
algorithm	string Specify the algorithm type used for distance calculation, eg: jensen_shannon_divergence, l_infinity.
modelMonitoringJob	string From a particular monitoring job.
modelMonitoringSchedule	string From a particular monitoring schedule.
objectiveType	string One of the supported monitoring objectives: raw-feature-drift prediction-output-drift feature-attribution
statsName	string If not specified, will return all the stats_names.

GoogleCloudAiplatformV1beta1SearchModelMonitoringStatsRequest

Request message for ModelMonitoringService.SearchModelMonitoringStats.

Fields
pageSize	integer (int32 format) The standard list page size.
pageToken	string A page token received from a previous ModelMonitoringService.SearchModelMonitoringStats call.
statsFilter	object (GoogleCloudAiplatformV1beta1SearchModelMonitoringStatsFilter) Filter for search different stats.
timeInterval	object (GoogleTypeInterval) The time interval for which results should be returned.

GoogleCloudAiplatformV1beta1SearchModelMonitoringStatsResponse

Response message for ModelMonitoringService.SearchModelMonitoringStats.

Fields
monitoringStats[]	object (GoogleCloudAiplatformV1beta1ModelMonitoringStats) Stats retrieved for requested objectives.
nextPageToken	string The page token that can be used by the next ModelMonitoringService.SearchModelMonitoringStats call.

GoogleCloudAiplatformV1beta1SearchNearestEntitiesRequest

The request message for FeatureOnlineStoreService.SearchNearestEntities.

Fields
query	object (GoogleCloudAiplatformV1beta1NearestNeighborQuery) Required. The query.
returnFullEntity	boolean Optional. If set to true, the full entities (including all vector values and metadata) of the nearest neighbors are returned; otherwise only entity id of the nearest neighbors will be returned. Note that returning full entities will significantly increase the latency and cost of the query.

GoogleCloudAiplatformV1beta1SearchNearestEntitiesResponse

Response message for FeatureOnlineStoreService.SearchNearestEntities

Fields
nearestNeighbors	object (GoogleCloudAiplatformV1beta1NearestNeighbors) The nearest neighbors of the query entity.

GoogleCloudAiplatformV1beta1ServiceAccountSpec

Configuration for the use of custom service account to run the workloads.

Fields
enableCustomServiceAccount	boolean Required. If true, custom user-managed service account is enforced to run any workloads (for example, Vertex Jobs) on the resource. Otherwise, uses the Vertex AI Custom Code Service Agent.
serviceAccount	string Optional. Required when all below conditions are met * enable_custom_service_account is true; * any runtime is specified via ResourceRuntimeSpec on creation time, for example, Ray The users must have iam.serviceAccounts.actAs permission on this service account and then the specified runtime containers will run as it. Do not set this field if you want to submit jobs using custom service account to this PersistentResource after creation, but only specify the service_account inside the job.

GoogleCloudAiplatformV1beta1ShieldedVmConfig

A set of Shielded Instance options. See Images using supported Shielded VM features.

Fields
enableSecureBoot	boolean Defines whether the instance has Secure Boot enabled. Secure Boot helps ensure that the system only runs authentic software by verifying the digital signature of all boot components, and halting the boot process if signature verification fails.

GoogleCloudAiplatformV1beta1SmoothGradConfig

Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

Fields
featureNoiseSigma	object (GoogleCloudAiplatformV1beta1FeatureNoiseSigma) This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noiseSigma	number (float format) This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisySampleCount	integer (int32 format) The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.

GoogleCloudAiplatformV1beta1SpecialistPool

SpecialistPool represents customers' own workforce to work on their data labeling jobs. It includes a group of specialist managers and workers. Managers are responsible for managing the workers in this pool as well as customers' data labeling jobs associated with this pool. Customers create specialist pool as well as start data labeling jobs on Cloud, managers and workers handle the jobs using CrowdCompute console.

Fields
displayName	string Required. The user-defined name of the SpecialistPool. The name can be up to 128 characters long and can consist of any UTF-8 characters. This field should be unique on project-level.
name	string Required. The resource name of the SpecialistPool.
pendingDataLabelingJobs[]	string Output only. The resource name of the pending data labeling jobs.
specialistManagerEmails[]	string The email addresses of the managers in the SpecialistPool.
specialistManagersCount	integer (int32 format) Output only. The number of managers in this SpecialistPool.
specialistWorkerEmails[]	string The email addresses of workers in the SpecialistPool.

GoogleCloudAiplatformV1beta1StartNotebookRuntimeOperationMetadata

Metadata information for NotebookService.StartNotebookRuntime.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.
progressMessage	string A human-readable message that shows the intermediate progress details of NotebookRuntime.

GoogleCloudAiplatformV1beta1StratifiedSplit

Assigns input data to the training, validation, and test sets so that the distribution of values found in the categorical column (as specified by the key field) is mirrored within each split. The fraction values determine the relative sizes of the splits. For example, if the specified column has three values, with 50% of the rows having value "A", 25% value "B", and 25% value "C", and the split fractions are specified as 80/10/10, then the training set will constitute 80% of the training data, with about 50% of the training set rows having the value "A" for the specified column, about 25% having the value "B", and about 25% having the value "C". Only the top 500 occurring values are used; any values not in the top 500 values are randomly assigned to a split. If less than three rows contain a specific value, those rows are randomly assigned. Supported only for tabular Datasets.

Fields
key	string Required. The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
testFraction	number (double format) The fraction of the input data that is to be used to evaluate the Model.
trainingFraction	number (double format) The fraction of the input data that is to be used to train the Model.
validationFraction	number (double format) The fraction of the input data that is to be used to validate the Model.

GoogleCloudAiplatformV1beta1StreamingFetchFeatureValuesRequest

Request message for FeatureOnlineStoreService.StreamingFetchFeatureValues. For the entities requested, all features under the requested feature view will be returned.

Fields
dataFormat	enum Specify response data format. If not set, KeyValue format will be used.
Enum type. Can be one of the following:
FEATURE_VIEW_DATA_FORMAT_UNSPECIFIED	Not set. Will be treated as the KeyValue format.
KEY_VALUE	Return response data in key-value format.
PROTO_STRUCT	Return response data in proto Struct format.
dataKeys[]	object (GoogleCloudAiplatformV1beta1FeatureViewDataKey) (No description provided)

GoogleCloudAiplatformV1beta1StreamingFetchFeatureValuesResponse

Response message for FeatureOnlineStoreService.StreamingFetchFeatureValues.

Fields
data[]	object (GoogleCloudAiplatformV1beta1FetchFeatureValuesResponse) (No description provided)
dataKeysWithError[]	object (GoogleCloudAiplatformV1beta1FeatureViewDataKey) (No description provided)
status	object (GoogleRpcStatus) Response status. If OK, then StreamingFetchFeatureValuesResponse.data will be populated. Otherwise StreamingFetchFeatureValuesResponse.data_keys_with_error will be populated with the appropriate data keys. The error only applies to the listed data keys - the stream will remain open for further FeatureOnlineStoreService.StreamingFetchFeatureValuesRequest requests.

GoogleCloudAiplatformV1beta1StreamingPredictRequest

Request message for PredictionService.StreamingPredict. The first message must contain endpoint field and optionally input. The subsequent messages must contain input.

Fields
inputs[]	object (GoogleCloudAiplatformV1beta1Tensor) The prediction input.
parameters	object (GoogleCloudAiplatformV1beta1Tensor) The parameters that govern the prediction.

GoogleCloudAiplatformV1beta1StreamingPredictResponse

Response message for PredictionService.StreamingPredict.

Fields
outputs[]	object (GoogleCloudAiplatformV1beta1Tensor) The prediction output.
parameters	object (GoogleCloudAiplatformV1beta1Tensor) The parameters that govern the prediction.

GoogleCloudAiplatformV1beta1StreamingReadFeatureValuesRequest

Request message for FeaturestoreOnlineServingService.StreamingFeatureValuesRead.

Fields
entityIds[]	string Required. IDs of entities to read Feature values of. The maximum number of IDs is 100. For example, for a machine learning model predicting user clicks on a website, an entity ID could be user_123.
featureSelector	object (GoogleCloudAiplatformV1beta1FeatureSelector) Required. Selector choosing Features of the target EntityType. Feature IDs will be deduplicated.

GoogleCloudAiplatformV1beta1StringArray

A list of string values.

Fields
values[]	string A list of string values.

GoogleCloudAiplatformV1beta1Study

A message representing a Study.

Fields
createTime	string (Timestamp format) Output only. Time at which the study was created.
displayName	string Required. Describes the Study, default value is empty string.
inactiveReason	string Output only. A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.
name	string Output only. The name of a study. The study's globally unique identifier. Format: projects/{project}/locations/{location}/studies/{study}
state	enum Output only. The detailed state of a Study.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	The study state is unspecified.
ACTIVE	The study is active.
INACTIVE	The study is stopped due to an internal error.
COMPLETED	The study is done when the service exhausts the parameter search space or max_trial_count is reached.
studySpec	object (GoogleCloudAiplatformV1beta1StudySpec) Required. Configuration of the Study.

GoogleCloudAiplatformV1beta1StudySpec

Represents specification of a Study.

Fields
algorithm	enum The search algorithm specified for the Study.
Enum type. Can be one of the following:
ALGORITHM_UNSPECIFIED	The default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
GRID_SEARCH	Simple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE.
RANDOM_SEARCH	Simple random search within the feasible space.
convexAutomatedStoppingSpec	object (GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpec) The automated early stopping spec using convex stopping rule.
convexStopConfig	object (GoogleCloudAiplatformV1beta1StudySpecConvexStopConfig) Deprecated. The automated early stopping using convex stopping rule.
decayCurveStoppingSpec	object (GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpec) The automated early stopping spec using decay curve rule.
measurementSelectionType	enum Describe which measurement selection type will be used
Enum type. Can be one of the following:
MEASUREMENT_SELECTION_TYPE_UNSPECIFIED	Will be treated as LAST_MEASUREMENT.
LAST_MEASUREMENT	Use the last measurement reported.
BEST_MEASUREMENT	Use the best measurement reported.
medianAutomatedStoppingSpec	object (GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpec) The automated early stopping spec using median rule.
metrics[]	object (GoogleCloudAiplatformV1beta1StudySpecMetricSpec) Required. Metric specs for the Study.
observationNoise	enum The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
Enum type. Can be one of the following:
OBSERVATION_NOISE_UNSPECIFIED	The default noise level chosen by Vertex AI.
LOW	Vertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
HIGH	Vertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
parameters[]	object (GoogleCloudAiplatformV1beta1StudySpecParameterSpec) Required. The set of parameters to tune.
studyStoppingConfig	object (GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfig) Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
transferLearningConfig	object (GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfig) The configuration info/options for transfer learning. Currently supported for Vertex AI Vizier service, not HyperParameterTuningJob

GoogleCloudAiplatformV1beta1StudySpecConvexAutomatedStoppingSpec

Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model.

Fields
learningRateParameterName	string The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
maxStepCount	string (int64 format) Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
minMeasurementCount	string (int64 format) The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
minStepCount	string (int64 format) Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
updateAllStoppedTrials	boolean ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
useElapsedDuration	boolean This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.

GoogleCloudAiplatformV1beta1StudySpecConvexStopConfig

Configuration for ConvexStopPolicy.

Fields
autoregressiveOrder	string (int64 format) The number of Trial measurements used in autoregressive model for value prediction. A trial won't be considered early stopping if has fewer measurement points.
learningRateParameterName	string The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
maxNumSteps	string (int64 format) Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. When use_steps is false, this field is set to the maximum elapsed seconds.
minNumSteps	string (int64 format) Minimum number of steps for a trial to complete. Trials which do not have a measurement with num_steps > min_num_steps won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_num_steps is set to be one-tenth of the max_num_steps. When use_steps is false, this field is set to the minimum elapsed seconds.
useSeconds	boolean This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_seconds==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_seconds==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.

GoogleCloudAiplatformV1beta1StudySpecDecayCurveAutomatedStoppingSpec

The decay curve automated stopping rule builds a Gaussian Process Regressor to predict the final objective value of a Trial based on the already completed Trials and the intermediate measurements of the current Trial. Early stopping is requested for the current Trial if there is very low probability to exceed the optimal value found so far.

Fields
useElapsedDuration	boolean True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.

GoogleCloudAiplatformV1beta1StudySpecMedianAutomatedStoppingSpec

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.

Fields
useElapsedDuration	boolean True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.

GoogleCloudAiplatformV1beta1StudySpecMetricSpec

Represents a metric to optimize.

Fields
goal	enum Required. The optimization goal of the metric.
Enum type. Can be one of the following:
GOAL_TYPE_UNSPECIFIED	Goal Type will default to maximize.
MAXIMIZE	Maximize the goal metric.
MINIMIZE	Minimize the goal metric.
metricId	string Required. The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
safetyConfig	object (GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfig) Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.

GoogleCloudAiplatformV1beta1StudySpecMetricSpecSafetyMetricConfig

Used in safe optimization to specify threshold levels and risk tolerance.

Fields
desiredMinSafeTrialsFraction	number (double format) Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
safetyThreshold	number (double format) Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.

GoogleCloudAiplatformV1beta1StudySpecParameterSpec

Represents a single parameter to optimize.

Fields
categoricalValueSpec	object (GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpec) The value spec for a 'CATEGORICAL' parameter.
conditionalParameterSpecs[]	object (GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpec) A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
discreteValueSpec	object (GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpec) The value spec for a 'DISCRETE' parameter.
doubleValueSpec	object (GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpec) The value spec for a 'DOUBLE' parameter.
integerValueSpec	object (GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpec) The value spec for an 'INTEGER' parameter.
parameterId	string Required. The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
scaleType	enum How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
Enum type. Can be one of the following:
SCALE_TYPE_UNSPECIFIED	By default, no scaling is applied.
UNIT_LINEAR_SCALE	Scales the feasible space to (0, 1) linearly.
UNIT_LOG_SCALE	Scales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
UNIT_REVERSE_LOG_SCALE	Scales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpec

Value specification for a parameter in CATEGORICAL type.

Fields
defaultValue	string A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
values[]	string Required. The list of possible categories.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpec

Represents a parameter spec with condition from its parent parameter.

Fields
parameterSpec	object (GoogleCloudAiplatformV1beta1StudySpecParameterSpec) Required. The spec for a conditional parameter.
parentCategoricalValues	object (GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueCondition) The spec for matching values from a parent parameter of CATEGORICAL type.
parentDiscreteValues	object (GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueCondition) The spec for matching values from a parent parameter of DISCRETE type.
parentIntValues	object (GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueCondition) The spec for matching values from a parent parameter of INTEGER type.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueCondition

Represents the spec to match categorical values from parent parameter.

Fields
values[]	string Required. Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in categorical_value_spec of parent parameter.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueCondition

Represents the spec to match discrete values from parent parameter.

Fields
values[]	number (double format) Required. Matches values of the parent parameter of 'DISCRETE' type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueCondition

Represents the spec to match integer values from parent parameter.

Fields
values[]	string (int64 format) Required. Matches values of the parent parameter of 'INTEGER' type. All values must lie in integer_value_spec of parent parameter.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpec

Value specification for a parameter in DISCRETE type.

Fields
defaultValue	number (double format) A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
values[]	number (double format) Required. A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpec

Value specification for a parameter in DOUBLE type.

Fields
defaultValue	number (double format) A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
maxValue	number (double format) Required. Inclusive maximum value of the parameter.
minValue	number (double format) Required. Inclusive minimum value of the parameter.

GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpec

Value specification for a parameter in INTEGER type.

Fields
defaultValue	string (int64 format) A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
maxValue	string (int64 format) Required. Inclusive maximum value of the parameter.
minValue	string (int64 format) Required. Inclusive minimum value of the parameter.

GoogleCloudAiplatformV1beta1StudySpecStudyStoppingConfig

The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection.

Fields
maxDurationNoProgress	string (Duration format) If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
maxNumTrials	integer (int32 format) If there are more than this many trials, stop the study.
maxNumTrialsNoProgress	integer (int32 format) If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
maximumRuntimeConstraint	object (GoogleCloudAiplatformV1beta1StudyTimeConstraint) If the specified time or duration has passed, stop the study.
minNumTrials	integer (int32 format) If there are fewer than this many COMPLETED trials, do not stop the study.
minimumRuntimeConstraint	object (GoogleCloudAiplatformV1beta1StudyTimeConstraint) Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
shouldStopAsap	boolean If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).

GoogleCloudAiplatformV1beta1StudySpecTransferLearningConfig

This contains flag for manually disabling transfer learning for a study. The names of prior studies being used for transfer learning (if any) are also listed here.

Fields
disableTransferLearning	boolean Flag to to manually prevent vizier from using transfer learning on a new study. Otherwise, vizier will automatically determine whether or not to use transfer learning.
priorStudyNames[]	string Output only. Names of previously completed studies

GoogleCloudAiplatformV1beta1StudyTimeConstraint

Time-based Constraint for Study

Fields
endTime	string (Timestamp format) Compares the wallclock time to this time. Must use UTC timezone.
maxDuration	string (Duration format) Counts the wallclock time passed since the creation of this Study.

GoogleCloudAiplatformV1beta1SuggestTrialsMetadata

Details of operations that perform Trials suggestion.

Fields
clientId	string 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.
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for suggesting Trials.

GoogleCloudAiplatformV1beta1SuggestTrialsRequest

Request message for VizierService.SuggestTrials.

Fields
clientId	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[]	object (GoogleCloudAiplatformV1beta1TrialContext) 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.
suggestionCount	integer (int32 format) Required. The number of suggestions requested. It must be positive.

GoogleCloudAiplatformV1beta1SuggestTrialsResponse

Response message for VizierService.SuggestTrials.

Fields
endTime	string (Timestamp format) The time at which operation processing completed.
startTime	string (Timestamp format) The time at which the operation was started.
studyState	enum The state of the Study.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	The study state is unspecified.
ACTIVE	The study is active.
INACTIVE	The study is stopped due to an internal error.
COMPLETED	The study is done when the service exhausts the parameter search space or max_trial_count is reached.
trials[]	object (GoogleCloudAiplatformV1beta1Trial) A list of Trials.

GoogleCloudAiplatformV1beta1SummarizationHelpfulnessInput

Input for summarization helpfulness metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1SummarizationHelpfulnessInstance) Required. Summarization helpfulness instance.
metricSpec	object (GoogleCloudAiplatformV1beta1SummarizationHelpfulnessSpec) Required. Spec for summarization helpfulness score metric.

GoogleCloudAiplatformV1beta1SummarizationHelpfulnessInstance

Spec for summarization helpfulness instance.

Fields
context	string Required. Text to be summarized.
instruction	string Optional. Summarization prompt for LLM.
prediction	string Required. Output of the evaluated model.
reference	string Optional. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1SummarizationHelpfulnessResult

Spec for summarization helpfulness result.

Fields
confidence	number (float format) Output only. Confidence for summarization helpfulness score.
explanation	string Output only. Explanation for summarization helpfulness score.
score	number (float format) Output only. Summarization Helpfulness score.

GoogleCloudAiplatformV1beta1SummarizationHelpfulnessSpec

Spec for summarization helpfulness score metric.

Fields
useReference	boolean Optional. Whether to use instance.reference to compute summarization helpfulness.
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1SummarizationQualityInput

Input for summarization quality metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1SummarizationQualityInstance) Required. Summarization quality instance.
metricSpec	object (GoogleCloudAiplatformV1beta1SummarizationQualitySpec) Required. Spec for summarization quality score metric.

GoogleCloudAiplatformV1beta1SummarizationQualityInstance

Spec for summarization quality instance.

Fields
context	string Required. Text to be summarized.
instruction	string Required. Summarization prompt for LLM.
prediction	string Required. Output of the evaluated model.
reference	string Optional. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1SummarizationQualityResult

Spec for summarization quality result.

Fields
confidence	number (float format) Output only. Confidence for summarization quality score.
explanation	string Output only. Explanation for summarization quality score.
score	number (float format) Output only. Summarization Quality score.

GoogleCloudAiplatformV1beta1SummarizationQualitySpec

Spec for summarization quality score metric.

Fields
useReference	boolean Optional. Whether to use instance.reference to compute summarization quality.
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1SummarizationVerbosityInput

Input for summarization verbosity metric.

Fields
instance	object (GoogleCloudAiplatformV1beta1SummarizationVerbosityInstance) Required. Summarization verbosity instance.
metricSpec	object (GoogleCloudAiplatformV1beta1SummarizationVerbositySpec) Required. Spec for summarization verbosity score metric.

GoogleCloudAiplatformV1beta1SummarizationVerbosityInstance

Spec for summarization verbosity instance.

Fields
context	string Required. Text to be summarized.
instruction	string Optional. Summarization prompt for LLM.
prediction	string Required. Output of the evaluated model.
reference	string Optional. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1SummarizationVerbosityResult

Spec for summarization verbosity result.

Fields
confidence	number (float format) Output only. Confidence for summarization verbosity score.
explanation	string Output only. Explanation for summarization verbosity score.
score	number (float format) Output only. Summarization Verbosity score.

GoogleCloudAiplatformV1beta1SummarizationVerbositySpec

Spec for summarization verbosity score metric.

Fields
useReference	boolean Optional. Whether to use instance.reference to compute summarization verbosity.
version	integer (int32 format) Optional. Which version to use for evaluation.

GoogleCloudAiplatformV1beta1SupervisedHyperParameters

Hyperparameters for SFT.

Fields
adapterSize	enum Optional. Adapter size for tuning.
Enum type. Can be one of the following:
ADAPTER_SIZE_UNSPECIFIED	Adapter size is unspecified.
ADAPTER_SIZE_ONE	Adapter size 1.
ADAPTER_SIZE_FOUR	Adapter size 4.
ADAPTER_SIZE_EIGHT	Adapter size 8.
ADAPTER_SIZE_SIXTEEN	Adapter size 16.
epochCount	string (int64 format) Optional. Number of complete passes the model makes over the entire training dataset during training.
learningRateMultiplier	number (double format) Optional. Multiplier for adjusting the default learning rate.

GoogleCloudAiplatformV1beta1SupervisedTuningDataStats

Tuning data statistics for Supervised Tuning.

Fields
totalBillableCharacterCount	string (int64 format) Output only. Number of billable characters in the tuning dataset.
totalTuningCharacterCount	string (int64 format) Output only. Number of tuning characters in the tuning dataset.
tuningDatasetExampleCount	string (int64 format) Output only. Number of examples in the tuning dataset.
tuningStepCount	string (int64 format) Output only. Number of tuning steps for this Tuning Job.
userDatasetExamples[]	object (GoogleCloudAiplatformV1beta1Content) Output only. Sample user messages in the training dataset uri.
userInputTokenDistribution	object (GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistribution) Output only. Dataset distributions for the user input tokens.
userMessagePerExampleDistribution	object (GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistribution) Output only. Dataset distributions for the messages per example.
userOutputTokenDistribution	object (GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistribution) Output only. Dataset distributions for the user output tokens.

GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistribution

Dataset distribution for Supervised Tuning.

Fields
buckets[]	object (GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistributionDatasetBucket) Output only. Defines the histogram bucket.
max	number (double format) Output only. The maximum of the population values.
mean	number (double format) Output only. The arithmetic mean of the values in the population.
median	number (double format) Output only. The median of the values in the population.
min	number (double format) Output only. The minimum of the population values.
p5	number (double format) Output only. The 5th percentile of the values in the population.
p95	number (double format) Output only. The 95th percentile of the values in the population.
sum	string (int64 format) Output only. Sum of a given population of values.

GoogleCloudAiplatformV1beta1SupervisedTuningDatasetDistributionDatasetBucket

Dataset bucket used to create a histogram for the distribution given a population of values.

Fields
count	number (double format) Output only. Number of values in the bucket.
left	number (double format) Output only. Left bound of the bucket.
right	number (double format) Output only. Right bound of the bucket.

GoogleCloudAiplatformV1beta1SupervisedTuningSpec

Tuning Spec for Supervised Tuning.

Fields
hyperParameters	object (GoogleCloudAiplatformV1beta1SupervisedHyperParameters) Optional. Hyperparameters for SFT.
trainingDatasetUri	string Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
validationDatasetUri	string Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.

GoogleCloudAiplatformV1beta1SyncFeatureViewResponse

Respose message for FeatureOnlineStoreAdminService.SyncFeatureView.

Fields
featureViewSync	string Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}/featureViewSyncs/{feature_view_sync}

GoogleCloudAiplatformV1beta1TFRecordDestination

The storage details for TFRecord output content.

Fields
gcsDestination	object (GoogleCloudAiplatformV1beta1GcsDestination) Required. Google Cloud Storage location.

GoogleCloudAiplatformV1beta1Tensor

A tensor value type.

Fields
boolVal[]	boolean Type specific representations that make it easy to create tensor protos in all languages. Only the representation corresponding to "dtype" can be set. The values hold the flattened representation of the tensor in row major order. BOOL
bytesVal[]	string (bytes format) STRING
doubleVal[]	number (double format) DOUBLE
dtype	enum The data type of tensor.
Enum type. Can be one of the following:
DATA_TYPE_UNSPECIFIED	Not a legal value for DataType. Used to indicate a DataType field has not been set.
BOOL	Data types that all computation devices are expected to be capable to support.
STRING	(No description provided)
FLOAT	(No description provided)
DOUBLE	(No description provided)
INT8	(No description provided)
INT16	(No description provided)
INT32	(No description provided)
INT64	(No description provided)
UINT8	(No description provided)
UINT16	(No description provided)
UINT32	(No description provided)
UINT64	(No description provided)
floatVal[]	number (float format) FLOAT
int64Val[]	string (int64 format) INT64
intVal[]	integer (int32 format) INT_8 INT_16 INT_32
listVal[]	object (GoogleCloudAiplatformV1beta1Tensor) A list of tensor values.
shape[]	string (int64 format) Shape of the tensor.
stringVal[]	string STRING
structVal	map (key: string, value: object (GoogleCloudAiplatformV1beta1Tensor)) A map of string to tensor.
tensorVal	string (bytes format) Serialized raw tensor content.
uint64Val[]	string (uint64 format) UINT64
uintVal[]	integer (uint32 format) UINT8 UINT16 UINT32

GoogleCloudAiplatformV1beta1Tensorboard

Tensorboard is a physical database that stores users' training metrics. A default Tensorboard is provided in each region of a Google Cloud project. If needed users can also create extra Tensorboards in their projects.

Fields
blobStoragePathPrefix	string Output only. Consumer project Cloud Storage path prefix used to store blob data, which can either be a bucket or directory. Does not end with a '/'.
createTime	string (Timestamp format) Output only. Timestamp when this Tensorboard was created.
description	string Description of this Tensorboard.
displayName	string Required. User provided name of this Tensorboard.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key spec for a Tensorboard. If set, this Tensorboard and all sub-resources of this Tensorboard will be secured by this key.
etag	string Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
isDefault	boolean Used to indicate if the TensorBoard instance is the default one. Each project & region can have at most one default TensorBoard instance. Creation of a default TensorBoard instance and updating an existing TensorBoard instance to be default will mark all other TensorBoard instances (if any) as non default.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your Tensorboards. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Tensorboard (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
name	string Output only. Name of the Tensorboard. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
runCount	integer (int32 format) Output only. The number of Runs stored in this Tensorboard.
updateTime	string (Timestamp format) Output only. Timestamp when this Tensorboard was last updated.

GoogleCloudAiplatformV1beta1TensorboardBlob

One blob (e.g, image, graph) viewable on a blob metric plot.

Fields
data	string (bytes format) Optional. The bytes of the blob is not present unless it's returned by the ReadTensorboardBlobData endpoint.
id	string Output only. A URI safe key uniquely identifying a blob. Can be used to locate the blob stored in the Cloud Storage bucket of the consumer project.

GoogleCloudAiplatformV1beta1TensorboardBlobSequence

One point viewable on a blob metric plot, but mostly just a wrapper message to work around repeated fields can't be used directly within oneof fields.

Fields
values[]	object (GoogleCloudAiplatformV1beta1TensorboardBlob) List of blobs contained within the sequence.

GoogleCloudAiplatformV1beta1TensorboardExperiment

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

Fields
createTime	string (Timestamp format) Output only. Timestamp when this TensorboardExperiment was created.
description	string Description of this TensorboardExperiment.
displayName	string User provided name of this TensorboardExperiment.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your TensorboardExperiment. Label keys and values cannot be longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with aiplatform.googleapis.com/ and are immutable. The following system labels exist for each Dataset: * aiplatform.googleapis.com/dataset_metadata_schema: output only. Its value is the metadata_schema's title.
name	string Output only. Name of the TensorboardExperiment. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}
source	string Immutable. Source of the TensorboardExperiment. Example: a custom training job.
updateTime	string (Timestamp format) Output only. Timestamp when this TensorboardExperiment was last updated.

GoogleCloudAiplatformV1beta1TensorboardRun

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

Fields
createTime	string (Timestamp format) Output only. Timestamp when this TensorboardRun was created.
description	string Description of this TensorboardRun.
displayName	string Required. User provided name of this TensorboardRun. This value must be unique among all TensorboardRuns belonging to the same parent TensorboardExperiment.
etag	string Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map (key: string, value: string) The labels with user-defined metadata to organize your TensorboardRuns. This field will be used to filter and visualize Runs in the Tensorboard UI. For example, a Vertex AI training job can set a label aiplatform.googleapis.com/training_job_id=xxxxx to all the runs created within that job. An end user can set a label experiment_id=xxxxx for all the runs produced in a Jupyter notebook. These runs can be grouped by a label value and visualized together in the Tensorboard UI. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one TensorboardRun (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
name	string Output only. Name of the TensorboardRun. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}
updateTime	string (Timestamp format) Output only. Timestamp when this TensorboardRun was last updated.

GoogleCloudAiplatformV1beta1TensorboardTensor

One point viewable on a tensor metric plot.

Fields
value	string (bytes format) Required. Serialized form of https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto
versionNumber	integer (int32 format) Optional. Version number of TensorProto used to serialize value.

GoogleCloudAiplatformV1beta1TensorboardTimeSeries

TensorboardTimeSeries maps to times series produced in training runs

Fields
createTime	string (Timestamp format) Output only. Timestamp when this TensorboardTimeSeries was created.
description	string Description of this TensorboardTimeSeries.
displayName	string Required. User provided name of this TensorboardTimeSeries. This value should be unique among all TensorboardTimeSeries resources belonging to the same TensorboardRun resource (parent resource).
etag	string Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
metadata	object (GoogleCloudAiplatformV1beta1TensorboardTimeSeriesMetadata) Output only. Scalar, Tensor, or Blob metadata for this TensorboardTimeSeries.
name	string Output only. Name of the TensorboardTimeSeries.
pluginData	string (bytes format) Data of the current plugin, with the size limited to 65KB.
pluginName	string Immutable. Name of the plugin this time series pertain to. Such as Scalar, Tensor, Blob
updateTime	string (Timestamp format) Output only. Timestamp when this TensorboardTimeSeries was last updated.
valueType	enum Required. Immutable. Type of TensorboardTimeSeries value.
Enum type. Can be one of the following:
VALUE_TYPE_UNSPECIFIED	The value type is unspecified.
SCALAR	Used for TensorboardTimeSeries that is a list of scalars. E.g. accuracy of a model over epochs/time.
TENSOR	Used for TensorboardTimeSeries that is a list of tensors. E.g. histograms of weights of layer in a model over epoch/time.
BLOB_SEQUENCE	Used for TensorboardTimeSeries that is a list of blob sequences. E.g. set of sample images with labels over epochs/time.

GoogleCloudAiplatformV1beta1TensorboardTimeSeriesMetadata

Describes metadata for a TensorboardTimeSeries.

Fields
maxBlobSequenceLength	string (int64 format) Output only. The largest blob sequence length (number of blobs) of all data points in this time series, if its ValueType is BLOB_SEQUENCE.
maxStep	string (int64 format) Output only. Max step index of all data points within a TensorboardTimeSeries.
maxWallTime	string (Timestamp format) Output only. Max wall clock timestamp of all data points within a TensorboardTimeSeries.

GoogleCloudAiplatformV1beta1ThresholdConfig

The config for feature monitoring threshold.

Fields

value

number (double format) Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

GoogleCloudAiplatformV1beta1TimeSeriesData

All the data stored in a TensorboardTimeSeries.

Fields
tensorboardTimeSeriesId	string Required. The ID of the TensorboardTimeSeries, which will become the final component of the TensorboardTimeSeries' resource name
valueType	enum Required. Immutable. The value type of this time series. All the values in this time series data must match this value type.
Enum type. Can be one of the following:
VALUE_TYPE_UNSPECIFIED	The value type is unspecified.
SCALAR	Used for TensorboardTimeSeries that is a list of scalars. E.g. accuracy of a model over epochs/time.
TENSOR	Used for TensorboardTimeSeries that is a list of tensors. E.g. histograms of weights of layer in a model over epoch/time.
BLOB_SEQUENCE	Used for TensorboardTimeSeries that is a list of blob sequences. E.g. set of sample images with labels over epochs/time.
values[]	object (GoogleCloudAiplatformV1beta1TimeSeriesDataPoint) Required. Data points in this time series.

GoogleCloudAiplatformV1beta1TimeSeriesDataPoint

A TensorboardTimeSeries data point.

Fields
blobs	object (GoogleCloudAiplatformV1beta1TensorboardBlobSequence) A blob sequence value.
scalar	object (GoogleCloudAiplatformV1beta1Scalar) A scalar value.
step	string (int64 format) Step index of this data point within the run.
tensor	object (GoogleCloudAiplatformV1beta1TensorboardTensor) A tensor value.
wallTime	string (Timestamp format) Wall clock timestamp when this data point is generated by the end user.

GoogleCloudAiplatformV1beta1TimestampSplit

Assigns input data to training, validation, and test sets based on a provided timestamps. The youngest data pieces are assigned to training set, next to validation set, and the oldest to the test set. Supported only for tabular Datasets.

Fields
key	string Required. The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
testFraction	number (double format) The fraction of the input data that is to be used to evaluate the Model.
trainingFraction	number (double format) The fraction of the input data that is to be used to train the Model.
validationFraction	number (double format) The fraction of the input data that is to be used to validate the Model.

GoogleCloudAiplatformV1beta1TokensInfo

Tokens info with a list of tokens and the corresponding list of token ids.

Fields
tokenIds[]	string (int64 format) A list of token ids from the input.
tokens[]	string (bytes format) A list of tokens from the input.

GoogleCloudAiplatformV1beta1Tool

Tool details that the model may use to generate response. A Tool is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. A Tool object should contain exactly one type of Tool (e.g FunctionDeclaration, Retrieval or GoogleSearchRetrieval).

Fields
functionDeclarations[]	object (GoogleCloudAiplatformV1beta1FunctionDeclaration) Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 64 function declarations can be provided.
retrieval	object (GoogleCloudAiplatformV1beta1Retrieval) Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation.

GoogleCloudAiplatformV1beta1ToolCallValidInput

Input for tool call valid metric.

Fields
instances[]	object (GoogleCloudAiplatformV1beta1ToolCallValidInstance) Required. Repeated tool call valid instances.
metricSpec	object (GoogleCloudAiplatformV1beta1ToolCallValidSpec) Required. Spec for tool call valid metric.

GoogleCloudAiplatformV1beta1ToolCallValidInstance

Spec for tool call valid instance.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Required. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1ToolCallValidMetricValue

Tool call valid metric value for an instance.

Fields
score	number (float format) Output only. Tool call valid score.

GoogleCloudAiplatformV1beta1ToolCallValidResults

Results for tool call valid metric.

Fields
toolCallValidMetricValues[]	object (GoogleCloudAiplatformV1beta1ToolCallValidMetricValue) Output only. Tool call valid metric values.

GoogleCloudAiplatformV1beta1ToolConfig

Tool config. This config is shared for all tools provided in the request.

Fields
functionCallingConfig	object (GoogleCloudAiplatformV1beta1FunctionCallingConfig) Optional. Function calling config.

GoogleCloudAiplatformV1beta1ToolNameMatchInput

Input for tool name match metric.

Fields
instances[]	object (GoogleCloudAiplatformV1beta1ToolNameMatchInstance) Required. Repeated tool name match instances.
metricSpec	object (GoogleCloudAiplatformV1beta1ToolNameMatchSpec) Required. Spec for tool name match metric.

GoogleCloudAiplatformV1beta1ToolNameMatchInstance

Spec for tool name match instance.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Required. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1ToolNameMatchMetricValue

Tool name match metric value for an instance.

Fields
score	number (float format) Output only. Tool name match score.

GoogleCloudAiplatformV1beta1ToolNameMatchResults

Results for tool name match metric.

Fields
toolNameMatchMetricValues[]	object (GoogleCloudAiplatformV1beta1ToolNameMatchMetricValue) Output only. Tool name match metric values.

GoogleCloudAiplatformV1beta1ToolParameterKVMatchInput

Input for tool parameter key value match metric.

Fields
instances[]	object (GoogleCloudAiplatformV1beta1ToolParameterKVMatchInstance) Required. Repeated tool parameter key value match instances.
metricSpec	object (GoogleCloudAiplatformV1beta1ToolParameterKVMatchSpec) Required. Spec for tool parameter key value match metric.

GoogleCloudAiplatformV1beta1ToolParameterKVMatchInstance

Spec for tool parameter key value match instance.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Required. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1ToolParameterKVMatchMetricValue

Tool parameter key value match metric value for an instance.

Fields
score	number (float format) Output only. Tool parameter key value match score.

GoogleCloudAiplatformV1beta1ToolParameterKVMatchResults

Results for tool parameter key value match metric.

Fields
toolParameterKvMatchMetricValues[]	object (GoogleCloudAiplatformV1beta1ToolParameterKVMatchMetricValue) Output only. Tool parameter key value match metric values.

GoogleCloudAiplatformV1beta1ToolParameterKVMatchSpec

Spec for tool parameter key value match metric.

Fields
useStrictStringMatch	boolean Optional. Whether to use STRCIT string match on parameter values.

GoogleCloudAiplatformV1beta1ToolParameterKeyMatchInput

Input for tool parameter key match metric.

Fields
instances[]	object (GoogleCloudAiplatformV1beta1ToolParameterKeyMatchInstance) Required. Repeated tool parameter key match instances.
metricSpec	object (GoogleCloudAiplatformV1beta1ToolParameterKeyMatchSpec) Required. Spec for tool parameter key match metric.

GoogleCloudAiplatformV1beta1ToolParameterKeyMatchInstance

Spec for tool parameter key match instance.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Required. Ground truth used to compare against the prediction.

GoogleCloudAiplatformV1beta1ToolParameterKeyMatchMetricValue

Tool parameter key match metric value for an instance.

Fields
score	number (float format) Output only. Tool parameter key match score.

GoogleCloudAiplatformV1beta1ToolParameterKeyMatchResults

Results for tool parameter key match metric.

Fields
toolParameterKeyMatchMetricValues[]	object (GoogleCloudAiplatformV1beta1ToolParameterKeyMatchMetricValue) Output only. Tool parameter key match metric values.

GoogleCloudAiplatformV1beta1ToolUseExample

A single example of the tool usage.

Fields
displayName	string Required. The display name for example.
extensionOperation	object (GoogleCloudAiplatformV1beta1ToolUseExampleExtensionOperation) Extension operation to call.
functionName	string Function name to call.
query	string Required. Query that should be routed to this tool.
requestParams	map (key: string, value: any) Request parameters used for executing this tool.
responseParams	map (key: string, value: any) Response parameters generated by this tool.
responseSummary	string Summary of the tool response to the user query.

GoogleCloudAiplatformV1beta1ToolUseExampleExtensionOperation

Identifies one operation of the extension.

Fields
extension	string Resource name of the extension.
operationId	string Required. Operation ID of the extension.

GoogleCloudAiplatformV1beta1TrainingConfig

CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.

Fields
timeoutTrainingMilliHours	string (int64 format) The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.

GoogleCloudAiplatformV1beta1TrainingPipeline

The TrainingPipeline orchestrates tasks associated with training a Model. It always executes the training task, and optionally may also export data from Vertex AI's Dataset which becomes the training input, upload the Model to Vertex AI, and evaluate the Model.

Fields
createTime	string (Timestamp format) Output only. Time when the TrainingPipeline was created.
displayName	string Required. The user-defined name of this TrainingPipeline.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.
endTime	string (Timestamp format) Output only. Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.
error	object (GoogleRpcStatus) Output only. Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
inputDataConfig	object (GoogleCloudAiplatformV1beta1InputDataConfig) Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline's training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration.
labels	map (key: string, value: string) The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
modelId	string Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
modelToUpload	object (GoogleCloudAiplatformV1beta1Model) Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline's training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline's state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload's resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is.
name	string Output only. Resource name of the TrainingPipeline.
parentModel	string Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model.
startTime	string (Timestamp format) Output only. Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.
state	enum Output only. The detailed state of the pipeline.
Enum type. Can be one of the following:
PIPELINE_STATE_UNSPECIFIED	The pipeline state is unspecified.
PIPELINE_STATE_QUEUED	The pipeline has been created or resumed, and processing has not yet begun.
PIPELINE_STATE_PENDING	The service is preparing to run the pipeline.
PIPELINE_STATE_RUNNING	The pipeline is in progress.
PIPELINE_STATE_SUCCEEDED	The pipeline completed successfully.
PIPELINE_STATE_FAILED	The pipeline failed.
PIPELINE_STATE_CANCELLING	The pipeline is being cancelled. From this state, the pipeline may only go to either PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
PIPELINE_STATE_CANCELLED	The pipeline has been cancelled.
PIPELINE_STATE_PAUSED	The pipeline has been stopped, and can be resumed.
trainingTaskDefinition	string Required. A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
trainingTaskInputs	any Required. The training task's parameter(s), as specified in the training_task_definition's inputs.
trainingTaskMetadata	any Output only. The metadata information as specified in the training_task_definition's metadata. This metadata is an auxiliary runtime and final information about the training task. While the pipeline is running this information is populated only at a best effort basis. Only present if the pipeline's training_task_definition contains metadata object.
updateTime	string (Timestamp format) Output only. Time when the TrainingPipeline was most recently updated.

GoogleCloudAiplatformV1beta1Trial

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.

Fields
clientId	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	string Output only. The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
endTime	string (Timestamp format) Output only. Time when the Trial's status changed to SUCCEEDED or INFEASIBLE.
finalMeasurement	object (GoogleCloudAiplatformV1beta1Measurement) Output only. The final measurement containing the objective value.
id	string Output only. The identifier of the Trial assigned by the service.
infeasibleReason	string Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is INFEASIBLE.
measurements[]	object (GoogleCloudAiplatformV1beta1Measurement) 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.
name	string Output only. Resource name of the Trial assigned by the service.
parameters[]	object (GoogleCloudAiplatformV1beta1TrialParameter) Output only. The parameters of the Trial.
startTime	string (Timestamp format) Output only. Time when the Trial was started.
state	enum Output only. The detailed state of the Trial.
Enum type. Can be one of the following:
STATE_UNSPECIFIED	The Trial state is unspecified.
REQUESTED	Indicates that a specific Trial has been requested, but it has not yet been suggested by the service.
ACTIVE	Indicates that the Trial has been suggested.
STOPPING	Indicates that the Trial should stop according to the service.
SUCCEEDED	Indicates that the Trial is completed successfully.
INFEASIBLE	Indicates that the Trial should not be attempted again. The service will set a Trial to INFEASIBLE when it's done but missing the final_measurement.
webAccessUris	map (key: string, value: string) Output only. URIs for accessing interactive shells (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.

GoogleCloudAiplatformV1beta1TrialContext

Next ID: 3

Fields
description	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[]	object (GoogleCloudAiplatformV1beta1TrialParameter) 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.

GoogleCloudAiplatformV1beta1TrialParameter

A message representing a parameter to be tuned.

Fields
parameterId	string Output only. The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
value	any 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'.

GoogleCloudAiplatformV1beta1TunedModel

The Model Registry Model and Online Prediction Endpoint assiociated with this TuningJob.

Fields
endpoint	string Output only. A resource name of an Endpoint. Format: projects/{project}/locations/{location}/endpoints/{endpoint}.
model	string Output only. The resource name of the TunedModel. Format: projects/{project}/locations/{location}/models/{model}.

GoogleCloudAiplatformV1beta1TuningDataStats

The tuning data statistic values for TuningJob.

Fields
supervisedTuningDataStats	object (GoogleCloudAiplatformV1beta1SupervisedTuningDataStats) The SFT Tuning data stats.

GoogleCloudAiplatformV1beta1TuningJob

Represents a TuningJob that runs with Google owned models.

Fields
baseModel	string The base model that is being tuned, e.g., "gemini-1.0-pro-002".
createTime	string (Timestamp format) Output only. Time when the TuningJob was created.
description	string Optional. The description of the TuningJob.
encryptionSpec	object (GoogleCloudAiplatformV1beta1EncryptionSpec) Customer-managed encryption key options for a TuningJob. If this is set, then all resources created by the TuningJob will be encrypted with the provided encryption key.
endTime	string (Timestamp format) Output only. Time when the TuningJob entered any of the following JobStates: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED, JOB_STATE_EXPIRED.
error	object (GoogleRpcStatus) Output only. Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
experiment	string Output only. The Experiment associated with this TuningJob.
labels	map (key: string, value: string) Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
name	string Output only. Identifier. Resource name of a TuningJob. Format: projects/{project}/locations/{location}/tuningJobs/{tuning_job}
startTime	string (Timestamp format) Output only. Time when the TuningJob for the first time entered the JOB_STATE_RUNNING state.
state	enum Output only. The detailed state of the job.
Enum type. Can be one of the following:
JOB_STATE_UNSPECIFIED	The job state is unspecified.
JOB_STATE_QUEUED	The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING	The service is preparing to run the job.
JOB_STATE_RUNNING	The job is in progress.
JOB_STATE_SUCCEEDED	The job completed successfully.
JOB_STATE_FAILED	The job failed.
JOB_STATE_CANCELLING	The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED	The job has been cancelled.
JOB_STATE_PAUSED	The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED	The job has expired.
JOB_STATE_UPDATING	The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED	The job is partially succeeded, some results may be missing due to errors.
supervisedTuningSpec	object (GoogleCloudAiplatformV1beta1SupervisedTuningSpec) Tuning Spec for Supervised Fine Tuning.
tunedModel	object (GoogleCloudAiplatformV1beta1TunedModel) Output only. The tuned model resources assiociated with this TuningJob.
tunedModelDisplayName	string Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters.
tuningDataStats	object (GoogleCloudAiplatformV1beta1TuningDataStats) Output only. The tuning data statistics associated with this TuningJob.
updateTime	string (Timestamp format) Output only. Time when the TuningJob was most recently updated.

GoogleCloudAiplatformV1beta1UndeployIndexOperationMetadata

Runtime operation information for IndexEndpointService.UndeployIndex.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1UndeployIndexRequest

Request message for IndexEndpointService.UndeployIndex.

Fields
deployedIndexId	string Required. The ID of the DeployedIndex to be undeployed from the IndexEndpoint.

GoogleCloudAiplatformV1beta1UndeployModelOperationMetadata

Runtime operation information for EndpointService.UndeployModel.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1UndeployModelRequest

Request message for EndpointService.UndeployModel.

Fields
deployedModelId	string Required. The ID of the DeployedModel to be undeployed from the Endpoint.
trafficSplit	map (key: string, value: integer (int32 format)) If this field is provided, then the Endpoint's traffic_split will be overwritten with it. If last DeployedModel is being undeployed from the Endpoint, the [Endpoint.traffic_split] will always end up empty when this call returns. A DeployedModel will be successfully undeployed only if it doesn't have any traffic assigned to it when this method executes, or if this field unassigns any traffic to it.

GoogleCloudAiplatformV1beta1UndeploySolverOperationMetadata

Runtime operation information for SolverService.UndeploySolver.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The generic operation information.

GoogleCloudAiplatformV1beta1UnmanagedContainerModel

Contains model information necessary to perform batch prediction without requiring a full model import.

Fields
artifactUri	string The path to the directory containing the Model artifact and any of its supporting files.
containerSpec	object (GoogleCloudAiplatformV1beta1ModelContainerSpec) Input only. The specification of the container that is to be used when deploying this Model.
predictSchemata	object (GoogleCloudAiplatformV1beta1PredictSchemata) Contains the schemata used in Model's predictions and explanations

GoogleCloudAiplatformV1beta1UpdateDeploymentResourcePoolOperationMetadata

Runtime operation information for UpdateDeploymentResourcePool method.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1UpdateExplanationDatasetOperationMetadata

Runtime operation information for ModelService.UpdateExplanationDataset.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.

GoogleCloudAiplatformV1beta1UpdateExplanationDatasetRequest

Request message for ModelService.UpdateExplanationDataset.

Fields
examples	object (GoogleCloudAiplatformV1beta1Examples) The example config containing the location of the dataset.

GoogleCloudAiplatformV1beta1UpdateFeatureGroupOperationMetadata

Details of operations that perform update FeatureGroup.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for FeatureGroup.

GoogleCloudAiplatformV1beta1UpdateFeatureOnlineStoreOperationMetadata

Details of operations that perform update FeatureOnlineStore.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for FeatureOnlineStore.

GoogleCloudAiplatformV1beta1UpdateFeatureOperationMetadata

Details of operations that perform update Feature.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Feature Update.

GoogleCloudAiplatformV1beta1UpdateFeatureViewOperationMetadata

Details of operations that perform update FeatureView.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for FeatureView Update.

GoogleCloudAiplatformV1beta1UpdateFeaturestoreOperationMetadata

Details of operations that perform update Featurestore.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Featurestore.

GoogleCloudAiplatformV1beta1UpdateIndexOperationMetadata

Runtime operation information for IndexService.UpdateIndex.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.
nearestNeighborSearchOperationMetadata	object (GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadata) The operation metadata with regard to Matching Engine Index operation.

GoogleCloudAiplatformV1beta1UpdateModelDeploymentMonitoringJobOperationMetadata

Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1UpdateModelMonitorOperationMetadata

Runtime operation information for ModelMonitoringService.UpdateModelMonitor.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.

GoogleCloudAiplatformV1beta1UpdatePersistentResourceOperationMetadata

Details of operations that perform update PersistentResource.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for PersistentResource.
progressMessage	string Progress Message for Update LRO

GoogleCloudAiplatformV1beta1UpdateSpecialistPoolOperationMetadata

Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.
specialistPool	string Output only. The name of the SpecialistPool to which the specialists are being added. Format: projects/{project_id}/locations/{location_id}/specialistPools/{specialist_pool}

GoogleCloudAiplatformV1beta1UpdateTensorboardOperationMetadata

Details of operations that perform update Tensorboard.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) Operation metadata for Tensorboard.

GoogleCloudAiplatformV1beta1UpgradeNotebookRuntimeOperationMetadata

Metadata information for NotebookService.UpgradeNotebookRuntime.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The operation generic information.
progressMessage	string A human-readable message that shows the intermediate progress details of NotebookRuntime.

GoogleCloudAiplatformV1beta1UploadModelOperationMetadata

Details of ModelService.UploadModel operation.

Fields
genericMetadata	object (GoogleCloudAiplatformV1beta1GenericOperationMetadata) The common part of the operation metadata.

GoogleCloudAiplatformV1beta1UploadModelRequest

Request message for ModelService.UploadModel.

Fields
model	object (GoogleCloudAiplatformV1beta1Model) Required. The Model to create.
modelId	string Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
parentModel	string Optional. The resource name of the model into which to upload the version. Only specify this field when uploading a new version.
serviceAccount	string Optional. The user-provided custom service account to use to do the model upload. If empty, Vertex AI Service Agent will be used to access resources needed to upload the model. This account must belong to the target project where the model is uploaded to, i.e., the project specified in the parent field of this request and have necessary read permissions (to Google Cloud Storage, Artifact Registry, etc.).

GoogleCloudAiplatformV1beta1UploadModelResponse

Response message of ModelService.UploadModel operation.

Fields
model	string The name of the uploaded Model resource. Format: projects/{project}/locations/{location}/models/{model}
modelVersionId	string Output only. The version ID of the model that is uploaded.

GoogleCloudAiplatformV1beta1UploadRagFileConfig

Config for uploading RagFile.

Fields
ragFileChunkingConfig	object (GoogleCloudAiplatformV1beta1RagFileChunkingConfig) Specifies the size and overlap of chunks after uploading RagFile.

GoogleCloudAiplatformV1beta1UploadRagFileRequest

Request message for VertexRagDataService.UploadRagFile.

Fields
ragFile	object (GoogleCloudAiplatformV1beta1RagFile) Required. The RagFile to upload.
uploadRagFileConfig	object (GoogleCloudAiplatformV1beta1UploadRagFileConfig) Required. The config for the RagFiles to be uploaded into the RagCorpus. VertexRagDataService.UploadRagFile.

GoogleCloudAiplatformV1beta1UploadRagFileResponse

Response message for VertexRagDataService.UploadRagFile.

Fields
error	object (GoogleRpcStatus) The error that occurred while processing the RagFile.
ragFile	object (GoogleCloudAiplatformV1beta1RagFile) The RagFile that had been uploaded into the RagCorpus.

GoogleCloudAiplatformV1beta1UpsertDatapointsRequest

Request message for IndexService.UpsertDatapoints

Fields
datapoints[]	object (GoogleCloudAiplatformV1beta1IndexDatapoint) A list of datapoints to be created/updated.
updateMask	string (FieldMask format) Optional. Update mask is used to specify the fields to be overwritten in the datapoints by the update. The fields specified in the update_mask are relative to each IndexDatapoint inside datapoints, not the full request. Updatable fields: * Use all_restricts to update both restricts and numeric_restricts.

GoogleCloudAiplatformV1beta1UserActionReference

References an API call. It contains more information about long running operation and Jobs that are triggered by the API call.

Fields
dataLabelingJob	string For API calls that start a LabelingJob. Resource name of the LabelingJob. Format: projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}
method	string The method name of the API RPC call. For example, "/google.cloud.aiplatform.{apiVersion}.DatasetService.CreateDataset"
operation	string For API calls that return a long running operation. Resource name of the long running operation. Format: projects/{project}/locations/{location}/operations/{operation}

GoogleCloudAiplatformV1beta1Value

Value is the value of the field.

Fields
doubleValue	number (double format) A double value.
intValue	string (int64 format) An integer value.
stringValue	string A string value.

GoogleCloudAiplatformV1beta1VertexAISearch

Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation

Fields
datastore	string Required. Fully-qualified Vertex AI Search's datastore resource ID. Format: projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}

GoogleCloudAiplatformV1beta1VertexRagStore

Retrieve from Vertex RAG Store for grounding.

Fields
ragCorpora[]	string Optional. Deprecated. Please use rag_resources instead.
ragResources[]	object (GoogleCloudAiplatformV1beta1VertexRagStoreRagResource) Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.
similarityTopK	integer (int32 format) Optional. Number of top k results to return from the selected corpora.
vectorDistanceThreshold	number (double format) Optional. Only return results with vector distance smaller than the threshold.

GoogleCloudAiplatformV1beta1VertexRagStoreRagResource

The definition of the Rag resource.

Fields
ragCorpus	string Optional. RagCorpora resource name. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}
ragFileIds[]	string Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field.

GoogleCloudAiplatformV1beta1VideoMetadata

Metadata describes the input video content.

Fields
endOffset	string (Duration format) Optional. The end offset of the video.
startOffset	string (Duration format) Optional. The start offset of the video.

GoogleCloudAiplatformV1beta1WorkerPoolSpec

Represents the spec of a worker pool in a job.

Fields
containerSpec	object (GoogleCloudAiplatformV1beta1ContainerSpec) The custom container task.
diskSpec	object (GoogleCloudAiplatformV1beta1DiskSpec) Disk spec.
machineSpec	object (GoogleCloudAiplatformV1beta1MachineSpec) Optional. Immutable. The specification of a single machine.
nfsMounts[]	object (GoogleCloudAiplatformV1beta1NfsMount) Optional. List of NFS mount spec.
pythonPackageSpec	object (GoogleCloudAiplatformV1beta1PythonPackageSpec) The Python packaged task.
replicaCount	string (int64 format) Optional. The number of worker replicas to use for this worker pool.

GoogleCloudAiplatformV1beta1WriteFeatureValuesPayload

Contains Feature values to be written for a specific entity.

Fields
entityId	string Required. The ID of the entity.
featureValues	map (key: string, value: object (GoogleCloudAiplatformV1beta1FeatureValue)) Required. Feature values to be written, mapping from Feature ID to value. Up to 100,000 feature_values entries may be written across all payloads. The feature generation time, aligned by days, must be no older than five years (1825 days) and no later than one year (366 days) in the future.

GoogleCloudAiplatformV1beta1WriteFeatureValuesRequest

Request message for FeaturestoreOnlineServingService.WriteFeatureValues.

Fields
payloads[]	object (GoogleCloudAiplatformV1beta1WriteFeatureValuesPayload) Required. The entities to be written. Up to 100,000 feature values can be written across all payloads.

GoogleCloudAiplatformV1beta1WriteTensorboardExperimentDataRequest

Request message for TensorboardService.WriteTensorboardExperimentData.

Fields
writeRunDataRequests[]	object (GoogleCloudAiplatformV1beta1WriteTensorboardRunDataRequest) Required. Requests containing per-run TensorboardTimeSeries data to write.

GoogleCloudAiplatformV1beta1WriteTensorboardRunDataRequest

Request message for TensorboardService.WriteTensorboardRunData.

Fields
tensorboardRun	string Required. The resource name of the TensorboardRun to write data to. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}
timeSeriesData[]	object (GoogleCloudAiplatformV1beta1TimeSeriesData) Required. The TensorboardTimeSeries data to write. Values with in a time series are indexed by their step value. Repeated writes to the same step will overwrite the existing value for that step. The upper limit of data points per write request is 5000.

GoogleCloudAiplatformV1beta1XraiAttribution

An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 Supported only by image Models.

Fields
blurBaselineConfig	object (GoogleCloudAiplatformV1beta1BlurBaselineConfig) Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig	object (GoogleCloudAiplatformV1beta1SmoothGradConfig) Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount	integer (int32 format) Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.

GoogleCloudLocationListLocationsResponse

The response message for Locations.ListLocations.

Fields
locations[]	object (GoogleCloudLocationLocation) A list of locations that matches the specified filter in the request.
nextPageToken	string The standard List next-page token.

GoogleCloudLocationLocation

A resource that represents a Google Cloud location.

Fields
displayName	string The friendly name for this location, typically a nearby city name. For example, "Tokyo".
labels	map (key: string, value: string) Cross-service attributes for the location. For example {"cloud.googleapis.com/region": "us-east1"}
locationId	string The canonical id for this location. For example: "us-east1".
metadata	map (key: string, value: any) Service-specific metadata. For example the available capacity at the given location.
name	string Resource name for the location, which may vary between implementations. For example: "projects/example-project/locations/us-east1"

GoogleIamV1Binding

Associates members, or principals, with a role.

Fields
condition	object (GoogleTypeExpr) The condition that is associated with this binding. If the condition evaluates to true, then this binding applies to the current request. If the condition evaluates to false, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding. To learn which resources support conditions in their IAM policies, see the IAM documentation.
members[]	string Specifies the principals requesting access for a Google Cloud resource. members can have the following values: * allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. * allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. * user:{emailid}: An email address that represents a specific Google account. For example, [e-mail] . * serviceAccount:{emailid}: An email address that represents a Google service account. For example, [e-mail]. * serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]: An identifier for a Kubernetes service account. For example, my-project.svc.id.goog[my-namespace/my-kubernetes-sa]. * group:{emailid}: An email address that represents a Google group. For example, [e-mail]. * domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com. * principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workforce identity pool. * principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}: All workforce identities in a group. * principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All workforce identities with a specific attribute value. * principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*: All identities in a workforce identity pool. * principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workload identity pool. * principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}: A workload identity pool group. * principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All identities in a workload identity pool with a certain attribute. * principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*: All identities in a workload identity pool. * deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, [e-mail]?uid=123456789012345678901. If the user is recovered, this value reverts to user:{emailid} and the recovered user retains the role in the binding. * deleted:serviceAccount:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, [e-mail]?uid=123456789012345678901. If the service account is undeleted, this value reverts to serviceAccount:{emailid} and the undeleted service account retains the role in the binding. * deleted:group:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, [e-mail]?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding. * deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: Deleted single identity in a workforce identity pool. For example, deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value.
role	string Role that is assigned to the list of members, or principals. For example, roles/viewer, roles/editor, or roles/owner. For an overview of the IAM roles and permissions, see the IAM documentation. For a list of the available pre-defined roles, see here.

GoogleIamV1GetIamPolicyRequest

Request message for GetIamPolicy method.

Fields
options	object (GoogleIamV1GetPolicyOptions) OPTIONAL: A GetPolicyOptions object for specifying options to GetIamPolicy.

GoogleIamV1GetPolicyOptions

Encapsulates settings provided to GetIamPolicy.

Fields

requestedPolicyVersion

integer (int32 format) Optional. The maximum policy version that will be used to format the policy. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset. The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1. To learn which resources support conditions in their IAM policies, see the IAM documentation.

GoogleIamV1Policy

An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources. A Policy is a collection of bindings. A binding binds one or more members, or principals, to a single role. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role. For some types of Google Cloud resources, a binding can also specify a condition, which is a logical expression that allows access to a resource only if the expression evaluates to true. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the IAM documentation. JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:[e-mail]", "group:[e-mail]", "domain:google.com", "serviceAccount:[e-mail]" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:[e-mail]" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: bindings: - members: - user:[e-mail] - group:[e-mail] - domain:google.com - serviceAccount:[e-mail] role: roles/resourcemanager.organizationAdmin - members: - user:[e-mail] role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 For a description of IAM and its features, see the IAM documentation.

Fields
bindings[]	object (GoogleIamV1Binding) Associates a list of members, or principals, with a role. Optionally, may specify a condition that determines how and when the bindings are applied. Each of the bindings must contain at least one principal. The bindings in a Policy can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the bindings grant 50 different roles to user:[e-mail], and not to any other principal, then you can add another 1,450 principals to the bindings in the Policy.
etag	string (bytes format) etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy. Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.
version	integer (int32 format) Specifies the format of the policy. Valid values are 0, 1, and 3. Requests that specify an invalid value are rejected. Any operation that affects conditional role bindings must specify version 3. This requirement applies to the following operations: * Getting a policy that includes a conditional role binding * Adding a conditional role binding to a policy * Changing a conditional role binding in a policy * Removing any role binding, with or without a condition, from a policy that includes conditions Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost. If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset. To learn which resources support conditions in their IAM policies, see the IAM documentation.

GoogleIamV1SetIamPolicyRequest

Request message for SetIamPolicy method.

Fields
policy	object (GoogleIamV1Policy) REQUIRED: The complete policy to be applied to the resource. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Google Cloud services (such as Projects) might reject them.

GoogleIamV1TestIamPermissionsRequest

Request message for TestIamPermissions method.

Fields
permissions[]	string The set of permissions to check for the resource. Permissions with wildcards (such as * or storage.*) are not allowed. For more information see IAM Overview.

GoogleIamV1TestIamPermissionsResponse

Response message for TestIamPermissions method.

Fields
permissions[]	string A subset of TestPermissionsRequest.permissions that the caller is allowed.

GoogleLongrunningListOperationsResponse

The response message for Operations.ListOperations.

Fields
nextPageToken	string The standard List next-page token.
operations[]	object (GoogleLongrunningOperation) A list of operations that matches the specified filter in the request.

GoogleLongrunningOperation

This resource represents a long-running operation that is the result of a network API call.

Fields
done	boolean 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	object (GoogleRpcStatus) The error result of the operation in case of failure or cancellation.
metadata	map (key: string, value: any) 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.
name	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	map (key: string, value: any) 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.

GoogleRpcStatus

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. 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.

Fields
code	integer (int32 format) The status code, which should be an enum value of google.rpc.Code.
details[]	object A list of messages that carry the error details. There is a common set of message types for APIs to use.
message	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.

GoogleTypeColor

Represents a color in the RGBA color space. This representation is designed for simplicity of conversion to and from color representations in various languages over compactness. For example, the fields of this representation can be trivially provided to the constructor of java.awt.Color in Java; it can also be trivially provided to UIColor's +colorWithRed:green:blue:alpha method in iOS; and, with just a little work, it can be easily formatted into a CSS rgba() string in JavaScript. This reference page doesn't have information about the absolute color space that should be used to interpret the RGB value—for example, sRGB, Adobe RGB, DCI-P3, and BT.2020. By default, applications should assume the sRGB color space. When color equality needs to be decided, implementations, unless documented otherwise, treat two colors as equal if all their red, green, blue, and alpha values each differ by at most 1e-5. Example (Java): import com.google.type.Color; // ... public static java.awt.Color fromProto(Color protocolor) { float alpha = protocolor.hasAlpha() ? protocolor.getAlpha().getValue() : 1.0; return new java.awt.Color( protocolor.getRed(), protocolor.getGreen(), protocolor.getBlue(), alpha); } public static Color toProto(java.awt.Color color) { float red = (float) color.getRed(); float green = (float) color.getGreen(); float blue = (float) color.getBlue(); float denominator = 255.0; Color.Builder resultBuilder = Color .newBuilder() .setRed(red / denominator) .setGreen(green / denominator) .setBlue(blue / denominator); int alpha = color.getAlpha(); if (alpha != 255) { result.setAlpha( FloatValue .newBuilder() .setValue(((float) alpha) / denominator) .build()); } return resultBuilder.build(); } // ... Example (iOS / Obj-C): // ... static UIColor fromProto(Color protocolor) { float red = [protocolor red]; float green = [protocolor green]; float blue = [protocolor blue]; FloatValue alpha_wrapper = [protocolor alpha]; float alpha = 1.0; if (alpha_wrapper != nil) { alpha = [alpha_wrapper value]; } return [UIColor colorWithRed:red green:green blue:blue alpha:alpha]; } static Color toProto(UIColor color) { CGFloat red, green, blue, alpha; if (![color getRed:&red green:&green blue:&blue alpha:&alpha]) { return nil; } Color result = [[Color alloc] init]; [result setRed:red]; [result setGreen:green]; [result setBlue:blue]; if (alpha <= 0.9999) { [result setAlpha:floatWrapperWithValue(alpha)]; } [result autorelease]; return result; } // ... Example (JavaScript): // ... var protoToCssColor = function(rgb_color) { var redFrac = rgb_color.red || 0.0; var greenFrac = rgb_color.green || 0.0; var blueFrac = rgb_color.blue || 0.0; var red = Math.floor(redFrac * 255); var green = Math.floor(greenFrac * 255); var blue = Math.floor(blueFrac * 255); if (!('alpha' in rgb_color)) { return rgbToCssColor(red, green, blue); } var alphaFrac = rgb_color.alpha.value || 0.0; var rgbParams = [red, green, blue].join(','); return ['rgba(', rgbParams, ',', alphaFrac, ')'].join(''); }; var rgbToCssColor = function(red, green, blue) { var rgbNumber = new Number((red << 16) | (green << 8) | blue); var hexString = rgbNumber.toString(16); var missingZeros = 6 - hexString.length; var resultBuilder = ['#']; for (var i = 0; i < missingZeros; i++) { resultBuilder.push('0'); } resultBuilder.push(hexString); return resultBuilder.join(''); }; // ...

Fields
alpha	number (float format) The fraction of this color that should be applied to the pixel. That is, the final pixel color is defined by the equation: pixel color = alpha * (this color) + (1.0 - alpha) * (background color) This means that a value of 1.0 corresponds to a solid color, whereas a value of 0.0 corresponds to a completely transparent color. This uses a wrapper message rather than a simple float scalar so that it is possible to distinguish between a default value and the value being unset. If omitted, this color object is rendered as a solid color (as if the alpha value had been explicitly given a value of 1.0).
blue	number (float format) The amount of blue in the color as a value in the interval [0, 1].
green	number (float format) The amount of green in the color as a value in the interval [0, 1].
red	number (float format) The amount of red in the color as a value in the interval [0, 1].

GoogleTypeDate

Represents a whole or partial calendar date, such as a birthday. The time of day and time zone are either specified elsewhere or are insignificant. The date is relative to the Gregorian Calendar. This can represent one of the following: * A full date, with non-zero year, month, and day values. * A month and day, with a zero year (for example, an anniversary). * A year on its own, with a zero month and a zero day. * A year and month, with a zero day (for example, a credit card expiration date). Related types: * google.type.TimeOfDay * google.type.DateTime * google.protobuf.Timestamp

Fields
day	integer (int32 format) Day of a month. Must be from 1 to 31 and valid for the year and month, or 0 to specify a year by itself or a year and month where the day isn't significant.
month	integer (int32 format) Month of a year. Must be from 1 to 12, or 0 to specify a year without a month and day.
year	integer (int32 format) Year of the date. Must be from 1 to 9999, or 0 to specify a date without a year.

GoogleTypeExpr

Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec. Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information.

Fields
description	string Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
expression	string Textual representation of an expression in Common Expression Language syntax.
location	string Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
title	string Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.

GoogleTypeInterval

Represents a time interval, encoded as a Timestamp start (inclusive) and a Timestamp end (exclusive). The start must be less than or equal to the end. When the start equals the end, the interval is empty (matches no time). When both start and end are unspecified, the interval matches any time.

Fields
endTime	string (Timestamp format) Optional. Exclusive end of the interval. If specified, a Timestamp matching this interval will have to be before the end.
startTime	string (Timestamp format) Optional. Inclusive start of the interval. If specified, a Timestamp matching this interval will have to be the same or after the start.

GoogleTypeMoney

Represents an amount of money with its currency type.

Fields
currencyCode	string The three-letter currency code defined in ISO 4217.
nanos	integer (int32 format) Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If units is positive, nanos must be positive or zero. If units is zero, nanos can be positive, zero, or negative. If units is negative, nanos must be negative or zero. For example $-1.75 is represented as units=-1 and nanos=-750,000,000.
units	string (int64 format) The whole units of the amount. For example if currencyCode is "USD", then 1 unit is one US dollar.