googleapis
Version:
Google APIs Client Library for Node.js
826 lines • 1.65 MB
TypeScript
/// <reference types="node" />
import { OAuth2Client, JWT, Compute, UserRefreshClient, BaseExternalAccountClient, GaxiosPromise, GoogleConfigurable, MethodOptions, StreamMethodOptions, GlobalOptions, GoogleAuth, BodyResponseCallback, APIRequestContext } from 'googleapis-common';
import { Readable } from 'stream';
export declare namespace aiplatform_v1beta1 {
export interface Options extends GlobalOptions {
version: 'v1beta1';
}
interface StandardParameters {
/**
* Auth client or API Key for the request
*/
auth?: string | OAuth2Client | JWT | Compute | UserRefreshClient | BaseExternalAccountClient | GoogleAuth;
/**
* V1 error format.
*/
'$.xgafv'?: string;
/**
* OAuth access token.
*/
access_token?: string;
/**
* Data format for response.
*/
alt?: string;
/**
* JSONP
*/
callback?: string;
/**
* Selector specifying which fields to include in a partial response.
*/
fields?: string;
/**
* API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
*/
key?: string;
/**
* OAuth 2.0 token for the current user.
*/
oauth_token?: string;
/**
* Returns response with indentations and line breaks.
*/
prettyPrint?: boolean;
/**
* Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
*/
quotaUser?: string;
/**
* Legacy upload protocol for media (e.g. "media", "multipart").
*/
uploadType?: string;
/**
* Upload protocol for media (e.g. "raw", "multipart").
*/
upload_protocol?: string;
}
/**
* Vertex AI API
*
* Train high-quality custom machine learning models with minimal machine learning expertise and effort.
*
* @example
* ```js
* const {google} = require('googleapis');
* const aiplatform = google.aiplatform('v1beta1');
* ```
*/
export class Aiplatform {
context: APIRequestContext;
projects: Resource$Projects;
publishers: Resource$Publishers;
constructor(options: GlobalOptions, google?: GoogleConfigurable);
}
/**
* Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; \} service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); \} Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); \} Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged.
*/
export interface Schema$GoogleApiHttpBody {
/**
* The HTTP Content-Type header value specifying the content type of the body.
*/
contentType?: string | null;
/**
* The HTTP request/response body as raw binary.
*/
data?: string | null;
/**
* Application specific response metadata. Must be set in the first response for streaming APIs.
*/
extensions?: Array<{
[key: string]: any;
}> | null;
}
/**
* 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.
*/
export interface Schema$GoogleCloudAiplatformV1beta1ActiveLearningConfig {
/**
* Max number of human labeled DataItems.
*/
maxDataItemCount?: string | null;
/**
* Max percent of total DataItems for human labeling.
*/
maxDataItemPercentage?: number | null;
/**
* Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
*/
sampleConfig?: Schema$GoogleCloudAiplatformV1beta1SampleConfig;
/**
* 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.
*/
trainingConfig?: Schema$GoogleCloudAiplatformV1beta1TrainingConfig;
}
/**
* Request message for MetadataService.AddContextArtifactsAndExecutions.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AddContextArtifactsAndExecutionsRequest {
/**
* The resource names of the Artifacts to attribute to the Context. Format: `projects/{project\}/locations/{location\}/metadataStores/{metadatastore\}/artifacts/{artifact\}`
*/
artifacts?: string[] | null;
/**
* The resource names of the Executions to associate with the Context. Format: `projects/{project\}/locations/{location\}/metadataStores/{metadatastore\}/executions/{execution\}`
*/
executions?: string[] | null;
}
/**
* Response message for MetadataService.AddContextArtifactsAndExecutions.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AddContextArtifactsAndExecutionsResponse {
}
/**
* Request message for MetadataService.AddContextChildren.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AddContextChildrenRequest {
/**
* The resource names of the child Contexts.
*/
childContexts?: string[] | null;
}
/**
* Response message for MetadataService.AddContextChildren.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AddContextChildrenResponse {
}
/**
* Request message for MetadataService.AddExecutionEvents.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AddExecutionEventsRequest {
/**
* The Events to create and add.
*/
events?: Schema$GoogleCloudAiplatformV1beta1Event[];
}
/**
* Response message for MetadataService.AddExecutionEvents.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AddExecutionEventsResponse {
}
/**
* Request message for VizierService.AddTrialMeasurement.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AddTrialMeasurementRequest {
/**
* Required. The measurement to be added to a Trial.
*/
measurement?: Schema$GoogleCloudAiplatformV1beta1Measurement;
}
/**
* Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.
*/
export interface Schema$GoogleCloudAiplatformV1beta1Annotation {
/**
* Output only. The source of the Annotation.
*/
annotationSource?: Schema$GoogleCloudAiplatformV1beta1UserActionReference;
/**
* Output only. Timestamp when this Annotation was created.
*/
createTime?: string | null;
/**
* Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
*/
etag?: string | null;
/**
* 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.
*/
labels?: {
[key: string]: string;
} | null;
/**
* Output only. Resource name of the Annotation.
*/
name?: string | null;
/**
* Required. The schema of the payload can be found in payload_schema.
*/
payload?: any | null;
/**
* Required. Google Cloud Storage URI points to a YAML file describing payload. The schema is defined as an [OpenAPI 3.0.2 Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). 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.
*/
payloadSchemaUri?: string | null;
/**
* Output only. Timestamp when this Annotation was last updated.
*/
updateTime?: string | null;
}
/**
* Identifies a concept with which DataItems may be annotated with.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AnnotationSpec {
/**
* Output only. Timestamp when this AnnotationSpec was created.
*/
createTime?: string | null;
/**
* Required. The user-defined name of the AnnotationSpec. The name can be up to 128 characters long and can consist of any UTF-8 characters.
*/
displayName?: string | null;
/**
* Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
*/
etag?: string | null;
/**
* Output only. Resource name of the AnnotationSpec.
*/
name?: string | null;
/**
* Output only. Timestamp when AnnotationSpec was last updated.
*/
updateTime?: string | null;
}
/**
* Instance of a general artifact.
*/
export interface Schema$GoogleCloudAiplatformV1beta1Artifact {
/**
* Output only. Timestamp when this Artifact was created.
*/
createTime?: string | null;
/**
* Description of the Artifact
*/
description?: string | null;
/**
* User provided display name of the Artifact. May be up to 128 Unicode characters.
*/
displayName?: string | null;
/**
* An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
*/
etag?: string | null;
/**
* 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).
*/
labels?: {
[key: string]: string;
} | null;
/**
* Properties of the Artifact. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.
*/
metadata?: {
[key: string]: any;
} | null;
/**
* Output only. The resource name of the Artifact.
*/
name?: string | null;
/**
* 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.
*/
schemaTitle?: string | null;
/**
* 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.
*/
schemaVersion?: string | null;
/**
* 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.
*/
state?: string | null;
/**
* Output only. Timestamp when this Artifact was last updated.
*/
updateTime?: string | null;
/**
* The uniform resource identifier of the artifact file. May be empty if there is no actual artifact file.
*/
uri?: string | null;
}
/**
* Metadata information for NotebookService.AssignNotebookRuntime.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AssignNotebookRuntimeOperationMetadata {
/**
* The operation generic information.
*/
genericMetadata?: Schema$GoogleCloudAiplatformV1beta1GenericOperationMetadata;
/**
* A human-readable message that shows the intermediate progress details of NotebookRuntime.
*/
progressMessage?: string | null;
}
/**
* Request message for NotebookService.AssignNotebookRuntime.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AssignNotebookRuntimeRequest {
/**
* Required. Provide runtime specific information (e.g. runtime owner, notebook id) used for NotebookRuntime assignment.
*/
notebookRuntime?: Schema$GoogleCloudAiplatformV1beta1NotebookRuntime;
/**
* Optional. User specified ID for the notebook runtime.
*/
notebookRuntimeId?: string | null;
/**
* Required. The resource name of the NotebookRuntimeTemplate based on which a NotebookRuntime will be assigned (reuse or create a new one).
*/
notebookRuntimeTemplate?: string | null;
}
/**
* Attribution that explains a particular prediction output.
*/
export interface Schema$GoogleCloudAiplatformV1beta1Attribution {
/**
* 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](/vertex-ai/docs/explainable-ai/overview) for more information.
*/
approximationError?: number | null;
/**
* 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.
*/
baselineOutputValue?: number | null;
/**
* 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).
*/
featureAttributions?: any | null;
/**
* 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.
*/
instanceOutputValue?: number | null;
/**
* 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.
*/
outputDisplayName?: string | null;
/**
* 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.
*/
outputIndex?: number[] | null;
/**
* Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs.
*/
outputName?: string | null;
}
/**
* 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.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AutomaticResources {
/**
* 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.
*/
maxReplicaCount?: number | null;
/**
* 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.
*/
minReplicaCount?: number | null;
}
/**
* The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AutoscalingMetricSpec {
/**
* Required. The resource metric name. Supported metrics: * For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization`
*/
metricName?: string | null;
/**
* The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
*/
target?: number | null;
}
/**
* The storage details for Avro input content.
*/
export interface Schema$GoogleCloudAiplatformV1beta1AvroSource {
/**
* Required. Google Cloud Storage location.
*/
gcsSource?: Schema$GoogleCloudAiplatformV1beta1GcsSource;
}
/**
* Details of operations that perform batch create Features.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchCreateFeaturesOperationMetadata {
/**
* Operation metadata for Feature.
*/
genericMetadata?: Schema$GoogleCloudAiplatformV1beta1GenericOperationMetadata;
}
/**
* Request message for FeaturestoreService.BatchCreateFeatures.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchCreateFeaturesRequest {
/**
* 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.
*/
requests?: Schema$GoogleCloudAiplatformV1beta1CreateFeatureRequest[];
}
/**
* Response message for FeaturestoreService.BatchCreateFeatures.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchCreateFeaturesResponse {
/**
* The Features created.
*/
features?: Schema$GoogleCloudAiplatformV1beta1Feature[];
}
/**
* Request message for TensorboardService.BatchCreateTensorboardRuns.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsRequest {
/**
* Required. The request message specifying the TensorboardRuns to create. A maximum of 1000 TensorboardRuns can be created in a batch.
*/
requests?: Schema$GoogleCloudAiplatformV1beta1CreateTensorboardRunRequest[];
}
/**
* Response message for TensorboardService.BatchCreateTensorboardRuns.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchCreateTensorboardRunsResponse {
/**
* The created TensorboardRuns.
*/
tensorboardRuns?: Schema$GoogleCloudAiplatformV1beta1TensorboardRun[];
}
/**
* Request message for TensorboardService.BatchCreateTensorboardTimeSeries.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesRequest {
/**
* Required. The request message specifying the TensorboardTimeSeries to create. A maximum of 1000 TensorboardTimeSeries can be created in a batch.
*/
requests?: Schema$GoogleCloudAiplatformV1beta1CreateTensorboardTimeSeriesRequest[];
}
/**
* Response message for TensorboardService.BatchCreateTensorboardTimeSeries.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchCreateTensorboardTimeSeriesResponse {
/**
* The created TensorboardTimeSeries.
*/
tensorboardTimeSeries?: Schema$GoogleCloudAiplatformV1beta1TensorboardTimeSeries[];
}
/**
* A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchDedicatedResources {
/**
* Required. Immutable. The specification of a single machine.
*/
machineSpec?: Schema$GoogleCloudAiplatformV1beta1MachineSpec;
/**
* Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
*/
maxReplicaCount?: number | null;
/**
* Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
*/
startingReplicaCount?: number | null;
}
/**
* Request message for ModelService.BatchImportEvaluatedAnnotations
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsRequest {
/**
* Required. Evaluated annotations resource to be imported.
*/
evaluatedAnnotations?: Schema$GoogleCloudAiplatformV1beta1EvaluatedAnnotation[];
}
/**
* Response message for ModelService.BatchImportEvaluatedAnnotations
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchImportEvaluatedAnnotationsResponse {
/**
* Output only. Number of EvaluatedAnnotations imported.
*/
importedEvaluatedAnnotationsCount?: number | null;
}
/**
* Request message for ModelService.BatchImportModelEvaluationSlices
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesRequest {
/**
* Required. Model evaluation slice resource to be imported.
*/
modelEvaluationSlices?: Schema$GoogleCloudAiplatformV1beta1ModelEvaluationSlice[];
}
/**
* Response message for ModelService.BatchImportModelEvaluationSlices
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchImportModelEvaluationSlicesResponse {
/**
* Output only. List of imported ModelEvaluationSlice.name.
*/
importedModelEvaluationSlices?: string[] | null;
}
/**
* Runtime operation information for MigrationService.BatchMigrateResources.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadata {
/**
* The common part of the operation metadata.
*/
genericMetadata?: Schema$GoogleCloudAiplatformV1beta1GenericOperationMetadata;
/**
* Partial results that reflect the latest migration operation progress.
*/
partialResults?: Schema$GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult[];
}
/**
* Represents a partial result in batch migration operation for one MigrateResourceRequest.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult {
/**
* Migrated dataset resource name.
*/
dataset?: string | null;
/**
* The error result of the migration request in case of failure.
*/
error?: Schema$GoogleRpcStatus;
/**
* Migrated model resource name.
*/
model?: string | null;
/**
* It's the same as the value in MigrateResourceRequest.migrate_resource_requests.
*/
request?: Schema$GoogleCloudAiplatformV1beta1MigrateResourceRequest;
}
/**
* Request message for MigrationService.BatchMigrateResources.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchMigrateResourcesRequest {
/**
* 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.
*/
migrateResourceRequests?: Schema$GoogleCloudAiplatformV1beta1MigrateResourceRequest[];
}
/**
* Response message for MigrationService.BatchMigrateResources.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchMigrateResourcesResponse {
/**
* Successfully migrated resources.
*/
migrateResourceResponses?: Schema$GoogleCloudAiplatformV1beta1MigrateResourceResponse[];
}
/**
* A job that uses a Model to produce predictions on multiple input instances. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchPredictionJob {
/**
* Output only. Statistics on completed and failed prediction instances.
*/
completionStats?: Schema$GoogleCloudAiplatformV1beta1CompletionStats;
/**
* Output only. Time when the BatchPredictionJob was created.
*/
createTime?: string | null;
/**
* The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
*/
dedicatedResources?: Schema$GoogleCloudAiplatformV1beta1BatchDedicatedResources;
/**
* 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](https://cloud.google.com/logging/pricing). User can disable container logging by setting this flag to true.
*/
disableContainerLogging?: boolean | null;
/**
* Required. The user-defined name of this BatchPredictionJob.
*/
displayName?: string | null;
/**
* 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.
*/
encryptionSpec?: Schema$GoogleCloudAiplatformV1beta1EncryptionSpec;
/**
* Output only. Time when the BatchPredictionJob entered any of the following states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`.
*/
endTime?: string | null;
/**
* Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
*/
error?: Schema$GoogleRpcStatus;
/**
* 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.
*/
explanationSpec?: Schema$GoogleCloudAiplatformV1beta1ExplanationSpec;
/**
* Generate explanation with the batch prediction results. When set to `true`, the batch prediction output changes based on the `predictions_format` field of the BatchPredictionJob.output_config object: * `bigquery`: output includes a column named `explanation`. The value is a struct that conforms to the Explanation object. * `jsonl`: The JSON objects on each line include an additional entry keyed `explanation`. The value of the entry is a JSON object that conforms to the Explanation object. * `csv`: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
*/
generateExplanation?: boolean | null;
/**
* Required. Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
*/
inputConfig?: Schema$GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig;
/**
* Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
*/
instanceConfig?: Schema$GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig;
/**
* The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
*/
labels?: {
[key: string]: string;
} | null;
/**
* Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
*/
manualBatchTuningParameters?: Schema$GoogleCloudAiplatformV1beta1ManualBatchTuningParameters;
/**
* 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\}`
*/
model?: string | null;
/**
* 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.
*/
modelMonitoringConfig?: Schema$GoogleCloudAiplatformV1beta1ModelMonitoringConfig;
/**
* Get batch prediction job monitoring statistics.
*/
modelMonitoringStatsAnomalies?: Schema$GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomalies[];
/**
* Output only. The running status of the model monitoring pipeline.
*/
modelMonitoringStatus?: Schema$GoogleRpcStatus;
/**
* The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
*/
modelParameters?: any | null;
/**
* Output only. The version ID of the Model that produces the predictions via this job.
*/
modelVersionId?: string | null;
/**
* Output only. Resource name of the BatchPredictionJob.
*/
name?: string | null;
/**
* Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
*/
outputConfig?: Schema$GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig;
/**
* Output only. Information further describing the output of this job.
*/
outputInfo?: Schema$GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfo;
/**
* Output only. Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
*/
partialFailures?: Schema$GoogleRpcStatus[];
/**
* 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.
*/
resourcesConsumed?: Schema$GoogleCloudAiplatformV1beta1ResourcesConsumed;
/**
* The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the `iam.serviceAccounts.actAs` permission on this service account.
*/
serviceAccount?: string | null;
/**
* Output only. Time when the BatchPredictionJob for the first time entered the `JOB_STATE_RUNNING` state.
*/
startTime?: string | null;
/**
* Output only. The detailed state of the job.
*/
state?: string | null;
/**
* Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
*/
unmanagedContainerModel?: Schema$GoogleCloudAiplatformV1beta1UnmanagedContainerModel;
/**
* Output only. Time when the BatchPredictionJob was most recently updated.
*/
updateTime?: string | null;
}
/**
* 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.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig {
/**
* The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
*/
bigquerySource?: Schema$GoogleCloudAiplatformV1beta1BigQuerySource;
/**
* The Cloud Storage location for the input instances.
*/
gcsSource?: Schema$GoogleCloudAiplatformV1beta1GcsSource;
/**
* Required. The format in which instances are given, must be one of the Model's supported_input_storage_formats.
*/
instancesFormat?: string | null;
}
/**
* Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig {
/**
* 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, CSV, BigQuery or TfRecord.
*/
excludedFields?: string[] | null;
/**
* 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, CSV, BigQuery or TfRecord.
*/
includedFields?: string[] | null;
/**
* 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.
*/
instanceType?: string | null;
/**
* 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.
*/
keyField?: string | null;
}
/**
* Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig {
/**
* The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name `prediction__` where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, `predictions`, and `errors`. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The `predictions` table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The `errors` table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only `code` and `message`.
*/
bigqueryDestination?: Schema$GoogleCloudAiplatformV1beta1BigQueryDestination;
/**
* 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 additional `errors_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 additional `error` field which as value has google.rpc.Status containing only `code` and `message` fields.
*/
gcsDestination?: Schema$GoogleCloudAiplatformV1beta1GcsDestination;
/**
* Required. The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
*/
predictionsFormat?: string | null;
}
/**
* Further describes this job's output. Supplements output_config.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfo {
/**
* Output only. The path of the BigQuery dataset created, in `bq://projectId.bqDatasetId` format, into which the prediction output is written.
*/
bigqueryOutputDataset?: string | null;
/**
* 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.
*/
bigqueryOutputTable?: string | null;
/**
* Output only. The full path of the Cloud Storage directory created, into which the prediction output is written.
*/
gcsOutputDirectory?: string | null;
}
/**
* Details of operations that batch reads Feature values.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchReadFeatureValuesOperationMetadata {
/**
* Operation metadata for Featurestore batch read Features values.
*/
genericMetadata?: Schema$GoogleCloudAiplatformV1beta1GenericOperationMetadata;
}
/**
* Request message for FeaturestoreService.BatchReadFeatureValues.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequest {
/**
* Similar to csv_read_instances, but from BigQuery source.
*/
bigqueryReadInstances?: Schema$GoogleCloudAiplatformV1beta1BigQuerySource;
/**
* 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`.
*/
csvReadInstances?: Schema$GoogleCloudAiplatformV1beta1CsvSource;
/**
* Required. Specifies output location and format.
*/
destination?: Schema$GoogleCloudAiplatformV1beta1FeatureValueDestination;
/**
* Required. Specifies EntityType grouping Features to read values of and settings.
*/
entityTypeSpecs?: Schema$GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestEntityTypeSpec[];
/**
* When not empty, the specified fields in the *_read_instances source will be joined as-is in the output, in addition to those fields from the Featurestore Entity. For BigQuery source, the type of the pass-through values will be automatically inferred. For CSV source, the pass-through values will be passed as opaque bytes.
*/
passThroughFields?: Schema$GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestPassThroughField[];
/**
* 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.
*/
startTime?: string | null;
}
/**
* Selects Features of an EntityType to read values of and specifies read settings.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestEntityTypeSpec {
/**
* Required. ID of the EntityType to select Features. The EntityType id is the entity_type_id specified during EntityType creation.
*/
entityTypeId?: string | null;
/**
* Required. Selectors choosing which Feature values to read from the EntityType.
*/
featureSelector?: Schema$GoogleCloudAiplatformV1beta1FeatureSelector;
/**
* Per-Feature settings for the batch read.
*/
settings?: Schema$GoogleCloudAiplatformV1beta1DestinationFeatureSetting[];
}
/**
* Describe pass-through fields in read_instance source.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchReadFeatureValuesRequestPassThroughField {
/**
* 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.
*/
fieldName?: string | null;
}
/**
* Response message for FeaturestoreService.BatchReadFeatureValues.
*/
export interface Schema$GoogleCloudAiplatformV1beta1BatchReadFeatureValuesResponse {
}
/**
* Response message for Tensorboa