As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

Types for Google Cloud Aiplatform v1 API

class google.cloud.aiplatform_v1.types.AcceleratorType(value)

Bases: Enum

Represents a hardware accelerator type.

Values:

ACCELERATOR_TYPE_UNSPECIFIED (0):

Unspecified accelerator type, which means no accelerator.

NVIDIA_TESLA_K80 (1):

Deprecated: Nvidia Tesla K80 GPU has reached end of support, see https://cloud.google.com/compute/docs/eol/k80-eol.

NVIDIA_TESLA_P100 (2):

Nvidia Tesla P100 GPU.

NVIDIA_TESLA_V100 (3):

Nvidia Tesla V100 GPU.

NVIDIA_TESLA_P4 (4):

Nvidia Tesla P4 GPU.

NVIDIA_TESLA_T4 (5):

Nvidia Tesla T4 GPU.

NVIDIA_TESLA_A100 (8):

Nvidia Tesla A100 GPU.

NVIDIA_A100_80GB (9):

Nvidia A100 80GB GPU.

NVIDIA_L4 (11):

Nvidia L4 GPU.

NVIDIA_H100_80GB (13):

Nvidia H100 80Gb GPU.

TPU_V2 (6):

TPU v2.

TPU_V3 (7):

TPU v3.

TPU_V4_POD (10):

TPU v4.

TPU_V5_LITEPOD (12):

TPU v5.

class google.cloud.aiplatform_v1.types.ActiveLearningConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

max_data_item_count

Max number of human labeled DataItems.

This field is a member of oneof human_labeling_budget.

Type:

int

max_data_item_percentage

Max percent of total DataItems for human labeling.

This field is a member of oneof human_labeling_budget.

Type:

int

sample_config

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

Type:

google.cloud.aiplatform_v1.types.SampleConfig

training_config

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.

Type:

google.cloud.aiplatform_v1.types.TrainingConfig

class google.cloud.aiplatform_v1.types.AddContextArtifactsAndExecutionsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.AddContextArtifactsAndExecutions][google.cloud.aiplatform.v1.MetadataService.AddContextArtifactsAndExecutions].

context

Required. The resource name of the Context that the Artifacts and Executions belong to. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}

Type:

str

artifacts

The resource names of the Artifacts to attribute to the Context.

Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}

Type:

MutableSequence[str]

executions

The resource names of the Executions to associate with the Context.

Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.AddContextArtifactsAndExecutionsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.AddContextArtifactsAndExecutions][google.cloud.aiplatform.v1.MetadataService.AddContextArtifactsAndExecutions].

class google.cloud.aiplatform_v1.types.AddContextChildrenRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.AddContextChildren][google.cloud.aiplatform.v1.MetadataService.AddContextChildren].

context

Required. The resource name of the parent Context.

Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}

Type:

str

child_contexts

The resource names of the child Contexts.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.AddContextChildrenResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.AddContextChildren][google.cloud.aiplatform.v1.MetadataService.AddContextChildren].

class google.cloud.aiplatform_v1.types.AddExecutionEventsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.AddExecutionEvents][google.cloud.aiplatform.v1.MetadataService.AddExecutionEvents].

execution

Required. The resource name of the Execution that the Events connect Artifacts with. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}

Type:

str

events

The Events to create and add.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Event]

class google.cloud.aiplatform_v1.types.AddExecutionEventsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.AddExecutionEvents][google.cloud.aiplatform.v1.MetadataService.AddExecutionEvents].

class google.cloud.aiplatform_v1.types.AddTrialMeasurementRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.AddTrialMeasurement][google.cloud.aiplatform.v1.VizierService.AddTrialMeasurement].

trial_name

Required. The name of the trial to add measurement. Format: projects/{project}/locations/{location}/studies/{study}/trials/{trial}

Type:

str

measurement

Required. The measurement to be added to a Trial.

Type:

google.cloud.aiplatform_v1.types.Measurement

class google.cloud.aiplatform_v1.types.Annotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. Resource name of the Annotation.

Type:

str

payload_schema_uri

Required. Google Cloud Storage URI points to a YAML file describing [payload][google.cloud.aiplatform.v1.Annotation.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][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri].

Type:

str

payload

Required. The schema of the payload can be found in [payload_schema][google.cloud.aiplatform.v1.Annotation.payload_schema_uri].

Type:

google.protobuf.struct_pb2.Value

create_time

Output only. Timestamp when this Annotation was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this Annotation was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

etag

Optional. Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

annotation_source

Output only. The source of the Annotation.

Type:

google.cloud.aiplatform_v1.types.UserActionReference

labels

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][google.cloud.aiplatform.v1.Annotation.payload_schema_uri] title.

Type:

MutableMapping[str, str]

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.AnnotationSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Identifies a concept with which DataItems may be annotated with.

name

Output only. Resource name of the AnnotationSpec.

Type:

str

display_name

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.

Type:

str

create_time

Output only. Timestamp when this AnnotationSpec was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when AnnotationSpec was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

etag

Optional. Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

class google.cloud.aiplatform_v1.types.Artifact(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Instance of a general artifact.

name

Output only. The resource name of the Artifact.

Type:

str

display_name

User provided display name of the Artifact. May be up to 128 Unicode characters.

Type:

str

uri

The uniform resource identifier of the artifact file. May be empty if there is no actual artifact file.

Type:

str

etag

An eTag used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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).

Type:

MutableMapping[str, str]

create_time

Output only. Timestamp when this Artifact was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this Artifact was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

state

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.

Type:

google.cloud.aiplatform_v1.types.Artifact.State

schema_title

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.

Type:

str

schema_version

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.

Type:

str

metadata

Properties of the Artifact. Top level metadata keys’ heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.

Type:

google.protobuf.struct_pb2.Struct

description

Description of the Artifact

Type:

str

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class State(value)

Bases: Enum

Describes the state of the Artifact.

Values:

STATE_UNSPECIFIED (0):

Unspecified state for the Artifact.

PENDING (1):

A state used by systems like Vertex AI Pipelines to indicate that the underlying data item represented by this Artifact is being created.

LIVE (2):

A state indicating that the Artifact should exist, unless something external to the system deletes it.

class google.cloud.aiplatform_v1.types.AssignNotebookRuntimeOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata information for [NotebookService.AssignNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.AssignNotebookRuntime].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

progress_message

A human-readable message that shows the intermediate progress details of NotebookRuntime.

Type:

str

class google.cloud.aiplatform_v1.types.AssignNotebookRuntimeRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.AssignNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.AssignNotebookRuntime].

parent

Required. The resource name of the Location to get the NotebookRuntime assignment. Format: projects/{project}/locations/{location}

Type:

str

notebook_runtime_template

Required. The resource name of the NotebookRuntimeTemplate based on which a NotebookRuntime will be assigned (reuse or create a new one).

Type:

str

notebook_runtime

Required. Provide runtime specific information (e.g. runtime owner, notebook id) used for NotebookRuntime assignment.

Type:

google.cloud.aiplatform_v1.types.NotebookRuntime

notebook_runtime_id

Optional. User specified ID for the notebook runtime.

Type:

str

class google.cloud.aiplatform_v1.types.Attribution(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Attribution that explains a particular prediction output.

baseline_output_value

Output only. Model predicted output if the input instance is constructed from the baselines of all the features defined in [ExplanationMetadata.inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs]. The field name of the output is determined by the key in [ExplanationMetadata.outputs][google.cloud.aiplatform.v1.ExplanationMetadata.outputs].

If the Model’s predicted output has multiple dimensions (rank > 1), this is the value in the output located by [output_index][google.cloud.aiplatform.v1.Attribution.output_index].

If there are multiple baselines, their output values are averaged.

Type:

float

instance_output_value

Output only. Model predicted output on the corresponding [explanation instance][ExplainRequest.instances]. The field name of the output is determined by the key in [ExplanationMetadata.outputs][google.cloud.aiplatform.v1.ExplanationMetadata.outputs].

If the Model predicted output has multiple dimensions, this is the value in the output located by [output_index][google.cloud.aiplatform.v1.Attribution.output_index].

Type:

float

feature_attributions

Output only. Attributions of each explained feature. Features are extracted from the [prediction instances][google.cloud.aiplatform.v1.ExplainRequest.instances] according to [explanation metadata for inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].

The value is a struct, whose keys are the name of the feature. The values are how much the feature in the [instance][google.cloud.aiplatform.v1.ExplainRequest.instances] 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][google.protobuf.Value.number_value].

• If the feature is an array of scalar values, the attribution value is an [array][google.protobuf.Value.list_value].

• If the feature is a struct, the attribution value is a [struct][google.protobuf.Value.struct_value]. 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][google.cloud.aiplatform.v1.ExplanationMetadata.feature_attributions_schema_uri] field, pointed to by the [ExplanationSpec][google.cloud.aiplatform.v1.ExplanationSpec] field of the [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models] object, points to the schema file that describes the features and their attribution values (if it is populated).

Type:

google.protobuf.struct_pb2.Value

output_index

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.

Type:

MutableSequence[int]

output_display_name

Output only. The display name of the output identified by [output_index][google.cloud.aiplatform.v1.Attribution.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.

Type:

str

approximation_error

Output only. Error of [feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions] caused by approximation used in the explanation method. Lower value means more precise attributions.

• For Sampled Shapley [attribution][google.cloud.aiplatform.v1.ExplanationParameters.sampled_shapley_attribution], increasing [path_count][google.cloud.aiplatform.v1.SampledShapleyAttribution.path_count] might reduce the error.

• For Integrated Gradients [attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution], increasing [step_count][google.cloud.aiplatform.v1.IntegratedGradientsAttribution.step_count] might reduce the error.

• For [XRAI attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution], increasing [step_count][google.cloud.aiplatform.v1.XraiAttribution.step_count] might reduce the error.

See this introduction for more information.

Type:

float

output_name

Output only. Name of the explain output. Specified as the key in [ExplanationMetadata.outputs][google.cloud.aiplatform.v1.ExplanationMetadata.outputs].

Type:

str

class google.cloud.aiplatform_v1.types.AutomaticResources(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

min_replica_count

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][google.cloud.aiplatform.v1.AutomaticResources.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.

Type:

int

max_replica_count

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.

Type:

int

class google.cloud.aiplatform_v1.types.AutoscalingMetricSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

metric_name

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

Type:

str

target

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.

Type:

int

class google.cloud.aiplatform_v1.types.AvroSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The storage details for Avro input content.

gcs_source

Required. Google Cloud Storage location.

Type:

google.cloud.aiplatform_v1.types.GcsSource

class google.cloud.aiplatform_v1.types.BatchCancelPipelineJobsOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [PipelineService.BatchCancelPipelineJobs][google.cloud.aiplatform.v1.PipelineService.BatchCancelPipelineJobs].

generic_metadata

The common part of the operation metadata.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.BatchCancelPipelineJobsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.BatchCancelPipelineJobs][google.cloud.aiplatform.v1.PipelineService.BatchCancelPipelineJobs].

parent

Required. The name of the PipelineJobs’ parent resource. Format: projects/{project}/locations/{location}

Type:

str

names

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}

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.BatchCancelPipelineJobsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PipelineService.BatchCancelPipelineJobs][google.cloud.aiplatform.v1.PipelineService.BatchCancelPipelineJobs].

pipeline_jobs

PipelineJobs cancelled.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.PipelineJob]

class google.cloud.aiplatform_v1.types.BatchCreateFeaturesOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform batch create Features.

generic_metadata

Operation metadata for Feature.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.BatchCreateFeaturesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1.FeaturestoreService.BatchCreateFeatures]. Request message for [FeatureRegistryService.BatchCreateFeatures][google.cloud.aiplatform.v1.FeatureRegistryService.BatchCreateFeatures].

parent

Required. The resource name of the EntityType/FeatureGroup to create the batch of Features under. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} projects/{project}/locations/{location}/featureGroups/{feature_group}

Type:

str

requests

Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType / FeatureGroup. 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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.CreateFeatureRequest]

class google.cloud.aiplatform_v1.types.BatchCreateFeaturesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeaturestoreService.BatchCreateFeatures][google.cloud.aiplatform.v1.FeaturestoreService.BatchCreateFeatures].

features

The Features created.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Feature]

class google.cloud.aiplatform_v1.types.BatchCreateTensorboardRunsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.BatchCreateTensorboardRuns][google.cloud.aiplatform.v1.TensorboardService.BatchCreateTensorboardRuns].

parent

Required. The resource name of the TensorboardExperiment to create the TensorboardRuns in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment} The parent field in the CreateTensorboardRunRequest messages must match this field.

Type:

str

requests

Required. The request message specifying the TensorboardRuns to create. A maximum of 1000 TensorboardRuns can be created in a batch.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.CreateTensorboardRunRequest]

class google.cloud.aiplatform_v1.types.BatchCreateTensorboardRunsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.BatchCreateTensorboardRuns][google.cloud.aiplatform.v1.TensorboardService.BatchCreateTensorboardRuns].

tensorboard_runs

The created TensorboardRuns.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TensorboardRun]

class google.cloud.aiplatform_v1.types.BatchCreateTensorboardTimeSeriesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.BatchCreateTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.BatchCreateTensorboardTimeSeries].

parent

Required. The resource name of the TensorboardExperiment to create the TensorboardTimeSeries in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment} The TensorboardRuns referenced by the parent fields in the CreateTensorboardTimeSeriesRequest messages must be sub resources of this TensorboardExperiment.

Type:

str

requests

Required. The request message specifying the TensorboardTimeSeries to create. A maximum of 1000 TensorboardTimeSeries can be created in a batch.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.CreateTensorboardTimeSeriesRequest]

class google.cloud.aiplatform_v1.types.BatchCreateTensorboardTimeSeriesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.BatchCreateTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.BatchCreateTensorboardTimeSeries].

tensorboard_time_series

The created TensorboardTimeSeries.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TensorboardTimeSeries]

class google.cloud.aiplatform_v1.types.BatchDedicatedResources(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

machine_spec

Required. Immutable. The specification of a single machine.

Type:

google.cloud.aiplatform_v1.types.MachineSpec

starting_replica_count

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][google.cloud.aiplatform.v1.BatchDedicatedResources.max_replica_count]

Type:

int

max_replica_count

Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.

Type:

int

class google.cloud.aiplatform_v1.types.BatchDeletePipelineJobsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.BatchDeletePipelineJobs][google.cloud.aiplatform.v1.PipelineService.BatchDeletePipelineJobs].

parent

Required. The name of the PipelineJobs’ parent resource. Format: projects/{project}/locations/{location}

Type:

str

names

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}

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.BatchDeletePipelineJobsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PipelineService.BatchDeletePipelineJobs][google.cloud.aiplatform.v1.PipelineService.BatchDeletePipelineJobs].

pipeline_jobs

PipelineJobs deleted.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.PipelineJob]

class google.cloud.aiplatform_v1.types.BatchImportEvaluatedAnnotationsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.BatchImportEvaluatedAnnotations][google.cloud.aiplatform.v1.ModelService.BatchImportEvaluatedAnnotations]

parent

Required. The name of the parent ModelEvaluationSlice resource. Format: projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}

Type:

str

evaluated_annotations

Required. Evaluated annotations resource to be imported.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.EvaluatedAnnotation]

class google.cloud.aiplatform_v1.types.BatchImportEvaluatedAnnotationsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [ModelService.BatchImportEvaluatedAnnotations][google.cloud.aiplatform.v1.ModelService.BatchImportEvaluatedAnnotations]

imported_evaluated_annotations_count

Output only. Number of EvaluatedAnnotations imported.

Type:

int

class google.cloud.aiplatform_v1.types.BatchImportModelEvaluationSlicesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.BatchImportModelEvaluationSlices]

parent

Required. The name of the parent ModelEvaluation resource. Format: projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}

Type:

str

model_evaluation_slices

Required. Model evaluation slice resource to be imported.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ModelEvaluationSlice]

class google.cloud.aiplatform_v1.types.BatchImportModelEvaluationSlicesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.BatchImportModelEvaluationSlices]

imported_model_evaluation_slices

Output only. List of imported [ModelEvaluationSlice.name][google.cloud.aiplatform.v1.ModelEvaluationSlice.name].

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.BatchMigrateResourcesOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [MigrationService.BatchMigrateResources][google.cloud.aiplatform.v1.MigrationService.BatchMigrateResources].

generic_metadata

The common part of the operation metadata.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

partial_results

Partial results that reflect the latest migration operation progress.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.BatchMigrateResourcesOperationMetadata.PartialResult]

class PartialResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents a partial result in batch migration operation for one [MigrateResourceRequest][google.cloud.aiplatform.v1.MigrateResourceRequest].

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

error

The error result of the migration request in case of failure.

This field is a member of oneof result.

Type:

google.rpc.status_pb2.Status

model

Migrated model resource name.

This field is a member of oneof result.

Type:

str

dataset

Migrated dataset resource name.

This field is a member of oneof result.

Type:

str

request

It’s the same as the value in [BatchMigrateResourcesRequest.migrate_resource_requests][google.cloud.aiplatform.v1.BatchMigrateResourcesRequest.migrate_resource_requests].

Type:

google.cloud.aiplatform_v1.types.MigrateResourceRequest

class google.cloud.aiplatform_v1.types.BatchMigrateResourcesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MigrationService.BatchMigrateResources][google.cloud.aiplatform.v1.MigrationService.BatchMigrateResources].

parent

Required. The location of the migrated resource will live in. Format: projects/{project}/locations/{location}

Type:

str

migrate_resource_requests

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.MigrateResourceRequest]

class google.cloud.aiplatform_v1.types.BatchMigrateResourcesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MigrationService.BatchMigrateResources][google.cloud.aiplatform.v1.MigrationService.BatchMigrateResources].

migrate_resource_responses

Successfully migrated resources.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.MigrateResourceResponse]

class google.cloud.aiplatform_v1.types.BatchPredictionJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A job that uses a [Model][google.cloud.aiplatform.v1.BatchPredictionJob.model] to produce predictions on multiple [input instances][google.cloud.aiplatform.v1.BatchPredictionJob.input_config]. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

name

Output only. Resource name of the BatchPredictionJob.

Type:

str

display_name

Required. The user-defined name of this BatchPredictionJob.

Type:

str

model

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}

Type:

str

model_version_id

Output only. The version ID of the Model that produces the predictions via this job.

Type:

str

unmanaged_container_model

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.

Type:

google.cloud.aiplatform_v1.types.UnmanagedContainerModel

input_config

Required. Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the [Model’s][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata’s][google.cloud.aiplatform.v1.Model.predict_schemata] [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

Type:

google.cloud.aiplatform_v1.types.BatchPredictionJob.InputConfig

instance_config

Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.

Type:

google.cloud.aiplatform_v1.types.BatchPredictionJob.InstanceConfig

model_parameters

The parameters that govern the predictions. The schema of the parameters may be specified via the [Model’s][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata’s][google.cloud.aiplatform.v1.Model.predict_schemata] [parameters_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri].

Type:

google.protobuf.struct_pb2.Value

output_config

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][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata’s][google.cloud.aiplatform.v1.Model.predict_schemata] [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] and [prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri].

Type:

google.cloud.aiplatform_v1.types.BatchPredictionJob.OutputConfig

dedicated_resources

The config of resources used by the Model during the batch prediction. If the Model [supports][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types] 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.

Type:

google.cloud.aiplatform_v1.types.BatchDedicatedResources

service_account

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.

Type:

str

manual_batch_tuning_parameters

Immutable. Parameters configuring the batch behavior. Currently only applicable when [dedicated_resources][google.cloud.aiplatform.v1.BatchPredictionJob.dedicated_resources] are used (in other cases Vertex AI does the tuning itself).

Type:

google.cloud.aiplatform_v1.types.ManualBatchTuningParameters

generate_explanation

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][google.cloud.aiplatform.v1.BatchPredictionJob.output_config] object:

• bigquery: output includes a column named explanation. The value is a struct that conforms to the [Explanation][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.Explanation] object.

• csv: Generating explanations for CSV format is not supported.

If this field is set to true, either the [Model.explanation_spec][google.cloud.aiplatform.v1.Model.explanation_spec] or [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec] must be populated.

Type:

bool

explanation_spec

Explanation configuration for this BatchPredictionJob. Can be specified only if [generate_explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation] is set to true.

This value overrides the value of [Model.explanation_spec][google.cloud.aiplatform.v1.Model.explanation_spec]. All fields of [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec] are optional in the request. If a field of the [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec] object is not populated, the corresponding field of the [Model.explanation_spec][google.cloud.aiplatform.v1.Model.explanation_spec] object is inherited.

Type:

google.cloud.aiplatform_v1.types.ExplanationSpec

output_info

Output only. Information further describing the output of this job.

Type:

google.cloud.aiplatform_v1.types.BatchPredictionJob.OutputInfo

state

Output only. The detailed state of the job.

Type:

google.cloud.aiplatform_v1.types.JobState

error

Output only. Only populated when the job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

Type:

google.rpc.status_pb2.Status

partial_failures

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.

Type:

MutableSequence[google.rpc.status_pb2.Status]

resources_consumed

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.

Type:

google.cloud.aiplatform_v1.types.ResourcesConsumed

completion_stats

Output only. Statistics on completed and failed prediction instances.

Type:

google.cloud.aiplatform_v1.types.CompletionStats

create_time

Output only. Time when the BatchPredictionJob was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

start_time

Output only. Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

Output only. Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Time when the BatchPredictionJob was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

labels

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.

Type:

MutableMapping[str, str]

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

disable_container_logging

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.

Type:

bool

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class InputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configures the input to [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob]. See [Model.supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats] for Model’s supported input formats, and how instances should be expressed via any of them.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

gcs_source

The Cloud Storage location for the input instances.

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.GcsSource

bigquery_source

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.

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.BigQuerySource

instances_format

Required. The format in which instances are given, must be one of the [Model’s][google.cloud.aiplatform.v1.BatchPredictionJob.model] [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats].

Type:

str

class InstanceConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

instance_type

The format of the instance that the Model accepts. Vertex AI will convert compatible [batch prediction input instance formats][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.instances_format] 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][google.cloud.aiplatform.v1.BatchPredictionJob.InstanceConfig.included_fields] is populated. [included_fields][google.cloud.aiplatform.v1.BatchPredictionJob.InstanceConfig.included_fields] must be populated for specifying field orders.

  • For jsonl, if each line of the JSONL input is an object, [included_fields][google.cloud.aiplatform.v1.BatchPredictionJob.InstanceConfig.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][google.cloud.aiplatform.v1.BatchPredictionJob.InstanceConfig.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": <value>}, where <value> 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": <value>}, where <value> is the Base64-encoded string of the content of the file.

Type:

str

key_field

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][google.cloud.aiplatform.v1.BatchPredictionJob.InstanceConfig.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.

Type:

str

included_fields

Fields that will be included in the prediction instance that is sent to the Model.

If [instance_type][google.cloud.aiplatform.v1.BatchPredictionJob.InstanceConfig.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][google.cloud.aiplatform.v1.BatchPredictionJob.InstanceConfig.excluded_fields] must be empty.

The input must be JSONL with objects at each line, BigQuery or TfRecord.

Type:

MutableSequence[str]

excluded_fields

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][google.cloud.aiplatform.v1.BatchPredictionJob.InstanceConfig.key_field] is not specified.

When excluded_fields is populated, [included_fields][google.cloud.aiplatform.v1.BatchPredictionJob.InstanceConfig.included_fields] must be empty.

The input must be JSONL with objects at each line, BigQuery or TfRecord.

Type:

MutableSequence[str]

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class OutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configures the output of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob]. See [Model.supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats] for supported output formats, and how predictions are expressed via any of them.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

gcs_destination

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-<model-display-name>-<job-create-time>, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001.<extension>, predictions_0002.<extension>, …, predictions_N.<extension> are created where <extension> depends on chosen [predictions_format][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.predictions_format], and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both [instance][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] and [prediction][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri] schemata defined then each such file contains predictions as per the [predictions_format][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.predictions_format]. If prediction for any instance failed (partially or completely), then an additional errors_0001.<extension>, errors_0002.<extension>,…, errors_N.<extension> 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][google.rpc.Status] containing only code and message fields.

This field is a member of oneof destination.

Type:

google.cloud.aiplatform_v1.types.GcsDestination

bigquery_destination

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_<model-display-name>_<job-create-time> 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][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] and [prediction][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri] 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][google.rpc.Status] represented as a STRUCT, and containing only code and message.

This field is a member of oneof destination.

Type:

google.cloud.aiplatform_v1.types.BigQueryDestination

predictions_format

Required. The format in which Vertex AI gives the predictions, must be one of the [Model’s][google.cloud.aiplatform.v1.BatchPredictionJob.model] [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].

Type:

str

class OutputInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Further describes this job’s output. Supplements [output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

gcs_output_directory

Output only. The full path of the Cloud Storage directory created, into which the prediction output is written.

This field is a member of oneof output_location.

Type:

str

bigquery_output_dataset

Output only. The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.

This field is a member of oneof output_location.

Type:

str

bigquery_output_table

Output only. The name of the BigQuery table created, in predictions_<timestamp> format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.

Type:

str

class google.cloud.aiplatform_v1.types.BatchReadFeatureValuesOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that batch reads Feature values.

generic_metadata

Operation metadata for Featurestore batch read Features values.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.BatchReadFeatureValuesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.BatchReadFeatureValues].

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

csv_read_instances

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.

This field is a member of oneof read_option.

Type:

google.cloud.aiplatform_v1.types.CsvSource

bigquery_read_instances

Similar to csv_read_instances, but from BigQuery source.

This field is a member of oneof read_option.

Type:

google.cloud.aiplatform_v1.types.BigQuerySource

featurestore

Required. The resource name of the Featurestore from which to query Feature values. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

Type:

str

destination

Required. Specifies output location and format.

Type:

google.cloud.aiplatform_v1.types.FeatureValueDestination

pass_through_fields

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.BatchReadFeatureValuesRequest.PassThroughField]

entity_type_specs

Required. Specifies EntityType grouping Features to read values of and settings.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.BatchReadFeatureValuesRequest.EntityTypeSpec]

start_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

class EntityTypeSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

entity_type_id

Required. ID of the EntityType to select Features. The EntityType id is the [entity_type_id][google.cloud.aiplatform.v1.CreateEntityTypeRequest.entity_type_id] specified during EntityType creation.

Type:

str

feature_selector

Required. Selectors choosing which Feature values to read from the EntityType.

Type:

google.cloud.aiplatform_v1.types.FeatureSelector

settings

Per-Feature settings for the batch read.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DestinationFeatureSetting]

class PassThroughField(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Describe pass-through fields in read_instance source.

field_name

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][google.cloud.aiplatform.v1.Feature.name].

Type:

str

class google.cloud.aiplatform_v1.types.BatchReadFeatureValuesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeaturestoreService.BatchReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.BatchReadFeatureValues].

class google.cloud.aiplatform_v1.types.BatchReadTensorboardTimeSeriesDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.BatchReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.BatchReadTensorboardTimeSeriesData].

tensorboard

Required. The resource name of the Tensorboard containing TensorboardTimeSeries to read data from. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}. The TensorboardTimeSeries referenced by [time_series][google.cloud.aiplatform.v1.BatchReadTensorboardTimeSeriesDataRequest.time_series] must be sub resources of this Tensorboard.

Type:

str

time_series

Required. The resource names of the TensorboardTimeSeries to read data from. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.BatchReadTensorboardTimeSeriesDataResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.BatchReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.BatchReadTensorboardTimeSeriesData].

time_series_data

The returned time series data.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TimeSeriesData]

class google.cloud.aiplatform_v1.types.BigQueryDestination(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The BigQuery location for the output content.

output_uri

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.

Type:

str

class google.cloud.aiplatform_v1.types.BigQuerySource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The BigQuery location for the input content.

input_uri

Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms:

• BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

Type:

str

class google.cloud.aiplatform_v1.types.BleuInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for bleu metric.

metric_spec

Required. Spec for bleu score metric.

Type:

google.cloud.aiplatform_v1.types.BleuSpec

instances

Required. Repeated bleu instances.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.BleuInstance]

class google.cloud.aiplatform_v1.types.BleuInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for bleu instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Required. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

class google.cloud.aiplatform_v1.types.BleuMetricValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Bleu metric value for an instance.

score

Output only. Bleu score.

This field is a member of oneof _score.

Type:

float

class google.cloud.aiplatform_v1.types.BleuResults(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Results for bleu metric.

bleu_metric_values

Output only. Bleu metric values.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.BleuMetricValue]

class google.cloud.aiplatform_v1.types.BleuSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

use_effective_order

Optional. Whether to use_effective_order to compute bleu score.

Type:

bool

class google.cloud.aiplatform_v1.types.Blob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Content blob.

It’s preferred to send as [text][google.cloud.aiplatform.v1.Part.text] directly rather than raw bytes.

mime_type

Required. The IANA standard MIME type of the source data.

Type:

str

data

Required. Raw bytes.

Type:

bytes

class google.cloud.aiplatform_v1.types.BlurBaselineConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

max_blur_sigma

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.

Type:

float

class google.cloud.aiplatform_v1.types.BoolArray(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A list of boolean values.

values

A list of bool values.

Type:

MutableSequence[bool]

class google.cloud.aiplatform_v1.types.CancelBatchPredictionJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.CancelBatchPredictionJob][google.cloud.aiplatform.v1.JobService.CancelBatchPredictionJob].

name

Required. The name of the BatchPredictionJob to cancel. Format: projects/{project}/locations/{location}/batchPredictionJobs/{batch_prediction_job}

Type:

str

class google.cloud.aiplatform_v1.types.CancelCustomJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.CancelCustomJob][google.cloud.aiplatform.v1.JobService.CancelCustomJob].

name

Required. The name of the CustomJob to cancel. Format: projects/{project}/locations/{location}/customJobs/{custom_job}

Type:

str

class google.cloud.aiplatform_v1.types.CancelDataLabelingJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.CancelDataLabelingJob][google.cloud.aiplatform.v1.JobService.CancelDataLabelingJob].

name

Required. The name of the DataLabelingJob. Format: projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}

Type:

str

class google.cloud.aiplatform_v1.types.CancelHyperparameterTuningJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.CancelHyperparameterTuningJob][google.cloud.aiplatform.v1.JobService.CancelHyperparameterTuningJob].

name

Required. The name of the HyperparameterTuningJob to cancel. Format: projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyperparameter_tuning_job}

Type:

str

class google.cloud.aiplatform_v1.types.CancelNasJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.CancelNasJob][google.cloud.aiplatform.v1.JobService.CancelNasJob].

name

Required. The name of the NasJob to cancel. Format: projects/{project}/locations/{location}/nasJobs/{nas_job}

Type:

str

class google.cloud.aiplatform_v1.types.CancelPipelineJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.CancelPipelineJob][google.cloud.aiplatform.v1.PipelineService.CancelPipelineJob].

name

Required. The name of the PipelineJob to cancel. Format: projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}

Type:

str

class google.cloud.aiplatform_v1.types.CancelTrainingPipelineRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.CancelTrainingPipeline][google.cloud.aiplatform.v1.PipelineService.CancelTrainingPipeline].

name

Required. The name of the TrainingPipeline to cancel. Format: projects/{project}/locations/{location}/trainingPipelines/{training_pipeline}

Type:

str

class google.cloud.aiplatform_v1.types.CancelTuningJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [GenAiTuningService.CancelTuningJob][google.cloud.aiplatform.v1.GenAiTuningService.CancelTuningJob].

name

Required. The name of the TuningJob to cancel. Format: projects/{project}/locations/{location}/tuningJobs/{tuning_job}

Type:

str

class google.cloud.aiplatform_v1.types.Candidate(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A response candidate generated from the model.

index

Output only. Index of the candidate.

Type:

int

content

Output only. Content parts of the candidate.

Type:

google.cloud.aiplatform_v1.types.Content

score

Output only. Confidence score of the candidate.

Type:

float

avg_logprobs

Output only. Average log probability score of the candidate.

Type:

float

logprobs_result

Output only. Log-likelihood scores for the response tokens and top tokens

Type:

google.cloud.aiplatform_v1.types.LogprobsResult

finish_reason

Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.

Type:

google.cloud.aiplatform_v1.types.Candidate.FinishReason

safety_ratings

Output only. List of ratings for the safety of a response candidate. There is at most one rating per category.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.SafetyRating]

finish_message

Output only. Describes the reason the mode stopped generating tokens in more detail. This is only filled when finish_reason is set.

This field is a member of oneof _finish_message.

Type:

str

citation_metadata

Output only. Source attribution of the generated content.

Type:

google.cloud.aiplatform_v1.types.CitationMetadata

grounding_metadata

Output only. Metadata specifies sources used to ground generated content.

Type:

google.cloud.aiplatform_v1.types.GroundingMetadata

class FinishReason(value)

Bases: Enum

The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.

Values:

FINISH_REASON_UNSPECIFIED (0):

The finish reason is unspecified.

STOP (1):

Token generation reached a natural stopping point or a configured stop sequence.

MAX_TOKENS (2):

Token generation reached the configured maximum output tokens.

SAFETY (3):

Token generation stopped because the content potentially contains safety violations. NOTE: When streaming, [content][google.cloud.aiplatform.v1.Candidate.content] is empty if content filters blocks the output.

RECITATION (4):

Token generation stopped because the content potentially contains copyright violations.

OTHER (5):

All other reasons that stopped the token generation.

BLOCKLIST (6):

Token generation stopped because the content contains forbidden terms.

PROHIBITED_CONTENT (7):

Token generation stopped for potentially containing prohibited content.

SPII (8):

Token generation stopped because the content potentially contains Sensitive Personally Identifiable Information (SPII).

MALFORMED_FUNCTION_CALL (9):

The function call generated by the model is invalid.

class google.cloud.aiplatform_v1.types.CheckTrialEarlyStoppingStateMetatdata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

generic_metadata

Operation metadata for suggesting Trials.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

study

The name of the Study that the Trial belongs to.

Type:

str

trial

The Trial name.

Type:

str

class google.cloud.aiplatform_v1.types.CheckTrialEarlyStoppingStateRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.CheckTrialEarlyStoppingState][google.cloud.aiplatform.v1.VizierService.CheckTrialEarlyStoppingState].

trial_name

Required. The Trial’s name. Format: projects/{project}/locations/{location}/studies/{study}/trials/{trial}

Type:

str

class google.cloud.aiplatform_v1.types.CheckTrialEarlyStoppingStateResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [VizierService.CheckTrialEarlyStoppingState][google.cloud.aiplatform.v1.VizierService.CheckTrialEarlyStoppingState].

should_stop

True if the Trial should stop.

Type:

bool

class google.cloud.aiplatform_v1.types.Citation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Source attributions for content.

start_index

Output only. Start index into the content.

Type:

int

end_index

Output only. End index into the content.

Type:

int

uri

Output only. Url reference of the attribution.

Type:

str

title

Output only. Title of the attribution.

Type:

str

license_

Output only. License of the attribution.

Type:

str

publication_date

Output only. Publication date of the attribution.

Type:

google.type.date_pb2.Date

class google.cloud.aiplatform_v1.types.CitationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A collection of source attributions for a piece of content.

citations

Output only. List of citations.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Citation]

class google.cloud.aiplatform_v1.types.ClientConnectionConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configurations (e.g. inference timeout) that are applied on your endpoints.

inference_timeout

Customizable online prediction request timeout.

Type:

google.protobuf.duration_pb2.Duration

class google.cloud.aiplatform_v1.types.CoherenceInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for coherence metric.

metric_spec

Required. Spec for coherence score metric.

Type:

google.cloud.aiplatform_v1.types.CoherenceSpec

instance

Required. Coherence instance.

Type:

google.cloud.aiplatform_v1.types.CoherenceInstance

class google.cloud.aiplatform_v1.types.CoherenceInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for coherence instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

class google.cloud.aiplatform_v1.types.CoherenceResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for coherence result.

score

Output only. Coherence score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for coherence score.

Type:

str

confidence

Output only. Confidence for coherence score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.CoherenceSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for coherence score metric.

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.CometInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for Comet metric.

metric_spec

Required. Spec for comet metric.

Type:

google.cloud.aiplatform_v1.types.CometSpec

instance

Required. Comet instance.

Type:

google.cloud.aiplatform_v1.types.CometInstance

class google.cloud.aiplatform_v1.types.CometInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for Comet instance - The fields used for evaluation are dependent on the comet version.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Optional. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

source

Optional. Source text in original language.

This field is a member of oneof _source.

Type:

str

class google.cloud.aiplatform_v1.types.CometResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for Comet result - calculates the comet score for the given instance using the version specified in the spec.

score

Output only. Comet score. Range depends on version.

This field is a member of oneof _score.

Type:

float

class google.cloud.aiplatform_v1.types.CometSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for Comet metric.

version

Required. Which version to use for evaluation.

This field is a member of oneof _version.

Type:

google.cloud.aiplatform_v1.types.CometSpec.CometVersion

source_language

Optional. Source language in BCP-47 format.

Type:

str

target_language

Optional. Target language in BCP-47 format. Covers both prediction and reference.

Type:

str

class CometVersion(value)

Bases: Enum

Comet version options.

Values:

COMET_VERSION_UNSPECIFIED (0):

Comet version unspecified.

COMET_22_SRC_REF (2):

Comet 22 for translation + source + reference (source-reference-combined).

class google.cloud.aiplatform_v1.types.CompleteTrialRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.CompleteTrial][google.cloud.aiplatform.v1.VizierService.CompleteTrial].

name

Required. The Trial’s name. Format: projects/{project}/locations/{location}/studies/{study}/trials/{trial}

Type:

str

final_measurement

Optional. If provided, it will be used as the completed Trial’s final_measurement; Otherwise, the service will auto-select a previously reported measurement as the final-measurement

Type:

google.cloud.aiplatform_v1.types.Measurement

trial_infeasible

Optional. True if the Trial cannot be run with the given Parameter, and final_measurement will be ignored.

Type:

bool

infeasible_reason

Optional. A human readable reason why the trial was infeasible. This should only be provided if trial_infeasible is true.

Type:

str

class google.cloud.aiplatform_v1.types.CompletionStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

successful_count

Output only. The number of entities that had been processed successfully.

Type:

int

failed_count

Output only. The number of entities for which any error was encountered.

Type:

int

incomplete_count

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).

Type:

int

successful_forecast_point_count

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.

Type:

int

class google.cloud.aiplatform_v1.types.ComputeTokensRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for ComputeTokens RPC call.

endpoint

Required. The name of the Endpoint requested to get lists of tokens and token ids.

Type:

str

instances

Optional. 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.

Type:

MutableSequence[google.protobuf.struct_pb2.Value]

model

Optional. The name of the publisher model requested to serve the prediction. Format: projects/{project}/locations/{location}/publishers//models/

Type:

str

contents

Optional. Input content.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Content]

class google.cloud.aiplatform_v1.types.ComputeTokensResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for ComputeTokens RPC call.

tokens_info

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TokensInfo]

class google.cloud.aiplatform_v1.types.ContainerRegistryDestination(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The Container Registry location for the container image.

output_uri

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.

Type:

str

class google.cloud.aiplatform_v1.types.ContainerSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The spec of a Container.

image_uri

Required. The URI of a container image in the Container Registry that is to be run on each worker replica.

Type:

str

command

The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.

Type:

MutableSequence[str]

args

The arguments to be passed when starting the container.

Type:

MutableSequence[str]

env

Environment variables to be passed to the container. Maximum limit is 100.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.EnvVar]

class google.cloud.aiplatform_v1.types.Content(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

role

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.

Type:

str

parts

Required. Ordered Parts that constitute a single message. Parts may have different IANA MIME types.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Part]

class google.cloud.aiplatform_v1.types.Context(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Instance of a general context.

name

Immutable. The resource name of the Context.

Type:

str

display_name

User provided display name of the Context. May be up to 128 Unicode characters.

Type:

str

etag

An eTag used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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).

Type:

MutableMapping[str, str]

create_time

Output only. Timestamp when this Context was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this Context was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

parent_contexts

Output only. A list of resource names of Contexts that are parents of this Context. A Context may have at most 10 parent_contexts.

Type:

MutableSequence[str]

schema_title

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.

Type:

str

schema_version

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.

Type:

str

metadata

Properties of the Context. Top level metadata keys’ heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.

Type:

google.protobuf.struct_pb2.Struct

description

Description of the Context

Type:

str

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.CopyModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of [ModelService.CopyModel][google.cloud.aiplatform.v1.ModelService.CopyModel] operation.

generic_metadata

The common part of the operation metadata.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CopyModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.CopyModel][google.cloud.aiplatform.v1.ModelService.CopyModel].

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

model_id

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.

This field is a member of oneof destination_model.

Type:

str

parent_model

Optional. Specify this field to copy source_model into this existing Model as a new version. Format: projects/{project}/locations/{location}/models/{model}

This field is a member of oneof destination_model.

Type:

str

parent

Required. The resource name of the Location into which to copy the Model. Format: projects/{project}/locations/{location}

Type:

str

source_model

Required. The resource name of the Model to copy. That Model must be in the same Project. Format: projects/{project}/locations/{location}/models/{model}

Type:

str

encryption_spec

Customer-managed encryption key options. If this is set, then the Model copy will be encrypted with the provided encryption key.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

class google.cloud.aiplatform_v1.types.CopyModelResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message of [ModelService.CopyModel][google.cloud.aiplatform.v1.ModelService.CopyModel] operation.

model

The name of the copied Model resource. Format: projects/{project}/locations/{location}/models/{model}

Type:

str

model_version_id

Output only. The version ID of the model that is copied.

Type:

str

class google.cloud.aiplatform_v1.types.CountTokensRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.CountTokens][].

endpoint

Required. The name of the Endpoint requested to perform token counting. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

model

Optional. The name of the publisher model requested to serve the prediction. Format: projects/{project}/locations/{location}/publishers/*/models/*

Type:

str

instances

Optional. The instances that are the input to token counting call. Schema is identical to the prediction schema of the underlying model.

Type:

MutableSequence[google.protobuf.struct_pb2.Value]

contents

Optional. Input content.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Content]

system_instruction

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.

This field is a member of oneof _system_instruction.

Type:

google.cloud.aiplatform_v1.types.Content

tools

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Tool]

generation_config

Optional. Generation config that the model will use to generate the response.

This field is a member of oneof _generation_config.

Type:

google.cloud.aiplatform_v1.types.GenerationConfig

class google.cloud.aiplatform_v1.types.CountTokensResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PredictionService.CountTokens][].

total_tokens

The total number of tokens counted across all instances from the request.

Type:

int

total_billable_characters

The total number of billable characters counted across all instances from the request.

Type:

int

class google.cloud.aiplatform_v1.types.CreateArtifactRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.CreateArtifact][google.cloud.aiplatform.v1.MetadataService.CreateArtifact].

parent

Required. The resource name of the MetadataStore where the Artifact should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

artifact

Required. The Artifact to create.

Type:

google.cloud.aiplatform_v1.types.Artifact

artifact_id

The {artifact} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact} If not provided, the Artifact’s ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /[a-z][0-9]-/. Must be unique across all Artifacts in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can’t view the preexisting Artifact.)

Type:

str

class google.cloud.aiplatform_v1.types.CreateBatchPredictionJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.CreateBatchPredictionJob][google.cloud.aiplatform.v1.JobService.CreateBatchPredictionJob].

parent

Required. The resource name of the Location to create the BatchPredictionJob in. Format: projects/{project}/locations/{location}

Type:

str

batch_prediction_job

Required. The BatchPredictionJob to create.

Type:

google.cloud.aiplatform_v1.types.BatchPredictionJob

class google.cloud.aiplatform_v1.types.CreateContextRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.CreateContext][google.cloud.aiplatform.v1.MetadataService.CreateContext].

parent

Required. The resource name of the MetadataStore where the Context should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

context

Required. The Context to create.

Type:

google.cloud.aiplatform_v1.types.Context

context_id

The {context} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}. If not provided, the Context’s ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /[a-z][0-9]-/. Must be unique across all Contexts in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can’t view the preexisting Context.)

Type:

str

class google.cloud.aiplatform_v1.types.CreateCustomJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.CreateCustomJob][google.cloud.aiplatform.v1.JobService.CreateCustomJob].

parent

Required. The resource name of the Location to create the CustomJob in. Format: projects/{project}/locations/{location}

Type:

str

custom_job

Required. The CustomJob to create.

Type:

google.cloud.aiplatform_v1.types.CustomJob

class google.cloud.aiplatform_v1.types.CreateDataLabelingJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.CreateDataLabelingJob][google.cloud.aiplatform.v1.JobService.CreateDataLabelingJob].

parent

Required. The parent of the DataLabelingJob. Format: projects/{project}/locations/{location}

Type:

str

data_labeling_job

Required. The DataLabelingJob to create.

Type:

google.cloud.aiplatform_v1.types.DataLabelingJob

class google.cloud.aiplatform_v1.types.CreateDatasetOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [DatasetService.CreateDataset][google.cloud.aiplatform.v1.DatasetService.CreateDataset].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.CreateDataset][google.cloud.aiplatform.v1.DatasetService.CreateDataset].

parent

Required. The resource name of the Location to create the Dataset in. Format: projects/{project}/locations/{location}

Type:

str

dataset

Required. The Dataset to create.

Type:

google.cloud.aiplatform_v1.types.Dataset

class google.cloud.aiplatform_v1.types.CreateDatasetVersionOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [DatasetService.CreateDatasetVersion][google.cloud.aiplatform.v1.DatasetService.CreateDatasetVersion].

generic_metadata

The common part of the operation metadata.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateDatasetVersionRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.CreateDatasetVersion][google.cloud.aiplatform.v1.DatasetService.CreateDatasetVersion].

parent

Required. The name of the Dataset resource. Format: projects/{project}/locations/{location}/datasets/{dataset}

Type:

str

dataset_version

Required. The version to be created. The same CMEK policies with the original Dataset will be applied the dataset version. So here we don’t need to specify the EncryptionSpecType here.

Type:

google.cloud.aiplatform_v1.types.DatasetVersion

class google.cloud.aiplatform_v1.types.CreateDeploymentResourcePoolOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for CreateDeploymentResourcePool method.

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateDeploymentResourcePoolRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for CreateDeploymentResourcePool method.

parent

Required. The parent location resource where this DeploymentResourcePool will be created. Format: projects/{project}/locations/{location}

Type:

str

deployment_resource_pool

Required. The DeploymentResourcePool to create.

Type:

google.cloud.aiplatform_v1.types.DeploymentResourcePool

deployment_resource_pool_id

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])?$/.

Type:

str

class google.cloud.aiplatform_v1.types.CreateEndpointOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [EndpointService.CreateEndpoint][google.cloud.aiplatform.v1.EndpointService.CreateEndpoint].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateEndpointRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [EndpointService.CreateEndpoint][google.cloud.aiplatform.v1.EndpointService.CreateEndpoint].

parent

Required. The resource name of the Location to create the Endpoint in. Format: projects/{project}/locations/{location}

Type:

str

endpoint

Required. The Endpoint to create.

Type:

google.cloud.aiplatform_v1.types.Endpoint

endpoint_id

Immutable. The ID to use for endpoint, which will become the final component of the endpoint resource name. If not provided, Vertex AI will generate a value for this ID.

If the first character is a letter, this value may be up to 63 characters, and valid characters are [a-z0-9-]. The last character must be a letter or number.

If the first character is a number, this value may be up to 9 characters, and valid characters are [0-9] with no leading zeros.

When using HTTP/JSON, this field is populated based on a query string argument, such as ?endpoint_id=12345. This is the fallback for fields that are not included in either the URI or the body.

Type:

str

class google.cloud.aiplatform_v1.types.CreateEntityTypeOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform create EntityType.

generic_metadata

Operation metadata for EntityType.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateEntityTypeRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.CreateEntityType][google.cloud.aiplatform.v1.FeaturestoreService.CreateEntityType].

parent

Required. The resource name of the Featurestore to create EntityTypes. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

Type:

str

entity_type

The EntityType to create.

Type:

google.cloud.aiplatform_v1.types.EntityType

entity_type_id

Required. The ID to use for the EntityType, which will become the final component of the EntityType’s resource name.

This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number.

The value must be unique within a featurestore.

Type:

str

class google.cloud.aiplatform_v1.types.CreateExecutionRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.CreateExecution][google.cloud.aiplatform.v1.MetadataService.CreateExecution].

parent

Required. The resource name of the MetadataStore where the Execution should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

execution

Required. The Execution to create.

Type:

google.cloud.aiplatform_v1.types.Execution

execution_id

The {execution} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution} If not provided, the Execution’s ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /[a-z][0-9]-/. Must be unique across all Executions in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can’t view the preexisting Execution.)

Type:

str

class google.cloud.aiplatform_v1.types.CreateFeatureGroupOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform create FeatureGroup.

generic_metadata

Operation metadata for FeatureGroup.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateFeatureGroupRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureRegistryService.CreateFeatureGroup][google.cloud.aiplatform.v1.FeatureRegistryService.CreateFeatureGroup].

parent

Required. The resource name of the Location to create FeatureGroups. Format: projects/{project}/locations/{location}

Type:

str

feature_group

Required. The FeatureGroup to create.

Type:

google.cloud.aiplatform_v1.types.FeatureGroup

feature_group_id

Required. The ID to use for this FeatureGroup, which will become the final component of the FeatureGroup’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 the project and location.

Type:

str

class google.cloud.aiplatform_v1.types.CreateFeatureOnlineStoreOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform create FeatureOnlineStore.

generic_metadata

Operation metadata for FeatureOnlineStore.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateFeatureOnlineStoreRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.CreateFeatureOnlineStore][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.CreateFeatureOnlineStore].

parent

Required. The resource name of the Location to create FeatureOnlineStores. Format: projects/{project}/locations/{location}

Type:

str

feature_online_store

Required. The FeatureOnlineStore to create.

Type:

google.cloud.aiplatform_v1.types.FeatureOnlineStore

feature_online_store_id

Required. The ID to use for this FeatureOnlineStore, which will become the final component of the FeatureOnlineStore’s resource name.

This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number.

The value must be unique within the project and location.

Type:

str

class google.cloud.aiplatform_v1.types.CreateFeatureOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform create Feature.

generic_metadata

Operation metadata for Feature.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateFeatureRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.CreateFeature][google.cloud.aiplatform.v1.FeaturestoreService.CreateFeature]. Request message for [FeatureRegistryService.CreateFeature][google.cloud.aiplatform.v1.FeatureRegistryService.CreateFeature].

parent

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}

Type:

str

feature

Required. The Feature to create.

Type:

google.cloud.aiplatform_v1.types.Feature

feature_id

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.

Type:

str

class google.cloud.aiplatform_v1.types.CreateFeatureViewOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform create FeatureView.

generic_metadata

Operation metadata for FeatureView Create.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateFeatureViewRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.CreateFeatureView][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.CreateFeatureView].

parent

Required. The resource name of the FeatureOnlineStore to create FeatureViews. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}

Type:

str

feature_view

Required. The FeatureView to create.

Type:

google.cloud.aiplatform_v1.types.FeatureView

feature_view_id

Required. The ID to use for the FeatureView, which will become the final component of the FeatureView’s resource name.

This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number.

The value must be unique within a FeatureOnlineStore.

Type:

str

run_sync_immediately

Immutable. If set to true, one on demand sync will be run immediately, regardless whether the [FeatureView.sync_config][google.cloud.aiplatform.v1.FeatureView.sync_config] is configured or not.

Type:

bool

class google.cloud.aiplatform_v1.types.CreateFeaturestoreOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform create Featurestore.

generic_metadata

Operation metadata for Featurestore.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateFeaturestoreRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.CreateFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.CreateFeaturestore].

parent

Required. The resource name of the Location to create Featurestores. Format: projects/{project}/locations/{location}

Type:

str

featurestore

Required. The Featurestore to create.

Type:

google.cloud.aiplatform_v1.types.Featurestore

featurestore_id

Required. The ID to use for this Featurestore, which will become the final component of the Featurestore’s resource name.

This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number.

The value must be unique within the project and location.

Type:

str

class google.cloud.aiplatform_v1.types.CreateHyperparameterTuningJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.CreateHyperparameterTuningJob][google.cloud.aiplatform.v1.JobService.CreateHyperparameterTuningJob].

parent

Required. The resource name of the Location to create the HyperparameterTuningJob in. Format: projects/{project}/locations/{location}

Type:

str

hyperparameter_tuning_job

Required. The HyperparameterTuningJob to create.

Type:

google.cloud.aiplatform_v1.types.HyperparameterTuningJob

class google.cloud.aiplatform_v1.types.CreateIndexEndpointOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [IndexEndpointService.CreateIndexEndpoint][google.cloud.aiplatform.v1.IndexEndpointService.CreateIndexEndpoint].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateIndexEndpointRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexEndpointService.CreateIndexEndpoint][google.cloud.aiplatform.v1.IndexEndpointService.CreateIndexEndpoint].

parent

Required. The resource name of the Location to create the IndexEndpoint in. Format: projects/{project}/locations/{location}

Type:

str

index_endpoint

Required. The IndexEndpoint to create.

Type:

google.cloud.aiplatform_v1.types.IndexEndpoint

class google.cloud.aiplatform_v1.types.CreateIndexOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [IndexService.CreateIndex][google.cloud.aiplatform.v1.IndexService.CreateIndex].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

nearest_neighbor_search_operation_metadata

The operation metadata with regard to Matching Engine Index operation.

Type:

google.cloud.aiplatform_v1.types.NearestNeighborSearchOperationMetadata

class google.cloud.aiplatform_v1.types.CreateIndexRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexService.CreateIndex][google.cloud.aiplatform.v1.IndexService.CreateIndex].

parent

Required. The resource name of the Location to create the Index in. Format: projects/{project}/locations/{location}

Type:

str

index

Required. The Index to create.

Type:

google.cloud.aiplatform_v1.types.Index

class google.cloud.aiplatform_v1.types.CreateMetadataSchemaRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.CreateMetadataSchema][google.cloud.aiplatform.v1.MetadataService.CreateMetadataSchema].

parent

Required. The resource name of the MetadataStore where the MetadataSchema should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

metadata_schema

Required. The MetadataSchema to create.

Type:

google.cloud.aiplatform_v1.types.MetadataSchema

metadata_schema_id

The {metadata_schema} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/metadataSchemas/{metadataschema} If not provided, the MetadataStore’s ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /[a-z][0-9]-/. Must be unique across all MetadataSchemas in the parent Location. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can’t view the preexisting MetadataSchema.)

Type:

str

class google.cloud.aiplatform_v1.types.CreateMetadataStoreOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform [MetadataService.CreateMetadataStore][google.cloud.aiplatform.v1.MetadataService.CreateMetadataStore].

generic_metadata

Operation metadata for creating a MetadataStore.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateMetadataStoreRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.CreateMetadataStore][google.cloud.aiplatform.v1.MetadataService.CreateMetadataStore].

parent

Required. The resource name of the Location where the MetadataStore should be created. Format: projects/{project}/locations/{location}/

Type:

str

metadata_store

Required. The MetadataStore to create.

Type:

google.cloud.aiplatform_v1.types.MetadataStore

metadata_store_id

The {metadatastore} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore} If not provided, the MetadataStore’s ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /[a-z][0-9]-/. Must be unique across all MetadataStores in the parent Location. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can’t view the preexisting MetadataStore.)

Type:

str

class google.cloud.aiplatform_v1.types.CreateModelDeploymentMonitoringJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.CreateModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.CreateModelDeploymentMonitoringJob].

parent

Required. The parent of the ModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}

Type:

str

model_deployment_monitoring_job

Required. The ModelDeploymentMonitoringJob to create

Type:

google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringJob

class google.cloud.aiplatform_v1.types.CreateNasJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.CreateNasJob][google.cloud.aiplatform.v1.JobService.CreateNasJob].

parent

Required. The resource name of the Location to create the NasJob in. Format: projects/{project}/locations/{location}

Type:

str

nas_job

Required. The NasJob to create.

Type:

google.cloud.aiplatform_v1.types.NasJob

class google.cloud.aiplatform_v1.types.CreateNotebookExecutionJobOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata information for [NotebookService.CreateNotebookExecutionJob][google.cloud.aiplatform.v1.NotebookService.CreateNotebookExecutionJob].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

progress_message

A human-readable message that shows the intermediate progress details of NotebookRuntime.

Type:

str

class google.cloud.aiplatform_v1.types.CreateNotebookExecutionJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.CreateNotebookExecutionJob]

parent

Required. The resource name of the Location to create the NotebookExecutionJob. Format: projects/{project}/locations/{location}

Type:

str

notebook_execution_job

Required. The NotebookExecutionJob to create.

Type:

google.cloud.aiplatform_v1.types.NotebookExecutionJob

notebook_execution_job_id

Optional. User specified ID for the NotebookExecutionJob.

Type:

str

class google.cloud.aiplatform_v1.types.CreateNotebookRuntimeTemplateOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata information for [NotebookService.CreateNotebookRuntimeTemplate][google.cloud.aiplatform.v1.NotebookService.CreateNotebookRuntimeTemplate].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateNotebookRuntimeTemplateRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.CreateNotebookRuntimeTemplate][google.cloud.aiplatform.v1.NotebookService.CreateNotebookRuntimeTemplate].

parent

Required. The resource name of the Location to create the NotebookRuntimeTemplate. Format: projects/{project}/locations/{location}

Type:

str

notebook_runtime_template

Required. The NotebookRuntimeTemplate to create.

Type:

google.cloud.aiplatform_v1.types.NotebookRuntimeTemplate

notebook_runtime_template_id

Optional. User specified ID for the notebook runtime template.

Type:

str

class google.cloud.aiplatform_v1.types.CreatePersistentResourceOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform create PersistentResource.

generic_metadata

Operation metadata for PersistentResource.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

progress_message

Progress Message for Create LRO

Type:

str

class google.cloud.aiplatform_v1.types.CreatePersistentResourceRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PersistentResourceService.CreatePersistentResource][google.cloud.aiplatform.v1.PersistentResourceService.CreatePersistentResource].

parent

Required. The resource name of the Location to create the PersistentResource in. Format: projects/{project}/locations/{location}

Type:

str

persistent_resource

Required. The PersistentResource to create.

Type:

google.cloud.aiplatform_v1.types.PersistentResource

persistent_resource_id

Required. The ID to use for the PersistentResource, which become the final component of the PersistentResource’s resource name.

The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.

Type:

str

class google.cloud.aiplatform_v1.types.CreatePipelineJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.CreatePipelineJob][google.cloud.aiplatform.v1.PipelineService.CreatePipelineJob].

parent

Required. The resource name of the Location to create the PipelineJob in. Format: projects/{project}/locations/{location}

Type:

str

pipeline_job

Required. The PipelineJob to create.

Type:

google.cloud.aiplatform_v1.types.PipelineJob

pipeline_job_id

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][0-9]-/.

Type:

str

class google.cloud.aiplatform_v1.types.CreateRegistryFeatureOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform create FeatureGroup.

generic_metadata

Operation metadata for Feature.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateScheduleRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ScheduleService.CreateSchedule][google.cloud.aiplatform.v1.ScheduleService.CreateSchedule].

parent

Required. The resource name of the Location to create the Schedule in. Format: projects/{project}/locations/{location}

Type:

str

schedule

Required. The Schedule to create.

Type:

google.cloud.aiplatform_v1.types.Schedule

class google.cloud.aiplatform_v1.types.CreateSpecialistPoolOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [SpecialistPoolService.CreateSpecialistPool][google.cloud.aiplatform.v1.SpecialistPoolService.CreateSpecialistPool].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateSpecialistPoolRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [SpecialistPoolService.CreateSpecialistPool][google.cloud.aiplatform.v1.SpecialistPoolService.CreateSpecialistPool].

parent

Required. The parent Project name for the new SpecialistPool. The form is projects/{project}/locations/{location}.

Type:

str

specialist_pool

Required. The SpecialistPool to create.

Type:

google.cloud.aiplatform_v1.types.SpecialistPool

class google.cloud.aiplatform_v1.types.CreateStudyRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.CreateStudy][google.cloud.aiplatform.v1.VizierService.CreateStudy].

parent

Required. The resource name of the Location to create the CustomJob in. Format: projects/{project}/locations/{location}

Type:

str

study

Required. The Study configuration used to create the Study.

Type:

google.cloud.aiplatform_v1.types.Study

class google.cloud.aiplatform_v1.types.CreateTensorboardExperimentRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.CreateTensorboardExperiment][google.cloud.aiplatform.v1.TensorboardService.CreateTensorboardExperiment].

parent

Required. The resource name of the Tensorboard to create the TensorboardExperiment in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

Type:

str

tensorboard_experiment

The TensorboardExperiment to create.

Type:

google.cloud.aiplatform_v1.types.TensorboardExperiment

tensorboard_experiment_id

Required. The ID to use for the Tensorboard experiment, which becomes the final component of the Tensorboard experiment’s resource name.

This value should be 1-128 characters, and valid characters are /[a-z][0-9]-/.

Type:

str

class google.cloud.aiplatform_v1.types.CreateTensorboardOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform create Tensorboard.

generic_metadata

Operation metadata for Tensorboard.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.CreateTensorboardRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.CreateTensorboard][google.cloud.aiplatform.v1.TensorboardService.CreateTensorboard].

parent

Required. The resource name of the Location to create the Tensorboard in. Format: projects/{project}/locations/{location}

Type:

str

tensorboard

Required. The Tensorboard to create.

Type:

google.cloud.aiplatform_v1.types.Tensorboard

class google.cloud.aiplatform_v1.types.CreateTensorboardRunRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.CreateTensorboardRun][google.cloud.aiplatform.v1.TensorboardService.CreateTensorboardRun].

parent

Required. The resource name of the TensorboardExperiment to create the TensorboardRun in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}

Type:

str

tensorboard_run

Required. The TensorboardRun to create.

Type:

google.cloud.aiplatform_v1.types.TensorboardRun

tensorboard_run_id

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][0-9]-/.

Type:

str

class google.cloud.aiplatform_v1.types.CreateTensorboardTimeSeriesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.CreateTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.CreateTensorboardTimeSeries].

parent

Required. The resource name of the TensorboardRun to create the TensorboardTimeSeries in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}

Type:

str

tensorboard_time_series_id

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][a-z0-9-]{0, 127}”.

Type:

str

tensorboard_time_series

Required. The TensorboardTimeSeries to create.

Type:

google.cloud.aiplatform_v1.types.TensorboardTimeSeries

class google.cloud.aiplatform_v1.types.CreateTrainingPipelineRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.CreateTrainingPipeline][google.cloud.aiplatform.v1.PipelineService.CreateTrainingPipeline].

parent

Required. The resource name of the Location to create the TrainingPipeline in. Format: projects/{project}/locations/{location}

Type:

str

training_pipeline

Required. The TrainingPipeline to create.

Type:

google.cloud.aiplatform_v1.types.TrainingPipeline

class google.cloud.aiplatform_v1.types.CreateTrialRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.CreateTrial][google.cloud.aiplatform.v1.VizierService.CreateTrial].

parent

Required. The resource name of the Study to create the Trial in. Format: projects/{project}/locations/{location}/studies/{study}

Type:

str

trial

Required. The Trial to create.

Type:

google.cloud.aiplatform_v1.types.Trial

class google.cloud.aiplatform_v1.types.CreateTuningJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [GenAiTuningService.CreateTuningJob][google.cloud.aiplatform.v1.GenAiTuningService.CreateTuningJob].

parent

Required. The resource name of the Location to create the TuningJob in. Format: projects/{project}/locations/{location}

Type:

str

tuning_job

Required. The TuningJob to create.

Type:

google.cloud.aiplatform_v1.types.TuningJob

class google.cloud.aiplatform_v1.types.CsvDestination(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The storage details for CSV output content.

gcs_destination

Required. Google Cloud Storage location.

Type:

google.cloud.aiplatform_v1.types.GcsDestination

class google.cloud.aiplatform_v1.types.CsvSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The storage details for CSV input content.

gcs_source

Required. Google Cloud Storage location.

Type:

google.cloud.aiplatform_v1.types.GcsSource

class google.cloud.aiplatform_v1.types.CustomJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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).

name

Output only. Resource name of a CustomJob.

Type:

str

display_name

Required. The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

job_spec

Required. Job spec.

Type:

google.cloud.aiplatform_v1.types.CustomJobSpec

state

Output only. The detailed state of the job.

Type:

google.cloud.aiplatform_v1.types.JobState

create_time

Output only. Time when the CustomJob was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

start_time

Output only. Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

Output only. Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Time when the CustomJob was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

error

Output only. Only populated when job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

Type:

google.rpc.status_pb2.Status

labels

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.

Type:

MutableMapping[str, str]

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

web_access_uris

Output only. URIs for accessing interactive shells (one URI for each training node). Only available if [job_spec.enable_web_access][google.cloud.aiplatform.v1.CustomJobSpec.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.

Type:

MutableMapping[str, str]

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class WebAccessUrisEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.CustomJobSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents the spec of a CustomJob.

persistent_resource_id

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.

Type:

str

worker_pool_specs

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.WorkerPoolSpec]

scheduling

Scheduling options for a CustomJob.

Type:

google.cloud.aiplatform_v1.types.Scheduling

service_account

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.

Type:

str

network

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.

Type:

str

reserved_ip_ranges

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’].

Type:

MutableSequence[str]

base_output_directory

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][google.cloud.aiplatform.v1.Trial.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 = <base_output_directory>/model/

• AIP_CHECKPOINT_DIR = <base_output_directory>/checkpoints/

• AIP_TENSORBOARD_LOG_DIR = <base_output_directory>/logs/

For CustomJob backing a Trial of HyperparameterTuningJob:

• AIP_MODEL_DIR = <base_output_directory>/<trial_id>/model/

• AIP_CHECKPOINT_DIR = <base_output_directory>/<trial_id>/checkpoints/

• AIP_TENSORBOARD_LOG_DIR = <base_output_directory>/<trial_id>/logs/

Type:

google.cloud.aiplatform_v1.types.GcsDestination

protected_artifact_location_id

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

Type:

str

tensorboard

Optional. The name of a Vertex AI [Tensorboard][google.cloud.aiplatform.v1.Tensorboard] resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

Type:

str

enable_web_access

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][google.cloud.aiplatform.v1.CustomJob.web_access_uris] or [Trial.web_access_uris][google.cloud.aiplatform.v1.Trial.web_access_uris] (within [HyperparameterTuningJob.trials][google.cloud.aiplatform.v1.HyperparameterTuningJob.trials]).

Type:

bool

enable_dashboard_access

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][google.cloud.aiplatform.v1.CustomJob.web_access_uris] or [Trial.web_access_uris][google.cloud.aiplatform.v1.Trial.web_access_uris] (within [HyperparameterTuningJob.trials][google.cloud.aiplatform.v1.HyperparameterTuningJob.trials]).

Type:

bool

experiment

Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

Type:

str

experiment_run

Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

Type:

str

models

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.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.DataItem(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. The resource name of the DataItem.

Type:

str

create_time

Output only. Timestamp when this DataItem was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this DataItem was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

labels

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.

Type:

MutableMapping[str, str]

payload

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][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] dataItemSchemaUri field.

Type:

google.protobuf.struct_pb2.Value

etag

Optional. Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.DataItemView(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A container for a single DataItem and Annotations on it.

data_item

The DataItem.

Type:

google.cloud.aiplatform_v1.types.DataItem

annotations

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Annotation]

has_truncated_annotations

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.

Type:

bool

class google.cloud.aiplatform_v1.types.DataLabelingJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. Resource name of the DataLabelingJob.

Type:

str

display_name

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.

Type:

str

datasets

Required. Dataset resource names. Right now we only support labeling from a single Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}

Type:

MutableSequence[str]

annotation_labels

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.

Type:

MutableMapping[str, str]

labeler_count

Required. Number of labelers to work on each DataItem.

Type:

int

instruction_uri

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.

Type:

str

inputs_schema_uri

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.

Type:

str

inputs

Required. Input config parameters for the DataLabelingJob.

Type:

google.protobuf.struct_pb2.Value

state

Output only. The detailed state of the job.

Type:

google.cloud.aiplatform_v1.types.JobState

labeling_progress

Output only. Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.

Type:

int

current_spend

Output only. Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.

Type:

google.type.money_pb2.Money

create_time

Output only. Timestamp when this DataLabelingJob was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this DataLabelingJob was updated most recently.

Type:

google.protobuf.timestamp_pb2.Timestamp

error

Output only. DataLabelingJob errors. It is only populated when job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

Type:

google.rpc.status_pb2.Status

labels

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][google.cloud.aiplatform.v1.DataLabelingJob.inputs_schema_uri]’s title.

Type:

MutableMapping[str, str]

specialist_pools

The SpecialistPools’ resource names associated with this job.

Type:

MutableSequence[str]

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

active_learning_config

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.

Type:

google.cloud.aiplatform_v1.types.ActiveLearningConfig

class AnnotationLabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.Dataset(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A collection of DataItems and Annotations on them.

name

Output only. Identifier. The resource name of the Dataset.

Type:

str

display_name

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.

Type:

str

description

The description of the Dataset.

Type:

str

metadata_schema_uri

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/.

Type:

str

metadata

Required. Additional information about the Dataset.

Type:

google.protobuf.struct_pb2.Value

data_item_count

Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.

Type:

int

create_time

Output only. Timestamp when this Dataset was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this Dataset was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

etag

Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] title.

Type:

MutableMapping[str, str]

saved_queries

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][google.cloud.aiplatform.v1.SavedQuery.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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.SavedQuery]

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

metadata_artifact

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}.

Type:

str

model_reference

Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.

Type:

str

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.DatasetVersion(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Describes the dataset version.

name

Output only. Identifier. The resource name of the DatasetVersion.

Type:

str

create_time

Output only. Timestamp when this DatasetVersion was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this DatasetVersion was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

etag

Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

big_query_dataset_name

Output only. Name of the associated BigQuery dataset.

Type:

str

display_name

The user-defined name of the DatasetVersion. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

metadata

Required. Output only. Additional information about the DatasetVersion.

Type:

google.protobuf.struct_pb2.Value

model_reference

Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions.

Type:

str

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class google.cloud.aiplatform_v1.types.DedicatedResources(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

machine_spec

Required. Immutable. The specification of a single machine used by the prediction.

Type:

google.cloud.aiplatform_v1.types.MachineSpec

min_replica_count

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.

Type:

int

max_replica_count

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][google.cloud.aiplatform.v1.DedicatedResources.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).

Type:

int

autoscaling_metric_specs

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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.MachineSpec.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][google.cloud.aiplatform.v1.AutoscalingMetricSpec.metric_name] to aiplatform.googleapis.com/prediction/online/cpu/utilization and [autoscaling_metric_specs.target][google.cloud.aiplatform.v1.AutoscalingMetricSpec.target] to 80.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.AutoscalingMetricSpec]

spot

Optional. If true, schedule the deployment workload on spot VMs.

Type:

bool

class google.cloud.aiplatform_v1.types.DeleteArtifactRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.DeleteArtifact][google.cloud.aiplatform.v1.MetadataService.DeleteArtifact].

name

Required. The resource name of the Artifact to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}

Type:

str

etag

Optional. The etag of the Artifact to delete. If this is provided, it must match the server’s etag. Otherwise, the request will fail with a FAILED_PRECONDITION.

Type:

str

class google.cloud.aiplatform_v1.types.DeleteBatchPredictionJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.DeleteBatchPredictionJob][google.cloud.aiplatform.v1.JobService.DeleteBatchPredictionJob].

name

Required. The name of the BatchPredictionJob resource to be deleted. Format: projects/{project}/locations/{location}/batchPredictionJobs/{batch_prediction_job}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteContextRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.DeleteContext][google.cloud.aiplatform.v1.MetadataService.DeleteContext].

name

Required. The resource name of the Context to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}

Type:

str

force

The force deletion semantics is still undefined. Users should not use this field.

Type:

bool

etag

Optional. The etag of the Context to delete. If this is provided, it must match the server’s etag. Otherwise, the request will fail with a FAILED_PRECONDITION.

Type:

str

class google.cloud.aiplatform_v1.types.DeleteCustomJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.DeleteCustomJob][google.cloud.aiplatform.v1.JobService.DeleteCustomJob].

name

Required. The name of the CustomJob resource to be deleted. Format: projects/{project}/locations/{location}/customJobs/{custom_job}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteDataLabelingJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.DeleteDataLabelingJob][google.cloud.aiplatform.v1.JobService.DeleteDataLabelingJob].

name

Required. The name of the DataLabelingJob to be deleted. Format: projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.DeleteDataset][google.cloud.aiplatform.v1.DatasetService.DeleteDataset].

name

Required. The resource name of the Dataset to delete. Format: projects/{project}/locations/{location}/datasets/{dataset}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteDatasetVersionRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.DeleteDatasetVersion][google.cloud.aiplatform.v1.DatasetService.DeleteDatasetVersion].

name

Required. The resource name of the Dataset version to delete. Format: projects/{project}/locations/{location}/datasets/{dataset}/datasetVersions/{dataset_version}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteDeploymentResourcePoolRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for DeleteDeploymentResourcePool method.

name

Required. The name of the DeploymentResourcePool to delete. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteEndpointRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [EndpointService.DeleteEndpoint][google.cloud.aiplatform.v1.EndpointService.DeleteEndpoint].

name

Required. The name of the Endpoint resource to be deleted. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteEntityTypeRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.DeleteEntityType][google.cloud.aiplatform.v1.FeaturestoreService.DeleteEntityType].

name

Required. The name of the EntityType to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

Type:

str

force

If set to true, any Features for this EntityType will also be deleted. (Otherwise, the request will only work if the EntityType has no Features.)

Type:

bool

class google.cloud.aiplatform_v1.types.DeleteExecutionRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.DeleteExecution][google.cloud.aiplatform.v1.MetadataService.DeleteExecution].

name

Required. The resource name of the Execution to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}

Type:

str

etag

Optional. The etag of the Execution to delete. If this is provided, it must match the server’s etag. Otherwise, the request will fail with a FAILED_PRECONDITION.

Type:

str

class google.cloud.aiplatform_v1.types.DeleteFeatureGroupRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureRegistryService.DeleteFeatureGroup][google.cloud.aiplatform.v1.FeatureRegistryService.DeleteFeatureGroup].

name

Required. The name of the FeatureGroup to be deleted. Format: projects/{project}/locations/{location}/featureGroups/{feature_group}

Type:

str

force

If set to true, any Features under this FeatureGroup will also be deleted. (Otherwise, the request will only work if the FeatureGroup has no Features.)

Type:

bool

class google.cloud.aiplatform_v1.types.DeleteFeatureOnlineStoreRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.DeleteFeatureOnlineStore][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.DeleteFeatureOnlineStore].

name

Required. The name of the FeatureOnlineStore to be deleted. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}

Type:

str

force

If set to true, any FeatureViews and Features for this FeatureOnlineStore will also be deleted. (Otherwise, the request will only work if the FeatureOnlineStore has no FeatureViews.)

Type:

bool

class google.cloud.aiplatform_v1.types.DeleteFeatureRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.DeleteFeature][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeature]. Request message for [FeatureRegistryService.DeleteFeature][google.cloud.aiplatform.v1.FeatureRegistryService.DeleteFeature].

name

Required. The name of the Features to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteFeatureValuesOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that delete Feature values.

generic_metadata

Operation metadata for Featurestore delete Features values.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.DeleteFeatureValuesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.DeleteFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeatureValues].

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

select_entity

Select feature values to be deleted by specifying entities.

This field is a member of oneof DeleteOption.

Type:

google.cloud.aiplatform_v1.types.DeleteFeatureValuesRequest.SelectEntity

select_time_range_and_feature

Select feature values to be deleted by specifying time range and features.

This field is a member of oneof DeleteOption.

Type:

google.cloud.aiplatform_v1.types.DeleteFeatureValuesRequest.SelectTimeRangeAndFeature

entity_type

Required. The resource name of the EntityType grouping the Features for which values are being deleted from. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}

Type:

str

class SelectEntity(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

entity_id_selector

Required. Selectors choosing feature values of which entity id to be deleted from the EntityType.

Type:

google.cloud.aiplatform_v1.types.EntityIdSelector

class SelectTimeRangeAndFeature(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

time_range

Required. Select feature generated within a half-inclusive time range. The time range is lower inclusive and upper exclusive.

Type:

google.type.interval_pb2.Interval

feature_selector

Required. Selectors choosing which feature values to be deleted from the EntityType.

Type:

google.cloud.aiplatform_v1.types.FeatureSelector

skip_online_storage_delete

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.

Type:

bool

class google.cloud.aiplatform_v1.types.DeleteFeatureValuesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeaturestoreService.DeleteFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeatureValues].

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

select_entity

Response for request specifying the entities to delete

This field is a member of oneof response.

Type:

google.cloud.aiplatform_v1.types.DeleteFeatureValuesResponse.SelectEntity

select_time_range_and_feature

Response for request specifying time range and feature

This field is a member of oneof response.

Type:

google.cloud.aiplatform_v1.types.DeleteFeatureValuesResponse.SelectTimeRangeAndFeature

class SelectEntity(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message if the request uses the SelectEntity option.

offline_storage_deleted_entity_row_count

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.

Type:

int

online_storage_deleted_entity_count

The count of deleted entities in the online storage. Each entity ID corresponds to one entity.

Type:

int

class SelectTimeRangeAndFeature(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message if the request uses the SelectTimeRangeAndFeature option.

impacted_feature_count

The count of the features or columns impacted. This is the same as the feature count in the request.

Type:

int

offline_storage_modified_entity_row_count

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.

Type:

int

online_storage_modified_entity_count

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.

Type:

int

class google.cloud.aiplatform_v1.types.DeleteFeatureViewRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.DeleteFeatureView][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.DeleteFeatureView].

name

Required. The name of the FeatureView to be deleted. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteFeaturestoreRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.DeleteFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.DeleteFeaturestore].

name

Required. The name of the Featurestore to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

Type:

str

force

If set to true, any EntityTypes and Features for this Featurestore will also be deleted. (Otherwise, the request will only work if the Featurestore has no EntityTypes.)

Type:

bool

class google.cloud.aiplatform_v1.types.DeleteHyperparameterTuningJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.DeleteHyperparameterTuningJob][google.cloud.aiplatform.v1.JobService.DeleteHyperparameterTuningJob].

name

Required. The name of the HyperparameterTuningJob resource to be deleted. Format: projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyperparameter_tuning_job}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteIndexEndpointRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexEndpointService.DeleteIndexEndpoint][google.cloud.aiplatform.v1.IndexEndpointService.DeleteIndexEndpoint].

name

Required. The name of the IndexEndpoint resource to be deleted. Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteIndexRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexService.DeleteIndex][google.cloud.aiplatform.v1.IndexService.DeleteIndex].

name

Required. The name of the Index resource to be deleted. Format: projects/{project}/locations/{location}/indexes/{index}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteMetadataStoreOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform [MetadataService.DeleteMetadataStore][google.cloud.aiplatform.v1.MetadataService.DeleteMetadataStore].

generic_metadata

Operation metadata for deleting a MetadataStore.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.DeleteMetadataStoreRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.DeleteMetadataStore][google.cloud.aiplatform.v1.MetadataService.DeleteMetadataStore].

name

Required. The resource name of the MetadataStore to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

force

Deprecated: Field is no longer supported.

Type:

bool

class google.cloud.aiplatform_v1.types.DeleteModelDeploymentMonitoringJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.DeleteModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.DeleteModelDeploymentMonitoringJob].

name

Required. The resource name of the model monitoring job to delete. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.DeleteModel][google.cloud.aiplatform.v1.ModelService.DeleteModel].

name

Required. The name of the Model resource to be deleted. Format: projects/{project}/locations/{location}/models/{model}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteModelVersionRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.DeleteModelVersion][google.cloud.aiplatform.v1.ModelService.DeleteModelVersion].

name

Required. The name of the model version to be deleted, with a version ID explicitly included.

Example: projects/{project}/locations/{location}/models/{model}@1234

Type:

str

class google.cloud.aiplatform_v1.types.DeleteNasJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.DeleteNasJob][google.cloud.aiplatform.v1.JobService.DeleteNasJob].

name

Required. The name of the NasJob resource to be deleted. Format: projects/{project}/locations/{location}/nasJobs/{nas_job}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteNotebookExecutionJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.DeleteNotebookExecutionJob]

name

Required. The name of the NotebookExecutionJob resource to be deleted.

Type:

str

class google.cloud.aiplatform_v1.types.DeleteNotebookRuntimeRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.DeleteNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.DeleteNotebookRuntime].

name

Required. The name of the NotebookRuntime resource to be deleted. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner.

Type:

str

class google.cloud.aiplatform_v1.types.DeleteNotebookRuntimeTemplateRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.DeleteNotebookRuntimeTemplate][google.cloud.aiplatform.v1.NotebookService.DeleteNotebookRuntimeTemplate].

name

Required. The name of the NotebookRuntimeTemplate resource to be deleted. Format: projects/{project}/locations/{location}/notebookRuntimeTemplates/{notebook_runtime_template}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform deletes of any entities.

generic_metadata

The common part of the operation metadata.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.DeletePersistentResourceRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PersistentResourceService.DeletePersistentResource][google.cloud.aiplatform.v1.PersistentResourceService.DeletePersistentResource].

name

Required. The name of the PersistentResource to be deleted. Format: projects/{project}/locations/{location}/persistentResources/{persistent_resource}

Type:

str

class google.cloud.aiplatform_v1.types.DeletePipelineJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.DeletePipelineJob][google.cloud.aiplatform.v1.PipelineService.DeletePipelineJob].

name

Required. The name of the PipelineJob resource to be deleted. Format: projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteSavedQueryRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.DeleteSavedQuery][google.cloud.aiplatform.v1.DatasetService.DeleteSavedQuery].

name

Required. The resource name of the SavedQuery to delete. Format: projects/{project}/locations/{location}/datasets/{dataset}/savedQueries/{saved_query}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteScheduleRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ScheduleService.DeleteSchedule][google.cloud.aiplatform.v1.ScheduleService.DeleteSchedule].

name

Required. The name of the Schedule resource to be deleted. Format: projects/{project}/locations/{location}/schedules/{schedule}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteSpecialistPoolRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [SpecialistPoolService.DeleteSpecialistPool][google.cloud.aiplatform.v1.SpecialistPoolService.DeleteSpecialistPool].

name

Required. The resource name of the SpecialistPool to delete. Format: projects/{project}/locations/{location}/specialistPools/{specialist_pool}

Type:

str

force

If set to true, any specialist managers in this SpecialistPool will also be deleted. (Otherwise, the request will only work if the SpecialistPool has no specialist managers.)

Type:

bool

class google.cloud.aiplatform_v1.types.DeleteStudyRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.DeleteStudy][google.cloud.aiplatform.v1.VizierService.DeleteStudy].

name

Required. The name of the Study resource to be deleted. Format: projects/{project}/locations/{location}/studies/{study}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteTensorboardExperimentRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.DeleteTensorboardExperiment][google.cloud.aiplatform.v1.TensorboardService.DeleteTensorboardExperiment].

name

Required. The name of the TensorboardExperiment to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteTensorboardRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.DeleteTensorboard][google.cloud.aiplatform.v1.TensorboardService.DeleteTensorboard].

name

Required. The name of the Tensorboard to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteTensorboardRunRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.DeleteTensorboardRun][google.cloud.aiplatform.v1.TensorboardService.DeleteTensorboardRun].

name

Required. The name of the TensorboardRun to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteTensorboardTimeSeriesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.DeleteTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.DeleteTensorboardTimeSeries].

name

Required. The name of the TensorboardTimeSeries to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteTrainingPipelineRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.DeleteTrainingPipeline][google.cloud.aiplatform.v1.PipelineService.DeleteTrainingPipeline].

name

Required. The name of the TrainingPipeline resource to be deleted. Format: projects/{project}/locations/{location}/trainingPipelines/{training_pipeline}

Type:

str

class google.cloud.aiplatform_v1.types.DeleteTrialRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.DeleteTrial][google.cloud.aiplatform.v1.VizierService.DeleteTrial].

name

Required. The Trial’s name. Format: projects/{project}/locations/{location}/studies/{study}/trials/{trial}

Type:

str

class google.cloud.aiplatform_v1.types.DeployIndexOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [IndexEndpointService.DeployIndex][google.cloud.aiplatform.v1.IndexEndpointService.DeployIndex].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

deployed_index_id

The unique index id specified by user

Type:

str

class google.cloud.aiplatform_v1.types.DeployIndexRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexEndpointService.DeployIndex][google.cloud.aiplatform.v1.IndexEndpointService.DeployIndex].

index_endpoint

Required. The name of the IndexEndpoint resource into which to deploy an Index. Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

Type:

str

deployed_index

Required. The DeployedIndex to be created within the IndexEndpoint.

Type:

google.cloud.aiplatform_v1.types.DeployedIndex

class google.cloud.aiplatform_v1.types.DeployIndexResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [IndexEndpointService.DeployIndex][google.cloud.aiplatform.v1.IndexEndpointService.DeployIndex].

deployed_index

The DeployedIndex that had been deployed in the IndexEndpoint.

Type:

google.cloud.aiplatform_v1.types.DeployedIndex

class google.cloud.aiplatform_v1.types.DeployModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.DeployModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel].

endpoint

Required. The name of the Endpoint resource into which to deploy a Model. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

deployed_model

Required. The DeployedModel to be created within the Endpoint. Note that [Endpoint.traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split] must be updated for the DeployedModel to start receiving traffic, either as part of this call, or via [EndpointService.UpdateEndpoint][google.cloud.aiplatform.v1.EndpointService.UpdateEndpoint].

Type:

google.cloud.aiplatform_v1.types.DeployedModel

traffic_split

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][google.cloud.aiplatform.v1.Endpoint.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][google.cloud.aiplatform.v1.Endpoint.traffic_split] is not updated.

Type:

MutableMapping[str, int]

class TrafficSplitEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.DeployModelResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel].

deployed_model

The DeployedModel that had been deployed in the Endpoint.

Type:

google.cloud.aiplatform_v1.types.DeployedModel

class google.cloud.aiplatform_v1.types.DeployedIndex(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

id

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.

Type:

str

index

Required. The name of the Index this is the deployment of. We may refer to this Index as the DeployedIndex’s “original” Index.

Type:

str

display_name

The display name of the DeployedIndex. If not provided upon creation, the Index’s display_name is used.

Type:

str

create_time

Output only. Timestamp when the DeployedIndex was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

private_endpoints

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][google.cloud.aiplatform.v1.IndexEndpoint.network] is configured.

Type:

google.cloud.aiplatform_v1.types.IndexPrivateEndpoints

index_sync_time

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][google.cloud.aiplatform.v1.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][google.longrunning.Operations.ListOperations] the operations that are running on the original Index. Only the successfully completed Operations with [update_time][google.cloud.aiplatform.v1.GenericOperationMetadata.update_time] equal or before this sync time are contained in this DeployedIndex.

Type:

google.protobuf.timestamp_pb2.Timestamp

automatic_resources

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.

Type:

google.cloud.aiplatform_v1.types.AutomaticResources

dedicated_resources

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.

Type:

google.cloud.aiplatform_v1.types.DedicatedResources

enable_access_logging

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.

Type:

bool

deployed_index_auth_config

Optional. If set, the authentication is enabled for the private endpoint.

Type:

google.cloud.aiplatform_v1.types.DeployedIndexAuthConfig

reserved_ip_ranges

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.

Type:

MutableSequence[str]

deployment_group

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’).

Type:

str

psc_automation_configs

Optional. If set for PSC deployed index, PSC connection will be automatically created after deployment is done and the endpoint information is populated in private_endpoints.psc_automated_endpoints.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.PSCAutomationConfig]

class google.cloud.aiplatform_v1.types.DeployedIndexAuthConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

auth_provider

Defines the authentication provider that the DeployedIndex uses.

Type:

google.cloud.aiplatform_v1.types.DeployedIndexAuthConfig.AuthProvider

class AuthProvider(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

audiences

The list of JWT audiences. that are allowed to access. A JWT containing any of these audiences will be accepted.

Type:

MutableSequence[str]

allowed_issuers

A list of allowed JWT issuers. Each entry must be a valid Google service account, in the following format:

service-account-name@project-id.iam.gserviceaccount.com

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.DeployedIndexRef(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Points to a DeployedIndex.

index_endpoint

Immutable. A resource name of the IndexEndpoint.

Type:

str

deployed_index_id

Immutable. The ID of the DeployedIndex in the above IndexEndpoint.

Type:

str

display_name

Output only. The display name of the DeployedIndex.

Type:

str

class google.cloud.aiplatform_v1.types.DeployedModel(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

dedicated_resources

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

This field is a member of oneof prediction_resources.

Type:

google.cloud.aiplatform_v1.types.DedicatedResources

automatic_resources

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.

This field is a member of oneof prediction_resources.

Type:

google.cloud.aiplatform_v1.types.AutomaticResources

shared_resources

The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

This field is a member of oneof prediction_resources.

Type:

str

id

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]/.

Type:

str

model

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.

Type:

str

model_version_id

Output only. The version ID of the model that is deployed.

Type:

str

display_name

The display name of the DeployedModel. If not provided upon creation, the Model’s display_name is used.

Type:

str

create_time

Output only. Timestamp when the DeployedModel was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

explanation_spec

Explanation configuration for this DeployedModel.

When deploying a Model using [EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel], this value overrides the value of [Model.explanation_spec][google.cloud.aiplatform.v1.Model.explanation_spec]. All fields of [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] are optional in the request. If a field of [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] is not populated, the value of the same field of [Model.explanation_spec][google.cloud.aiplatform.v1.Model.explanation_spec] is inherited. If the corresponding [Model.explanation_spec][google.cloud.aiplatform.v1.Model.explanation_spec] is not populated, all fields of the [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] will be used for the explanation configuration.

Type:

google.cloud.aiplatform_v1.types.ExplanationSpec

disable_explanations

If true, deploy the model without explainable feature, regardless the existence of [Model.explanation_spec][google.cloud.aiplatform.v1.Model.explanation_spec] or [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec].

Type:

bool

service_account

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.

Type:

str

disable_container_logging

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.

Type:

bool

enable_access_logging

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.

Type:

bool

private_endpoints

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][google.cloud.aiplatform.v1.Endpoint.network] is configured.

Type:

google.cloud.aiplatform_v1.types.PrivateEndpoints

faster_deployment_config

Configuration for faster model deployment.

Type:

google.cloud.aiplatform_v1.types.FasterDeploymentConfig

system_labels

System labels to apply to Model Garden deployments. System labels are managed by Google for internal use only.

Type:

MutableMapping[str, str]

class SystemLabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.DeployedModelRef(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Points to a DeployedModel.

endpoint

Immutable. A resource name of an Endpoint.

Type:

str

deployed_model_id

Immutable. An ID of a DeployedModel in the above Endpoint.

Type:

str

class google.cloud.aiplatform_v1.types.DeploymentResourcePool(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Immutable. The resource name of the DeploymentResourcePool. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

Type:

str

dedicated_resources

Required. The underlying DedicatedResources that the DeploymentResourcePool uses.

Type:

google.cloud.aiplatform_v1.types.DedicatedResources

encryption_spec

Customer-managed encryption key spec for a DeploymentResourcePool. If set, this DeploymentResourcePool will be secured by this key. Endpoints and the DeploymentResourcePool they deploy in need to have the same EncryptionSpec.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

service_account

The service account that the DeploymentResourcePool’s container(s) run as. Specify the email address of the service account. If this service account is not specified, the container(s) run as a service account that doesn’t have access to the resource project.

Users deploying the Models to this DeploymentResourcePool must have the iam.serviceAccounts.actAs permission on this service account.

Type:

str

disable_container_logging

If the DeploymentResourcePool is deployed with custom-trained Models or AutoML Tabular Models, the container(s) of the DeploymentResourcePool 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.

Type:

bool

create_time

Output only. Timestamp when this DeploymentResourcePool was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class google.cloud.aiplatform_v1.types.DestinationFeatureSetting(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

feature_id

Required. The ID of the Feature to apply the setting to.

Type:

str

destination_field

Specify the field name in the export destination. If not specified, Feature ID is used.

Type:

str

class google.cloud.aiplatform_v1.types.DirectPredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.DirectPredict][google.cloud.aiplatform.v1.PredictionService.DirectPredict].

endpoint

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

inputs

The prediction input.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Tensor]

parameters

The parameters that govern the prediction.

Type:

google.cloud.aiplatform_v1.types.Tensor

class google.cloud.aiplatform_v1.types.DirectPredictResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PredictionService.DirectPredict][google.cloud.aiplatform.v1.PredictionService.DirectPredict].

outputs

The prediction output.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Tensor]

parameters

The parameters that govern the prediction.

Type:

google.cloud.aiplatform_v1.types.Tensor

class google.cloud.aiplatform_v1.types.DirectRawPredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.DirectRawPredict][google.cloud.aiplatform.v1.PredictionService.DirectRawPredict].

endpoint

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

method_name

Fully qualified name of the API method being invoked to perform predictions.

Format: /namespace.Service/Method/ Example: /tensorflow.serving.PredictionService/Predict

Type:

str

input

The prediction input.

Type:

bytes

class google.cloud.aiplatform_v1.types.DirectRawPredictResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PredictionService.DirectRawPredict][google.cloud.aiplatform.v1.PredictionService.DirectRawPredict].

output

The prediction output.

Type:

bytes

class google.cloud.aiplatform_v1.types.DiskSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents the spec of disk options.

boot_disk_type

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).

Type:

str

boot_disk_size_gb

Size in GB of the boot disk (default is 100GB).

Type:

int

class google.cloud.aiplatform_v1.types.DoubleArray(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A list of double values.

values

A list of double values.

Type:

MutableSequence[float]

class google.cloud.aiplatform_v1.types.DynamicRetrievalConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Describes the options to customize dynamic retrieval.

mode

The mode of the predictor to be used in dynamic retrieval.

Type:

google.cloud.aiplatform_v1.types.DynamicRetrievalConfig.Mode

dynamic_threshold

Optional. The threshold to be used in dynamic retrieval. If not set, a system default value is used.

This field is a member of oneof _dynamic_threshold.

Type:

float

class Mode(value)

Bases: Enum

The mode of the predictor to be used in dynamic retrieval.

Values:

MODE_UNSPECIFIED (0):

Always trigger retrieval.

MODE_DYNAMIC (1):

Run retrieval only when system decides it is necessary.

class google.cloud.aiplatform_v1.types.EncryptionSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

kms_key_name

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.

Type:

str

class google.cloud.aiplatform_v1.types.Endpoint(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. The resource name of the Endpoint.

Type:

str

display_name

Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

description

The description of the Endpoint.

Type:

str

deployed_models

Output only. The models deployed in this Endpoint. To add or remove DeployedModels use [EndpointService.DeployModel][google.cloud.aiplatform.v1.EndpointService.DeployModel] and [EndpointService.UndeployModel][google.cloud.aiplatform.v1.EndpointService.UndeployModel] respectively.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DeployedModel]

traffic_split

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.

Type:

MutableMapping[str, int]

etag

Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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.

Type:

MutableMapping[str, str]

create_time

Output only. Timestamp when this Endpoint was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this Endpoint was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

network

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][google.cloud.aiplatform.v1.Endpoint.network] or [enable_private_service_connect][google.cloud.aiplatform.v1.Endpoint.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.

Type:

str

enable_private_service_connect

Deprecated: If true, expose the Endpoint via private service connect.

Only one of the fields, [network][google.cloud.aiplatform.v1.Endpoint.network] or [enable_private_service_connect][google.cloud.aiplatform.v1.Endpoint.enable_private_service_connect], can be set.

Type:

bool

private_service_connect_config

Optional. Configuration for private service connect.

[network][google.cloud.aiplatform.v1.Endpoint.network] and [private_service_connect_config][google.cloud.aiplatform.v1.Endpoint.private_service_connect_config] are mutually exclusive.

Type:

google.cloud.aiplatform_v1.types.PrivateServiceConnectConfig

model_deployment_monitoring_job

Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by [JobService.CreateModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.CreateModelDeploymentMonitoringJob]. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

Type:

str

predict_request_response_logging_config

Configures the request-response logging for online prediction.

Type:

google.cloud.aiplatform_v1.types.PredictRequestResponseLoggingConfig

dedicated_endpoint_enabled

If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users’ traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won’t be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon.

Type:

bool

dedicated_endpoint_dns

Output only. DNS of the dedicated endpoint. Will only be populated if dedicated_endpoint_enabled is true. Format: https://{endpoint_id}.{region}-{project_number}.prediction.vertexai.goog.

Type:

str

client_connection_config

Configurations that are applied to the endpoint for online prediction.

Type:

google.cloud.aiplatform_v1.types.ClientConnectionConfig

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class TrafficSplitEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.EntityIdSelector(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Selector for entityId. Getting ids from the given source.

csv_source

Source of Csv

This field is a member of oneof EntityIdsSource.

Type:

google.cloud.aiplatform_v1.types.CsvSource

entity_id_field

Source column that holds entity IDs. If not provided, entity IDs are extracted from the column named entity_id.

Type:

str

class google.cloud.aiplatform_v1.types.EntityType(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

name

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.

Type:

str

description

Optional. Description of the EntityType.

Type:

str

create_time

Output only. Timestamp when this EntityType was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this EntityType was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

labels

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.

Type:

MutableMapping[str, str]

etag

Optional. Used to perform a consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

monitoring_config

Optional. The default monitoring configuration for all Features with value type ([Feature.ValueType][google.cloud.aiplatform.v1.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.

Type:

google.cloud.aiplatform_v1.types.FeaturestoreMonitoringConfig

offline_storage_ttl_days

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.

Type:

int

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.EnvVar(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Required. Name of the environment variable. Must be a valid C identifier.

Type:

str

value

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.

Type:

str

class google.cloud.aiplatform_v1.types.ErrorAnalysisAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Model error analysis for each annotation.

attributed_items

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

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ErrorAnalysisAnnotation.AttributedItem]

query_type

The query type used for finding the attributed items.

Type:

google.cloud.aiplatform_v1.types.ErrorAnalysisAnnotation.QueryType

outlier_score

The outlier score of this annotated item. Usually defined as the min of all distances from attributed items.

Type:

float

outlier_threshold

The threshold used to determine if this annotation is an outlier or not.

Type:

float

class AttributedItem(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

annotation_resource_name

The unique ID for each annotation. Used by FE to allocate the annotation in DB.

Type:

str

distance

The distance of this item to the annotation.

Type:

float

class QueryType(value)

Bases: Enum

The query type used for finding the attributed items.

Values:

QUERY_TYPE_UNSPECIFIED (0):

Unspecified query type for model error analysis.

ALL_SIMILAR (1):

Query similar samples across all classes in the dataset.

SAME_CLASS_SIMILAR (2):

Query similar samples from the same class of the input sample.

SAME_CLASS_DISSIMILAR (3):

Query dissimilar samples from the same class of the input sample.

class google.cloud.aiplatform_v1.types.EvaluateInstancesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for EvaluationService.EvaluateInstances.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

exact_match_input

Auto metric instances. Instances and metric spec for exact match metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.ExactMatchInput

bleu_input

Instances and metric spec for bleu metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.BleuInput

rouge_input

Instances and metric spec for rouge metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.RougeInput

fluency_input

LLM-based metric instance. General text generation metrics, applicable to other categories. Input for fluency metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.FluencyInput

coherence_input

Input for coherence metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.CoherenceInput

safety_input

Input for safety metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.SafetyInput

groundedness_input

Input for groundedness metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.GroundednessInput

fulfillment_input

Input for fulfillment metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.FulfillmentInput

summarization_quality_input

Input for summarization quality metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.SummarizationQualityInput

pairwise_summarization_quality_input

Input for pairwise summarization quality metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.PairwiseSummarizationQualityInput

summarization_helpfulness_input

Input for summarization helpfulness metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.SummarizationHelpfulnessInput

summarization_verbosity_input

Input for summarization verbosity metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.SummarizationVerbosityInput

question_answering_quality_input

Input for question answering quality metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringQualityInput

pairwise_question_answering_quality_input

Input for pairwise question answering quality metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.PairwiseQuestionAnsweringQualityInput

question_answering_relevance_input

Input for question answering relevance metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringRelevanceInput

question_answering_helpfulness_input

Input for question answering helpfulness metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringHelpfulnessInput

question_answering_correctness_input

Input for question answering correctness metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringCorrectnessInput

pointwise_metric_input

Input for pointwise metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.PointwiseMetricInput

pairwise_metric_input

Input for pairwise metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.PairwiseMetricInput

tool_call_valid_input

Tool call metric instances. Input for tool call valid metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.ToolCallValidInput

tool_name_match_input

Input for tool name match metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.ToolNameMatchInput

tool_parameter_key_match_input

Input for tool parameter key match metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.ToolParameterKeyMatchInput

tool_parameter_kv_match_input

Input for tool parameter key value match metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.ToolParameterKVMatchInput

comet_input

Translation metrics. Input for Comet metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.CometInput

metricx_input

Input for Metricx metric.

This field is a member of oneof metric_inputs.

Type:

google.cloud.aiplatform_v1.types.MetricxInput

location

Required. The resource name of the Location to evaluate the instances. Format: projects/{project}/locations/{location}

Type:

str

class google.cloud.aiplatform_v1.types.EvaluateInstancesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for EvaluationService.EvaluateInstances.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

exact_match_results

Auto metric evaluation results. Results for exact match metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.ExactMatchResults

bleu_results

Results for bleu metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.BleuResults

rouge_results

Results for rouge metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.RougeResults

fluency_result

LLM-based metric evaluation result. General text generation metrics, applicable to other categories. Result for fluency metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.FluencyResult

coherence_result

Result for coherence metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.CoherenceResult

safety_result

Result for safety metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.SafetyResult

groundedness_result

Result for groundedness metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.GroundednessResult

fulfillment_result

Result for fulfillment metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.FulfillmentResult

summarization_quality_result

Summarization only metrics. Result for summarization quality metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.SummarizationQualityResult

pairwise_summarization_quality_result

Result for pairwise summarization quality metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.PairwiseSummarizationQualityResult

summarization_helpfulness_result

Result for summarization helpfulness metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.SummarizationHelpfulnessResult

summarization_verbosity_result

Result for summarization verbosity metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.SummarizationVerbosityResult

question_answering_quality_result

Question answering only metrics. Result for question answering quality metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringQualityResult

pairwise_question_answering_quality_result

Result for pairwise question answering quality metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.PairwiseQuestionAnsweringQualityResult

question_answering_relevance_result

Result for question answering relevance metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringRelevanceResult

question_answering_helpfulness_result

Result for question answering helpfulness metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringHelpfulnessResult

question_answering_correctness_result

Result for question answering correctness metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringCorrectnessResult

pointwise_metric_result

Generic metrics. Result for pointwise metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.PointwiseMetricResult

pairwise_metric_result

Result for pairwise metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.PairwiseMetricResult

tool_call_valid_results

Tool call metrics. Results for tool call valid metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.ToolCallValidResults

tool_name_match_results

Results for tool name match metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.ToolNameMatchResults

tool_parameter_key_match_results

Results for tool parameter key match metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.ToolParameterKeyMatchResults

tool_parameter_kv_match_results

Results for tool parameter key value match metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.ToolParameterKVMatchResults

comet_result

Translation metrics. Result for Comet metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.CometResult

metricx_result

Result for Metricx metric.

This field is a member of oneof evaluation_results.

Type:

google.cloud.aiplatform_v1.types.MetricxResult

class google.cloud.aiplatform_v1.types.EvaluatedAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

True positive, false positive, or false negative.

EvaluatedAnnotation is only available under ModelEvaluationSlice with slice of annotationSpec dimension.

type_

Output only. Type of the EvaluatedAnnotation.

Type:

google.cloud.aiplatform_v1.types.EvaluatedAnnotation.EvaluatedAnnotationType

predictions

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][google.cloud.aiplatform.v1.EvaluatedAnnotation.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][google.cloud.aiplatform.v1.EvaluatedAnnotation.evaluated_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][google.cloud.aiplatform.v1.EvaluatedAnnotation.ground_truths] but not enough for a match.

The schema of the prediction is stored in [ModelEvaluation.annotation_schema_uri][google.cloud.aiplatform.v1.ModelEvaluation.annotation_schema_uri]

Type:

MutableSequence[google.protobuf.struct_pb2.Value]

ground_truths

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][google.cloud.aiplatform.v1.EvaluatedAnnotation.predictions].

For false positive, there are zero or more ground truth annotations that are similar to the only prediction in [predictions][google.cloud.aiplatform.v1.EvaluatedAnnotation.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][google.cloud.aiplatform.v1.ModelEvaluation.annotation_schema_uri]

Type:

MutableSequence[google.protobuf.struct_pb2.Value]

data_item_payload

Output only. The data item payload that the Model predicted this EvaluatedAnnotation on.

Type:

google.protobuf.struct_pb2.Value

evaluated_data_item_view_id

Output only. ID of the EvaluatedDataItemView under the same ancestor ModelEvaluation. The EvaluatedDataItemView consists of all ground truths and predictions on [data_item_payload][google.cloud.aiplatform.v1.EvaluatedAnnotation.data_item_payload].

Type:

str

explanations

Explanations of [predictions][google.cloud.aiplatform.v1.EvaluatedAnnotation.predictions]. Each element of the explanations indicates the explanation for one explanation Method.

The attributions list in the [EvaluatedAnnotationExplanation.explanation][google.cloud.aiplatform.v1.EvaluatedAnnotationExplanation.explanation] object corresponds to the [predictions][google.cloud.aiplatform.v1.EvaluatedAnnotation.predictions] list. For example, the second element in the attributions list explains the second element in the predictions list.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.EvaluatedAnnotationExplanation]

error_analysis_annotations

Annotations of model error analysis results.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ErrorAnalysisAnnotation]

class EvaluatedAnnotationType(value)

Bases: Enum

Describes the type of the EvaluatedAnnotation. The type is determined

Values:

EVALUATED_ANNOTATION_TYPE_UNSPECIFIED (0):

Invalid value.

TRUE_POSITIVE (1):

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 (2):

The EvaluatedAnnotation is false positive. It has a prediction created by the Model which does not match any ground truth annotation.

FALSE_NEGATIVE (3):

The EvaluatedAnnotation is false negative. It has a ground truth annotation which is not matched by any of the model created predictions.

class google.cloud.aiplatform_v1.types.EvaluatedAnnotationExplanation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Explanation result of the prediction produced by the Model.

explanation_type

Explanation type.

For AutoML Image Classification models, possible values are:

• image-integrated-gradients

• image-xrai

Type:

str

explanation

Explanation attribution response details.

Type:

google.cloud.aiplatform_v1.types.Explanation

class google.cloud.aiplatform_v1.types.Event(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

artifact

Required. The relative resource name of the Artifact in the Event.

Type:

str

execution

Output only. The relative resource name of the Execution in the Event.

Type:

str

event_time

Output only. Time the Event occurred.

Type:

google.protobuf.timestamp_pb2.Timestamp

type_

Required. The type of the Event.

Type:

google.cloud.aiplatform_v1.types.Event.Type

labels

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:

MutableMapping[str, str]

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class Type(value)

Bases: Enum

Describes whether an Event’s Artifact is the Execution’s input or output.

Values:

TYPE_UNSPECIFIED (0):

Unspecified whether input or output of the Execution.

INPUT (1):

An input of the Execution.

OUTPUT (2):

An output of the Execution.

class google.cloud.aiplatform_v1.types.ExactMatchInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for exact match metric.

metric_spec

Required. Spec for exact match metric.

Type:

google.cloud.aiplatform_v1.types.ExactMatchSpec

instances

Required. Repeated exact match instances.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ExactMatchInstance]

class google.cloud.aiplatform_v1.types.ExactMatchInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for exact match instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Required. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

class google.cloud.aiplatform_v1.types.ExactMatchMetricValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Exact match metric value for an instance.

score

Output only. Exact match score.

This field is a member of oneof _score.

Type:

float

class google.cloud.aiplatform_v1.types.ExactMatchResults(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Results for exact match metric.

exact_match_metric_values

Output only. Exact match metric values.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ExactMatchMetricValue]

class google.cloud.aiplatform_v1.types.ExactMatchSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0.

class google.cloud.aiplatform_v1.types.Examples(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

example_gcs_source

The Cloud Storage input instances.

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.Examples.ExampleGcsSource

nearest_neighbor_search_config

The full configuration for the generated index, the semantics are the same as [metadata][google.cloud.aiplatform.v1.Index.metadata] and should match NearestNeighborSearchConfig.

This field is a member of oneof config.

Type:

google.protobuf.struct_pb2.Value

presets

Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

This field is a member of oneof config.

Type:

google.cloud.aiplatform_v1.types.Presets

neighbor_count

The number of neighbors to return when querying for examples.

Type:

int

class ExampleGcsSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The Cloud Storage input instances.

data_format

The format in which instances are given, if not specified, assume it’s JSONL format. Currently only JSONL format is supported.

Type:

google.cloud.aiplatform_v1.types.Examples.ExampleGcsSource.DataFormat

gcs_source

The Cloud Storage location for the input instances.

Type:

google.cloud.aiplatform_v1.types.GcsSource

class DataFormat(value)

Bases: Enum

The format of the input example instances.

Values:

DATA_FORMAT_UNSPECIFIED (0):

Format unspecified, used when unset.

JSONL (1):

Examples are stored in JSONL files.

class google.cloud.aiplatform_v1.types.ExamplesOverride(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Overrides for example-based explanations.

neighbor_count

The number of neighbors to return.

Type:

int

crowding_count

The number of neighbors to return that have the same crowding tag.

Type:

int

restrictions

Restrict the resulting nearest neighbors to respect these constraints.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ExamplesRestrictionsNamespace]

return_embeddings

If true, return the embeddings instead of neighbors.

Type:

bool

data_format

The format of the data being provided with each call.

Type:

google.cloud.aiplatform_v1.types.ExamplesOverride.DataFormat

class DataFormat(value)

Bases: Enum

Data format enum.

Values:

DATA_FORMAT_UNSPECIFIED (0):

Unspecified format. Must not be used.

INSTANCES (1):

Provided data is a set of model inputs.

EMBEDDINGS (2):

Provided data is a set of embeddings.

class google.cloud.aiplatform_v1.types.ExamplesRestrictionsNamespace(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Restrictions namespace for example-based explanations overrides.

namespace_name

The namespace name.

Type:

str

allow

The list of allowed tags.

Type:

MutableSequence[str]

deny

The list of deny tags.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.Execution(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Instance of a general execution.

name

Output only. The resource name of the Execution.

Type:

str

display_name

User provided display name of the Execution. May be up to 128 Unicode characters.

Type:

str

state

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.

Type:

google.cloud.aiplatform_v1.types.Execution.State

etag

An eTag used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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).

Type:

MutableMapping[str, str]

create_time

Output only. Timestamp when this Execution was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this Execution was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

schema_title

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.

Type:

str

schema_version

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.

Type:

str

metadata

Properties of the Execution. Top level metadata keys’ heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.

Type:

google.protobuf.struct_pb2.Struct

description

Description of the Execution

Type:

str

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class State(value)

Bases: Enum

Describes the state of the Execution.

Values:

STATE_UNSPECIFIED (0):

Unspecified Execution state

NEW (1):

The Execution is new

RUNNING (2):

The Execution is running

COMPLETE (3):

The Execution has finished running

FAILED (4):

The Execution has failed

CACHED (5):

The Execution completed through Cache hit.

CANCELLED (6):

The Execution was cancelled.

class google.cloud.aiplatform_v1.types.ExplainRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].

endpoint

Required. The name of the Endpoint requested to serve the explanation. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

instances

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][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata’s][google.cloud.aiplatform.v1.Model.predict_schemata] [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

Type:

MutableSequence[google.protobuf.struct_pb2.Value]

parameters

The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint’s DeployedModels’ [Model’s ][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata’s][google.cloud.aiplatform.v1.Model.predict_schemata] [parameters_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri].

Type:

google.protobuf.struct_pb2.Value

explanation_spec_override

If specified, overrides the [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.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.

Type:

google.cloud.aiplatform_v1.types.ExplanationSpecOverride

deployed_model_id

If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding [Endpoint.traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split].

Type:

str

class google.cloud.aiplatform_v1.types.ExplainResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].

explanations

The explanations of the Model’s [PredictResponse.predictions][google.cloud.aiplatform.v1.PredictResponse.predictions].

It has the same number of elements as [instances][google.cloud.aiplatform.v1.ExplainRequest.instances] to be explained.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Explanation]

deployed_model_id

ID of the Endpoint’s DeployedModel that served this explanation.

Type:

str

predictions

The predictions that are the output of the predictions call. Same as [PredictResponse.predictions][google.cloud.aiplatform.v1.PredictResponse.predictions].

Type:

MutableSequence[google.protobuf.struct_pb2.Value]

class google.cloud.aiplatform_v1.types.Explanation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Explanation of a prediction (provided in [PredictResponse.predictions][google.cloud.aiplatform.v1.PredictResponse.predictions]) produced by the Model on a given [instance][google.cloud.aiplatform.v1.ExplainRequest.instances].

attributions

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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplanationParameters.top_k], the attributions are sorted by [instance_output_value][google.cloud.aiplatform.v1.Attribution.instance_output_value] in descending order. If [ExplanationParameters.output_indices][google.cloud.aiplatform.v1.ExplanationParameters.output_indices] is specified, the attributions are stored by [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index] in the same order as they appear in the output_indices.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Attribution]

neighbors

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Neighbor]

class google.cloud.aiplatform_v1.types.ExplanationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

inputs

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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.Attribution.feature_attributions] are keyed by this key (if not grouped with another feature).

For custom images, the key must match with the key in [instance][google.cloud.aiplatform.v1.ExplainRequest.instances].

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.ExplanationMetadata.InputMetadata]

outputs

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.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.ExplanationMetadata.OutputMetadata]

feature_attributions_schema_uri

Points to a YAML file stored on Google Cloud Storage describing the format of the [feature attributions][google.cloud.aiplatform.v1.Attribution.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.

Type:

str

latent_space_source

Name of the source to generate embeddings for example based explanations.

Type:

str

class InputMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata of the input of a feature.

Fields other than [InputMetadata.input_baselines][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.input_baselines] are applicable only for Models that are using Vertex AI-provided images for Tensorflow.

input_baselines

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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The schema of any single instance may be specified via Endpoint’s DeployedModels’ [Model’s][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata’s][google.cloud.aiplatform.v1.Model.predict_schemata] [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

Type:

MutableSequence[google.protobuf.struct_pb2.Value]

input_tensor_name

Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.

Type:

str

encoding

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.

Type:

google.cloud.aiplatform_v1.types.ExplanationMetadata.InputMetadata.Encoding

modality

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

Type:

str

feature_value_domain

The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.

Type:

google.cloud.aiplatform_v1.types.ExplanationMetadata.InputMetadata.FeatureValueDomain

indices_tensor_name

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.

Type:

str

dense_shape_tensor_name

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.

Type:

str

index_feature_mapping

A list of feature names for each index in the input tensor. Required when the input [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding] is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

Type:

MutableSequence[str]

encoded_tensor_name

Encoded tensor is a transformation of the input tensor. Must be provided if choosing [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution] or [XRAI attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution] and the input tensor is not differentiable.

An encoded tensor is generated if the input tensor is encoded by a lookup table.

Type:

str

encoded_baselines

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.

Type:

MutableSequence[google.protobuf.struct_pb2.Value]

visualization

Visualization configurations for image explanation.

Type:

google.cloud.aiplatform_v1.types.ExplanationMetadata.InputMetadata.Visualization

group_name

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][google.cloud.aiplatform.v1.Attribution.feature_attributions], keyed by the group name.

Type:

str

class Encoding(value)

Bases: Enum

Defines how a feature is encoded. Defaults to IDENTITY.

Values:

ENCODING_UNSPECIFIED (0):

Default value. This is the same as IDENTITY.

IDENTITY (1):

The tensor represents one feature.

BAG_OF_FEATURES (2):

The tensor represents a bag of features where each index maps to a feature. [InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1.ExplanationMetadata.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 (3):

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][google.cloud.aiplatform.v1.ExplanationMetadata.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 (4):

The tensor is a list of binaries representing whether a feature exists or not (1 indicates existence). [InputMetadata.index_feature_mapping][google.cloud.aiplatform.v1.ExplanationMetadata.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 (5):

The tensor is encoded into a 1-dimensional array represented by an encoded tensor. [InputMetadata.encoded_tensor_name][google.cloud.aiplatform.v1.ExplanationMetadata.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 (6):

Select this encoding when the input tensor is encoded into a 2-dimensional array represented by an encoded tensor. [InputMetadata.encoded_tensor_name][google.cloud.aiplatform.v1.ExplanationMetadata.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]]

class FeatureValueDomain(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

min_value

The minimum permissible value for this feature.

Type:

float

max_value

The maximum permissible value for this feature.

Type:

float

original_mean

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.

Type:

float

original_stddev

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.

Type:

float

class Visualization(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Visualization configurations for image explanation.

type_

Type of the image visualization. Only applicable to [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES.

Type:

google.cloud.aiplatform_v1.types.ExplanationMetadata.InputMetadata.Visualization.Type

polarity

Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.

Type:

google.cloud.aiplatform_v1.types.ExplanationMetadata.InputMetadata.Visualization.Polarity

color_map

The color scheme used for the highlighted areas.

Defaults to PINK_GREEN for [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution], which shows positive attributions in green and negative in pink.

Defaults to VIRIDIS for [XRAI attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution], which highlights the most influential regions in yellow and the least influential in blue.

Type:

google.cloud.aiplatform_v1.types.ExplanationMetadata.InputMetadata.Visualization.ColorMap

clip_percent_upperbound

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.

Type:

float

clip_percent_lowerbound

Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.

Type:

float

overlay_type

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.

Type:

google.cloud.aiplatform_v1.types.ExplanationMetadata.InputMetadata.Visualization.OverlayType

class ColorMap(value)

Bases: Enum

The color scheme used for highlighting areas.

Values:

COLOR_MAP_UNSPECIFIED (0):

Should not be used.

PINK_GREEN (1):

Positive: green. Negative: pink.

VIRIDIS (2):

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 (3):

Positive: red. Negative: red.

GREEN (4):

Positive: green. Negative: green.

RED_GREEN (6):

Positive: green. Negative: red.

PINK_WHITE_GREEN (5):

PiYG palette.

class OverlayType(value)

Bases: Enum

How the original image is displayed in the visualization.

Values:

OVERLAY_TYPE_UNSPECIFIED (0):

Default value. This is the same as NONE.

NONE (1):

No overlay.

ORIGINAL (2):

The attributions are shown on top of the original image.

GRAYSCALE (3):

The attributions are shown on top of grayscaled version of the original image.

MASK_BLACK (4):

The attributions are used as a mask to reveal predictive parts of the image and hide the un-predictive parts.

class Polarity(value)

Bases: Enum

Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.

Values:

POLARITY_UNSPECIFIED (0):

Default value. This is the same as POSITIVE.

POSITIVE (1):

Highlights the pixels/outlines that were most influential to the model’s prediction.

NEGATIVE (2):

Setting polarity to negative highlights areas that does not lead to the models’s current prediction.

BOTH (3):

Shows both positive and negative attributions.

class Type(value)

Bases: Enum

Type of the image visualization. Only applicable to [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].

Values:

TYPE_UNSPECIFIED (0):

Should not be used.

PIXELS (1):

Shows which pixel contributed to the image prediction.

OUTLINES (2):

Shows which region contributed to the image prediction by outlining the region.

class InputsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class OutputMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata of the prediction output to be explained.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

index_display_name_mapping

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][google.cloud.aiplatform.v1.Attribution.output_display_name] is populated by locating in the mapping with [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index].

This field is a member of oneof display_name_mapping.

Type:

google.protobuf.struct_pb2.Value

display_name_mapping_key

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][google.cloud.aiplatform.v1.Attribution.output_index] for a specific output.

This field is a member of oneof display_name_mapping.

Type:

str

output_tensor_name

Name of the output tensor. Required and is only applicable to Vertex AI provided images for Tensorflow.

Type:

str

class OutputsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.ExplanationMetadataOverride(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The [ExplanationMetadata][google.cloud.aiplatform.v1.ExplanationMetadata] entries that can be overridden at [online explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time.

inputs

Required. Overrides the [input metadata][google.cloud.aiplatform.v1.ExplanationMetadata.inputs] 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.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.ExplanationMetadataOverride.InputMetadataOverride]

class InputMetadataOverride(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The [input metadata][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata] entries to be overridden.

input_baselines

Baseline inputs for this feature.

This overrides the input_baseline field of the [ExplanationMetadata.InputMetadata][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata] object of the corresponding feature’s input metadata. If it’s not specified, the original baselines are not overridden.

Type:

MutableSequence[google.protobuf.struct_pb2.Value]

class InputsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.ExplanationParameters(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Parameters to configure explaining for Model’s predictions.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

sampled_shapley_attribution

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.

This field is a member of oneof method.

Type:

google.cloud.aiplatform_v1.types.SampledShapleyAttribution

integrated_gradients_attribution

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

This field is a member of oneof method.

Type:

google.cloud.aiplatform_v1.types.IntegratedGradientsAttribution

xrai_attribution

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.

This field is a member of oneof method.

Type:

google.cloud.aiplatform_v1.types.XraiAttribution

examples

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

This field is a member of oneof method.

Type:

google.cloud.aiplatform_v1.types.Examples

top_k

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.

Type:

int

output_indices

If populated, only returns attributions that have [output_index][google.cloud.aiplatform.v1.Attribution.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][google.cloud.aiplatform.v1.ExplanationParameters.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).

Type:

google.protobuf.struct_pb2.ListValue

class google.cloud.aiplatform_v1.types.ExplanationSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Specification of Model explanation.

parameters

Required. Parameters that configure explaining of the Model’s predictions.

Type:

google.cloud.aiplatform_v1.types.ExplanationParameters

metadata

Optional. Metadata describing the Model’s input and output for explanation.

Type:

google.cloud.aiplatform_v1.types.ExplanationMetadata

class google.cloud.aiplatform_v1.types.ExplanationSpecOverride(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The [ExplanationSpec][google.cloud.aiplatform.v1.ExplanationSpec] entries that can be overridden at [online explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time.

parameters

The parameters to be overridden. Note that the attribution method cannot be changed. If not specified, no parameter is overridden.

Type:

google.cloud.aiplatform_v1.types.ExplanationParameters

metadata

The metadata to be overridden. If not specified, no metadata is overridden.

Type:

google.cloud.aiplatform_v1.types.ExplanationMetadataOverride

examples_override

The example-based explanations parameter overrides.

Type:

google.cloud.aiplatform_v1.types.ExamplesOverride

class google.cloud.aiplatform_v1.types.ExportDataConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

gcs_destination

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-<dataset-display-name>-<timestamp-of-export-call> 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.

This field is a member of oneof destination.

Type:

google.cloud.aiplatform_v1.types.GcsDestination

fraction_split

Split based on fractions defining the size of each set.

This field is a member of oneof split.

Type:

google.cloud.aiplatform_v1.types.ExportFractionSplit

filter_split

Split based on the provided filters for each set.

This field is a member of oneof split.

Type:

google.cloud.aiplatform_v1.types.ExportFilterSplit

annotations_filter

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][google.cloud.aiplatform.v1.DatasetService.ListAnnotations].

Type:

str

saved_query_id

The ID of a SavedQuery (annotation set) under the Dataset specified by [ExportDataRequest.name][google.cloud.aiplatform.v1.ExportDataRequest.name] used for filtering Annotations for training.

Only used for custom training data export use cases. Only applicable to Datasets that have SavedQueries.

Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with [annotations_filter][google.cloud.aiplatform.v1.ExportDataConfig.annotations_filter], the Annotations used for training are filtered by both [saved_query_id][google.cloud.aiplatform.v1.ExportDataConfig.saved_query_id] and [annotations_filter][google.cloud.aiplatform.v1.ExportDataConfig.annotations_filter].

Only one of [saved_query_id][google.cloud.aiplatform.v1.ExportDataConfig.saved_query_id] and [annotation_schema_uri][google.cloud.aiplatform.v1.ExportDataConfig.annotation_schema_uri] should be specified as both of them represent the same thing: problem type.

Type:

str

annotation_schema_uri

The 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][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the Dataset specified by [ExportDataRequest.name][google.cloud.aiplatform.v1.ExportDataRequest.name].

Only used for custom training data export use cases. Only applicable to Datasets that have DataItems and Annotations.

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][google.cloud.aiplatform.v1.ExportDataConfig.annotations_filter], the Annotations used for training are filtered by both [annotations_filter][google.cloud.aiplatform.v1.ExportDataConfig.annotations_filter] and [annotation_schema_uri][google.cloud.aiplatform.v1.ExportDataConfig.annotation_schema_uri].

Type:

str

export_use

Indicates the usage of the exported files.

Type:

google.cloud.aiplatform_v1.types.ExportDataConfig.ExportUse

class ExportUse(value)

Bases: Enum

ExportUse indicates the usage of the exported files. It restricts file destination, format, annotations to be exported, whether to allow unannotated data to be exported and whether to clone files to temp Cloud Storage bucket.

Values:

EXPORT_USE_UNSPECIFIED (0):

Regular user export.

CUSTOM_CODE_TRAINING (6):

Export for custom code training.

class google.cloud.aiplatform_v1.types.ExportDataOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [DatasetService.ExportData][google.cloud.aiplatform.v1.DatasetService.ExportData].

generic_metadata

The common part of the operation metadata.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

gcs_output_directory

A Google Cloud Storage directory which path ends with ‘/’. The exported data is stored in the directory.

Type:

str

class google.cloud.aiplatform_v1.types.ExportDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.ExportData][google.cloud.aiplatform.v1.DatasetService.ExportData].

name

Required. The name of the Dataset resource. Format: projects/{project}/locations/{location}/datasets/{dataset}

Type:

str

export_config

Required. The desired output location.

Type:

google.cloud.aiplatform_v1.types.ExportDataConfig

class google.cloud.aiplatform_v1.types.ExportDataResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [DatasetService.ExportData][google.cloud.aiplatform.v1.DatasetService.ExportData].

exported_files

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-*).

Type:

MutableSequence[str]

data_stats

Only present for custom code training export use case. Records data stats, i.e., train/validation/test item/annotation counts calculated during the export operation.

Type:

google.cloud.aiplatform_v1.types.Model.DataStats

class google.cloud.aiplatform_v1.types.ExportFeatureValuesOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that exports Features values.

generic_metadata

Operation metadata for Featurestore export Feature values.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.ExportFeatureValuesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ExportFeatureValues].

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

snapshot_export

Exports the latest Feature values of all entities of the EntityType within a time range.

This field is a member of oneof mode.

Type:

google.cloud.aiplatform_v1.types.ExportFeatureValuesRequest.SnapshotExport

full_export

Exports all historical values of all entities of the EntityType within a time range

This field is a member of oneof mode.

Type:

google.cloud.aiplatform_v1.types.ExportFeatureValuesRequest.FullExport

entity_type

Required. The resource name of the EntityType from which to export Feature values. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

Type:

str

destination

Required. Specifies destination location and format.

Type:

google.cloud.aiplatform_v1.types.FeatureValueDestination

feature_selector

Required. Selects Features to export values of.

Type:

google.cloud.aiplatform_v1.types.FeatureSelector

settings

Per-Feature export settings.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DestinationFeatureSetting]

class FullExport(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

start_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

class SnapshotExport(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

snapshot_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

start_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

class google.cloud.aiplatform_v1.types.ExportFeatureValuesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeaturestoreService.ExportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ExportFeatureValues].

class google.cloud.aiplatform_v1.types.ExportFilterSplit(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

training_filter

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][google.cloud.aiplatform.v1.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.

Type:

str

validation_filter

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][google.cloud.aiplatform.v1.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.

Type:

str

test_filter

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][google.cloud.aiplatform.v1.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.

Type:

str

class google.cloud.aiplatform_v1.types.ExportFractionSplit(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

training_fraction

The fraction of the input data that is to be used to train the Model.

Type:

float

validation_fraction

The fraction of the input data that is to be used to validate the Model.

Type:

float

test_fraction

The fraction of the input data that is to be used to evaluate the Model.

Type:

float

class google.cloud.aiplatform_v1.types.ExportModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of [ModelService.ExportModel][google.cloud.aiplatform.v1.ModelService.ExportModel] operation.

generic_metadata

The common part of the operation metadata.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

output_info

Output only. Information further describing the output of this Model export.

Type:

google.cloud.aiplatform_v1.types.ExportModelOperationMetadata.OutputInfo

class OutputInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Further describes the output of the ExportModel. Supplements [ExportModelRequest.OutputConfig][google.cloud.aiplatform.v1.ExportModelRequest.OutputConfig].

artifact_output_uri

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.

Type:

str

image_output_uri

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.

Type:

str

class google.cloud.aiplatform_v1.types.ExportModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.ExportModel][google.cloud.aiplatform.v1.ModelService.ExportModel].

name

Required. The resource name of the Model to export. The resource name may contain version id or version alias to specify the version, if no version is specified, the default version will be exported.

Type:

str

output_config

Required. The desired output location and configuration.

Type:

google.cloud.aiplatform_v1.types.ExportModelRequest.OutputConfig

class OutputConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Output configuration for the Model export.

export_format_id

The ID of the format in which the Model must be exported. Each Model lists the [export formats it supports][google.cloud.aiplatform.v1.Model.supported_export_formats]. If no value is provided here, then the first from the list of the Model’s supported formats is used by default.

Type:

str

artifact_destination

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-<model-display-name>-<timestamp-of-export-call>”, 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.

Type:

google.cloud.aiplatform_v1.types.GcsDestination

image_destination

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.

Type:

google.cloud.aiplatform_v1.types.ContainerRegistryDestination

class google.cloud.aiplatform_v1.types.ExportModelResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message of [ModelService.ExportModel][google.cloud.aiplatform.v1.ModelService.ExportModel] operation.

class google.cloud.aiplatform_v1.types.ExportTensorboardTimeSeriesDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.ExportTensorboardTimeSeriesData].

tensorboard_time_series

Required. The resource name of the TensorboardTimeSeries to export data from. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}

Type:

str

filter

Exports the TensorboardTimeSeries’ data that match the filter expression.

Type:

str

page_size

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.

Type:

int

page_token

A page token, received from a previous [ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.ExportTensorboardTimeSeriesData] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.ExportTensorboardTimeSeriesData] must match the call that provided the page token.

Type:

str

order_by

Field to use to sort the TensorboardTimeSeries’ data. By default, TensorboardTimeSeries’ data is returned in a pseudo random order.

Type:

str

class google.cloud.aiplatform_v1.types.ExportTensorboardTimeSeriesDataResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.ExportTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.ExportTensorboardTimeSeriesData].

time_series_data_points

The returned time series data points.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TimeSeriesDataPoint]

next_page_token

A token, which can be sent as [page_token][google.cloud.aiplatform.v1.ExportTensorboardTimeSeriesDataRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.FasterDeploymentConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configuration for faster model deployment.

fast_tryout_enabled

If true, enable fast tryout feature for this deployed model.

Type:

bool

class google.cloud.aiplatform_v1.types.Feature(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

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.

Type:

str

description

Description of the Feature.

Type:

str

value_type

Immutable. Only applicable for Vertex AI Feature Store (Legacy). Type of Feature value.

Type:

google.cloud.aiplatform_v1.types.Feature.ValueType

create_time

Output only. Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Only applicable for Vertex AI Feature Store (Legacy). Timestamp when this EntityType was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

labels

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.

Type:

MutableMapping[str, str]

etag

Used to perform a consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

disable_monitoring

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][google.cloud.aiplatform.v1.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.

Type:

bool

monitoring_stats_anomalies

Output only. Only applicable for Vertex AI Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Feature.MonitoringStatsAnomaly]

version_column_name

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.

Type:

str

point_of_contact

Entity responsible for maintaining this feature. Can be comma separated list of email addresses or URIs.

Type:

str

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class MonitoringStatsAnomaly(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A list of historical [SnapshotAnalysis][google.cloud.aiplatform.v1.FeaturestoreMonitoringConfig.SnapshotAnalysis] or [ImportFeaturesAnalysis][google.cloud.aiplatform.v1.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis] stats requested by user, sorted by [FeatureStatsAnomaly.start_time][google.cloud.aiplatform.v1.FeatureStatsAnomaly.start_time] descending.

objective

Output only. The objective for each stats.

Type:

google.cloud.aiplatform_v1.types.Feature.MonitoringStatsAnomaly.Objective

feature_stats_anomaly

Output only. The stats and anomalies generated at specific timestamp.

Type:

google.cloud.aiplatform_v1.types.FeatureStatsAnomaly

class Objective(value)

Bases: Enum

If the objective in the request is both Import Feature Analysis and Snapshot Analysis, this objective could be one of them. Otherwise, this objective should be the same as the objective in the request.

Values:

OBJECTIVE_UNSPECIFIED (0):

If it’s OBJECTIVE_UNSPECIFIED, monitoring_stats will be empty.

IMPORT_FEATURE_ANALYSIS (1):

Stats are generated by Import Feature Analysis.

SNAPSHOT_ANALYSIS (2):

Stats are generated by Snapshot Analysis.

class ValueType(value)

Bases: Enum

Only applicable for Vertex AI Legacy Feature Store. An enum representing the value type of a feature.

Values:

VALUE_TYPE_UNSPECIFIED (0):

The value type is unspecified.

BOOL (1):

Used for Feature that is a boolean.

BOOL_ARRAY (2):

Used for Feature that is a list of boolean.

DOUBLE (3):

Used for Feature that is double.

DOUBLE_ARRAY (4):

Used for Feature that is a list of double.

INT64 (9):

Used for Feature that is INT64.

INT64_ARRAY (10):

Used for Feature that is a list of INT64.

STRING (11):

Used for Feature that is string.

STRING_ARRAY (12):

Used for Feature that is a list of String.

BYTES (13):

Used for Feature that is bytes.

STRUCT (14):

Used for Feature that is struct.

class google.cloud.aiplatform_v1.types.FeatureGroup(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Vertex AI Feature Group.

big_query

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.

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.FeatureGroup.BigQuery

name

Identifier. Name of the FeatureGroup. Format: projects/{project}/locations/{location}/featureGroups/{featureGroup}

Type:

str

create_time

Output only. Timestamp when this FeatureGroup was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this FeatureGroup was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

etag

Optional. Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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.

Type:

MutableMapping[str, str]

description

Optional. Description of the FeatureGroup.

Type:

str

class BigQuery(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input source type for BigQuery Tables and Views.

big_query_source

Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View.

Type:

google.cloud.aiplatform_v1.types.BigQuerySource

entity_id_columns

Optional. Columns to construct entity_id / row keys. If not provided defaults to entity_id.

Type:

MutableSequence[str]

static_data_source

Optional. Set if the data source is not a time-series.

Type:

bool

time_series

Optional. If the source is a time-series source, this can be set to control how downstream sources (ex: [FeatureView][google.cloud.aiplatform.v1.FeatureView] ) will treat time-series sources. If not set, will treat the source as a time-series source with feature_timestamp as timestamp column and no scan boundary.

Type:

google.cloud.aiplatform_v1.types.FeatureGroup.BigQuery.TimeSeries

dense

Optional. If set, all feature values will be fetched from a single row per unique entityId including nulls. If not set, will collapse all rows for each unique entityId into a singe row with any non-null values if present, if no non-null values are present will sync null. ex: If source has schema (entity_id, feature_timestamp, f0, f1) and the following rows: (e1, 2020-01-01T10:00:00.123Z, 10, 15) (e1, 2020-02-01T10:00:00.123Z, 20, null) If dense is set, (e1, 20, null) is synced to online stores. If dense is not set, (e1, 20, 15) is synced to online stores.

Type:

bool

class TimeSeries(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

timestamp_column

Optional. Column hosting timestamp values for a time-series source. Will be used to determine the latest feature_values for each entity. Optional. If not provided, column named feature_timestamp of type TIMESTAMP will be used.

Type:

str

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.FeatureNoiseSigma(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

noise_sigma

Noise sigma per feature. No noise is added to features that are not set.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FeatureNoiseSigma.NoiseSigmaForFeature]

class NoiseSigmaForFeature(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Noise sigma for a single feature.

name

The name of the input feature for which noise sigma is provided. The features are defined in [explanation metadata inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].

Type:

str

sigma

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][google.cloud.aiplatform.v1.SmoothGradConfig.noise_sigma] but represents the noise added to the current feature. Defaults to 0.1.

Type:

float

class google.cloud.aiplatform_v1.types.FeatureOnlineStore(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

bigtable

Contains settings for the Cloud Bigtable instance that will be created to serve featureValues for all FeatureViews under this FeatureOnlineStore.

This field is a member of oneof storage_type.

Type:

google.cloud.aiplatform_v1.types.FeatureOnlineStore.Bigtable

optimized

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][google.cloud.aiplatform.v1.PrivateServiceConnectConfig.enable_private_service_connect] to use private endpoint. Otherwise will use public endpoint by default.

This field is a member of oneof storage_type.

Type:

google.cloud.aiplatform_v1.types.FeatureOnlineStore.Optimized

name

Identifier. Name of the FeatureOnlineStore. Format: projects/{project}/locations/{location}/featureOnlineStores/{featureOnlineStore}

Type:

str

create_time

Output only. Timestamp when this FeatureOnlineStore was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this FeatureOnlineStore was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

etag

Optional. Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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.

Type:

MutableMapping[str, str]

state

Output only. State of the featureOnlineStore.

Type:

google.cloud.aiplatform_v1.types.FeatureOnlineStore.State

dedicated_serving_endpoint

Optional. The dedicated serving endpoint for this FeatureOnlineStore, which is different from common Vertex service endpoint.

Type:

google.cloud.aiplatform_v1.types.FeatureOnlineStore.DedicatedServingEndpoint

encryption_spec

Optional. Customer-managed encryption key spec for data storage. If set, online store will be secured by this key.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class Bigtable(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

auto_scaling

Required. Autoscaling config applied to Bigtable Instance.

Type:

google.cloud.aiplatform_v1.types.FeatureOnlineStore.Bigtable.AutoScaling

class AutoScaling(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

min_node_count

Required. The minimum number of nodes to scale down to. Must be greater than or equal to 1.

Type:

int

max_node_count

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’.

Type:

int

cpu_utilization_target

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%.

Type:

int

class DedicatedServingEndpoint(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

public_endpoint_domain_name

Output only. This field will be populated with the domain name to use for this FeatureOnlineStore

Type:

str

private_service_connect_config

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][google.cloud.aiplatform.v1.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.

Type:

google.cloud.aiplatform_v1.types.PrivateServiceConnectConfig

service_attachment

Output only. The name of the service attachment resource. Populated if private service connect is enabled and after FeatureViewSync is created.

Type:

str

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class Optimized(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Optimized storage type

class State(value)

Bases: Enum

Possible states a featureOnlineStore can have.

Values:

STATE_UNSPECIFIED (0):

Default value. This value is unused.

STABLE (1):

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 (2):

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.

class google.cloud.aiplatform_v1.types.FeatureSelector(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Selector for Features of an EntityType.

id_matcher

Required. Matches Features based on ID.

Type:

google.cloud.aiplatform_v1.types.IdMatcher

class google.cloud.aiplatform_v1.types.FeatureStatsAnomaly(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

score

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][google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW] and [ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT][google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT].

Type:

float

stats_uri

Path of the stats file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

Type:

str

anomaly_uri

Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/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] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).

Type:

str

distribution_deviation

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.

Type:

float

anomaly_detection_threshold

This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from [ThresholdConfig.value][google.cloud.aiplatform.v1.ThresholdConfig.value].

Type:

float

start_time

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).

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

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).

Type:

google.protobuf.timestamp_pb2.Timestamp

class google.cloud.aiplatform_v1.types.FeatureValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Value for a feature.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

bool_value

Bool type feature value.

This field is a member of oneof value.

Type:

bool

double_value

Double type feature value.

This field is a member of oneof value.

Type:

float

int64_value

Int64 feature value.

This field is a member of oneof value.

Type:

int

string_value

String feature value.

This field is a member of oneof value.

Type:

str

bool_array_value

A list of bool type feature value.

This field is a member of oneof value.

Type:

google.cloud.aiplatform_v1.types.BoolArray

double_array_value

A list of double type feature value.

This field is a member of oneof value.

Type:

google.cloud.aiplatform_v1.types.DoubleArray

int64_array_value

A list of int64 type feature value.

This field is a member of oneof value.

Type:

google.cloud.aiplatform_v1.types.Int64Array

string_array_value

A list of string type feature value.

This field is a member of oneof value.

Type:

google.cloud.aiplatform_v1.types.StringArray

bytes_value

Bytes feature value.

This field is a member of oneof value.

Type:

bytes

struct_value

A struct type feature value.

This field is a member of oneof value.

Type:

google.cloud.aiplatform_v1.types.StructValue

metadata

Metadata of feature value.

Type:

google.cloud.aiplatform_v1.types.FeatureValue.Metadata

class Metadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata of feature value.

generate_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

class google.cloud.aiplatform_v1.types.FeatureValueDestination(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A destination location for Feature values and format.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

bigquery_destination

Output in BigQuery format. [BigQueryDestination.output_uri][google.cloud.aiplatform.v1.BigQueryDestination.output_uri] in [FeatureValueDestination.bigquery_destination][google.cloud.aiplatform.v1.FeatureValueDestination.bigquery_destination] must refer to a table.

This field is a member of oneof destination.

Type:

google.cloud.aiplatform_v1.types.BigQueryDestination

tfrecord_destination

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

This field is a member of oneof destination.

Type:

google.cloud.aiplatform_v1.types.TFRecordDestination

csv_destination

Output in CSV format. Array Feature value types are not allowed in CSV format.

This field is a member of oneof destination.

Type:

google.cloud.aiplatform_v1.types.CsvDestination

class google.cloud.aiplatform_v1.types.FeatureValueList(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Container for list of values.

values

A list of feature values. All of them should be the same data type.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FeatureValue]

class google.cloud.aiplatform_v1.types.FeatureView(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

big_query_source

Optional. Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.FeatureView.BigQuerySource

feature_registry_source

Optional. Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.FeatureView.FeatureRegistrySource

vertex_rag_source

Optional. The Vertex RAG Source that the FeatureView is linked to.

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.FeatureView.VertexRagSource

name

Identifier. Name of the FeatureView. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}

Type:

str

create_time

Output only. Timestamp when this FeatureView was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this FeatureView was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

etag

Optional. Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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.

Type:

MutableMapping[str, str]

sync_config

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.

Type:

google.cloud.aiplatform_v1.types.FeatureView.SyncConfig

index_config

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.

Type:

google.cloud.aiplatform_v1.types.FeatureView.IndexConfig

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class BigQuerySource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

uri

Required. The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.

Type:

str

entity_id_columns

Required. Columns to construct entity_id / row keys.

Type:

MutableSequence[str]

class FeatureRegistrySource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

feature_groups

Required. List of features that need to be synced to Online Store.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FeatureView.FeatureRegistrySource.FeatureGroup]

project_number

Optional. The project number of the parent project of the Feature Groups.

This field is a member of oneof _project_number.

Type:

int

class FeatureGroup(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

feature_group_id

Required. Identifier of the feature group.

Type:

str

feature_ids

Required. Identifiers of features under the feature group.

Type:

MutableSequence[str]

class IndexConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configuration for vector indexing.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

tree_ah_config

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

This field is a member of oneof algorithm_config.

Type:

google.cloud.aiplatform_v1.types.FeatureView.IndexConfig.TreeAHConfig

brute_force_config

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.

This field is a member of oneof algorithm_config.

Type:

google.cloud.aiplatform_v1.types.FeatureView.IndexConfig.BruteForceConfig

embedding_column

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.

Type:

str

filter_columns

Optional. Columns of features that’re used to filter vector search results.

Type:

MutableSequence[str]

crowding_column

Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by [FeatureOnlineStoreService.SearchNearestEntities][google.cloud.aiplatform.v1.FeatureOnlineStoreService.SearchNearestEntities] to diversify search results. If [NearestNeighborQuery.per_crowding_attribute_neighbor_count][google.cloud.aiplatform.v1.NearestNeighborQuery.per_crowding_attribute_neighbor_count] is set to K in [SearchNearestEntitiesRequest][google.cloud.aiplatform.v1.SearchNearestEntitiesRequest], it’s guaranteed that no more than K entities of the same crowding attribute are returned in the response.

Type:

str

embedding_dimension

Optional. The number of dimensions of the input embedding.

This field is a member of oneof _embedding_dimension.

Type:

int

distance_measure_type

Optional. The distance measure used in nearest neighbor search.

Type:

google.cloud.aiplatform_v1.types.FeatureView.IndexConfig.DistanceMeasureType

class BruteForceConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configuration options for using brute force search.

class DistanceMeasureType(value)

Bases: Enum

The distance measure used in nearest neighbor search.

Values:

DISTANCE_MEASURE_TYPE_UNSPECIFIED (0):

Should not be set.

SQUARED_L2_DISTANCE (1):

Euclidean (L_2) Distance.

COSINE_DISTANCE (2):

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 (3):

Dot Product Distance. Defined as a negative of the dot product.

class TreeAHConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configuration options for the tree-AH algorithm.

leaf_node_embedding_count

Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.

This field is a member of oneof _leaf_node_embedding_count.

Type:

int

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class SyncConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configuration for Sync. Only one option is set.

cron

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 * * * *”.

Type:

str

continuous

Optional. If true, syncs the FeatureView in a continuous manner to Online Store.

Type:

bool

class VertexRagSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A Vertex Rag source for features that need to be synced to Online Store.

uri

Required. The BigQuery view/table URI that will be materialized on each manual sync trigger. The table/view is expected to have the following columns and types at least:

• corpus_id (STRING, NULLABLE/REQUIRED)

• file_id (STRING, NULLABLE/REQUIRED)

• chunk_id (STRING, NULLABLE/REQUIRED)

• chunk_data_type (STRING, NULLABLE/REQUIRED)

• chunk_data (STRING, NULLABLE/REQUIRED)

• embeddings (FLOAT, REPEATED)

• file_original_uri (STRING, NULLABLE/REQUIRED)

Type:

str

rag_corpus_id

Optional. The RAG corpus id corresponding to this FeatureView.

Type:

int

class google.cloud.aiplatform_v1.types.FeatureViewDataFormat(value)

Bases: Enum

Format of the data in the Feature View.

Values:

FEATURE_VIEW_DATA_FORMAT_UNSPECIFIED (0):

Not set. Will be treated as the KeyValue format.

KEY_VALUE (1):

Return response data in key-value format.

PROTO_STRUCT (2):

Return response data in proto Struct format.

class google.cloud.aiplatform_v1.types.FeatureViewDataKey(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Lookup key for a feature view.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

key

String key to use for lookup.

This field is a member of oneof key_oneof.

Type:

str

composite_key

The actual Entity ID will be composed from this struct. This should match with the way ID is defined in the FeatureView spec.

This field is a member of oneof key_oneof.

Type:

google.cloud.aiplatform_v1.types.FeatureViewDataKey.CompositeKey

class CompositeKey(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

ID that is comprised from several parts (columns).

parts

Parts to construct Entity ID. Should match with the same ID columns as defined in FeatureView in the same order.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.FeatureViewSync(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Identifier. Name of the FeatureViewSync. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}/featureViewSyncs/{feature_view_sync}

Type:

str

create_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

run_time

Output only. Time when this FeatureViewSync is finished.

Type:

google.type.interval_pb2.Interval

final_status

Output only. Final status of the FeatureViewSync.

Type:

google.rpc.status_pb2.Status

sync_summary

Output only. Summary of the sync job.

Type:

google.cloud.aiplatform_v1.types.FeatureViewSync.SyncSummary

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class SyncSummary(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

row_synced

Output only. Total number of rows synced.

Type:

int

total_slot

Output only. BigQuery slot milliseconds consumed for the sync job.

Type:

int

system_watermark_time

Lower bound of the system time watermark for the sync job. This is only set for continuously syncing feature views.

Type:

google.protobuf.timestamp_pb2.Timestamp

class google.cloud.aiplatform_v1.types.Featurestore(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

name

Output only. Name of the Featurestore. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

Type:

str

create_time

Output only. Timestamp when this Featurestore was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this Featurestore was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

etag

Optional. Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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.

Type:

MutableMapping[str, str]

online_serving_config

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.

Type:

google.cloud.aiplatform_v1.types.Featurestore.OnlineServingConfig

state

Output only. State of the featurestore.

Type:

google.cloud.aiplatform_v1.types.Featurestore.State

online_storage_ttl_days

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

Type:

int

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class OnlineServingConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

OnlineServingConfig specifies the details for provisioning online serving resources.

fixed_node_count

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.

Type:

int

scaling

Online serving scaling configuration. Only one of fixed_node_count and scaling can be set. Setting one will reset the other.

Type:

google.cloud.aiplatform_v1.types.Featurestore.OnlineServingConfig.Scaling

class Scaling(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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).

min_node_count

Required. The minimum number of nodes to scale down to. Must be greater than or equal to 1.

Type:

int

max_node_count

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’.

Type:

int

cpu_utilization_target

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.

Type:

int

class State(value)

Bases: Enum

Possible states a featurestore can have.

Values:

STATE_UNSPECIFIED (0):

Default value. This value is unused.

STABLE (1):

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 (2):

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.

class google.cloud.aiplatform_v1.types.FeaturestoreMonitoringConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configuration of how features in Featurestore are monitored.

snapshot_analysis

The config for Snapshot Analysis Based Feature Monitoring.

Type:

google.cloud.aiplatform_v1.types.FeaturestoreMonitoringConfig.SnapshotAnalysis

import_features_analysis

The config for ImportFeatures Analysis Based Feature Monitoring.

Type:

google.cloud.aiplatform_v1.types.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis

numerical_threshold_config

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][google.cloud.aiplatform.v1.Feature.ValueType]) DOUBLE or INT64).

Type:

google.cloud.aiplatform_v1.types.FeaturestoreMonitoringConfig.ThresholdConfig

categorical_threshold_config

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][google.cloud.aiplatform.v1.Feature.ValueType]) BOOL or STRING).

Type:

google.cloud.aiplatform_v1.types.FeaturestoreMonitoringConfig.ThresholdConfig

class ImportFeaturesAnalysis(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configuration of the Featurestore’s ImportFeature Analysis Based Monitoring. This type of analysis generates statistics for values of each Feature imported by every [ImportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ImportFeatureValues] operation.

state

Whether to enable / disable / inherite default hebavior for import features analysis.

Type:

google.cloud.aiplatform_v1.types.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.State

anomaly_detection_baseline

The baseline used to do anomaly detection for the statistics generated by import features analysis.

Type:

google.cloud.aiplatform_v1.types.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.Baseline

class Baseline(value)

Bases: Enum

Defines the baseline to do anomaly detection for feature values imported by each [ImportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ImportFeatureValues] operation.

Values:

BASELINE_UNSPECIFIED (0):

Should not be used.

LATEST_STATS (1):

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 (2):

Use the statistics generated by the most recent snapshot analysis if exists.

PREVIOUS_IMPORT_FEATURES_STATS (3):

Use the statistics generated by the previous import features analysis if exists.

class State(value)

Bases: Enum

The state defines whether to enable ImportFeature analysis.

Values:

STATE_UNSPECIFIED (0):

Should not be used.

DEFAULT (1):

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 (2):

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 (3):

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.

class SnapshotAnalysis(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

disabled

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.

Type:

bool

monitoring_interval_days

Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.

Type:

int

staleness_days

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.

Type:

int

class ThresholdConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The config for Featurestore Monitoring threshold.

value

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.

This field is a member of oneof threshold.

Type:

float

class google.cloud.aiplatform_v1.types.FetchFeatureValuesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreService.FetchFeatureValues][google.cloud.aiplatform.v1.FeatureOnlineStoreService.FetchFeatureValues]. All the features under the requested feature view will be returned.

feature_view

Required. FeatureView resource format projects/{project}/locations/{location}/featureOnlineStores/{featureOnlineStore}/featureViews/{featureView}

Type:

str

data_key

Optional. The request key to fetch feature values for.

Type:

google.cloud.aiplatform_v1.types.FeatureViewDataKey

data_format

Optional. Response data format. If not set, [FeatureViewDataFormat.KEY_VALUE][google.cloud.aiplatform.v1.FeatureViewDataFormat.KEY_VALUE] will be used.

Type:

google.cloud.aiplatform_v1.types.FeatureViewDataFormat

class google.cloud.aiplatform_v1.types.FetchFeatureValuesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeatureOnlineStoreService.FetchFeatureValues][google.cloud.aiplatform.v1.FeatureOnlineStoreService.FetchFeatureValues]

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

key_values

Feature values in KeyValue format.

This field is a member of oneof format.

Type:

google.cloud.aiplatform_v1.types.FetchFeatureValuesResponse.FeatureNameValuePairList

proto_struct

Feature values in proto Struct format.

This field is a member of oneof format.

Type:

google.protobuf.struct_pb2.Struct

data_key

The data key associated with this response. Will only be populated for [FeatureOnlineStoreService.StreamingFetchFeatureValues][] RPCs.

Type:

google.cloud.aiplatform_v1.types.FeatureViewDataKey

class FeatureNameValuePairList(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

features

List of feature names and values.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FetchFeatureValuesResponse.FeatureNameValuePairList.FeatureNameValuePair]

class FeatureNameValuePair(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Feature name & value pair.

value

Feature value.

This field is a member of oneof data.

Type:

google.cloud.aiplatform_v1.types.FeatureValue

name

Feature short name.

Type:

str

class google.cloud.aiplatform_v1.types.FileData(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

URI based data.

mime_type

Required. The IANA standard MIME type of the source data.

Type:

str

file_uri

Required. URI.

Type:

str

class google.cloud.aiplatform_v1.types.FilterSplit(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

training_filter

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][google.cloud.aiplatform.v1.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.

Type:

str

validation_filter

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][google.cloud.aiplatform.v1.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.

Type:

str

test_filter

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][google.cloud.aiplatform.v1.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.

Type:

str

class google.cloud.aiplatform_v1.types.FindNeighborsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The request message for [MatchService.FindNeighbors][google.cloud.aiplatform.v1.MatchService.FindNeighbors].

index_endpoint

Required. The name of the index endpoint. Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

Type:

str

deployed_index_id

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.

Type:

str

queries

The list of queries.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FindNeighborsRequest.Query]

return_full_datapoint

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.

Type:

bool

class Query(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

rrf

Optional. Represents RRF algorithm that combines search results.

This field is a member of oneof ranking.

Type:

google.cloud.aiplatform_v1.types.FindNeighborsRequest.Query.RRF

datapoint

Required. The datapoint/vector whose nearest neighbors should be searched for.

Type:

google.cloud.aiplatform_v1.types.IndexDatapoint

neighbor_count

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).

Type:

int

per_crowding_attribute_neighbor_count

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.

Type:

int

approximate_neighbor_count

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.

Type:

int

fraction_leaf_nodes_to_search_override

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.

Type:

float

class RRF(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Parameters for RRF algorithm that combines search results.

alpha

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.

Type:

float

class google.cloud.aiplatform_v1.types.FindNeighborsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The response message for [MatchService.FindNeighbors][google.cloud.aiplatform.v1.MatchService.FindNeighbors].

nearest_neighbors

The nearest neighbors of the query datapoints.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FindNeighborsResponse.NearestNeighbors]

class NearestNeighbors(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Nearest neighbors for one query.

id

The ID of the query datapoint.

Type:

str

neighbors

All its neighbors.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FindNeighborsResponse.Neighbor]

class Neighbor(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A neighbor of the query vector.

datapoint

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.

Type:

google.cloud.aiplatform_v1.types.IndexDatapoint

distance

The distance between the neighbor and the dense embedding query.

Type:

float

sparse_distance

The distance between the neighbor and the query sparse_embedding.

Type:

float

class google.cloud.aiplatform_v1.types.FluencyInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for fluency metric.

metric_spec

Required. Spec for fluency score metric.

Type:

google.cloud.aiplatform_v1.types.FluencySpec

instance

Required. Fluency instance.

Type:

google.cloud.aiplatform_v1.types.FluencyInstance

class google.cloud.aiplatform_v1.types.FluencyInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for fluency instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

class google.cloud.aiplatform_v1.types.FluencyResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for fluency result.

score

Output only. Fluency score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for fluency score.

Type:

str

confidence

Output only. Confidence for fluency score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.FluencySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for fluency score metric.

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.FractionSplit(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

training_fraction

The fraction of the input data that is to be used to train the Model.

Type:

float

validation_fraction

The fraction of the input data that is to be used to validate the Model.

Type:

float

test_fraction

The fraction of the input data that is to be used to evaluate the Model.

Type:

float

class google.cloud.aiplatform_v1.types.FulfillmentInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for fulfillment metric.

metric_spec

Required. Spec for fulfillment score metric.

Type:

google.cloud.aiplatform_v1.types.FulfillmentSpec

instance

Required. Fulfillment instance.

Type:

google.cloud.aiplatform_v1.types.FulfillmentInstance

class google.cloud.aiplatform_v1.types.FulfillmentInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for fulfillment instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

instruction

Required. Inference instruction prompt to compare prediction with.

This field is a member of oneof _instruction.

Type:

str

class google.cloud.aiplatform_v1.types.FulfillmentResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for fulfillment result.

score

Output only. Fulfillment score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for fulfillment score.

Type:

str

confidence

Output only. Confidence for fulfillment score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.FulfillmentSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for fulfillment metric.

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.FunctionCall(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

name

Required. The name of the function to call. Matches [FunctionDeclaration.name].

Type:

str

args

Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.

Type:

google.protobuf.struct_pb2.Struct

class google.cloud.aiplatform_v1.types.FunctionCallingConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Function calling config.

mode

Optional. Function calling mode.

Type:

google.cloud.aiplatform_v1.types.FunctionCallingConfig.Mode

allowed_function_names

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.

Type:

MutableSequence[str]

class Mode(value)

Bases: Enum

Function calling mode.

Values:

MODE_UNSPECIFIED (0):

Unspecified function calling mode. This value should not be used.

AUTO (1):

Default model behavior, model decides to predict either a function call or a natural language response.

ANY (2):

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 (3):

Model will not predict any function call. Model behavior is same as when not passing any function declarations.

class google.cloud.aiplatform_v1.types.FunctionDeclaration(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Structured representation of a function declaration as defined by the OpenAPI 3.0 specification. Included in this declaration are the function name, description, parameters and response type. 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.

name

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.

Type:

str

description

Optional. Description and purpose of the function. Model uses it to decide how and whether to call the function.

Type:

str

parameters

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

Type:

google.cloud.aiplatform_v1.types.Schema

response

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.

Type:

google.cloud.aiplatform_v1.types.Schema

class google.cloud.aiplatform_v1.types.FunctionResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

name

Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].

Type:

str

response

Required. The function response in JSON object format. Use “output” key to specify function output and “error” key to specify error details (if any). If “output” and “error” keys are not specified, then whole “response” is treated as function output.

Type:

google.protobuf.struct_pb2.Struct

class google.cloud.aiplatform_v1.types.GcsDestination(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

output_uri_prefix

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.

Type:

str

class google.cloud.aiplatform_v1.types.GcsSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The Google Cloud Storage location for the input content.

uris

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.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.GenerateContentRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.GenerateContent].

model

Required. The fully qualified name of the publisher model or tuned model endpoint to use.

Publisher model format: projects/{project}/locations/{location}/publishers/*/models/*

Tuned model endpoint format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

contents

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Content]

system_instruction

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.

This field is a member of oneof _system_instruction.

Type:

google.cloud.aiplatform_v1.types.Content

tools

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Tool]

tool_config

Optional. Tool config. This config is shared for all tools provided in the request.

Type:

google.cloud.aiplatform_v1.types.ToolConfig

labels

Optional. The labels with user-defined metadata for the request. It is used for billing and reporting only.

Label keys and values can be no longer than 63 characters (Unicode codepoints) and can only contain lowercase letters, numeric characters, underscores, and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter.

Type:

MutableMapping[str, str]

safety_settings

Optional. Per request settings for blocking unsafe content. Enforced on GenerateContentResponse.candidates.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.SafetySetting]

generation_config

Optional. Generation config.

Type:

google.cloud.aiplatform_v1.types.GenerationConfig

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.GenerateContentResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PredictionService.GenerateContent].

candidates

Output only. Generated candidates.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Candidate]

model_version

Output only. The model version used to generate the response.

Type:

str

prompt_feedback

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.

Type:

google.cloud.aiplatform_v1.types.GenerateContentResponse.PromptFeedback

usage_metadata

Usage metadata about the response(s).

Type:

google.cloud.aiplatform_v1.types.GenerateContentResponse.UsageMetadata

class PromptFeedback(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Content filter results for a prompt sent in the request.

block_reason

Output only. Blocked reason.

Type:

google.cloud.aiplatform_v1.types.GenerateContentResponse.PromptFeedback.BlockedReason

safety_ratings

Output only. Safety ratings.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.SafetyRating]

block_reason_message

Output only. A readable block reason message.

Type:

str

class BlockedReason(value)

Bases: Enum

Blocked reason enumeration.

Values:

BLOCKED_REASON_UNSPECIFIED (0):

Unspecified blocked reason.

SAFETY (1):

Candidates blocked due to safety.

OTHER (2):

Candidates blocked due to other reason.

BLOCKLIST (3):

Candidates blocked due to the terms which are included from the terminology blocklist.

PROHIBITED_CONTENT (4):

Candidates blocked due to prohibited content.

class UsageMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Usage metadata about response(s).

prompt_token_count

Number of tokens in the request. When cached_content is set, this is still the total effective prompt size meaning this includes the number of tokens in the cached content.

Type:

int

candidates_token_count

Number of tokens in the response(s).

Type:

int

total_token_count

Total token count for prompt and response candidates.

Type:

int

class google.cloud.aiplatform_v1.types.GenerationConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Generation config.

temperature

Optional. Controls the randomness of predictions.

This field is a member of oneof _temperature.

Type:

float

top_p

Optional. If specified, nucleus sampling will be used.

This field is a member of oneof _top_p.

Type:

float

top_k

Optional. If specified, top-k sampling will be used.

This field is a member of oneof _top_k.

Type:

float

candidate_count

Optional. Number of candidates to generate.

This field is a member of oneof _candidate_count.

Type:

int

max_output_tokens

Optional. The maximum number of output tokens to generate per message.

This field is a member of oneof _max_output_tokens.

Type:

int

stop_sequences

Optional. Stop sequences.

Type:

MutableSequence[str]

response_logprobs

Optional. If true, export the logprobs results in response.

This field is a member of oneof _response_logprobs.

Type:

bool

logprobs

Optional. Logit probabilities.

This field is a member of oneof _logprobs.

Type:

int

presence_penalty

Optional. Positive penalties.

This field is a member of oneof _presence_penalty.

Type:

float

frequency_penalty

Optional. Frequency penalties.

This field is a member of oneof _frequency_penalty.

Type:

float

seed

Optional. Seed.

This field is a member of oneof _seed.

Type:

int

response_mime_type

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.

Type:

str

response_schema

Optional. The Schema object allows the definition of input and output data types. These types can be objects, but also primitives and arrays. Represents a select subset of an OpenAPI 3.0 schema object. If set, a compatible response_mime_type must also be set. Compatible mimetypes: application/json: Schema for JSON response.

This field is a member of oneof _response_schema.

Type:

google.cloud.aiplatform_v1.types.Schema

routing_config

Optional. Routing configuration.

This field is a member of oneof _routing_config.

Type:

google.cloud.aiplatform_v1.types.GenerationConfig.RoutingConfig

class RoutingConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The configuration for routing the request to a specific model.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

auto_mode

Automated routing.

This field is a member of oneof routing_config.

Type:

google.cloud.aiplatform_v1.types.GenerationConfig.RoutingConfig.AutoRoutingMode

manual_mode

Manual routing.

This field is a member of oneof routing_config.

Type:

google.cloud.aiplatform_v1.types.GenerationConfig.RoutingConfig.ManualRoutingMode

class AutoRoutingMode(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference.

model_routing_preference

The model routing preference.

This field is a member of oneof _model_routing_preference.

Type:

google.cloud.aiplatform_v1.types.GenerationConfig.RoutingConfig.AutoRoutingMode.ModelRoutingPreference

class ModelRoutingPreference(value)

Bases: Enum

The model routing preference.

Values:

UNKNOWN (0):

Unspecified model routing preference.

PRIORITIZE_QUALITY (1):

Prefer higher quality over low cost.

BALANCED (2):

Balanced model routing preference.

PRIORITIZE_COST (3):

Prefer lower cost over higher quality.

class ManualRoutingMode(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

When manual routing is set, the specified model will be used directly.

model_name

The model name to use. Only the public LLM models are accepted. e.g. ‘gemini-1.5-pro-001’.

This field is a member of oneof _model_name.

Type:

str

class google.cloud.aiplatform_v1.types.GenericOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Generic Metadata shared by all operations.

partial_failures

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.

Type:

MutableSequence[google.rpc.status_pb2.Status]

create_time

Output only. Time when the operation was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

class google.cloud.aiplatform_v1.types.GenieSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

base_model_uri

Required. The public base model URI.

Type:

str

class google.cloud.aiplatform_v1.types.GetAnnotationSpecRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.GetAnnotationSpec][google.cloud.aiplatform.v1.DatasetService.GetAnnotationSpec].

name

Required. The name of the AnnotationSpec resource. Format: projects/{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{annotation_spec}

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.GetArtifactRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.GetArtifact][google.cloud.aiplatform.v1.MetadataService.GetArtifact].

name

Required. The resource name of the Artifact to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}

Type:

str

class google.cloud.aiplatform_v1.types.GetBatchPredictionJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.GetBatchPredictionJob][google.cloud.aiplatform.v1.JobService.GetBatchPredictionJob].

name

Required. The name of the BatchPredictionJob resource. Format: projects/{project}/locations/{location}/batchPredictionJobs/{batch_prediction_job}

Type:

str

class google.cloud.aiplatform_v1.types.GetContextRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.GetContext][google.cloud.aiplatform.v1.MetadataService.GetContext].

name

Required. The resource name of the Context to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}

Type:

str

class google.cloud.aiplatform_v1.types.GetCustomJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.GetCustomJob][google.cloud.aiplatform.v1.JobService.GetCustomJob].

name

Required. The name of the CustomJob resource. Format: projects/{project}/locations/{location}/customJobs/{custom_job}

Type:

str

class google.cloud.aiplatform_v1.types.GetDataLabelingJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.GetDataLabelingJob][google.cloud.aiplatform.v1.JobService.GetDataLabelingJob].

name

Required. The name of the DataLabelingJob. Format: projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}

Type:

str

class google.cloud.aiplatform_v1.types.GetDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.GetDataset][google.cloud.aiplatform.v1.DatasetService.GetDataset]. Next ID: 4

name

Required. The name of the Dataset resource.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.GetDatasetVersionRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.GetDatasetVersion][google.cloud.aiplatform.v1.DatasetService.GetDatasetVersion]. Next ID: 4

name

Required. The resource name of the Dataset version to delete. Format: projects/{project}/locations/{location}/datasets/{dataset}/datasetVersions/{dataset_version}

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.GetDeploymentResourcePoolRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for GetDeploymentResourcePool method.

name

Required. The name of the DeploymentResourcePool to retrieve. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

Type:

str

class google.cloud.aiplatform_v1.types.GetEndpointRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [EndpointService.GetEndpoint][google.cloud.aiplatform.v1.EndpointService.GetEndpoint]

name

Required. The name of the Endpoint resource. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

class google.cloud.aiplatform_v1.types.GetEntityTypeRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.GetEntityType][google.cloud.aiplatform.v1.FeaturestoreService.GetEntityType].

name

Required. The name of the EntityType resource. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

Type:

str

class google.cloud.aiplatform_v1.types.GetExecutionRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.GetExecution][google.cloud.aiplatform.v1.MetadataService.GetExecution].

name

Required. The resource name of the Execution to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}

Type:

str

class google.cloud.aiplatform_v1.types.GetFeatureGroupRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureRegistryService.GetFeatureGroup][google.cloud.aiplatform.v1.FeatureRegistryService.GetFeatureGroup].

name

Required. The name of the FeatureGroup resource.

Type:

str

class google.cloud.aiplatform_v1.types.GetFeatureOnlineStoreRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.GetFeatureOnlineStore][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.GetFeatureOnlineStore].

name

Required. The name of the FeatureOnlineStore resource.

Type:

str

class google.cloud.aiplatform_v1.types.GetFeatureRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.GetFeature][google.cloud.aiplatform.v1.FeaturestoreService.GetFeature]. Request message for [FeatureRegistryService.GetFeature][google.cloud.aiplatform.v1.FeatureRegistryService.GetFeature].

name

Required. The name of the Feature resource. 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}

Type:

str

class google.cloud.aiplatform_v1.types.GetFeatureViewRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.GetFeatureView][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.GetFeatureView].

name

Required. The name of the FeatureView resource. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}

Type:

str

class google.cloud.aiplatform_v1.types.GetFeatureViewSyncRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.GetFeatureViewSync][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.GetFeatureViewSync].

name

Required. The name of the FeatureViewSync resource. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}/featureViewSyncs/{feature_view_sync}

Type:

str

class google.cloud.aiplatform_v1.types.GetFeaturestoreRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.GetFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.GetFeaturestore].

name

Required. The name of the Featurestore resource.

Type:

str

class google.cloud.aiplatform_v1.types.GetHyperparameterTuningJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.GetHyperparameterTuningJob][google.cloud.aiplatform.v1.JobService.GetHyperparameterTuningJob].

name

Required. The name of the HyperparameterTuningJob resource. Format: projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyperparameter_tuning_job}

Type:

str

class google.cloud.aiplatform_v1.types.GetIndexEndpointRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexEndpointService.GetIndexEndpoint][google.cloud.aiplatform.v1.IndexEndpointService.GetIndexEndpoint]

name

Required. The name of the IndexEndpoint resource. Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

Type:

str

class google.cloud.aiplatform_v1.types.GetIndexRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexService.GetIndex][google.cloud.aiplatform.v1.IndexService.GetIndex]

name

Required. The name of the Index resource. Format: projects/{project}/locations/{location}/indexes/{index}

Type:

str

class google.cloud.aiplatform_v1.types.GetMetadataSchemaRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.GetMetadataSchema][google.cloud.aiplatform.v1.MetadataService.GetMetadataSchema].

name

Required. The resource name of the MetadataSchema to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/metadataSchemas/{metadataschema}

Type:

str

class google.cloud.aiplatform_v1.types.GetMetadataStoreRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.GetMetadataStore][google.cloud.aiplatform.v1.MetadataService.GetMetadataStore].

name

Required. The resource name of the MetadataStore to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

class google.cloud.aiplatform_v1.types.GetModelDeploymentMonitoringJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.GetModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.GetModelDeploymentMonitoringJob].

name

Required. The resource name of the ModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

Type:

str

class google.cloud.aiplatform_v1.types.GetModelEvaluationRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.GetModelEvaluation][google.cloud.aiplatform.v1.ModelService.GetModelEvaluation].

name

Required. The name of the ModelEvaluation resource. Format: projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}

Type:

str

class google.cloud.aiplatform_v1.types.GetModelEvaluationSliceRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.GetModelEvaluationSlice][google.cloud.aiplatform.v1.ModelService.GetModelEvaluationSlice].

name

Required. The name of the ModelEvaluationSlice resource. Format: projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}

Type:

str

class google.cloud.aiplatform_v1.types.GetModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.GetModel][google.cloud.aiplatform.v1.ModelService.GetModel].

name

Required. The name of the Model resource. 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.

Type:

str

class google.cloud.aiplatform_v1.types.GetNasJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.GetNasJob][google.cloud.aiplatform.v1.JobService.GetNasJob].

name

Required. The name of the NasJob resource. Format: projects/{project}/locations/{location}/nasJobs/{nas_job}

Type:

str

class google.cloud.aiplatform_v1.types.GetNasTrialDetailRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.GetNasTrialDetail][google.cloud.aiplatform.v1.JobService.GetNasTrialDetail].

name

Required. The name of the NasTrialDetail resource. Format: projects/{project}/locations/{location}/nasJobs/{nas_job}/nasTrialDetails/{nas_trial_detail}

Type:

str

class google.cloud.aiplatform_v1.types.GetNotebookExecutionJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.GetNotebookExecutionJob]

name

Required. The name of the NotebookExecutionJob resource.

Type:

str

view

Optional. The NotebookExecutionJob view. Defaults to BASIC.

Type:

google.cloud.aiplatform_v1.types.NotebookExecutionJobView

class google.cloud.aiplatform_v1.types.GetNotebookRuntimeRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.GetNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.GetNotebookRuntime]

name

Required. The name of the NotebookRuntime resource. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner.

Type:

str

class google.cloud.aiplatform_v1.types.GetNotebookRuntimeTemplateRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.GetNotebookRuntimeTemplate][google.cloud.aiplatform.v1.NotebookService.GetNotebookRuntimeTemplate]

name

Required. The name of the NotebookRuntimeTemplate resource. Format: projects/{project}/locations/{location}/notebookRuntimeTemplates/{notebook_runtime_template}

Type:

str

class google.cloud.aiplatform_v1.types.GetPersistentResourceRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PersistentResourceService.GetPersistentResource][google.cloud.aiplatform.v1.PersistentResourceService.GetPersistentResource].

name

Required. The name of the PersistentResource resource. Format: projects/{project_id_or_number}/locations/{location_id}/persistentResources/{persistent_resource_id}

Type:

str

class google.cloud.aiplatform_v1.types.GetPipelineJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.GetPipelineJob][google.cloud.aiplatform.v1.PipelineService.GetPipelineJob].

name

Required. The name of the PipelineJob resource. Format: projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}

Type:

str

class google.cloud.aiplatform_v1.types.GetPublisherModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelGardenService.GetPublisherModel][google.cloud.aiplatform.v1.ModelGardenService.GetPublisherModel]

name

Required. The name of the PublisherModel resource. Format: publishers/{publisher}/models/{publisher_model}

Type:

str

language_code

Optional. The IETF BCP-47 language code representing the language in which the publisher model’s text information should be written in.

Type:

str

view

Optional. PublisherModel view specifying which fields to read.

Type:

google.cloud.aiplatform_v1.types.PublisherModelView

is_hugging_face_model

Optional. Boolean indicates whether the requested model is a Hugging Face model.

Type:

bool

hugging_face_token

Optional. Token used to access Hugging Face gated models.

Type:

str

class google.cloud.aiplatform_v1.types.GetScheduleRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ScheduleService.GetSchedule][google.cloud.aiplatform.v1.ScheduleService.GetSchedule].

name

Required. The name of the Schedule resource. Format: projects/{project}/locations/{location}/schedules/{schedule}

Type:

str

class google.cloud.aiplatform_v1.types.GetSpecialistPoolRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [SpecialistPoolService.GetSpecialistPool][google.cloud.aiplatform.v1.SpecialistPoolService.GetSpecialistPool].

name

Required. The name of the SpecialistPool resource. The form is projects/{project}/locations/{location}/specialistPools/{specialist_pool}.

Type:

str

class google.cloud.aiplatform_v1.types.GetStudyRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.GetStudy][google.cloud.aiplatform.v1.VizierService.GetStudy].

name

Required. The name of the Study resource. Format: projects/{project}/locations/{location}/studies/{study}

Type:

str

class google.cloud.aiplatform_v1.types.GetTensorboardExperimentRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.GetTensorboardExperiment][google.cloud.aiplatform.v1.TensorboardService.GetTensorboardExperiment].

name

Required. The name of the TensorboardExperiment resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}

Type:

str

class google.cloud.aiplatform_v1.types.GetTensorboardRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.GetTensorboard][google.cloud.aiplatform.v1.TensorboardService.GetTensorboard].

name

Required. The name of the Tensorboard resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

Type:

str

class google.cloud.aiplatform_v1.types.GetTensorboardRunRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.GetTensorboardRun][google.cloud.aiplatform.v1.TensorboardService.GetTensorboardRun].

name

Required. The name of the TensorboardRun resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}

Type:

str

class google.cloud.aiplatform_v1.types.GetTensorboardTimeSeriesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.GetTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.GetTensorboardTimeSeries].

name

Required. The name of the TensorboardTimeSeries resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}

Type:

str

class google.cloud.aiplatform_v1.types.GetTrainingPipelineRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.GetTrainingPipeline][google.cloud.aiplatform.v1.PipelineService.GetTrainingPipeline].

name

Required. The name of the TrainingPipeline resource. Format: projects/{project}/locations/{location}/trainingPipelines/{training_pipeline}

Type:

str

class google.cloud.aiplatform_v1.types.GetTrialRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.GetTrial][google.cloud.aiplatform.v1.VizierService.GetTrial].

name

Required. The name of the Trial resource. Format: projects/{project}/locations/{location}/studies/{study}/trials/{trial}

Type:

str

class google.cloud.aiplatform_v1.types.GetTuningJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [GenAiTuningService.GetTuningJob][google.cloud.aiplatform.v1.GenAiTuningService.GetTuningJob].

name

Required. The name of the TuningJob resource. Format: projects/{project}/locations/{location}/tuningJobs/{tuning_job}

Type:

str

class google.cloud.aiplatform_v1.types.GoogleSearchRetrieval(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Tool to retrieve public web data for grounding, powered by Google.

dynamic_retrieval_config

Specifies the dynamic retrieval configuration for the given source.

Type:

google.cloud.aiplatform_v1.types.DynamicRetrievalConfig

class google.cloud.aiplatform_v1.types.GroundednessInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for groundedness metric.

metric_spec

Required. Spec for groundedness metric.

Type:

google.cloud.aiplatform_v1.types.GroundednessSpec

instance

Required. Groundedness instance.

Type:

google.cloud.aiplatform_v1.types.GroundednessInstance

class google.cloud.aiplatform_v1.types.GroundednessInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for groundedness instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

context

Required. Background information provided in context used to compare against the prediction.

This field is a member of oneof _context.

Type:

str

class google.cloud.aiplatform_v1.types.GroundednessResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for groundedness result.

score

Output only. Groundedness score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for groundedness score.

Type:

str

confidence

Output only. Confidence for groundedness score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.GroundednessSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for groundedness metric.

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.GroundingChunk(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Grounding chunk.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

web

Grounding chunk from the web.

This field is a member of oneof chunk_type.

Type:

google.cloud.aiplatform_v1.types.GroundingChunk.Web

retrieved_context

Grounding chunk from context retrieved by the retrieval tools.

This field is a member of oneof chunk_type.

Type:

google.cloud.aiplatform_v1.types.GroundingChunk.RetrievedContext

class RetrievedContext(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Chunk from context retrieved by the retrieval tools.

uri

URI reference of the attribution.

This field is a member of oneof _uri.

Type:

str

title

Title of the attribution.

This field is a member of oneof _title.

Type:

str

text

Text of the attribution.

This field is a member of oneof _text.

Type:

str

class Web(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Chunk from the web.

uri

URI reference of the chunk.

This field is a member of oneof _uri.

Type:

str

title

Title of the chunk.

This field is a member of oneof _title.

Type:

str

class google.cloud.aiplatform_v1.types.GroundingMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata returned to client when grounding is enabled.

web_search_queries

Optional. Web search queries for the following-up web search.

Type:

MutableSequence[str]

search_entry_point

Optional. Google search entry for the following-up web searches.

This field is a member of oneof _search_entry_point.

Type:

google.cloud.aiplatform_v1.types.SearchEntryPoint

grounding_chunks

List of supporting references retrieved from specified grounding source.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.GroundingChunk]

grounding_supports

Optional. List of grounding support.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.GroundingSupport]

retrieval_metadata

Optional. Output only. Retrieval metadata.

This field is a member of oneof _retrieval_metadata.

Type:

google.cloud.aiplatform_v1.types.RetrievalMetadata

class google.cloud.aiplatform_v1.types.GroundingSupport(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Grounding support.

segment

Segment of the content this support belongs to.

This field is a member of oneof _segment.

Type:

google.cloud.aiplatform_v1.types.Segment

grounding_chunk_indices

A list of indices (into ‘grounding_chunk’) specifying the citations associated with the claim. For instance [1,3,4] means that grounding_chunk[1], grounding_chunk[3], grounding_chunk[4] are the retrieved content attributed to the claim.

Type:

MutableSequence[int]

confidence_scores

Confidence score of the support references. Ranges from 0 to 1. 1 is the most confident. This list must have the same size as the grounding_chunk_indices.

Type:

MutableSequence[float]

class google.cloud.aiplatform_v1.types.HarmCategory(value)

Bases: Enum

Harm categories that will block the content.

Values:

HARM_CATEGORY_UNSPECIFIED (0):

The harm category is unspecified.

HARM_CATEGORY_HATE_SPEECH (1):

The harm category is hate speech.

HARM_CATEGORY_DANGEROUS_CONTENT (2):

The harm category is dangerous content.

HARM_CATEGORY_HARASSMENT (3):

The harm category is harassment.

HARM_CATEGORY_SEXUALLY_EXPLICIT (4):

The harm category is sexually explicit content.

HARM_CATEGORY_CIVIC_INTEGRITY (5):

The harm category is civic integrity.

class google.cloud.aiplatform_v1.types.HyperparameterTuningJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. Resource name of the HyperparameterTuningJob.

Type:

str

display_name

Required. The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

study_spec

Required. Study configuration of the HyperparameterTuningJob.

Type:

google.cloud.aiplatform_v1.types.StudySpec

max_trial_count

Required. The desired total number of Trials.

Type:

int

parallel_trial_count

Required. The desired number of Trials to run in parallel.

Type:

int

max_failed_trial_count

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.

Type:

int

trial_job_spec

Required. The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.

Type:

google.cloud.aiplatform_v1.types.CustomJobSpec

trials

Output only. Trials of the HyperparameterTuningJob.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Trial]

state

Output only. The detailed state of the job.

Type:

google.cloud.aiplatform_v1.types.JobState

create_time

Output only. Time when the HyperparameterTuningJob was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

start_time

Output only. Time when the HyperparameterTuningJob for the first time entered the JOB_STATE_RUNNING state.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

Output only. Time when the HyperparameterTuningJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Time when the HyperparameterTuningJob was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

error

Output only. Only populated when job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

Type:

google.rpc.status_pb2.Status

labels

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.

Type:

MutableMapping[str, str]

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.IdMatcher(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Matcher for Features of an EntityType by Feature ID.

ids

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.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.ImportDataConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

gcs_source

The Google Cloud Storage location for the input content.

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.GcsSource

data_item_labels

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][google.cloud.aiplatform.v1.ImportDataConfig.import_schema_uri], e.g. jsonl file.

Type:

MutableMapping[str, str]

annotation_labels

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][google.cloud.aiplatform.v1.Annotation.payload], [payload_schema_uri][google.cloud.aiplatform.v1.Annotation.payload_schema_uri] and all of their [labels][google.cloud.aiplatform.v1.Annotation.labels] are the same. These labels will be overridden by Annotation labels specified inside index file referenced by [import_schema_uri][google.cloud.aiplatform.v1.ImportDataConfig.import_schema_uri], e.g. jsonl file.

Type:

MutableMapping[str, str]

import_schema_uri

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.

Type:

str

class AnnotationLabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class DataItemLabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.ImportDataOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [DatasetService.ImportData][google.cloud.aiplatform.v1.DatasetService.ImportData].

generic_metadata

The common part of the operation metadata.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.ImportDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.ImportData][google.cloud.aiplatform.v1.DatasetService.ImportData].

name

Required. The name of the Dataset resource. Format: projects/{project}/locations/{location}/datasets/{dataset}

Type:

str

import_configs

Required. The desired input locations. The contents of all input locations will be imported in one batch.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ImportDataConfig]

class google.cloud.aiplatform_v1.types.ImportDataResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [DatasetService.ImportData][google.cloud.aiplatform.v1.DatasetService.ImportData].

class google.cloud.aiplatform_v1.types.ImportFeatureValuesOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform import Feature values.

generic_metadata

Operation metadata for Featurestore import Feature values.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

imported_entity_count

Number of entities that have been imported by the operation.

Type:

int

imported_feature_value_count

Number of Feature values that have been imported by the operation.

Type:

int

source_uris

The source URI from where Feature values are imported.

Type:

MutableSequence[str]

invalid_row_count

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).

Type:

int

timestamp_outside_retention_rows_count

The number rows that weren’t ingested due to having timestamps outside the retention boundary.

Type:

int

blocking_operation_ids

List of ImportFeatureValues operations running under a single EntityType that are blocking this operation.

Type:

MutableSequence[int]

class google.cloud.aiplatform_v1.types.ImportFeatureValuesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ImportFeatureValues].

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

avro_source

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.AvroSource

bigquery_source

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.BigQuerySource

csv_source

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.CsvSource

feature_time_field

Source column that holds the Feature timestamp for all Feature values in each entity.

This field is a member of oneof feature_time_source.

Type:

str

feature_time

Single Feature timestamp for all entities being imported. The timestamp must not have higher than millisecond precision.

This field is a member of oneof feature_time_source.

Type:

google.protobuf.timestamp_pb2.Timestamp

entity_type

Required. The resource name of the EntityType grouping the Features for which values are being imported. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}

Type:

str

entity_id_field

Source column that holds entity IDs. If not provided, entity IDs are extracted from the column named entity_id.

Type:

str

feature_specs

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ImportFeatureValuesRequest.FeatureSpec]

disable_online_serving

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.

Type:

bool

worker_count

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.

Type:

int

disable_ingestion_analysis

If true, API doesn’t start ingestion analysis pipeline.

Type:

bool

class FeatureSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Defines the Feature value(s) to import.

id

Required. ID of the Feature to import values of. This Feature must exist in the target EntityType, or the request will fail.

Type:

str

source_field

Source column to get the Feature values from. If not set, uses the column with the same name as the Feature ID.

Type:

str

class google.cloud.aiplatform_v1.types.ImportFeatureValuesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeaturestoreService.ImportFeatureValues][google.cloud.aiplatform.v1.FeaturestoreService.ImportFeatureValues].

imported_entity_count

Number of entities that have been imported by the operation.

Type:

int

imported_feature_value_count

Number of Feature values that have been imported by the operation.

Type:

int

invalid_row_count

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).

Type:

int

timestamp_outside_retention_rows_count

The number rows that weren’t ingested due to having feature timestamps outside the retention boundary.

Type:

int

class google.cloud.aiplatform_v1.types.ImportModelEvaluationRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.ImportModelEvaluation][google.cloud.aiplatform.v1.ModelService.ImportModelEvaluation]

parent

Required. The name of the parent model resource. Format: projects/{project}/locations/{location}/models/{model}

Type:

str

model_evaluation

Required. Model evaluation resource to be imported.

Type:

google.cloud.aiplatform_v1.types.ModelEvaluation

class google.cloud.aiplatform_v1.types.Index(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. The resource name of the Index.

Type:

str

display_name

Required. The display name of the Index. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

description

The description of the Index.

Type:

str

metadata_schema_uri

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.

Type:

str

metadata

An additional information about the Index; the schema of the metadata can be found in [metadata_schema][google.cloud.aiplatform.v1.Index.metadata_schema_uri].

Type:

google.protobuf.struct_pb2.Value

deployed_indexes

Output only. The pointers to DeployedIndexes created from this Index. An Index can be only deleted if all its DeployedIndexes had been undeployed first.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DeployedIndexRef]

etag

Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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.

Type:

MutableMapping[str, str]

create_time

Output only. Timestamp when this Index was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

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] [google.cloud.aiplatform.v1.GenericOperationMetadata.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.

Type:

google.protobuf.timestamp_pb2.Timestamp

index_stats

Output only. Stats of the index resource.

Type:

google.cloud.aiplatform_v1.types.IndexStats

index_update_method

Immutable. The update method to use with this Index. If not set, BATCH_UPDATE will be used by default.

Type:

google.cloud.aiplatform_v1.types.Index.IndexUpdateMethod

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class IndexUpdateMethod(value)

Bases: Enum

The update method of an Index.

Values:

INDEX_UPDATE_METHOD_UNSPECIFIED (0):

Should not be used.

BATCH_UPDATE (1):

BatchUpdate: user can call UpdateIndex with files on Cloud Storage of Datapoints to update.

STREAM_UPDATE (2):

StreamUpdate: user can call UpsertDatapoints/DeleteDatapoints to update the Index and the updates will be applied in corresponding DeployedIndexes in nearly real-time.

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.IndexDatapoint(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A datapoint of Index.

datapoint_id

Required. Unique identifier of the datapoint.

Type:

str

feature_vector

Required. Feature embedding vector for dense index. An array of numbers with the length of [NearestNeighborSearchConfig.dimensions].

Type:

MutableSequence[float]

sparse_embedding

Optional. Feature embedding vector for sparse index.

Type:

google.cloud.aiplatform_v1.types.IndexDatapoint.SparseEmbedding

restricts

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

Type:

MutableSequence[google.cloud.aiplatform_v1.types.IndexDatapoint.Restriction]

numeric_restricts

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.IndexDatapoint.NumericRestriction]

crowding_tag

Optional. CrowdingTag of the datapoint, the number of neighbors to return in each crowding can be configured during query.

Type:

google.cloud.aiplatform_v1.types.IndexDatapoint.CrowdingTag

class CrowdingTag(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

crowding_attribute

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.

Type:

str

class NumericRestriction(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

value_int

Represents 64 bit integer.

This field is a member of oneof Value.

Type:

int

value_float

Represents 32 bit float.

This field is a member of oneof Value.

Type:

float

value_double

Represents 64 bit float.

This field is a member of oneof Value.

Type:

float

namespace

The namespace of this restriction. e.g.: cost.

Type:

str

op

This MUST be specified for queries and must NOT be specified for datapoints.

Type:

google.cloud.aiplatform_v1.types.IndexDatapoint.NumericRestriction.Operator

class Operator(value)

Bases: Enum

Which comparison operator to use. Should be specified for queries only; specifying this for a datapoint is an error.

Datapoints for which Operator is true relative to the query’s Value field will be allowlisted.

Values:

OPERATOR_UNSPECIFIED (0):

Default value of the enum.

LESS (1):

Datapoints are eligible iff their value is < the query’s.

LESS_EQUAL (2):

Datapoints are eligible iff their value is <= the query’s.

EQUAL (3):

Datapoints are eligible iff their value is == the query’s.

GREATER_EQUAL (4):

Datapoints are eligible iff their value is >= the query’s.

GREATER (5):

Datapoints are eligible iff their value is > the query’s.

NOT_EQUAL (6):

Datapoints are eligible iff their value is != the query’s.

class Restriction(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

namespace

The namespace of this restriction. e.g.: color.

Type:

str

allow_list

The attributes to allow in this namespace. e.g.: ‘red’

Type:

MutableSequence[str]

deny_list

The attributes to deny in this namespace. e.g.: ‘blue’

Type:

MutableSequence[str]

class SparseEmbedding(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

values

Required. The list of embedding values of the sparse vector.

Type:

MutableSequence[float]

dimensions

Required. The list of indexes for the embedding values of the sparse vector.

Type:

MutableSequence[int]

class google.cloud.aiplatform_v1.types.IndexEndpoint(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. The resource name of the IndexEndpoint.

Type:

str

display_name

Required. The display name of the IndexEndpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

description

The description of the IndexEndpoint.

Type:

str

deployed_indexes

Output only. The indexes deployed in this endpoint.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DeployedIndex]

etag

Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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.

Type:

MutableMapping[str, str]

create_time

Output only. Timestamp when this IndexEndpoint was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

network

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][google.cloud.aiplatform.v1.IndexEndpoint.network] and [private_service_connect_config][google.cloud.aiplatform.v1.IndexEndpoint.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.

Type:

str

enable_private_service_connect

Optional. Deprecated: If true, expose the IndexEndpoint via private service connect.

Only one of the fields, [network][google.cloud.aiplatform.v1.IndexEndpoint.network] or [enable_private_service_connect][google.cloud.aiplatform.v1.IndexEndpoint.enable_private_service_connect], can be set.

Type:

bool

private_service_connect_config

Optional. Configuration for private service connect.

[network][google.cloud.aiplatform.v1.IndexEndpoint.network] and [private_service_connect_config][google.cloud.aiplatform.v1.IndexEndpoint.private_service_connect_config] are mutually exclusive.

Type:

google.cloud.aiplatform_v1.types.PrivateServiceConnectConfig

public_endpoint_enabled

Optional. If true, the deployed index will be accessible through public endpoint.

Type:

bool

public_endpoint_domain_name

Output only. If [public_endpoint_enabled][google.cloud.aiplatform.v1.IndexEndpoint.public_endpoint_enabled] is true, this field will be populated with the domain name to use for this index endpoint.

Type:

str

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.IndexPrivateEndpoints(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

match_grpc_address

Output only. The ip address used to send match gRPC requests.

Type:

str

service_attachment

Output only. The name of the service attachment resource. Populated if private service connect is enabled.

Type:

str

psc_automated_endpoints

Output only. PscAutomatedEndpoints is populated if private service connect is enabled if PscAutomatedConfig is set.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.PscAutomatedEndpoints]

class google.cloud.aiplatform_v1.types.IndexStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Stats of the Index.

vectors_count

Output only. The number of dense vectors in the Index.

Type:

int

sparse_vectors_count

Output only. The number of sparse vectors in the Index.

Type:

int

shards_count

Output only. The number of shards in the Index.

Type:

int

class google.cloud.aiplatform_v1.types.InputDataConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

fraction_split

Split based on fractions defining the size of each set.

This field is a member of oneof split.

Type:

google.cloud.aiplatform_v1.types.FractionSplit

filter_split

Split based on the provided filters for each set.

This field is a member of oneof split.

Type:

google.cloud.aiplatform_v1.types.FilterSplit

predefined_split

Supported only for tabular Datasets.

Split based on a predefined key.

This field is a member of oneof split.

Type:

google.cloud.aiplatform_v1.types.PredefinedSplit

timestamp_split

Supported only for tabular Datasets.

Split based on the timestamp of the input data pieces.

This field is a member of oneof split.

Type:

google.cloud.aiplatform_v1.types.TimestampSplit

stratified_split

Supported only for tabular Datasets.

Split based on the distribution of the specified column.

This field is a member of oneof split.

Type:

google.cloud.aiplatform_v1.types.StratifiedSplit

gcs_destination

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-<dataset-id>-<annotation-type>-<timestamp-of-training-call> 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}”.

This field is a member of oneof destination.

Type:

google.cloud.aiplatform_v1.types.GcsDestination

bigquery_destination

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_<dataset-id>_<annotation-type>_<timestamp-of-training-call> 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”

• AIP_VALIDATION_DATA_URI = “bigquery_destination.dataset_**.validation”

• AIP_TEST_DATA_URI = “bigquery_destination.dataset_**.test”.

This field is a member of oneof destination.

Type:

google.cloud.aiplatform_v1.types.BigQueryDestination

dataset_id

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] [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. For tabular Datasets, all their data is exported to training, to pick and choose from.

Type:

str

annotations_filter

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][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.

Type:

str

annotation_schema_uri

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][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the Dataset specified by [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.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][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], the Annotations used for training are filtered by both [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] and [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri].

Type:

str

saved_query_id

Only applicable to Datasets that have SavedQueries.

The ID of a SavedQuery (annotation set) under the Dataset specified by [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.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][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], the Annotations used for training are filtered by both [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] and [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter].

Only one of [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] and [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] should be specified as both of them represent the same thing: problem type.

Type:

str

persist_ml_use_assignment

Whether to persist the ML use assignment to data item system labels.

Type:

bool

class google.cloud.aiplatform_v1.types.Int64Array(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A list of int64 values.

values

A list of int64 values.

Type:

MutableSequence[int]

class google.cloud.aiplatform_v1.types.IntegratedGradientsAttribution(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

step_count

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.

Type:

int

smooth_grad_config

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

Type:

google.cloud.aiplatform_v1.types.SmoothGradConfig

blur_baseline_config

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

Type:

google.cloud.aiplatform_v1.types.BlurBaselineConfig

class google.cloud.aiplatform_v1.types.JobState(value)

Bases: Enum

Describes the state of a job.

Values:

JOB_STATE_UNSPECIFIED (0):

The job state is unspecified.

JOB_STATE_QUEUED (1):

The job has been just created or resumed and processing has not yet begun.

JOB_STATE_PENDING (2):

The service is preparing to run the job.

JOB_STATE_RUNNING (3):

The job is in progress.

JOB_STATE_SUCCEEDED (4):

The job completed successfully.

JOB_STATE_FAILED (5):

The job failed.

JOB_STATE_CANCELLING (6):

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 (7):

The job has been cancelled.

JOB_STATE_PAUSED (8):

The job has been stopped, and can be resumed.

JOB_STATE_EXPIRED (9):

The job has expired.

JOB_STATE_UPDATING (10):

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 (11):

The job is partially succeeded, some results may be missing due to errors.

class google.cloud.aiplatform_v1.types.LargeModelReference(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Contains information about the Large Model.

name

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.

Type:

str

class google.cloud.aiplatform_v1.types.LineageSubgraph(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

artifacts

The Artifact nodes in the subgraph.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Artifact]

executions

The Execution nodes in the subgraph.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Execution]

events

The Event edges between Artifacts and Executions in the subgraph.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Event]

class google.cloud.aiplatform_v1.types.ListAnnotationsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations].

parent

Required. The resource name of the DataItem to list Annotations from. Format: projects/{project}/locations/{location}/datasets/{dataset}/dataItems/{data_item}

Type:

str

filter

The standard list filter.

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending.

Type:

str

class google.cloud.aiplatform_v1.types.ListAnnotationsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [DatasetService.ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations].

annotations

A list of Annotations that matches the specified filter in the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Annotation]

next_page_token

The standard List next-page token.

Type:

str

class google.cloud.aiplatform_v1.types.ListArtifactsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.ListArtifacts][google.cloud.aiplatform.v1.MetadataService.ListArtifacts].

parent

Required. The MetadataStore whose Artifacts should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

page_size

The maximum number of Artifacts to return. The service may return fewer. Must be in range 1-1000, inclusive. Defaults to 100.

Type:

int

page_token

A page token, received from a previous [MetadataService.ListArtifacts][google.cloud.aiplatform.v1.MetadataService.ListArtifacts] call. Provide this to retrieve the subsequent page.

When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.)

Type:

str

filter

Filter specifying the boolean condition for the Artifacts to satisfy in order to be part of the result set. The syntax to define filter query is based on https://google.aip.dev/160. The supported set of filters include the following:

• Attribute filtering: For example: display_name = "test". Supported fields include: name, display_name, uri, state, schema_title, create_time, and update_time. Time fields, such as create_time and update_time, require values specified in RFC-3339 format. For example: create_time = "2020-11-19T11:30:00-04:00"

• Metadata field: To filter on metadata fields use traversal operation as follows: metadata.<field_name>.<type_value>. For example: metadata.field_1.number_value = 10.0 In case the field name contains special characters (such as colon), one can embed it inside double quote. For example: metadata."field:1".number_value = 10.0

• Context based filtering: To filter Artifacts based on the contexts to which they belong, use the function operator with the full resource name in_context(<context-name>). For example: in_context("projects/<project_number>/locations/<location>/metadataStores/<metadatastore_name>/contexts/<context-id>")

Each of the above supported filter types can be combined together using logical operators (AND & OR). Maximum nested expression depth allowed is 5.

For example: display_name = "test" AND metadata.field1.bool_value = true.

Type:

str

order_by

How the list of messages is ordered. Specify the values to order by and an ordering operation. The default sorting order is ascending. To specify descending order for a field, users append a ” desc” suffix; for example: “foo desc, bar”. Subfields are specified with a . character, such as foo.bar. see https://google.aip.dev/132#ordering for more details.

Type:

str

class google.cloud.aiplatform_v1.types.ListArtifactsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.ListArtifacts][google.cloud.aiplatform.v1.MetadataService.ListArtifacts].

artifacts

The Artifacts retrieved from the MetadataStore.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Artifact]

next_page_token

A token, which can be sent as [ListArtifactsRequest.page_token][google.cloud.aiplatform.v1.ListArtifactsRequest.page_token] to retrieve the next page. If this field is not populated, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListBatchPredictionJobsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.ListBatchPredictionJobs][google.cloud.aiplatform.v1.JobService.ListBatchPredictionJobs].

parent

Required. The resource name of the Location to list the BatchPredictionJobs from. Format: projects/{project}/locations/{location}

Type:

str

filter

The standard list filter.

Supported fields:

• display_name supports =, != comparisons, and : wildcard.

• model_display_name supports =, != comparisons.

• state supports =, != comparisons.

• create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format.

• labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence

Some examples of using the filter are:

• state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"

• state!="JOB_STATE_FAILED" OR display_name="my_job"

• NOT display_name="my_job"

• create_time>"2021-05-18T00:00:00Z"

• labels.keyA=valueA

• labels.keyB:*

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [ListBatchPredictionJobsResponse.next_page_token][google.cloud.aiplatform.v1.ListBatchPredictionJobsResponse.next_page_token] of the previous [JobService.ListBatchPredictionJobs][google.cloud.aiplatform.v1.JobService.ListBatchPredictionJobs] call.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListBatchPredictionJobsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [JobService.ListBatchPredictionJobs][google.cloud.aiplatform.v1.JobService.ListBatchPredictionJobs]

batch_prediction_jobs

List of BatchPredictionJobs in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.BatchPredictionJob]

next_page_token

A token to retrieve the next page of results. Pass to [ListBatchPredictionJobsRequest.page_token][google.cloud.aiplatform.v1.ListBatchPredictionJobsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListContextsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.ListContexts][google.cloud.aiplatform.v1.MetadataService.ListContexts]

parent

Required. The MetadataStore whose Contexts should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

page_size

The maximum number of Contexts to return. The service may return fewer. Must be in range 1-1000, inclusive. Defaults to 100.

Type:

int

page_token

A page token, received from a previous [MetadataService.ListContexts][google.cloud.aiplatform.v1.MetadataService.ListContexts] call. Provide this to retrieve the subsequent page.

When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.)

Type:

str

filter

Filter specifying the boolean condition for the Contexts to satisfy in order to be part of the result set. The syntax to define filter query is based on https://google.aip.dev/160. Following are the supported set of filters:

• Attribute filtering: For example: display_name = "test". Supported fields include: name, display_name, schema_title, create_time, and update_time. Time fields, such as create_time and update_time, require values specified in RFC-3339 format. For example: create_time = "2020-11-19T11:30:00-04:00".

• Metadata field: To filter on metadata fields use traversal operation as follows: metadata.<field_name>.<type_value>. For example: metadata.field_1.number_value = 10.0. In case the field name contains special characters (such as colon), one can embed it inside double quote. For example: metadata."field:1".number_value = 10.0

• Parent Child filtering: To filter Contexts based on parent-child relationship use the HAS operator as follows:

  parent_contexts:
  "projects/<project_number>/locations/<location>/metadataStores/<metadatastore_name>/contexts/<context_id>"
  child_contexts:
  "projects/<project_number>/locations/<location>/metadataStores/<metadatastore_name>/contexts/<context_id>"
  

Each of the above supported filters can be combined together using logical operators (AND & OR). Maximum nested expression depth allowed is 5.

For example: display_name = "test" AND metadata.field1.bool_value = true.

Type:

str

order_by

How the list of messages is ordered. Specify the values to order by and an ordering operation. The default sorting order is ascending. To specify descending order for a field, users append a ” desc” suffix; for example: “foo desc, bar”. Subfields are specified with a . character, such as foo.bar. see https://google.aip.dev/132#ordering for more details.

Type:

str

class google.cloud.aiplatform_v1.types.ListContextsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.ListContexts][google.cloud.aiplatform.v1.MetadataService.ListContexts].

contexts

The Contexts retrieved from the MetadataStore.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Context]

next_page_token

A token, which can be sent as [ListContextsRequest.page_token][google.cloud.aiplatform.v1.ListContextsRequest.page_token] to retrieve the next page. If this field is not populated, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListCustomJobsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.ListCustomJobs][google.cloud.aiplatform.v1.JobService.ListCustomJobs].

parent

Required. The resource name of the Location to list the CustomJobs from. Format: projects/{project}/locations/{location}

Type:

str

filter

The standard list filter.

Supported fields:

• display_name supports =, != comparisons, and : wildcard.

• state supports =, != comparisons.

• create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format.

• labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence

Some examples of using the filter are:

• state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"

• state!="JOB_STATE_FAILED" OR display_name="my_job"

• NOT display_name="my_job"

• create_time>"2021-05-18T00:00:00Z"

• labels.keyA=valueA

• labels.keyB:*

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [ListCustomJobsResponse.next_page_token][google.cloud.aiplatform.v1.ListCustomJobsResponse.next_page_token] of the previous [JobService.ListCustomJobs][google.cloud.aiplatform.v1.JobService.ListCustomJobs] call.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListCustomJobsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [JobService.ListCustomJobs][google.cloud.aiplatform.v1.JobService.ListCustomJobs]

custom_jobs

List of CustomJobs in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.CustomJob]

next_page_token

A token to retrieve the next page of results. Pass to [ListCustomJobsRequest.page_token][google.cloud.aiplatform.v1.ListCustomJobsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListDataItemsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.ListDataItems][google.cloud.aiplatform.v1.DatasetService.ListDataItems].

parent

Required. The resource name of the Dataset to list DataItems from. Format: projects/{project}/locations/{location}/datasets/{dataset}

Type:

str

filter

The standard list filter.

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending.

Type:

str

class google.cloud.aiplatform_v1.types.ListDataItemsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [DatasetService.ListDataItems][google.cloud.aiplatform.v1.DatasetService.ListDataItems].

data_items

A list of DataItems that matches the specified filter in the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DataItem]

next_page_token

The standard List next-page token.

Type:

str

class google.cloud.aiplatform_v1.types.ListDataLabelingJobsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.ListDataLabelingJobs][google.cloud.aiplatform.v1.JobService.ListDataLabelingJobs].

parent

Required. The parent of the DataLabelingJob. Format: projects/{project}/locations/{location}

Type:

str

filter

The standard list filter.

Supported fields:

• display_name supports =, != comparisons, and : wildcard.

• state supports =, != comparisons.

• create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format.

• labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence

Some examples of using the filter are:

• state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"

• state!="JOB_STATE_FAILED" OR display_name="my_job"

• NOT display_name="my_job"

• create_time>"2021-05-18T00:00:00Z"

• labels.keyA=valueA

• labels.keyB:*

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token.

Type:

str

read_mask

Mask specifying which fields to read. FieldMask represents a set of symbolic field paths. For example, the mask can be paths: "name". The “name” here is a field in DataLabelingJob. If this field is not set, all fields of the DataLabelingJob are returned.

Type:

google.protobuf.field_mask_pb2.FieldMask

order_by

A comma-separated list of fields to order by, sorted in ascending order by default. Use desc after a field name for descending.

Type:

str

class google.cloud.aiplatform_v1.types.ListDataLabelingJobsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [JobService.ListDataLabelingJobs][google.cloud.aiplatform.v1.JobService.ListDataLabelingJobs].

data_labeling_jobs

A list of DataLabelingJobs that matches the specified filter in the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DataLabelingJob]

next_page_token

The standard List next-page token.

Type:

str

class google.cloud.aiplatform_v1.types.ListDatasetVersionsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.ListDatasetVersions][google.cloud.aiplatform.v1.DatasetService.ListDatasetVersions].

parent

Required. The resource name of the Dataset to list DatasetVersions from. Format: projects/{project}/locations/{location}/datasets/{dataset}

Type:

str

filter

Optional. The standard list filter.

Type:

str

page_size

Optional. The standard list page size.

Type:

int

page_token

Optional. The standard list page token.

Type:

str

read_mask

Optional. Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

order_by

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending.

Type:

str

class google.cloud.aiplatform_v1.types.ListDatasetVersionsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [DatasetService.ListDatasetVersions][google.cloud.aiplatform.v1.DatasetService.ListDatasetVersions].

dataset_versions

A list of DatasetVersions that matches the specified filter in the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DatasetVersion]

next_page_token

The standard List next-page token.

Type:

str

class google.cloud.aiplatform_v1.types.ListDatasetsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.ListDatasets][google.cloud.aiplatform.v1.DatasetService.ListDatasets].

parent

Required. The name of the Dataset’s parent resource. Format: projects/{project}/locations/{location}

Type:

str

filter

An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

• display_name: supports = and !=

• metadata_schema_uri: supports = and !=

• labels supports general map functions that is:

  • labels.key=value - key:value equality

  • `labels.key:* or labels:key - key existence

  • A key including a space must be quoted. labels."a key".

Some examples:

• displayName="myDisplayName"

• labels.myKey="myValue"

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported fields:

• display_name

• create_time

• update_time

Type:

str

class google.cloud.aiplatform_v1.types.ListDatasetsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [DatasetService.ListDatasets][google.cloud.aiplatform.v1.DatasetService.ListDatasets].

datasets

A list of Datasets that matches the specified filter in the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Dataset]

next_page_token

The standard List next-page token.

Type:

str

class google.cloud.aiplatform_v1.types.ListDeploymentResourcePoolsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for ListDeploymentResourcePools method.

parent

Required. The parent Location which owns this collection of DeploymentResourcePools. Format: projects/{project}/locations/{location}

Type:

str

page_size

The maximum number of DeploymentResourcePools to return. The service may return fewer than this value.

Type:

int

page_token

A page token, received from a previous ListDeploymentResourcePools call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to ListDeploymentResourcePools must match the call that provided the page token.

Type:

str

class google.cloud.aiplatform_v1.types.ListDeploymentResourcePoolsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for ListDeploymentResourcePools method.

deployment_resource_pools

The DeploymentResourcePools from the specified location.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DeploymentResourcePool]

next_page_token

A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListEndpointsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [EndpointService.ListEndpoints][google.cloud.aiplatform.v1.EndpointService.ListEndpoints].

parent

Required. The resource name of the Location from which to list the Endpoints. Format: projects/{project}/locations/{location}

Type:

str

filter

Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

• endpoint supports = and !=. endpoint represents the Endpoint ID, i.e. the last segment of the Endpoint’s [resource name][google.cloud.aiplatform.v1.Endpoint.name].

• display_name supports = and !=.

• labels supports general map functions that is:

  • labels.key=value - key:value equality

  • labels.key:* or labels:key - key existence

  • A key including a space must be quoted. labels."a key".

• base_model_name only supports =.

Some examples:

• endpoint=1

• displayName="myDisplayName"

• labels.myKey="myValue"

• baseModelName="text-bison"

Type:

str

page_size

Optional. The standard list page size.

Type:

int

page_token

Optional. The standard list page token. Typically obtained via [ListEndpointsResponse.next_page_token][google.cloud.aiplatform.v1.ListEndpointsResponse.next_page_token] of the previous [EndpointService.ListEndpoints][google.cloud.aiplatform.v1.EndpointService.ListEndpoints] call.

Type:

str

read_mask

Optional. Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported fields:

• display_name

• create_time

• update_time

Example: display_name, create_time desc.

Type:

str

class google.cloud.aiplatform_v1.types.ListEndpointsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [EndpointService.ListEndpoints][google.cloud.aiplatform.v1.EndpointService.ListEndpoints].

endpoints

List of Endpoints in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Endpoint]

next_page_token

A token to retrieve the next page of results. Pass to [ListEndpointsRequest.page_token][google.cloud.aiplatform.v1.ListEndpointsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListEntityTypesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1.FeaturestoreService.ListEntityTypes].

parent

Required. The resource name of the Featurestore to list EntityTypes. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

Type:

str

filter

Lists the EntityTypes that match the filter expression. The following filters are supported:

• create_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format.

• update_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format.

• labels: Supports key-value equality as well as key presence.

Examples:

• create_time > \"2020-01-31T15:30:00.000000Z\" OR update_time > \"2020-01-31T15:30:00.000000Z\" –> EntityTypes created or updated after 2020-01-31T15:30:00.000000Z.

• labels.active = yes AND labels.env = prod –> EntityTypes having both (active: yes) and (env: prod) labels.

• labels.env: * –> Any EntityType which has a label with ‘env’ as the key.

Type:

str

page_size

The maximum number of EntityTypes to return. The service may return fewer than this value. If unspecified, at most 1000 EntityTypes will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000.

Type:

int

page_token

A page token, received from a previous [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1.FeaturestoreService.ListEntityTypes] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1.FeaturestoreService.ListEntityTypes] must match the call that provided the page token.

Type:

str

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending.

Supported fields:

• entity_type_id

• create_time

• update_time

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListEntityTypesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeaturestoreService.ListEntityTypes][google.cloud.aiplatform.v1.FeaturestoreService.ListEntityTypes].

entity_types

The EntityTypes matching the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.EntityType]

next_page_token

A token, which can be sent as [ListEntityTypesRequest.page_token][google.cloud.aiplatform.v1.ListEntityTypesRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListExecutionsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.ListExecutions][google.cloud.aiplatform.v1.MetadataService.ListExecutions].

parent

Required. The MetadataStore whose Executions should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

page_size

The maximum number of Executions to return. The service may return fewer. Must be in range 1-1000, inclusive. Defaults to 100.

Type:

int

page_token

A page token, received from a previous [MetadataService.ListExecutions][google.cloud.aiplatform.v1.MetadataService.ListExecutions] call. Provide this to retrieve the subsequent page.

When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with an INVALID_ARGUMENT error.)

Type:

str

filter

Filter specifying the boolean condition for the Executions to satisfy in order to be part of the result set. The syntax to define filter query is based on https://google.aip.dev/160. Following are the supported set of filters:

• Attribute filtering: For example: display_name = "test". Supported fields include: name, display_name, state, schema_title, create_time, and update_time. Time fields, such as create_time and update_time, require values specified in RFC-3339 format. For example: create_time = "2020-11-19T11:30:00-04:00".

• Metadata field: To filter on metadata fields use traversal operation as follows: metadata.<field_name>.<type_value> For example: metadata.field_1.number_value = 10.0 In case the field name contains special characters (such as colon), one can embed it inside double quote. For example: metadata."field:1".number_value = 10.0

• Context based filtering: To filter Executions based on the contexts to which they belong use the function operator with the full resource name: in_context(<context-name>). For example: in_context("projects/<project_number>/locations/<location>/metadataStores/<metadatastore_name>/contexts/<context-id>")

Each of the above supported filters can be combined together using logical operators (AND & OR). Maximum nested expression depth allowed is 5.

For example: display_name = "test" AND metadata.field1.bool_value = true.

Type:

str

order_by

How the list of messages is ordered. Specify the values to order by and an ordering operation. The default sorting order is ascending. To specify descending order for a field, users append a ” desc” suffix; for example: “foo desc, bar”. Subfields are specified with a . character, such as foo.bar. see https://google.aip.dev/132#ordering for more details.

Type:

str

class google.cloud.aiplatform_v1.types.ListExecutionsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.ListExecutions][google.cloud.aiplatform.v1.MetadataService.ListExecutions].

executions

The Executions retrieved from the MetadataStore.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Execution]

next_page_token

A token, which can be sent as [ListExecutionsRequest.page_token][google.cloud.aiplatform.v1.ListExecutionsRequest.page_token] to retrieve the next page. If this field is not populated, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListFeatureGroupsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureRegistryService.ListFeatureGroups][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatureGroups].

parent

Required. The resource name of the Location to list FeatureGroups. Format: projects/{project}/locations/{location}

Type:

str

filter

Lists the FeatureGroups that match the filter expression. The following fields are supported:

• create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

• update_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

• labels: Supports key-value equality and key presence.

Examples:

• create_time > "2020-01-01" OR update_time > "2020-01-01" FeatureGroups created or updated after 2020-01-01.

• labels.env = "prod" FeatureGroups with label “env” set to “prod”.

Type:

str

page_size

The maximum number of FeatureGroups to return. The service may return fewer than this value. If unspecified, at most 100 FeatureGroups will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100.

Type:

int

page_token

A page token, received from a previous [FeatureRegistryService.ListFeatureGroups][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatureGroups] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [FeatureRegistryService.ListFeatureGroups][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatureGroups] must match the call that provided the page token.

Type:

str

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported Fields:

• create_time

• update_time

Type:

str

class google.cloud.aiplatform_v1.types.ListFeatureGroupsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeatureRegistryService.ListFeatureGroups][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatureGroups].

feature_groups

The FeatureGroups matching the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FeatureGroup]

next_page_token

A token, which can be sent as [ListFeatureGroupsRequest.page_token][google.cloud.aiplatform.v1.ListFeatureGroupsRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListFeatureOnlineStoresRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.ListFeatureOnlineStores][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureOnlineStores].

parent

Required. The resource name of the Location to list FeatureOnlineStores. Format: projects/{project}/locations/{location}

Type:

str

filter

Lists the FeatureOnlineStores that match the filter expression. The following fields are supported:

• create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

• update_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

• labels: Supports key-value equality and key presence.

Examples:

• create_time > "2020-01-01" OR update_time > "2020-01-01" FeatureOnlineStores created or updated after 2020-01-01.

• labels.env = "prod" FeatureOnlineStores with label “env” set to “prod”.

Type:

str

page_size

The maximum number of FeatureOnlineStores to return. The service may return fewer than this value. If unspecified, at most 100 FeatureOnlineStores will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100.

Type:

int

page_token

A page token, received from a previous [FeatureOnlineStoreAdminService.ListFeatureOnlineStores][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureOnlineStores] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [FeatureOnlineStoreAdminService.ListFeatureOnlineStores][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureOnlineStores] must match the call that provided the page token.

Type:

str

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported Fields:

• create_time

• update_time

Type:

str

class google.cloud.aiplatform_v1.types.ListFeatureOnlineStoresResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeatureOnlineStoreAdminService.ListFeatureOnlineStores][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureOnlineStores].

feature_online_stores

The FeatureOnlineStores matching the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FeatureOnlineStore]

next_page_token

A token, which can be sent as [ListFeatureOnlineStoresRequest.page_token][google.cloud.aiplatform.v1.ListFeatureOnlineStoresRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListFeatureViewSyncsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.ListFeatureViewSyncs][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureViewSyncs].

parent

Required. The resource name of the FeatureView to list FeatureViewSyncs. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}

Type:

str

filter

Lists the FeatureViewSyncs that match the filter expression. The following filters are supported:

• create_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format.

Examples:

• create_time > \"2020-01-31T15:30:00.000000Z\" –> FeatureViewSyncs created after 2020-01-31T15:30:00.000000Z.

Type:

str

page_size

The maximum number of FeatureViewSyncs to return. The service may return fewer than this value. If unspecified, at most 1000 FeatureViewSyncs will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000.

Type:

int

page_token

A page token, received from a previous [FeatureOnlineStoreAdminService.ListFeatureViewSyncs][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureViewSyncs] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [FeatureOnlineStoreAdminService.ListFeatureViewSyncs][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureViewSyncs] must match the call that provided the page token.

Type:

str

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending.

Supported fields:

• create_time

Type:

str

class google.cloud.aiplatform_v1.types.ListFeatureViewSyncsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeatureOnlineStoreAdminService.ListFeatureViewSyncs][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureViewSyncs].

feature_view_syncs

The FeatureViewSyncs matching the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FeatureViewSync]

next_page_token

A token, which can be sent as [ListFeatureViewSyncsRequest.page_token][google.cloud.aiplatform.v1.ListFeatureViewSyncsRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListFeatureViewsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.ListFeatureViews][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureViews].

parent

Required. The resource name of the FeatureOnlineStore to list FeatureViews. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}

Type:

str

filter

Lists the FeatureViews that match the filter expression. The following filters are supported:

• create_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format.

• update_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format.

• labels: Supports key-value equality as well as key presence.

Examples:

• create_time > \"2020-01-31T15:30:00.000000Z\" OR update_time > \"2020-01-31T15:30:00.000000Z\" –> FeatureViews created or updated after 2020-01-31T15:30:00.000000Z.

• labels.active = yes AND labels.env = prod –> FeatureViews having both (active: yes) and (env: prod) labels.

• labels.env: * –> Any FeatureView which has a label with ‘env’ as the key.

Type:

str

page_size

The maximum number of FeatureViews to return. The service may return fewer than this value. If unspecified, at most 1000 FeatureViews will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000.

Type:

int

page_token

A page token, received from a previous [FeatureOnlineStoreAdminService.ListFeatureViews][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureViews] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [FeatureOnlineStoreAdminService.ListFeatureViews][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureViews] must match the call that provided the page token.

Type:

str

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending.

Supported fields:

• feature_view_id

• create_time

• update_time

Type:

str

class google.cloud.aiplatform_v1.types.ListFeatureViewsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeatureOnlineStoreAdminService.ListFeatureViews][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.ListFeatureViews].

feature_views

The FeatureViews matching the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FeatureView]

next_page_token

A token, which can be sent as [ListFeatureViewsRequest.page_token][google.cloud.aiplatform.v1.ListFeatureViewsRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListFeaturesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1.FeaturestoreService.ListFeatures]. Request message for [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatures].

parent

Required. The resource name of the Location to list Features. 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}

Type:

str

filter

Lists the Features that match the filter expression. The following filters are supported:

• value_type: Supports = and != comparisons.

• create_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format.

• update_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format.

• labels: Supports key-value equality as well as key presence.

Examples:

• value_type = DOUBLE –> Features whose type is DOUBLE.

• create_time > \"2020-01-31T15:30:00.000000Z\" OR update_time > \"2020-01-31T15:30:00.000000Z\" –> EntityTypes created or updated after 2020-01-31T15:30:00.000000Z.

• labels.active = yes AND labels.env = prod –> Features having both (active: yes) and (env: prod) labels.

• labels.env: * –> Any Feature which has a label with ‘env’ as the key.

Type:

str

page_size

The maximum number of Features to return. The service may return fewer than this value. If unspecified, at most 1000 Features will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000.

Type:

int

page_token

A page token, received from a previous [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1.FeaturestoreService.ListFeatures] call or [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatures] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1.FeaturestoreService.ListFeatures] or [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatures] must match the call that provided the page token.

Type:

str

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported fields:

• feature_id

• value_type (Not supported for FeatureRegistry Feature)

• create_time

• update_time

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

latest_stats_count

Only applicable for Vertex AI Feature Store (Legacy). If set, return the most recent [ListFeaturesRequest.latest_stats_count][google.cloud.aiplatform.v1.ListFeaturesRequest.latest_stats_count] of stats for each Feature in response. Valid value is [0, 10]. If number of stats exists < [ListFeaturesRequest.latest_stats_count][google.cloud.aiplatform.v1.ListFeaturesRequest.latest_stats_count], return all existing stats.

Type:

int

class google.cloud.aiplatform_v1.types.ListFeaturesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeaturestoreService.ListFeatures][google.cloud.aiplatform.v1.FeaturestoreService.ListFeatures]. Response message for [FeatureRegistryService.ListFeatures][google.cloud.aiplatform.v1.FeatureRegistryService.ListFeatures].

features

The Features matching the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Feature]

next_page_token

A token, which can be sent as [ListFeaturesRequest.page_token][google.cloud.aiplatform.v1.ListFeaturesRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListFeaturestoresRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1.FeaturestoreService.ListFeaturestores].

parent

Required. The resource name of the Location to list Featurestores. Format: projects/{project}/locations/{location}

Type:

str

filter

Lists the featurestores that match the filter expression. The following fields are supported:

• create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

• update_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

• online_serving_config.fixed_node_count: Supports =, !=, <, >, <=, and >= comparisons.

• labels: Supports key-value equality and key presence.

Examples:

• create_time > "2020-01-01" OR update_time > "2020-01-01" Featurestores created or updated after 2020-01-01.

• labels.env = "prod" Featurestores with label “env” set to “prod”.

Type:

str

page_size

The maximum number of Featurestores to return. The service may return fewer than this value. If unspecified, at most 100 Featurestores will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100.

Type:

int

page_token

A page token, received from a previous [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1.FeaturestoreService.ListFeaturestores] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1.FeaturestoreService.ListFeaturestores] must match the call that provided the page token.

Type:

str

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported Fields:

• create_time

• update_time

• online_serving_config.fixed_node_count

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListFeaturestoresResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeaturestoreService.ListFeaturestores][google.cloud.aiplatform.v1.FeaturestoreService.ListFeaturestores].

featurestores

The Featurestores matching the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Featurestore]

next_page_token

A token, which can be sent as [ListFeaturestoresRequest.page_token][google.cloud.aiplatform.v1.ListFeaturestoresRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListHyperparameterTuningJobsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.ListHyperparameterTuningJobs][google.cloud.aiplatform.v1.JobService.ListHyperparameterTuningJobs].

parent

Required. The resource name of the Location to list the HyperparameterTuningJobs from. Format: projects/{project}/locations/{location}

Type:

str

filter

The standard list filter.

Supported fields:

• display_name supports =, != comparisons, and : wildcard.

• state supports =, != comparisons.

• create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format.

• labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence

Some examples of using the filter are:

• state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"

• state!="JOB_STATE_FAILED" OR display_name="my_job"

• NOT display_name="my_job"

• create_time>"2021-05-18T00:00:00Z"

• labels.keyA=valueA

• labels.keyB:*

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [ListHyperparameterTuningJobsResponse.next_page_token][google.cloud.aiplatform.v1.ListHyperparameterTuningJobsResponse.next_page_token] of the previous [JobService.ListHyperparameterTuningJobs][google.cloud.aiplatform.v1.JobService.ListHyperparameterTuningJobs] call.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListHyperparameterTuningJobsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [JobService.ListHyperparameterTuningJobs][google.cloud.aiplatform.v1.JobService.ListHyperparameterTuningJobs]

hyperparameter_tuning_jobs

List of HyperparameterTuningJobs in the requested page. [HyperparameterTuningJob.trials][google.cloud.aiplatform.v1.HyperparameterTuningJob.trials] of the jobs will be not be returned.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.HyperparameterTuningJob]

next_page_token

A token to retrieve the next page of results. Pass to [ListHyperparameterTuningJobsRequest.page_token][google.cloud.aiplatform.v1.ListHyperparameterTuningJobsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListIndexEndpointsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexEndpointService.ListIndexEndpoints][google.cloud.aiplatform.v1.IndexEndpointService.ListIndexEndpoints].

parent

Required. The resource name of the Location from which to list the IndexEndpoints. Format: projects/{project}/locations/{location}

Type:

str

filter

Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

• index_endpoint supports = and !=. index_endpoint represents the IndexEndpoint ID, ie. the last segment of the IndexEndpoint’s [resourcename][google.cloud.aiplatform.v1.IndexEndpoint.name].

• display_name supports =, != and regex() (uses re2 syntax)

• labels supports general map functions that is: labels.key=value - key:value equality labels.key:* or labels:key - key existence A key including a space must be quoted.labels.”a key”`.

Some examples:

• index_endpoint="1"

• display_name="myDisplayName"

• `regex(display_name, “^A”) -> The display name starts with an A.

• labels.myKey="myValue"

Type:

str

page_size

Optional. The standard list page size.

Type:

int

page_token

Optional. The standard list page token. Typically obtained via [ListIndexEndpointsResponse.next_page_token][google.cloud.aiplatform.v1.ListIndexEndpointsResponse.next_page_token] of the previous [IndexEndpointService.ListIndexEndpoints][google.cloud.aiplatform.v1.IndexEndpointService.ListIndexEndpoints] call.

Type:

str

read_mask

Optional. Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListIndexEndpointsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [IndexEndpointService.ListIndexEndpoints][google.cloud.aiplatform.v1.IndexEndpointService.ListIndexEndpoints].

index_endpoints

List of IndexEndpoints in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.IndexEndpoint]

next_page_token

A token to retrieve next page of results. Pass to [ListIndexEndpointsRequest.page_token][google.cloud.aiplatform.v1.ListIndexEndpointsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListIndexesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexService.ListIndexes][google.cloud.aiplatform.v1.IndexService.ListIndexes].

parent

Required. The resource name of the Location from which to list the Indexes. Format: projects/{project}/locations/{location}

Type:

str

filter

The standard list filter.

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [ListIndexesResponse.next_page_token][google.cloud.aiplatform.v1.ListIndexesResponse.next_page_token] of the previous [IndexService.ListIndexes][google.cloud.aiplatform.v1.IndexService.ListIndexes] call.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListIndexesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [IndexService.ListIndexes][google.cloud.aiplatform.v1.IndexService.ListIndexes].

indexes

List of indexes in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Index]

next_page_token

A token to retrieve next page of results. Pass to [ListIndexesRequest.page_token][google.cloud.aiplatform.v1.ListIndexesRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListMetadataSchemasRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.ListMetadataSchemas][google.cloud.aiplatform.v1.MetadataService.ListMetadataSchemas].

parent

Required. The MetadataStore whose MetadataSchemas should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

page_size

The maximum number of MetadataSchemas to return. The service may return fewer. Must be in range 1-1000, inclusive. Defaults to 100.

Type:

int

page_token

A page token, received from a previous [MetadataService.ListMetadataSchemas][google.cloud.aiplatform.v1.MetadataService.ListMetadataSchemas] call. Provide this to retrieve the next page.

When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.)

Type:

str

filter

A query to filter available MetadataSchemas for matching results.

Type:

str

class google.cloud.aiplatform_v1.types.ListMetadataSchemasResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.ListMetadataSchemas][google.cloud.aiplatform.v1.MetadataService.ListMetadataSchemas].

metadata_schemas

The MetadataSchemas found for the MetadataStore.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.MetadataSchema]

next_page_token

A token, which can be sent as [ListMetadataSchemasRequest.page_token][google.cloud.aiplatform.v1.ListMetadataSchemasRequest.page_token] to retrieve the next page. If this field is not populated, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListMetadataStoresRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.ListMetadataStores][google.cloud.aiplatform.v1.MetadataService.ListMetadataStores].

parent

Required. The Location whose MetadataStores should be listed. Format: projects/{project}/locations/{location}

Type:

str

page_size

The maximum number of Metadata Stores to return. The service may return fewer. Must be in range 1-1000, inclusive. Defaults to 100.

Type:

int

page_token

A page token, received from a previous [MetadataService.ListMetadataStores][google.cloud.aiplatform.v1.MetadataService.ListMetadataStores] call. Provide this to retrieve the subsequent page.

When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.)

Type:

str

class google.cloud.aiplatform_v1.types.ListMetadataStoresResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.ListMetadataStores][google.cloud.aiplatform.v1.MetadataService.ListMetadataStores].

metadata_stores

The MetadataStores found for the Location.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.MetadataStore]

next_page_token

A token, which can be sent as [ListMetadataStoresRequest.page_token][google.cloud.aiplatform.v1.ListMetadataStoresRequest.page_token] to retrieve the next page. If this field is not populated, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListModelDeploymentMonitoringJobsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.ListModelDeploymentMonitoringJobs][google.cloud.aiplatform.v1.JobService.ListModelDeploymentMonitoringJobs].

parent

Required. The parent of the ModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}

Type:

str

filter

The standard list filter.

Supported fields:

• display_name supports =, != comparisons, and : wildcard.

• state supports =, != comparisons.

• create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format.

• labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence

Some examples of using the filter are:

• state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"

• state!="JOB_STATE_FAILED" OR display_name="my_job"

• NOT display_name="my_job"

• create_time>"2021-05-18T00:00:00Z"

• labels.keyA=valueA

• labels.keyB:*

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token.

Type:

str

read_mask

Mask specifying which fields to read

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListModelDeploymentMonitoringJobsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [JobService.ListModelDeploymentMonitoringJobs][google.cloud.aiplatform.v1.JobService.ListModelDeploymentMonitoringJobs].

model_deployment_monitoring_jobs

A list of ModelDeploymentMonitoringJobs that matches the specified filter in the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringJob]

next_page_token

The standard List next-page token.

Type:

str

class google.cloud.aiplatform_v1.types.ListModelEvaluationSlicesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.ListModelEvaluationSlices].

parent

Required. The resource name of the ModelEvaluation to list the ModelEvaluationSlices from. Format: projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}

Type:

str

filter

The standard list filter.

• slice.dimension - for =.

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [ListModelEvaluationSlicesResponse.next_page_token][google.cloud.aiplatform.v1.ListModelEvaluationSlicesResponse.next_page_token] of the previous [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.ListModelEvaluationSlices] call.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListModelEvaluationSlicesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.ListModelEvaluationSlices].

model_evaluation_slices

List of ModelEvaluations in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ModelEvaluationSlice]

next_page_token

A token to retrieve next page of results. Pass to [ListModelEvaluationSlicesRequest.page_token][google.cloud.aiplatform.v1.ListModelEvaluationSlicesRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListModelEvaluationsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1.ModelService.ListModelEvaluations].

parent

Required. The resource name of the Model to list the ModelEvaluations from. Format: projects/{project}/locations/{location}/models/{model}

Type:

str

filter

The standard list filter.

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [ListModelEvaluationsResponse.next_page_token][google.cloud.aiplatform.v1.ListModelEvaluationsResponse.next_page_token] of the previous [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1.ModelService.ListModelEvaluations] call.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListModelEvaluationsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1.ModelService.ListModelEvaluations].

model_evaluations

List of ModelEvaluations in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ModelEvaluation]

next_page_token

A token to retrieve next page of results. Pass to [ListModelEvaluationsRequest.page_token][google.cloud.aiplatform.v1.ListModelEvaluationsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListModelVersionsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.ListModelVersions][google.cloud.aiplatform.v1.ModelService.ListModelVersions].

name

Required. The name of the model to list versions for.

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [next_page_token][google.cloud.aiplatform.v1.ListModelVersionsResponse.next_page_token] of the previous [ListModelVersions][google.cloud.aiplatform.v1.ModelService.ListModelVersions] call.

Type:

str

filter

An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

• labels supports general map functions that is:

  • labels.key=value - key:value equality

  • `labels.key:* or labels:key - key existence

  • A key including a space must be quoted. labels."a key".

Some examples:

• labels.myKey="myValue"

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported fields:

• create_time

• update_time

Example: update_time asc, create_time desc.

Type:

str

class google.cloud.aiplatform_v1.types.ListModelVersionsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [ModelService.ListModelVersions][google.cloud.aiplatform.v1.ModelService.ListModelVersions]

models

List of Model versions in the requested page. In the returned Model name field, version ID instead of regvision tag will be included.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Model]

next_page_token

A token to retrieve the next page of results. Pass to [ListModelVersionsRequest.page_token][google.cloud.aiplatform.v1.ListModelVersionsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListModelsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.ListModels][google.cloud.aiplatform.v1.ModelService.ListModels].

parent

Required. The resource name of the Location to list the Models from. Format: projects/{project}/locations/{location}

Type:

str

filter

An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

• model supports = and !=. model represents the Model ID, i.e. the last segment of the Model’s [resource name][google.cloud.aiplatform.v1.Model.name].

• display_name supports = and !=

• labels supports general map functions that is:

  • labels.key=value - key:value equality

  • `labels.key:* or labels:key - key existence

  • A key including a space must be quoted. labels."a key".

• base_model_name only supports =

Some examples:

• model=1234

• displayName="myDisplayName"

• labels.myKey="myValue"

• baseModelName="text-bison"

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [ListModelsResponse.next_page_token][google.cloud.aiplatform.v1.ListModelsResponse.next_page_token] of the previous [ModelService.ListModels][google.cloud.aiplatform.v1.ModelService.ListModels] call.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported fields:

• display_name

• create_time

• update_time

Example: display_name, create_time desc.

Type:

str

class google.cloud.aiplatform_v1.types.ListModelsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [ModelService.ListModels][google.cloud.aiplatform.v1.ModelService.ListModels]

models

List of Models in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Model]

next_page_token

A token to retrieve next page of results. Pass to [ListModelsRequest.page_token][google.cloud.aiplatform.v1.ListModelsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListNasJobsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.ListNasJobs][google.cloud.aiplatform.v1.JobService.ListNasJobs].

parent

Required. The resource name of the Location to list the NasJobs from. Format: projects/{project}/locations/{location}

Type:

str

filter

The standard list filter.

Supported fields:

• display_name supports =, != comparisons, and : wildcard.

• state supports =, != comparisons.

• create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format.

• labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence

Some examples of using the filter are:

• state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*"

• state!="JOB_STATE_FAILED" OR display_name="my_job"

• NOT display_name="my_job"

• create_time>"2021-05-18T00:00:00Z"

• labels.keyA=valueA

• labels.keyB:*

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [ListNasJobsResponse.next_page_token][google.cloud.aiplatform.v1.ListNasJobsResponse.next_page_token] of the previous [JobService.ListNasJobs][google.cloud.aiplatform.v1.JobService.ListNasJobs] call.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListNasJobsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [JobService.ListNasJobs][google.cloud.aiplatform.v1.JobService.ListNasJobs]

nas_jobs

List of NasJobs in the requested page. [NasJob.nas_job_output][google.cloud.aiplatform.v1.NasJob.nas_job_output] of the jobs will not be returned.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NasJob]

next_page_token

A token to retrieve the next page of results. Pass to [ListNasJobsRequest.page_token][google.cloud.aiplatform.v1.ListNasJobsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListNasTrialDetailsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.ListNasTrialDetails][google.cloud.aiplatform.v1.JobService.ListNasTrialDetails].

parent

Required. The name of the NasJob resource. Format: projects/{project}/locations/{location}/nasJobs/{nas_job}

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [ListNasTrialDetailsResponse.next_page_token][google.cloud.aiplatform.v1.ListNasTrialDetailsResponse.next_page_token] of the previous [JobService.ListNasTrialDetails][google.cloud.aiplatform.v1.JobService.ListNasTrialDetails] call.

Type:

str

class google.cloud.aiplatform_v1.types.ListNasTrialDetailsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [JobService.ListNasTrialDetails][google.cloud.aiplatform.v1.JobService.ListNasTrialDetails]

nas_trial_details

List of top NasTrials in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NasTrialDetail]

next_page_token

A token to retrieve the next page of results. Pass to [ListNasTrialDetailsRequest.page_token][google.cloud.aiplatform.v1.ListNasTrialDetailsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListNotebookExecutionJobsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.ListNotebookExecutionJobs]

parent

Required. The resource name of the Location from which to list the NotebookExecutionJobs. Format: projects/{project}/locations/{location}

Type:

str

filter

Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

• notebookExecutionJob supports = and !=. notebookExecutionJob represents the NotebookExecutionJob ID.

• displayName supports = and != and regex.

• schedule supports = and != and regex.

Some examples:

• notebookExecutionJob="123"

• notebookExecutionJob="my-execution-job"

• displayName="myDisplayName" and displayName=~"myDisplayNameRegex"

Type:

str

page_size

Optional. The standard list page size.

Type:

int

page_token

Optional. The standard list page token. Typically obtained via [ListNotebookExecutionJobsResponse.next_page_token][google.cloud.aiplatform.v1.ListNotebookExecutionJobsResponse.next_page_token] of the previous [NotebookService.ListNotebookExecutionJobs][google.cloud.aiplatform.v1.NotebookService.ListNotebookExecutionJobs] call.

Type:

str

order_by

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported fields:

• display_name

• create_time

• update_time

Example: display_name, create_time desc.

Type:

str

view

Optional. The NotebookExecutionJob view. Defaults to BASIC.

Type:

google.cloud.aiplatform_v1.types.NotebookExecutionJobView

class google.cloud.aiplatform_v1.types.ListNotebookExecutionJobsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [NotebookService.CreateNotebookExecutionJob]

notebook_execution_jobs

List of NotebookExecutionJobs in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NotebookExecutionJob]

next_page_token

A token to retrieve next page of results. Pass to [ListNotebookExecutionJobsRequest.page_token][google.cloud.aiplatform.v1.ListNotebookExecutionJobsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListNotebookRuntimeTemplatesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.ListNotebookRuntimeTemplates][google.cloud.aiplatform.v1.NotebookService.ListNotebookRuntimeTemplates].

parent

Required. The resource name of the Location from which to list the NotebookRuntimeTemplates. Format: projects/{project}/locations/{location}

Type:

str

filter

Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

• notebookRuntimeTemplate supports = and !=. notebookRuntimeTemplate represents the NotebookRuntimeTemplate ID, i.e. the last segment of the NotebookRuntimeTemplate’s [resource name] [google.cloud.aiplatform.v1.NotebookRuntimeTemplate.name].

• display_name supports = and !=

• labels supports general map functions that is:

  • labels.key=value - key:value equality

  • `labels.key:* or labels:key - key existence

  • A key including a space must be quoted. labels."a key".

• notebookRuntimeType supports = and !=. notebookRuntimeType enum: [USER_DEFINED, ONE_CLICK].

Some examples:

• notebookRuntimeTemplate=notebookRuntimeTemplate123

• displayName="myDisplayName"

• labels.myKey="myValue"

• notebookRuntimeType=USER_DEFINED

Type:

str

page_size

Optional. The standard list page size.

Type:

int

page_token

Optional. The standard list page token. Typically obtained via [ListNotebookRuntimeTemplatesResponse.next_page_token][google.cloud.aiplatform.v1.ListNotebookRuntimeTemplatesResponse.next_page_token] of the previous [NotebookService.ListNotebookRuntimeTemplates][google.cloud.aiplatform.v1.NotebookService.ListNotebookRuntimeTemplates] call.

Type:

str

read_mask

Optional. Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

order_by

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported fields:

• display_name

• create_time

• update_time

Example: display_name, create_time desc.

Type:

str

class google.cloud.aiplatform_v1.types.ListNotebookRuntimeTemplatesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [NotebookService.ListNotebookRuntimeTemplates][google.cloud.aiplatform.v1.NotebookService.ListNotebookRuntimeTemplates].

notebook_runtime_templates

List of NotebookRuntimeTemplates in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NotebookRuntimeTemplate]

next_page_token

A token to retrieve next page of results. Pass to [ListNotebookRuntimeTemplatesRequest.page_token][google.cloud.aiplatform.v1.ListNotebookRuntimeTemplatesRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListNotebookRuntimesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.ListNotebookRuntimes][google.cloud.aiplatform.v1.NotebookService.ListNotebookRuntimes].

parent

Required. The resource name of the Location from which to list the NotebookRuntimes. Format: projects/{project}/locations/{location}

Type:

str

filter

Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

• notebookRuntime supports = and !=. notebookRuntime represents the NotebookRuntime ID, i.e. the last segment of the NotebookRuntime’s [resource name] [google.cloud.aiplatform.v1.NotebookRuntime.name].

• displayName supports = and != and regex.

• notebookRuntimeTemplate supports = and !=. notebookRuntimeTemplate represents the NotebookRuntimeTemplate ID, i.e. the last segment of the NotebookRuntimeTemplate’s [resource name] [google.cloud.aiplatform.v1.NotebookRuntimeTemplate.name].

• healthState supports = and !=. healthState enum: [HEALTHY, UNHEALTHY, HEALTH_STATE_UNSPECIFIED].

• runtimeState supports = and !=. runtimeState enum: [RUNTIME_STATE_UNSPECIFIED, RUNNING, BEING_STARTED, BEING_STOPPED, STOPPED, BEING_UPGRADED, ERROR, INVALID].

• runtimeUser supports = and !=.

• API version is UI only: uiState supports = and !=. uiState enum: [UI_RESOURCE_STATE_UNSPECIFIED, UI_RESOURCE_STATE_BEING_CREATED, UI_RESOURCE_STATE_ACTIVE, UI_RESOURCE_STATE_BEING_DELETED, UI_RESOURCE_STATE_CREATION_FAILED].

• notebookRuntimeType supports = and !=. notebookRuntimeType enum: [USER_DEFINED, ONE_CLICK].

Some examples:

• notebookRuntime="notebookRuntime123"

• displayName="myDisplayName" and displayName=~"myDisplayNameRegex"

• notebookRuntimeTemplate="notebookRuntimeTemplate321"

• healthState=HEALTHY

• runtimeState=RUNNING

• runtimeUser="test@google.com"

• uiState=UI_RESOURCE_STATE_BEING_DELETED

• notebookRuntimeType=USER_DEFINED

Type:

str

page_size

Optional. The standard list page size.

Type:

int

page_token

Optional. The standard list page token. Typically obtained via [ListNotebookRuntimesResponse.next_page_token][google.cloud.aiplatform.v1.ListNotebookRuntimesResponse.next_page_token] of the previous [NotebookService.ListNotebookRuntimes][google.cloud.aiplatform.v1.NotebookService.ListNotebookRuntimes] call.

Type:

str

read_mask

Optional. Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

order_by

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported fields:

• display_name

• create_time

• update_time

Example: display_name, create_time desc.

Type:

str

class google.cloud.aiplatform_v1.types.ListNotebookRuntimesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [NotebookService.ListNotebookRuntimes][google.cloud.aiplatform.v1.NotebookService.ListNotebookRuntimes].

notebook_runtimes

List of NotebookRuntimes in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NotebookRuntime]

next_page_token

A token to retrieve next page of results. Pass to [ListNotebookRuntimesRequest.page_token][google.cloud.aiplatform.v1.ListNotebookRuntimesRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListOptimalTrialsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.ListOptimalTrials][google.cloud.aiplatform.v1.VizierService.ListOptimalTrials].

parent

Required. The name of the Study that the optimal Trial belongs to.

Type:

str

class google.cloud.aiplatform_v1.types.ListOptimalTrialsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [VizierService.ListOptimalTrials][google.cloud.aiplatform.v1.VizierService.ListOptimalTrials].

optimal_trials

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

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Trial]

class google.cloud.aiplatform_v1.types.ListPersistentResourcesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PersistentResourceService.ListPersistentResources][google.cloud.aiplatform.v1.PersistentResourceService.ListPersistentResources].

parent

Required. The resource name of the Location to list the PersistentResources from. Format: projects/{project}/locations/{location}

Type:

str

page_size

Optional. The standard list page size.

Type:

int

page_token

Optional. The standard list page token. Typically obtained via [ListPersistentResourcesResponse.next_page_token][google.cloud.aiplatform.v1.ListPersistentResourcesResponse.next_page_token] of the previous [PersistentResourceService.ListPersistentResource][] call.

Type:

str

class google.cloud.aiplatform_v1.types.ListPersistentResourcesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PersistentResourceService.ListPersistentResources][google.cloud.aiplatform.v1.PersistentResourceService.ListPersistentResources]

persistent_resources

Type:

MutableSequence[google.cloud.aiplatform_v1.types.PersistentResource]

next_page_token

A token to retrieve next page of results. Pass to [ListPersistentResourcesRequest.page_token][google.cloud.aiplatform.v1.ListPersistentResourcesRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListPipelineJobsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.ListPipelineJobs][google.cloud.aiplatform.v1.PipelineService.ListPipelineJobs].

parent

Required. The resource name of the Location to list the PipelineJobs from. Format: projects/{project}/locations/{location}

Type:

str

filter

Lists the PipelineJobs that match the filter expression. The following fields are supported:

• pipeline_name: Supports = and != comparisons.

• display_name: Supports =, != comparisons, and : wildcard.

• pipeline_job_user_id: Supports =, != comparisons, and : wildcard. for example, can check if pipeline’s display_name contains step by doing display_name:”step”

• state: Supports = and != comparisons.

• create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

• update_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

• end_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

• labels: Supports key-value equality and key presence.

• template_uri: Supports =, != comparisons, and : wildcard.

• template_metadata.version: Supports =, != comparisons, and : wildcard.

Filter expressions can be combined together using logical operators (AND & OR). For example: pipeline_name="test" AND create_time>"2020-05-18T13:30:00Z".

The syntax to define filter expression is based on https://google.aip.dev/160.

Examples:

• create_time>"2021-05-18T00:00:00Z" OR update_time>"2020-05-18T00:00:00Z" PipelineJobs created or updated after 2020-05-18 00:00:00 UTC.

• labels.env = "prod" PipelineJobs with label “env” set to “prod”.

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [ListPipelineJobsResponse.next_page_token][google.cloud.aiplatform.v1.ListPipelineJobsResponse.next_page_token] of the previous [PipelineService.ListPipelineJobs][google.cloud.aiplatform.v1.PipelineService.ListPipelineJobs] call.

Type:

str

order_by

A comma-separated list of fields to order by. The default sort order is in ascending order. Use “desc” after a field name for descending. You can have multiple order_by fields provided e.g. “create_time desc, end_time”, “end_time, start_time, update_time” For example, using “create_time desc, end_time” will order results by create time in descending order, and if there are multiple jobs having the same create time, order them by the end time in ascending order. if order_by is not specified, it will order by default order is create time in descending order. Supported fields:

• create_time

• update_time

• end_time

• start_time

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListPipelineJobsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PipelineService.ListPipelineJobs][google.cloud.aiplatform.v1.PipelineService.ListPipelineJobs]

pipeline_jobs

List of PipelineJobs in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.PipelineJob]

next_page_token

A token to retrieve the next page of results. Pass to [ListPipelineJobsRequest.page_token][google.cloud.aiplatform.v1.ListPipelineJobsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListSavedQueriesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.ListSavedQueries][google.cloud.aiplatform.v1.DatasetService.ListSavedQueries].

parent

Required. The resource name of the Dataset to list SavedQueries from. Format: projects/{project}/locations/{location}/datasets/{dataset}

Type:

str

filter

The standard list filter.

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending.

Type:

str

class google.cloud.aiplatform_v1.types.ListSavedQueriesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [DatasetService.ListSavedQueries][google.cloud.aiplatform.v1.DatasetService.ListSavedQueries].

saved_queries

A list of SavedQueries that match the specified filter in the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.SavedQuery]

next_page_token

The standard List next-page token.

Type:

str

class google.cloud.aiplatform_v1.types.ListSchedulesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ScheduleService.ListSchedules][google.cloud.aiplatform.v1.ScheduleService.ListSchedules].

parent

Required. The resource name of the Location to list the Schedules from. Format: projects/{project}/locations/{location}

Type:

str

filter

Lists the Schedules that match the filter expression. The following fields are supported:

• display_name: Supports =, != comparisons, and : wildcard.

• state: Supports = and != comparisons.

• request: Supports existence of the <request_type> check. (e.g. create_pipeline_job_request:* –> Schedule has create_pipeline_job_request).

• create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

• start_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

• end_time: Supports =, !=, <, >, <=, >= comparisons and :* existence check. Values must be in RFC 3339 format.

• next_run_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format.

Filter expressions can be combined together using logical operators (NOT, AND & OR). The syntax to define filter expression is based on https://google.aip.dev/160.

Examples:

• state="ACTIVE" AND display_name:"my_schedule_*"

• NOT display_name="my_schedule"

• create_time>"2021-05-18T00:00:00Z"

• end_time>"2021-05-18T00:00:00Z" OR NOT end_time:*

• create_pipeline_job_request:*

Type:

str

page_size

The standard list page size. Default to 100 if not specified.

Type:

int

page_token

The standard list page token. Typically obtained via [ListSchedulesResponse.next_page_token][google.cloud.aiplatform.v1.ListSchedulesResponse.next_page_token] of the previous [ScheduleService.ListSchedules][google.cloud.aiplatform.v1.ScheduleService.ListSchedules] call.

Type:

str

order_by

A comma-separated list of fields to order by. The default sort order is in ascending order. Use “desc” after a field name for descending. You can have multiple order_by fields provided.

For example, using “create_time desc, end_time” will order results by create time in descending order, and if there are multiple schedules having the same create time, order them by the end time in ascending order.

If order_by is not specified, it will order by default with create_time in descending order.

Supported fields:

• create_time

• start_time

• end_time

• next_run_time

Type:

str

class google.cloud.aiplatform_v1.types.ListSchedulesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [ScheduleService.ListSchedules][google.cloud.aiplatform.v1.ScheduleService.ListSchedules]

schedules

List of Schedules in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Schedule]

next_page_token

A token to retrieve the next page of results. Pass to [ListSchedulesRequest.page_token][google.cloud.aiplatform.v1.ListSchedulesRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListSpecialistPoolsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [SpecialistPoolService.ListSpecialistPools][google.cloud.aiplatform.v1.SpecialistPoolService.ListSpecialistPools].

parent

Required. The name of the SpecialistPool’s parent resource. Format: projects/{project}/locations/{location}

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained by [ListSpecialistPoolsResponse.next_page_token][google.cloud.aiplatform.v1.ListSpecialistPoolsResponse.next_page_token] of the previous [SpecialistPoolService.ListSpecialistPools][google.cloud.aiplatform.v1.SpecialistPoolService.ListSpecialistPools] call. Return first page if empty.

Type:

str

read_mask

Mask specifying which fields to read. FieldMask represents a set of

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListSpecialistPoolsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [SpecialistPoolService.ListSpecialistPools][google.cloud.aiplatform.v1.SpecialistPoolService.ListSpecialistPools].

specialist_pools

A list of SpecialistPools that matches the specified filter in the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.SpecialistPool]

next_page_token

The standard List next-page token.

Type:

str

class google.cloud.aiplatform_v1.types.ListStudiesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.ListStudies][google.cloud.aiplatform.v1.VizierService.ListStudies].

parent

Required. The resource name of the Location to list the Study from. Format: projects/{project}/locations/{location}

Type:

str

page_token

Optional. A page token to request the next page of results. If unspecified, there are no subsequent pages.

Type:

str

page_size

Optional. The maximum number of studies to return per “page” of results. If unspecified, service will pick an appropriate default.

Type:

int

class google.cloud.aiplatform_v1.types.ListStudiesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [VizierService.ListStudies][google.cloud.aiplatform.v1.VizierService.ListStudies].

studies

The studies associated with the project.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Study]

next_page_token

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.

Type:

str

class google.cloud.aiplatform_v1.types.ListTensorboardExperimentsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.ListTensorboardExperiments][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardExperiments].

parent

Required. The resource name of the Tensorboard to list TensorboardExperiments. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

Type:

str

filter

Lists the TensorboardExperiments that match the filter expression.

Type:

str

page_size

The maximum number of TensorboardExperiments to return. The service may return fewer than this value. If unspecified, at most 50 TensorboardExperiments are returned. The maximum value is 1000; values above 1000 are coerced to 1000.

Type:

int

page_token

A page token, received from a previous [TensorboardService.ListTensorboardExperiments][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardExperiments] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [TensorboardService.ListTensorboardExperiments][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardExperiments] must match the call that provided the page token.

Type:

str

order_by

Field to use to sort the list.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListTensorboardExperimentsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.ListTensorboardExperiments][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardExperiments].

tensorboard_experiments

The TensorboardExperiments mathching the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TensorboardExperiment]

next_page_token

A token, which can be sent as [ListTensorboardExperimentsRequest.page_token][google.cloud.aiplatform.v1.ListTensorboardExperimentsRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListTensorboardRunsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.ListTensorboardRuns][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardRuns].

parent

Required. The resource name of the TensorboardExperiment to list TensorboardRuns. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}

Type:

str

filter

Lists the TensorboardRuns that match the filter expression.

Type:

str

page_size

The maximum number of TensorboardRuns to return. The service may return fewer than this value. If unspecified, at most 50 TensorboardRuns are returned. The maximum value is 1000; values above 1000 are coerced to 1000.

Type:

int

page_token

A page token, received from a previous [TensorboardService.ListTensorboardRuns][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardRuns] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [TensorboardService.ListTensorboardRuns][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardRuns] must match the call that provided the page token.

Type:

str

order_by

Field to use to sort the list.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListTensorboardRunsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.ListTensorboardRuns][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardRuns].

tensorboard_runs

The TensorboardRuns mathching the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TensorboardRun]

next_page_token

A token, which can be sent as [ListTensorboardRunsRequest.page_token][google.cloud.aiplatform.v1.ListTensorboardRunsRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListTensorboardTimeSeriesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.ListTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardTimeSeries].

parent

Required. The resource name of the TensorboardRun to list TensorboardTimeSeries. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}

Type:

str

filter

Lists the TensorboardTimeSeries that match the filter expression.

Type:

str

page_size

The maximum number of TensorboardTimeSeries to return. The service may return fewer than this value. If unspecified, at most 50 TensorboardTimeSeries are returned. The maximum value is 1000; values above 1000 are coerced to 1000.

Type:

int

page_token

A page token, received from a previous [TensorboardService.ListTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardTimeSeries] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [TensorboardService.ListTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardTimeSeries] must match the call that provided the page token.

Type:

str

order_by

Field to use to sort the list.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListTensorboardTimeSeriesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.ListTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.ListTensorboardTimeSeries].

tensorboard_time_series

The TensorboardTimeSeries mathching the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TensorboardTimeSeries]

next_page_token

A token, which can be sent as [ListTensorboardTimeSeriesRequest.page_token][google.cloud.aiplatform.v1.ListTensorboardTimeSeriesRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListTensorboardsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.ListTensorboards][google.cloud.aiplatform.v1.TensorboardService.ListTensorboards].

parent

Required. The resource name of the Location to list Tensorboards. Format: projects/{project}/locations/{location}

Type:

str

filter

Lists the Tensorboards that match the filter expression.

Type:

str

page_size

The maximum number of Tensorboards to return. The service may return fewer than this value. If unspecified, at most 100 Tensorboards are returned. The maximum value is 100; values above 100 are coerced to 100.

Type:

int

page_token

A page token, received from a previous [TensorboardService.ListTensorboards][google.cloud.aiplatform.v1.TensorboardService.ListTensorboards] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [TensorboardService.ListTensorboards][google.cloud.aiplatform.v1.TensorboardService.ListTensorboards] must match the call that provided the page token.

Type:

str

order_by

Field to use to sort the list.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListTensorboardsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.ListTensorboards][google.cloud.aiplatform.v1.TensorboardService.ListTensorboards].

tensorboards

The Tensorboards mathching the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Tensorboard]

next_page_token

A token, which can be sent as [ListTensorboardsRequest.page_token][google.cloud.aiplatform.v1.ListTensorboardsRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.ListTrainingPipelinesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PipelineService.ListTrainingPipelines][google.cloud.aiplatform.v1.PipelineService.ListTrainingPipelines].

parent

Required. The resource name of the Location to list the TrainingPipelines from. Format: projects/{project}/locations/{location}

Type:

str

filter

The standard list filter.

Supported fields:

• display_name supports =, != comparisons, and : wildcard.

• state supports =, != comparisons.

• training_task_definition =, != comparisons, and : wildcard.

• create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format.

• labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence

Some examples of using the filter are:

• state="PIPELINE_STATE_SUCCEEDED" AND display_name:"my_pipeline_*"

• state!="PIPELINE_STATE_FAILED" OR display_name="my_pipeline"

• NOT display_name="my_pipeline"

• create_time>"2021-05-18T00:00:00Z"

• training_task_definition:"*automl_text_classification*"

Type:

str

page_size

The standard list page size.

Type:

int

page_token

The standard list page token. Typically obtained via [ListTrainingPipelinesResponse.next_page_token][google.cloud.aiplatform.v1.ListTrainingPipelinesResponse.next_page_token] of the previous [PipelineService.ListTrainingPipelines][google.cloud.aiplatform.v1.PipelineService.ListTrainingPipelines] call.

Type:

str

read_mask

Mask specifying which fields to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.ListTrainingPipelinesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PipelineService.ListTrainingPipelines][google.cloud.aiplatform.v1.PipelineService.ListTrainingPipelines]

training_pipelines

List of TrainingPipelines in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TrainingPipeline]

next_page_token

A token to retrieve the next page of results. Pass to [ListTrainingPipelinesRequest.page_token][google.cloud.aiplatform.v1.ListTrainingPipelinesRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.ListTrialsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.ListTrials][google.cloud.aiplatform.v1.VizierService.ListTrials].

parent

Required. The resource name of the Study to list the Trial from. Format: projects/{project}/locations/{location}/studies/{study}

Type:

str

page_token

Optional. A page token to request the next page of results. If unspecified, there are no subsequent pages.

Type:

str

page_size

Optional. The number of Trials to retrieve per “page” of results. If unspecified, the service will pick an appropriate default.

Type:

int

class google.cloud.aiplatform_v1.types.ListTrialsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [VizierService.ListTrials][google.cloud.aiplatform.v1.VizierService.ListTrials].

trials

The Trials associated with the Study.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Trial]

next_page_token

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.

Type:

str

class google.cloud.aiplatform_v1.types.ListTuningJobsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [GenAiTuningService.ListTuningJobs][google.cloud.aiplatform.v1.GenAiTuningService.ListTuningJobs].

parent

Required. The resource name of the Location to list the TuningJobs from. Format: projects/{project}/locations/{location}

Type:

str

filter

Optional. The standard list filter.

Type:

str

page_size

Optional. The standard list page size.

Type:

int

page_token

Optional. The standard list page token. Typically obtained via [ListTuningJobsResponse.next_page_token][google.cloud.aiplatform.v1.ListTuningJobsResponse.next_page_token] of the previous GenAiTuningService.ListTuningJob][] call.

Type:

str

class google.cloud.aiplatform_v1.types.ListTuningJobsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [GenAiTuningService.ListTuningJobs][google.cloud.aiplatform.v1.GenAiTuningService.ListTuningJobs]

tuning_jobs

List of TuningJobs in the requested page.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TuningJob]

next_page_token

A token to retrieve the next page of results. Pass to [ListTuningJobsRequest.page_token][google.cloud.aiplatform.v1.ListTuningJobsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.LogprobsResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Logprobs Result

top_candidates

Length = total number of decoding steps.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.LogprobsResult.TopCandidates]

chosen_candidates

Length = total number of decoding steps. The chosen candidates may or may not be in top_candidates.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.LogprobsResult.Candidate]

class Candidate(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Candidate for the logprobs token and score.

token

The candidate’s token string value.

This field is a member of oneof _token.

Type:

str

token_id

The candidate’s token id value.

This field is a member of oneof _token_id.

Type:

int

log_probability

The candidate’s log probability.

This field is a member of oneof _log_probability.

Type:

float

class TopCandidates(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Candidates with top log probabilities at each decoding step.

candidates

Sorted by log probability in descending order.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.LogprobsResult.Candidate]

class google.cloud.aiplatform_v1.types.LookupStudyRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.LookupStudy][google.cloud.aiplatform.v1.VizierService.LookupStudy].

parent

Required. The resource name of the Location to get the Study from. Format: projects/{project}/locations/{location}

Type:

str

display_name

Required. The user-defined display name of the Study

Type:

str

class google.cloud.aiplatform_v1.types.MachineSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Specification of a single machine.

machine_type

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][google.cloud.aiplatform.v1.DeployedModel] this field is optional, and the default value is n1-standard-2. For [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob] or as part of [WorkerPoolSpec][google.cloud.aiplatform.v1.WorkerPoolSpec] this field is required.

Type:

str

accelerator_type

Immutable. The type of accelerator(s) that may be attached to the machine as per [accelerator_count][google.cloud.aiplatform.v1.MachineSpec.accelerator_count].

Type:

google.cloud.aiplatform_v1.types.AcceleratorType

accelerator_count

The number of accelerators to attach to the machine.

Type:

int

tpu_topology

Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: “2x2x1”).

Type:

str

reservation_affinity

Optional. Immutable. Configuration controlling how this resource pool consumes reservation.

Type:

google.cloud.aiplatform_v1.types.ReservationAffinity

class google.cloud.aiplatform_v1.types.ManualBatchTuningParameters(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Manual batch tuning parameters.

batch_size

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.

Type:

int

class google.cloud.aiplatform_v1.types.Measurement(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

elapsed_duration

Output only. Time that the Trial has been running at the point of this Measurement.

Type:

google.protobuf.duration_pb2.Duration

step_count

Output only. The number of steps the machine learning model has been trained for. Must be non-negative.

Type:

int

metrics

Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Measurement.Metric]

class Metric(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A message representing a metric in the measurement.

metric_id

Output only. The ID of the Metric. The Metric should be defined in [StudySpec’s Metrics][google.cloud.aiplatform.v1.StudySpec.metrics].

Type:

str

value

Output only. The value for this metric.

Type:

float

class google.cloud.aiplatform_v1.types.MergeVersionAliasesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.MergeVersionAliases][google.cloud.aiplatform.v1.ModelService.MergeVersionAliases].

name

Required. The name of the model version to merge aliases, with a version ID explicitly included.

Example: projects/{project}/locations/{location}/models/{model}@1234

Type:

str

version_aliases

Required. The set of version aliases to merge. The alias should be at most 128 characters, and match [a-z][a-zA-Z0-9-]{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.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.MetadataSchema(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Instance of a general MetadataSchema.

name

Output only. The resource name of the MetadataSchema.

Type:

str

schema_version

The version of the MetadataSchema. The version’s format must match the following regular expression: ^[0-9]+[.][0-9]+[.][0-9]+$, which would allow to order/compare different versions. Example: 1.0.0, 1.0.1, etc.

Type:

str

schema

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

Type:

str

schema_type

The type of the MetadataSchema. This is a property that identifies which metadata types will use the MetadataSchema.

Type:

google.cloud.aiplatform_v1.types.MetadataSchema.MetadataSchemaType

create_time

Output only. Timestamp when this MetadataSchema was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

description

Description of the Metadata Schema

Type:

str

class MetadataSchemaType(value)

Bases: Enum

Describes the type of the MetadataSchema.

Values:

METADATA_SCHEMA_TYPE_UNSPECIFIED (0):

Unspecified type for the MetadataSchema.

ARTIFACT_TYPE (1):

A type indicating that the MetadataSchema will be used by Artifacts.

EXECUTION_TYPE (2):

A typee indicating that the MetadataSchema will be used by Executions.

CONTEXT_TYPE (3):

A state indicating that the MetadataSchema will be used by Contexts.

class google.cloud.aiplatform_v1.types.MetadataStore(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. The resource name of the MetadataStore instance.

Type:

str

create_time

Output only. Timestamp when this MetadataStore was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this MetadataStore was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

description

Description of the MetadataStore.

Type:

str

state

Output only. State information of the MetadataStore.

Type:

google.cloud.aiplatform_v1.types.MetadataStore.MetadataStoreState

dataplex_config

Optional. Dataplex integration settings.

Type:

google.cloud.aiplatform_v1.types.MetadataStore.DataplexConfig

class DataplexConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents Dataplex integration settings.

enabled_pipelines_lineage

Optional. Whether or not Data Lineage synchronization is enabled for Vertex Pipelines.

Type:

bool

class MetadataStoreState(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents state information for a MetadataStore.

disk_utilization_bytes

The disk utilization of the MetadataStore in bytes.

Type:

int

class google.cloud.aiplatform_v1.types.MetricxInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for MetricX metric.

metric_spec

Required. Spec for Metricx metric.

Type:

google.cloud.aiplatform_v1.types.MetricxSpec

instance

Required. Metricx instance.

Type:

google.cloud.aiplatform_v1.types.MetricxInstance

class google.cloud.aiplatform_v1.types.MetricxInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for MetricX instance - The fields used for evaluation are dependent on the MetricX version.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Optional. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

source

Optional. Source text in original language.

This field is a member of oneof _source.

Type:

str

class google.cloud.aiplatform_v1.types.MetricxResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for MetricX result - calculates the MetricX score for the given instance using the version specified in the spec.

score

Output only. MetricX score. Range depends on version.

This field is a member of oneof _score.

Type:

float

class google.cloud.aiplatform_v1.types.MetricxSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for MetricX metric.

version

Required. Which version to use for evaluation.

This field is a member of oneof _version.

Type:

google.cloud.aiplatform_v1.types.MetricxSpec.MetricxVersion

source_language

Optional. Source language in BCP-47 format.

Type:

str

target_language

Optional. Target language in BCP-47 format. Covers both prediction and reference.

Type:

str

class MetricxVersion(value)

Bases: Enum

MetricX Version options.

Values:

METRICX_VERSION_UNSPECIFIED (0):

MetricX version unspecified.

METRICX_24_REF (1):

MetricX 2024 (2.6) for translation + reference (reference-based).

METRICX_24_SRC (2):

MetricX 2024 (2.6) for translation + source (QE).

METRICX_24_SRC_REF (3):

MetricX 2024 (2.6) for translation + source + reference (source-reference-combined).

class google.cloud.aiplatform_v1.types.MigratableResource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

ml_engine_model_version

Output only. Represents one Version in ml.googleapis.com.

This field is a member of oneof resource.

Type:

google.cloud.aiplatform_v1.types.MigratableResource.MlEngineModelVersion

automl_model

Output only. Represents one Model in automl.googleapis.com.

This field is a member of oneof resource.

Type:

google.cloud.aiplatform_v1.types.MigratableResource.AutomlModel

automl_dataset

Output only. Represents one Dataset in automl.googleapis.com.

This field is a member of oneof resource.

Type:

google.cloud.aiplatform_v1.types.MigratableResource.AutomlDataset

data_labeling_dataset

Output only. Represents one Dataset in datalabeling.googleapis.com.

This field is a member of oneof resource.

Type:

google.cloud.aiplatform_v1.types.MigratableResource.DataLabelingDataset

last_migrate_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

last_update_time

Output only. Timestamp when this MigratableResource was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

class AutomlDataset(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents one Dataset in automl.googleapis.com.

dataset

Full resource name of automl Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}.

Type:

str

dataset_display_name

The Dataset’s display name in automl.googleapis.com.

Type:

str

class AutomlModel(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents one Model in automl.googleapis.com.

model

Full resource name of automl Model. Format: projects/{project}/locations/{location}/models/{model}.

Type:

str

model_display_name

The Model’s display name in automl.googleapis.com.

Type:

str

class DataLabelingDataset(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents one Dataset in datalabeling.googleapis.com.

dataset

Full resource name of data labeling Dataset. Format: projects/{project}/datasets/{dataset}.

Type:

str

dataset_display_name

The Dataset’s display name in datalabeling.googleapis.com.

Type:

str

data_labeling_annotated_datasets

The migratable AnnotatedDataset in datalabeling.googleapis.com belongs to the data labeling Dataset.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.MigratableResource.DataLabelingDataset.DataLabelingAnnotatedDataset]

class DataLabelingAnnotatedDataset(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents one AnnotatedDataset in datalabeling.googleapis.com.

annotated_dataset

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

Type:

str

annotated_dataset_display_name

The AnnotatedDataset’s display name in datalabeling.googleapis.com.

Type:

str

class MlEngineModelVersion(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents one model Version in ml.googleapis.com.

endpoint

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

Type:

str

version

Full resource name of ml engine model Version. Format: projects/{project}/models/{model}/versions/{version}.

Type:

str

class google.cloud.aiplatform_v1.types.MigrateResourceRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

migrate_ml_engine_model_version_config

Config for migrating Version in ml.googleapis.com to Vertex AI’s Model.

This field is a member of oneof request.

Type:

google.cloud.aiplatform_v1.types.MigrateResourceRequest.MigrateMlEngineModelVersionConfig

migrate_automl_model_config

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

This field is a member of oneof request.

Type:

google.cloud.aiplatform_v1.types.MigrateResourceRequest.MigrateAutomlModelConfig

migrate_automl_dataset_config

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

This field is a member of oneof request.

Type:

google.cloud.aiplatform_v1.types.MigrateResourceRequest.MigrateAutomlDatasetConfig

migrate_data_labeling_dataset_config

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

This field is a member of oneof request.

Type:

google.cloud.aiplatform_v1.types.MigrateResourceRequest.MigrateDataLabelingDatasetConfig

class MigrateAutomlDatasetConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

dataset

Required. Full resource name of automl Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}.

Type:

str

dataset_display_name

Required. Display name of the Dataset in Vertex AI. System will pick a display name if unspecified.

Type:

str

class MigrateAutomlModelConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

model

Required. Full resource name of automl Model. Format: projects/{project}/locations/{location}/models/{model}.

Type:

str

model_display_name

Optional. Display name of the model in Vertex AI. System will pick a display name if unspecified.

Type:

str

class MigrateDataLabelingDatasetConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

dataset

Required. Full resource name of data labeling Dataset. Format: projects/{project}/datasets/{dataset}.

Type:

str

dataset_display_name

Optional. Display name of the Dataset in Vertex AI. System will pick a display name if unspecified.

Type:

str

migrate_data_labeling_annotated_dataset_configs

Optional. Configs for migrating AnnotatedDataset in datalabeling.googleapis.com to Vertex AI’s SavedQuery. The specified AnnotatedDatasets have to belong to the datalabeling Dataset.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.MigrateResourceRequest.MigrateDataLabelingDatasetConfig.MigrateDataLabelingAnnotatedDatasetConfig]

class MigrateDataLabelingAnnotatedDatasetConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

annotated_dataset

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

Type:

str

class MigrateMlEngineModelVersionConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

endpoint

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

Type:

str

model_version

Required. Full resource name of ml engine model version. Format: projects/{project}/models/{model}/versions/{version}.

Type:

str

model_display_name

Required. Display name of the model in Vertex AI. System will pick a display name if unspecified.

Type:

str

class google.cloud.aiplatform_v1.types.MigrateResourceResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Describes a successfully migrated resource.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

dataset

Migrated Dataset’s resource name.

This field is a member of oneof migrated_resource.

Type:

str

model

Migrated Model’s resource name.

This field is a member of oneof migrated_resource.

Type:

str

migratable_resource

Before migration, the identifier in ml.googleapis.com, automl.googleapis.com or datalabeling.googleapis.com.

Type:

google.cloud.aiplatform_v1.types.MigratableResource

class google.cloud.aiplatform_v1.types.Model(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A trained machine learning Model.

name

The resource name of the Model.

Type:

str

version_id

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.

Type:

str

version_aliases

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][a-zA-Z0-9-]{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.

Type:

MutableSequence[str]

version_create_time

Output only. Timestamp when this version was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

version_update_time

Output only. Timestamp when this version was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

display_name

Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

description

The description of the Model.

Type:

str

version_description

The description of this version.

Type:

str

predict_schemata

The schemata that describe formats of the Model’s predictions and explanations as given and returned via [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] and [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].

Type:

google.cloud.aiplatform_v1.types.PredictSchemata

metadata_schema_uri

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.

Type:

str

metadata

Immutable. An additional information about the Model; the schema of the metadata can be found in [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri]. Unset if the Model does not have any additional information.

Type:

google.protobuf.struct_pb2.Value

supported_export_formats

Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Model.ExportFormat]

training_pipeline

Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.

Type:

str

pipeline_job

Optional. This field is populated if the model is produced by a pipeline job.

Type:

str

container_spec

Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel], and all binaries it contains are copied and stored internally by Vertex AI. Not required for AutoML Models.

Type:

google.cloud.aiplatform_v1.types.ModelContainerSpec

artifact_uri

Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.

Type:

str

supported_deployment_resources_types

Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the [Endpoint.deployed_models][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.Endpoint] and does not support online predictions ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] or [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]). Such a Model can serve predictions by using a [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it has at least one entry each in [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats] and [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Model.DeploymentResourcesType]

supported_input_storage_formats

Output only. The formats this Model supports in [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config]. If [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

• 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][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

• tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

• tf-record-gzip Similar to tf-record, but the file is gzipped. Uses [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

• bigquery Each instance is a single row in BigQuery. Uses [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

• file-list Each line of the file is the location of an instance to process, uses gcs_source field of the [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig] object.

If this Model doesn’t support any of these formats it means it cannot be used with a [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob]. However, if it has [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types], it could serve online predictions by using [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] or [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].

Type:

MutableSequence[str]

supported_output_storage_formats

Output only. The formats this Model supports in [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config]. If both [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] and [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

• 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][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

• bigquery Each prediction is a single row in a BigQuery table, uses [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination] .

If this Model doesn’t support any of these formats it means it cannot be used with a [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob]. However, if it has [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types], it could serve online predictions by using [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict] or [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].

Type:

MutableSequence[str]

create_time

Output only. Timestamp when this Model was uploaded into Vertex AI.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this Model was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

deployed_models

Output only. The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DeployedModelRef]

explanation_spec

The default explanation specification for this Model.

The Model can be used for [requesting explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if it is populated. The Model can be used for [batch explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation] if it is populated.

All fields of the explanation_spec can be overridden by [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] of [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model], or [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec] of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

If the default explanation specification is not set for this Model, this Model can still be used for [requesting explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by setting [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] of [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model] and for [batch explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation] by setting [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec] of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

Type:

google.cloud.aiplatform_v1.types.ExplanationSpec

etag

Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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.

Type:

MutableMapping[str, str]

data_stats

Stats of data used for training or evaluating the Model.

Only populated when the Model is trained by a TrainingPipeline with [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].

Type:

google.cloud.aiplatform_v1.types.Model.DataStats

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

model_source_info

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.

Type:

google.cloud.aiplatform_v1.types.ModelSourceInfo

original_model_info

Output only. If this Model is a copy of another Model, this contains info about the original.

Type:

google.cloud.aiplatform_v1.types.Model.OriginalModelInfo

metadata_artifact

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}.

Type:

str

base_model_source

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

Type:

google.cloud.aiplatform_v1.types.Model.BaseModelSource

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class BaseModelSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

model_garden_source

Source information of Model Garden models.

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.ModelGardenSource

genie_source

Information about the base model of Genie models.

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.GenieSource

class DataStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Stats of data used for train or evaluate the Model.

training_data_items_count

Number of DataItems that were used for training this Model.

Type:

int

validation_data_items_count

Number of DataItems that were used for validating this Model during training.

Type:

int

test_data_items_count

Number of DataItems that were used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test DataItems used by the first evaluation. If the Model is not evaluated, the number is 0.

Type:

int

training_annotations_count

Number of Annotations that are used for training this Model.

Type:

int

validation_annotations_count

Number of Annotations that are used for validating this Model during training.

Type:

int

test_annotations_count

Number of Annotations that are used for evaluating this Model. If the Model is evaluated multiple times, this will be the number of test Annotations used by the first evaluation. If the Model is not evaluated, the number is 0.

Type:

int

class DeploymentResourcesType(value)

Bases: Enum

Identifies a type of Model’s prediction resources.

Values:

DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED (0):

Should not be used.

DEDICATED_RESOURCES (1):

Resources that are dedicated to the [DeployedModel][google.cloud.aiplatform.v1.DeployedModel], and that need a higher degree of manual configuration.

AUTOMATIC_RESOURCES (2):

Resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.

SHARED_RESOURCES (3):

Resources that can be shared by multiple [DeployedModels][google.cloud.aiplatform.v1.DeployedModel]. A pre-configured [DeploymentResourcePool][google.cloud.aiplatform.v1.DeploymentResourcePool] is required.

class ExportFormat(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

id

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.

Type:

str

exportable_contents

Output only. The content of this Model that may be exported.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Model.ExportFormat.ExportableContent]

class ExportableContent(value)

Bases: Enum

The Model content that can be exported.

Values:

EXPORTABLE_CONTENT_UNSPECIFIED (0):

Should not be used.

ARTIFACT (1):

Model artifact and any of its supported files. Will be exported to the location specified by the artifactDestination field of the [ExportModelRequest.output_config][google.cloud.aiplatform.v1.ExportModelRequest.output_config] object.

IMAGE (2):

The container image that is to be used when deploying this Model. Will be exported to the location specified by the imageDestination field of the [ExportModelRequest.output_config][google.cloud.aiplatform.v1.ExportModelRequest.output_config] object.

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class OriginalModelInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

model

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}

Type:

str

class google.cloud.aiplatform_v1.types.ModelContainerSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

image_uri

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][google.cloud.aiplatform.v1.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.

Type:

str

command

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][google.cloud.aiplatform.v1.ModelContainerSpec.args] field or the container’s `CMD <https://docs.docker.com/engine/reference/builder/#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 <https://docs.docker.com/engine/reference/builder/#understand-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 <https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#notes>`__.

In this field, you can reference environment variables set by Vertex AI and environment variables set in the [env][google.cloud.aiplatform.v1.ModelContainerSpec.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.

Type:

MutableSequence[str]

args

Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container’s `CMD <https://docs.docker.com/engine/reference/builder/#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][google.cloud.aiplatform.v1.ModelContainerSpec.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 <https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#notes>`__.

If you don’t specify this field and don’t specify the command field, then the container’s `ENTRYPOINT <https://docs.docker.com/engine/reference/builder/#cmd>`__ and CMD determine what runs based on their default behavior. See the Docker documentation about how ``CMD` and ENTRYPOINT interact <https://docs.docker.com/engine/reference/builder/#understand-how-cmd-and-entrypoint-interact>`__.

In this field, you can reference environment variables set by Vertex AI and environment variables set in the [env][google.cloud.aiplatform.v1.ModelContainerSpec.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.

Type:

MutableSequence[str]

env

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][google.cloud.aiplatform.v1.ModelContainerSpec.command] and [args][google.cloud.aiplatform.v1.ModelContainerSpec.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:

[
  {
    "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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.EnvVar]

ports

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:

[
  {
    "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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Port]

predict_route

Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using [projects.locations.endpoints.predict][google.cloud.aiplatform.v1.PredictionService.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][google.cloud.aiplatform.v1.ModelContainerSpec.ports] field.

If you don’t specify this field, it defaults to the following value when you [deploy this Model to an Endpoint][google.cloud.aiplatform.v1.EndpointService.DeployModel]: /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 <https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables>`__.)

• DEPLOYED_MODEL: [DeployedModel.id][google.cloud.aiplatform.v1.DeployedModel.id] of the DeployedModel. (Vertex AI makes this value available to your container code as the `AIP_DEPLOYED_MODEL_ID environment variable <https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables>`__.)

Type:

str

health_route

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][google.cloud.aiplatform.v1.ModelContainerSpec.ports] field.

If you don’t specify this field, it defaults to the following value when you [deploy this Model to an Endpoint][google.cloud.aiplatform.v1.EndpointService.DeployModel]: /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 <https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables>`__.)

• DEPLOYED_MODEL: [DeployedModel.id][google.cloud.aiplatform.v1.DeployedModel.id] of the DeployedModel. (Vertex AI makes this value available to your container code as the `AIP_DEPLOYED_MODEL_ID environment variable <https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#aip-variables>`__.)

Type:

str

grpc_ports

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Port]

deployment_timeout

Immutable. Deployment timeout. Limit for deployment timeout is 2 hours.

Type:

google.protobuf.duration_pb2.Duration

shared_memory_size_mb

Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes.

Type:

int

startup_probe

Immutable. Specification for Kubernetes startup probe.

Type:

google.cloud.aiplatform_v1.types.Probe

health_probe

Immutable. Specification for Kubernetes readiness probe.

Type:

google.cloud.aiplatform_v1.types.Probe

class google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringBigQueryTable(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

log_source

The source of log.

Type:

google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringBigQueryTable.LogSource

log_type

The type of log.

Type:

google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringBigQueryTable.LogType

bigquery_table_path

The created BigQuery table to store logs. Customer could do their own query & analysis. Format: bq://<project_id>.model_deployment_monitoring_<endpoint_id>.<tolower(log_source)>_<tolower(log_type)>

Type:

str

request_response_logging_schema_version

Output only. The schema version of the request/response logging BigQuery table. Default to v1 if unset.

Type:

str

class LogSource(value)

Bases: Enum

Indicates where does the log come from.

Values:

LOG_SOURCE_UNSPECIFIED (0):

Unspecified source.

TRAINING (1):

Logs coming from Training dataset.

SERVING (2):

Logs coming from Serving traffic.

class LogType(value)

Bases: Enum

Indicates what type of traffic does the log belong to.

Values:

LOG_TYPE_UNSPECIFIED (0):

Unspecified type.

PREDICT (1):

Predict logs.

EXPLAIN (2):

Explain logs.

class google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

name

Output only. Resource name of a ModelDeploymentMonitoringJob.

Type:

str

display_name

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.

Type:

str

endpoint

Required. Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

state

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’.

Type:

google.cloud.aiplatform_v1.types.JobState

schedule_state

Output only. Schedule state when the monitoring job is in Running state.

Type:

google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringJob.MonitoringScheduleState

latest_monitoring_pipeline_metadata

Output only. Latest triggered monitoring pipeline metadata.

Type:

google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata

model_deployment_monitoring_objective_configs

Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringObjectiveConfig]

model_deployment_monitoring_schedule_config

Required. Schedule config for running the monitoring job.

Type:

google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringScheduleConfig

logging_sampling_strategy

Required. Sample Strategy for logging.

Type:

google.cloud.aiplatform_v1.types.SamplingStrategy

model_monitoring_alert_config

Alert config for model monitoring.

Type:

google.cloud.aiplatform_v1.types.ModelMonitoringAlertConfig

predict_instance_schema_uri

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.

Type:

str

sample_predict_instance

Sample Predict instance, same format as [PredictRequest.instances][google.cloud.aiplatform.v1.PredictRequest.instances], this can be set as a replacement of [ModelDeploymentMonitoringJob.predict_instance_schema_uri][google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.predict_instance_schema_uri]. If not set, we will generate predict schema from collected predict requests.

Type:

google.protobuf.struct_pb2.Value

analysis_instance_schema_uri

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][google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.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.

Type:

str

bigquery_tables

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

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringBigQueryTable]

log_ttl

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.

Type:

google.protobuf.duration_pb2.Duration

labels

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.

Type:

MutableMapping[str, str]

create_time

Output only. Timestamp when this ModelDeploymentMonitoringJob was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

Type:

google.protobuf.timestamp_pb2.Timestamp

next_schedule_time

Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.

Type:

google.protobuf.timestamp_pb2.Timestamp

stats_anomalies_base_directory

Stats anomalies base folder path.

Type:

google.cloud.aiplatform_v1.types.GcsDestination

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

enable_monitoring_pipeline_logs

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.

Type:

bool

error

Output only. Only populated when the job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

Type:

google.rpc.status_pb2.Status

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class LatestMonitoringPipelineMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

All metadata of most recent monitoring pipelines.

run_time

The time that most recent monitoring pipelines that is related to this run.

Type:

google.protobuf.timestamp_pb2.Timestamp

status

The status of the most recent monitoring pipeline.

Type:

google.rpc.status_pb2.Status

class MonitoringScheduleState(value)

Bases: Enum

The state to Specify the monitoring pipeline.

Values:

MONITORING_SCHEDULE_STATE_UNSPECIFIED (0):

Unspecified state.

PENDING (1):

The pipeline is picked up and wait to run.

OFFLINE (2):

The pipeline is offline and will be scheduled for next run.

RUNNING (3):

The pipeline is running.

class google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringObjectiveConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

ModelDeploymentMonitoringObjectiveConfig contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig.

deployed_model_id

The DeployedModel ID of the objective config.

Type:

str

objective_config

The objective config of for the modelmonitoring job of this deployed model.

Type:

google.cloud.aiplatform_v1.types.ModelMonitoringObjectiveConfig

class google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringObjectiveType(value)

Bases: Enum

The Model Monitoring Objective types.

Values:

MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED (0):

Default value, should not be set.

RAW_FEATURE_SKEW (1):

Raw feature values’ stats to detect skew between Training-Prediction datasets.

RAW_FEATURE_DRIFT (2):

Raw feature values’ stats to detect drift between Serving-Prediction datasets.

FEATURE_ATTRIBUTION_SKEW (3):

Feature attribution scores to detect skew between Training-Prediction datasets.

FEATURE_ATTRIBUTION_DRIFT (4):

Feature attribution scores to detect skew between Prediction datasets collected within different time windows.

class google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringScheduleConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The config for scheduling monitoring job.

monitor_interval

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.

Type:

google.protobuf.duration_pb2.Duration

monitor_window

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][google.cloud.aiplatform.v1.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.

Type:

google.protobuf.duration_pb2.Duration

class google.cloud.aiplatform_v1.types.ModelEvaluation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. The resource name of the ModelEvaluation.

Type:

str

display_name

The display name of the ModelEvaluation.

Type:

str

metrics_schema_uri

Points to a YAML file stored on Google Cloud Storage describing the [metrics][google.cloud.aiplatform.v1.ModelEvaluation.metrics] of this ModelEvaluation. The schema is defined as an OpenAPI 3.0.2 Schema Object.

Type:

str

metrics

Evaluation metrics of the Model. The schema of the metrics is stored in [metrics_schema_uri][google.cloud.aiplatform.v1.ModelEvaluation.metrics_schema_uri]

Type:

google.protobuf.struct_pb2.Value

create_time

Output only. Timestamp when this ModelEvaluation was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

slice_dimensions

All possible [dimensions][google.cloud.aiplatform.v1.ModelEvaluationSlice.Slice.dimension] of ModelEvaluationSlices. The dimensions can be used as the filter of the [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.ListModelEvaluationSlices] request, in the form of slice.dimension = <dimension>.

Type:

MutableSequence[str]

data_item_schema_uri

Points to a YAML file stored on Google Cloud Storage describing [EvaluatedDataItemView.data_item_payload][] and [EvaluatedAnnotation.data_item_payload][google.cloud.aiplatform.v1.EvaluatedAnnotation.data_item_payload]. The schema is defined as an OpenAPI 3.0.2 Schema Object.

This field is not populated if there are neither EvaluatedDataItemViews nor EvaluatedAnnotations under this ModelEvaluation.

Type:

str

annotation_schema_uri

Points to a YAML file stored on Google Cloud Storage describing [EvaluatedDataItemView.predictions][], [EvaluatedDataItemView.ground_truths][], [EvaluatedAnnotation.predictions][google.cloud.aiplatform.v1.EvaluatedAnnotation.predictions], and [EvaluatedAnnotation.ground_truths][google.cloud.aiplatform.v1.EvaluatedAnnotation.ground_truths]. The schema is defined as an OpenAPI 3.0.2 Schema Object.

This field is not populated if there are neither EvaluatedDataItemViews nor EvaluatedAnnotations under this ModelEvaluation.

Type:

str

model_explanation

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.

Type:

google.cloud.aiplatform_v1.types.ModelExplanation

explanation_specs

Describes the values of [ExplanationSpec][google.cloud.aiplatform.v1.ExplanationSpec] that are used for explaining the predicted values on the evaluated data.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ModelEvaluation.ModelEvaluationExplanationSpec]

metadata

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”.

Type:

google.protobuf.struct_pb2.Value

class ModelEvaluationExplanationSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

explanation_type

Explanation type.

For AutoML Image Classification models, possible values are:

• image-integrated-gradients

• image-xrai

Type:

str

explanation_spec

Explanation spec details.

Type:

google.cloud.aiplatform_v1.types.ExplanationSpec

class google.cloud.aiplatform_v1.types.ModelEvaluationSlice(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. The resource name of the ModelEvaluationSlice.

Type:

str

slice_

Output only. The slice of the test data that is used to evaluate the Model.

Type:

google.cloud.aiplatform_v1.types.ModelEvaluationSlice.Slice

metrics_schema_uri

Output only. Points to a YAML file stored on Google Cloud Storage describing the [metrics][google.cloud.aiplatform.v1.ModelEvaluationSlice.metrics] of this ModelEvaluationSlice. The schema is defined as an OpenAPI 3.0.2 Schema Object.

Type:

str

metrics

Output only. Sliced evaluation metrics of the Model. The schema of the metrics is stored in [metrics_schema_uri][google.cloud.aiplatform.v1.ModelEvaluationSlice.metrics_schema_uri]

Type:

google.protobuf.struct_pb2.Value

create_time

Output only. Timestamp when this ModelEvaluationSlice was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

model_explanation

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.

Type:

google.cloud.aiplatform_v1.types.ModelExplanation

class Slice(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Definition of a slice.

dimension

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][google.cloud.aiplatform.v1.AnnotationSpec.display_name] equals to [value][google.cloud.aiplatform.v1.ModelEvaluationSlice.Slice.value].

• slice: This slice is a user customized slice defined by its SliceSpec.

Type:

str

value

Output only. The value of the dimension in this slice.

Type:

str

slice_spec

Output only. Specification for how the data was sliced.

Type:

google.cloud.aiplatform_v1.types.ModelEvaluationSlice.Slice.SliceSpec

class SliceSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Specification for how the data should be sliced.

configs

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.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.ModelEvaluationSlice.Slice.SliceSpec.SliceConfig]

class ConfigsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class Range(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

low

Inclusive low value for the range.

Type:

float

high

Exclusive high value for the range.

Type:

float

class SliceConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

value

A unique specific value for a given feature. Example: { "value": { "string_value": "12345" } }

This field is a member of oneof kind.

Type:

google.cloud.aiplatform_v1.types.ModelEvaluationSlice.Slice.SliceSpec.Value

range_

A range of values for a numerical feature. Example: {"range":{"low":10000.0,"high":50000.0}} will capture 12345 and 23334 in the slice.

This field is a member of oneof kind.

Type:

google.cloud.aiplatform_v1.types.ModelEvaluationSlice.Slice.SliceSpec.Range

all_values

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}}

This field is a member of oneof kind.

Type:

google.protobuf.wrappers_pb2.BoolValue

class Value(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Single value that supports strings and floats.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

string_value

String type.

This field is a member of oneof kind.

Type:

str

float_value

Float type.

This field is a member of oneof kind.

Type:

float

class google.cloud.aiplatform_v1.types.ModelExplanation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

mean_attributions

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][google.cloud.aiplatform.v1.Attribution.output_index] can be used to identify which output this attribution is explaining.

The [baselineOutputValue][google.cloud.aiplatform.v1.Attribution.baseline_output_value], [instanceOutputValue][google.cloud.aiplatform.v1.Attribution.instance_output_value] and [featureAttributions][google.cloud.aiplatform.v1.Attribution.feature_attributions] 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][google.cloud.aiplatform.v1.Attribution.approximation_error] is not populated.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Attribution]

class google.cloud.aiplatform_v1.types.ModelGardenSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

public_model_name

Required. The model garden source model resource name.

Type:

str

class google.cloud.aiplatform_v1.types.ModelMonitoringAlertConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The alert config for model monitoring.

email_alert_config

Email alert config.

This field is a member of oneof alert.

Type:

google.cloud.aiplatform_v1.types.ModelMonitoringAlertConfig.EmailAlertConfig

enable_logging

Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto [ModelMonitoringStatsAnomalies][google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies]. This can be further synced to Pub/Sub or any other services supported by Cloud Logging.

Type:

bool

notification_channels

Resource names of the NotificationChannels to send alert. Must be of the format projects/<project_id_or_number>/notificationChannels/<channel_id>

Type:

MutableSequence[str]

class EmailAlertConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The config for email alert.

user_emails

The email addresses to send the alert.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.ModelMonitoringObjectiveConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

training_dataset

Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.

Type:

google.cloud.aiplatform_v1.types.ModelMonitoringObjectiveConfig.TrainingDataset

training_prediction_skew_detection_config

The config for skew between training data and prediction data.

Type:

google.cloud.aiplatform_v1.types.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig

prediction_drift_detection_config

The config for drift of prediction data.

Type:

google.cloud.aiplatform_v1.types.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig

explanation_config

The config for integrating with Vertex Explainable AI.

Type:

google.cloud.aiplatform_v1.types.ModelMonitoringObjectiveConfig.ExplanationConfig

class ExplanationConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

enable_feature_attributes

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.

Type:

bool

explanation_baseline

Predictions generated by the BatchPredictionJob using baseline dataset.

Type:

google.cloud.aiplatform_v1.types.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline

class ExplanationBaseline(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Output from [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob] for Model Monitoring baseline dataset, which can be used to generate baseline attribution scores.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

gcs

Cloud Storage location for BatchExplain output.

This field is a member of oneof destination.

Type:

google.cloud.aiplatform_v1.types.GcsDestination

bigquery

BigQuery location for BatchExplain output.

This field is a member of oneof destination.

Type:

google.cloud.aiplatform_v1.types.BigQueryDestination

prediction_format

The storage format of the predictions generated BatchPrediction job.

Type:

google.cloud.aiplatform_v1.types.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.PredictionFormat

class PredictionFormat(value)

Bases: Enum

The storage format of the predictions generated BatchPrediction job.

Values:

PREDICTION_FORMAT_UNSPECIFIED (0):

Should not be set.

JSONL (2):

Predictions are in JSONL files.

BIGQUERY (3):

Predictions are in BigQuery.

class PredictionDriftDetectionConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The config for Prediction data drift detection.

drift_thresholds

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.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.ThresholdConfig]

attribution_score_drift_thresholds

Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.ThresholdConfig]

default_drift_threshold

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.

Type:

google.cloud.aiplatform_v1.types.ThresholdConfig

class AttributionScoreDriftThresholdsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class DriftThresholdsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class TrainingDataset(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Training Dataset information.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

dataset

The resource name of the Dataset used to train this Model.

This field is a member of oneof data_source.

Type:

str

gcs_source

The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.

This field is a member of oneof data_source.

Type:

google.cloud.aiplatform_v1.types.GcsSource

bigquery_source

The BigQuery table of the unmanaged Dataset used to train this Model.

This field is a member of oneof data_source.

Type:

google.cloud.aiplatform_v1.types.BigQuerySource

data_format

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.

Type:

str

target_field

The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

Type:

str

logging_sampling_strategy

Strategy to sample data from Training Dataset. If not set, we process the whole dataset.

Type:

google.cloud.aiplatform_v1.types.SamplingStrategy

class TrainingPredictionSkewDetectionConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

skew_thresholds

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.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.ThresholdConfig]

attribution_score_skew_thresholds

Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.ThresholdConfig]

default_skew_threshold

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.

Type:

google.cloud.aiplatform_v1.types.ThresholdConfig

class AttributionScoreSkewThresholdsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class SkewThresholdsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.ModelMonitoringStatsAnomalies(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Statistics and anomalies generated by Model Monitoring.

objective

Model Monitoring Objective those stats and anomalies belonging to.

Type:

google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringObjectiveType

deployed_model_id

Deployed Model ID.

Type:

str

anomaly_count

Number of anomalies within all stats.

Type:

int

feature_stats

A list of historical Stats and Anomalies generated for all Features.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies]

class FeatureHistoricStatsAnomalies(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Historical Stats (and Anomalies) for a specific Feature.

feature_display_name

Display Name of the Feature.

Type:

str

threshold

Threshold for anomaly detection.

Type:

google.cloud.aiplatform_v1.types.ThresholdConfig

training_stats

Stats calculated for the Training Dataset.

Type:

google.cloud.aiplatform_v1.types.FeatureStatsAnomaly

prediction_stats

A list of historical stats generated by different time window’s Prediction Dataset.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FeatureStatsAnomaly]

class google.cloud.aiplatform_v1.types.ModelSourceInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Detail description of the source information of the model.

source_type

Type of the model source.

Type:

google.cloud.aiplatform_v1.types.ModelSourceInfo.ModelSourceType

copy

If this Model is copy of another Model. If true then [source_type][google.cloud.aiplatform.v1.ModelSourceInfo.source_type] pertains to the original.

Type:

bool

class ModelSourceType(value)

Bases: Enum

Source of the model. Different from objective field, this ModelSourceType enum indicates the source from which the model was accessed or obtained, whereas the objective indicates the overall aim or function of this model.

Values:

MODEL_SOURCE_TYPE_UNSPECIFIED (0):

Should not be used.

AUTOML (1):

The Model is uploaded by automl training pipeline.

CUSTOM (2):

The Model is uploaded by user or custom training pipeline.

BQML (3):

The Model is registered and sync’ed from BigQuery ML.

MODEL_GARDEN (4):

The Model is saved or tuned from Model Garden.

GENIE (5):

The Model is saved or tuned from Genie.

CUSTOM_TEXT_EMBEDDING (6):

The Model is uploaded by text embedding finetuning pipeline.

MARKETPLACE (7):

The Model is saved or tuned from Marketplace.

class google.cloud.aiplatform_v1.types.MutateDeployedIndexOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [IndexEndpointService.MutateDeployedIndex][google.cloud.aiplatform.v1.IndexEndpointService.MutateDeployedIndex].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

deployed_index_id

The unique index id specified by user

Type:

str

class google.cloud.aiplatform_v1.types.MutateDeployedIndexRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexEndpointService.MutateDeployedIndex][google.cloud.aiplatform.v1.IndexEndpointService.MutateDeployedIndex].

index_endpoint

Required. The name of the IndexEndpoint resource into which to deploy an Index. Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

Type:

str

deployed_index

Required. The DeployedIndex to be updated within the IndexEndpoint. Currently, the updatable fields are [DeployedIndex.automatic_resources][google.cloud.aiplatform.v1.DeployedIndex.automatic_resources] and [DeployedIndex.dedicated_resources][google.cloud.aiplatform.v1.DeployedIndex.dedicated_resources]

Type:

google.cloud.aiplatform_v1.types.DeployedIndex

class google.cloud.aiplatform_v1.types.MutateDeployedIndexResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [IndexEndpointService.MutateDeployedIndex][google.cloud.aiplatform.v1.IndexEndpointService.MutateDeployedIndex].

deployed_index

The DeployedIndex that had been updated in the IndexEndpoint.

Type:

google.cloud.aiplatform_v1.types.DeployedIndex

class google.cloud.aiplatform_v1.types.MutateDeployedModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [EndpointService.MutateDeployedModel][google.cloud.aiplatform.v1.EndpointService.MutateDeployedModel].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.MutateDeployedModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [EndpointService.MutateDeployedModel][google.cloud.aiplatform.v1.EndpointService.MutateDeployedModel].

endpoint

Required. The name of the Endpoint resource into which to mutate a DeployedModel. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

deployed_model

Required. The DeployedModel to be mutated within the Endpoint. Only the following fields can be mutated:

• min_replica_count in either [DedicatedResources][google.cloud.aiplatform.v1.DedicatedResources] or [AutomaticResources][google.cloud.aiplatform.v1.AutomaticResources]

• max_replica_count in either [DedicatedResources][google.cloud.aiplatform.v1.DedicatedResources] or [AutomaticResources][google.cloud.aiplatform.v1.AutomaticResources]

• [autoscaling_metric_specs][google.cloud.aiplatform.v1.DedicatedResources.autoscaling_metric_specs]

• disable_container_logging (v1 only)

• enable_container_logging (v1beta1 only)

Type:

google.cloud.aiplatform_v1.types.DeployedModel

update_mask

Required. The update mask applies to the resource. See [google.protobuf.FieldMask][google.protobuf.FieldMask].

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.MutateDeployedModelResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [EndpointService.MutateDeployedModel][google.cloud.aiplatform.v1.EndpointService.MutateDeployedModel].

deployed_model

The DeployedModel that’s being mutated.

Type:

google.cloud.aiplatform_v1.types.DeployedModel

class google.cloud.aiplatform_v1.types.NasJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents a Neural Architecture Search (NAS) job.

name

Output only. Resource name of the NasJob.

Type:

str

display_name

Required. The display name of the NasJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

nas_job_spec

Required. The specification of a NasJob.

Type:

google.cloud.aiplatform_v1.types.NasJobSpec

nas_job_output

Output only. Output of the NasJob.

Type:

google.cloud.aiplatform_v1.types.NasJobOutput

state

Output only. The detailed state of the job.

Type:

google.cloud.aiplatform_v1.types.JobState

create_time

Output only. Time when the NasJob was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

start_time

Output only. Time when the NasJob for the first time entered the JOB_STATE_RUNNING state.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

Output only. Time when the NasJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Time when the NasJob was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

error

Output only. Only populated when job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

Type:

google.rpc.status_pb2.Status

labels

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.

Type:

MutableMapping[str, str]

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

enable_restricted_image_training

Optional. Enable a separation of Custom model training and restricted image training for tenant project.

Type:

bool

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.NasJobOutput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents a uCAIP NasJob output.

multi_trial_job_output

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

This field is a member of oneof output.

Type:

google.cloud.aiplatform_v1.types.NasJobOutput.MultiTrialJobOutput

class MultiTrialJobOutput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

search_trials

Output only. List of NasTrials that were started as part of search stage.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NasTrial]

train_trials

Output only. List of NasTrials that were started as part of train stage.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NasTrial]

class google.cloud.aiplatform_v1.types.NasJobSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents the spec of a NasJob.

multi_trial_algorithm_spec

The spec of multi-trial algorithms.

This field is a member of oneof nas_algorithm_spec.

Type:

google.cloud.aiplatform_v1.types.NasJobSpec.MultiTrialAlgorithmSpec

resume_nas_job_id

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.

Type:

str

search_space_spec

It defines the search space for Neural Architecture Search (NAS).

Type:

str

class MultiTrialAlgorithmSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

multi_trial_algorithm

The multi-trial Neural Architecture Search (NAS) algorithm type. Defaults to REINFORCEMENT_LEARNING.

Type:

google.cloud.aiplatform_v1.types.NasJobSpec.MultiTrialAlgorithmSpec.MultiTrialAlgorithm

metric

Metric specs for the NAS job. Validation for this field is done at multi_trial_algorithm_spec field.

Type:

google.cloud.aiplatform_v1.types.NasJobSpec.MultiTrialAlgorithmSpec.MetricSpec

search_trial_spec

Required. Spec for search trials.

Type:

google.cloud.aiplatform_v1.types.NasJobSpec.MultiTrialAlgorithmSpec.SearchTrialSpec

train_trial_spec

Spec for train trials. Top N [TrainTrialSpec.max_parallel_trial_count] search trials will be trained for every M [TrainTrialSpec.frequency] trials searched.

Type:

google.cloud.aiplatform_v1.types.NasJobSpec.MultiTrialAlgorithmSpec.TrainTrialSpec

class MetricSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents a metric to optimize.

metric_id

Required. The ID of the metric. Must not contain whitespaces.

Type:

str

goal

Required. The optimization goal of the metric.

Type:

google.cloud.aiplatform_v1.types.NasJobSpec.MultiTrialAlgorithmSpec.MetricSpec.GoalType

class GoalType(value)

Bases: Enum

The available types of optimization goals.

Values:

GOAL_TYPE_UNSPECIFIED (0):

Goal Type will default to maximize.

MAXIMIZE (1):

Maximize the goal metric.

MINIMIZE (2):

Minimize the goal metric.

class MultiTrialAlgorithm(value)

Bases: Enum

The available types of multi-trial algorithms.

Values:

MULTI_TRIAL_ALGORITHM_UNSPECIFIED (0):

Defaults to REINFORCEMENT_LEARNING.

REINFORCEMENT_LEARNING (1):

The Reinforcement Learning Algorithm for Multi-trial Neural Architecture Search (NAS).

GRID_SEARCH (2):

The Grid Search Algorithm for Multi-trial Neural Architecture Search (NAS).

class SearchTrialSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represent spec for search trials.

search_trial_job_spec

Required. The spec of a search trial job. The same spec applies to all search trials.

Type:

google.cloud.aiplatform_v1.types.CustomJobSpec

max_trial_count

Required. The maximum number of Neural Architecture Search (NAS) trials to run.

Type:

int

max_parallel_trial_count

Required. The maximum number of trials to run in parallel.

Type:

int

max_failed_trial_count

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.

Type:

int

class TrainTrialSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represent spec for train trials.

train_trial_job_spec

Required. The spec of a train trial job. The same spec applies to all train trials.

Type:

google.cloud.aiplatform_v1.types.CustomJobSpec

max_parallel_trial_count

Required. The maximum number of trials to run in parallel.

Type:

int

frequency

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.

Type:

int

class google.cloud.aiplatform_v1.types.NasTrial(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents a uCAIP NasJob trial.

id

Output only. The identifier of the NasTrial assigned by the service.

Type:

str

state

Output only. The detailed state of the NasTrial.

Type:

google.cloud.aiplatform_v1.types.NasTrial.State

final_measurement

Output only. The final measurement containing the objective value.

Type:

google.cloud.aiplatform_v1.types.Measurement

start_time

Output only. Time when the NasTrial was started.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

Output only. Time when the NasTrial’s status changed to SUCCEEDED or INFEASIBLE.

Type:

google.protobuf.timestamp_pb2.Timestamp

class State(value)

Bases: Enum

Describes a NasTrial state.

Values:

STATE_UNSPECIFIED (0):

The NasTrial state is unspecified.

REQUESTED (1):

Indicates that a specific NasTrial has been requested, but it has not yet been suggested by the service.

ACTIVE (2):

Indicates that the NasTrial has been suggested.

STOPPING (3):

Indicates that the NasTrial should stop according to the service.

SUCCEEDED (4):

Indicates that the NasTrial is completed successfully.

INFEASIBLE (5):

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.

class google.cloud.aiplatform_v1.types.NasTrialDetail(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. Resource name of the NasTrialDetail.

Type:

str

parameters

The parameters for the NasJob NasTrial.

Type:

str

search_trial

The requested search NasTrial.

Type:

google.cloud.aiplatform_v1.types.NasTrial

train_trial

The train NasTrial corresponding to [search_trial][google.cloud.aiplatform.v1.NasTrialDetail.search_trial]. Only populated if [search_trial][google.cloud.aiplatform.v1.NasTrialDetail.search_trial] is used for training.

Type:

google.cloud.aiplatform_v1.types.NasTrial

class google.cloud.aiplatform_v1.types.NearestNeighborQuery(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A query to find a number of similar entities.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

entity_id

Optional. The entity id whose similar entities should be searched for. If embedding is set, search will use embedding instead of entity_id.

This field is a member of oneof instance.

Type:

str

embedding

Optional. The embedding vector that be used for similar search.

This field is a member of oneof instance.

Type:

google.cloud.aiplatform_v1.types.NearestNeighborQuery.Embedding

neighbor_count

Optional. The number of similar entities to be retrieved from feature view for each query.

Type:

int

string_filters

Optional. The list of string filters.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NearestNeighborQuery.StringFilter]

numeric_filters

Optional. The list of numeric filters.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NearestNeighborQuery.NumericFilter]

per_crowding_attribute_neighbor_count

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.

Type:

int

parameters

Optional. Parameters that can be set to tune query on the fly.

Type:

google.cloud.aiplatform_v1.types.NearestNeighborQuery.Parameters

class Embedding(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The embedding vector.

value

Optional. Individual value in the embedding.

Type:

MutableSequence[float]

class NumericFilter(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Numeric filter is used to search a subset of the entities by using boolean rules on numeric columns. For example: Database Point 0: {name: “a” value_int: 42} {name: “b” value_float: 1.0} Database Point 1: {name: “a” value_int: 10} {name: “b” value_float: 2.0} Database Point 2: {name: “a” value_int: -1} {name: “b” value_float: 3.0} Query: {name: “a” value_int: 12 operator: LESS} // Matches Point 1, 2 {name: “b” value_float: 2.0 operator: EQUAL} // Matches Point 1

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

value_int

int value type.

This field is a member of oneof Value.

Type:

int

value_float

float value type.

This field is a member of oneof Value.

Type:

float

value_double

double value type.

This field is a member of oneof Value.

Type:

float

name

Required. Column name in BigQuery that used as filters.

Type:

str

op

Optional. This MUST be specified for queries and must NOT be specified for database points.

This field is a member of oneof _op.

Type:

google.cloud.aiplatform_v1.types.NearestNeighborQuery.NumericFilter.Operator

class Operator(value)

Bases: Enum

Datapoints for which Operator is true relative to the query’s Value field will be allowlisted.

Values:

OPERATOR_UNSPECIFIED (0):

Unspecified operator.

LESS (1):

Entities are eligible if their value is < the query’s.

LESS_EQUAL (2):

Entities are eligible if their value is <= the query’s.

EQUAL (3):

Entities are eligible if their value is == the query’s.

GREATER_EQUAL (4):

Entities are eligible if their value is >= the query’s.

GREATER (5):

Entities are eligible if their value is > the query’s.

NOT_EQUAL (6):

Entities are eligible if their value is != the query’s.

class Parameters(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

approximate_neighbor_candidates

Optional. The number of neighbors to find via approximate search before exact reordering is performed; if set, this value must be > neighbor_count.

Type:

int

leaf_nodes_search_fraction

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.

Type:

float

class StringFilter(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

name

Required. Column names in BigQuery that used as filters.

Type:

str

allow_tokens

Optional. The allowed tokens.

Type:

MutableSequence[str]

deny_tokens

Optional. The denied tokens.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.NearestNeighborSearchOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation metadata with regard to Matching Engine Index.

content_validation_stats

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][google.cloud.aiplatform.v1.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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NearestNeighborSearchOperationMetadata.ContentValidationStats]

data_bytes_count

The ingested data size in bytes.

Type:

int

class ContentValidationStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

source_gcs_uri

Cloud Storage URI pointing to the original file in user’s bucket.

Type:

str

valid_record_count

Number of records in this file that were successfully processed.

Type:

int

invalid_record_count

Number of records in this file we skipped due to validate errors.

Type:

int

partial_errors

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NearestNeighborSearchOperationMetadata.RecordError]

valid_sparse_record_count

Number of sparse records in this file that were successfully processed.

Type:

int

invalid_sparse_record_count

Number of sparse records in this file we skipped due to validate errors.

Type:

int

class RecordError(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

error_type

The error type of this record.

Type:

google.cloud.aiplatform_v1.types.NearestNeighborSearchOperationMetadata.RecordError.RecordErrorType

error_message

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.

Type:

str

source_gcs_uri

Cloud Storage URI pointing to the original file in user’s bucket.

Type:

str

embedding_id

Empty if the embedding id is failed to parse.

Type:

str

raw_record

The original content of this record.

Type:

str

class RecordErrorType(value)

Bases: Enum

Values:

ERROR_TYPE_UNSPECIFIED (0):

Default, shall not be used.

EMPTY_LINE (1):

The record is empty.

INVALID_JSON_SYNTAX (2):

Invalid json format.

INVALID_CSV_SYNTAX (3):

Invalid csv format.

INVALID_AVRO_SYNTAX (4):

Invalid avro format.

INVALID_EMBEDDING_ID (5):

The embedding id is not valid.

EMBEDDING_SIZE_MISMATCH (6):

The size of the dense embedding vectors does not match with the specified dimension.

NAMESPACE_MISSING (7):

The namespace field is missing.

PARSING_ERROR (8):

Generic catch-all error. Only used for validation failure where the root cause cannot be easily retrieved programmatically.

DUPLICATE_NAMESPACE (9):

There are multiple restricts with the same namespace value.

OP_IN_DATAPOINT (10):

Numeric restrict has operator specified in datapoint.

MULTIPLE_VALUES (11):

Numeric restrict has multiple values specified.

INVALID_NUMERIC_VALUE (12):

Numeric restrict has invalid numeric value specified.

INVALID_ENCODING (13):

File is not in UTF_8 format.

INVALID_SPARSE_DIMENSIONS (14):

Error parsing sparse dimensions field.

INVALID_TOKEN_VALUE (15):

Token restrict value is invalid.

INVALID_SPARSE_EMBEDDING (16):

Invalid sparse embedding.

INVALID_EMBEDDING (17):

Invalid dense embedding.

class google.cloud.aiplatform_v1.types.NearestNeighbors(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Nearest neighbors for one query.

neighbors

All its neighbors.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NearestNeighbors.Neighbor]

class Neighbor(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A neighbor of the query vector.

entity_id

The id of the similar entity.

Type:

str

distance

The distance between the neighbor and the query vector.

Type:

float

entity_key_values

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.

Type:

google.cloud.aiplatform_v1.types.FetchFeatureValuesResponse

class google.cloud.aiplatform_v1.types.Neighbor(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Neighbors for example-based explanations.

neighbor_id

Output only. The neighbor id.

Type:

str

neighbor_distance

Output only. The neighbor distance.

Type:

float

class google.cloud.aiplatform_v1.types.NetworkSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Network spec.

enable_internet_access

Whether to enable public internet access. Default false.

Type:

bool

network

The full name of the Google Compute Engine network

Type:

str

subnetwork

The name of the subnet that this instance is in. Format: projects/{project_id_or_number}/regions/{region}/subnetworks/{subnetwork_id}

Type:

str

class google.cloud.aiplatform_v1.types.NfsMount(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

server

Required. IP address of the NFS server.

Type:

str

path

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

Type:

str

mount_point

Required. Destination mount path. The NFS will be mounted for the user under /mnt/nfs/<mount_point>

Type:

str

class google.cloud.aiplatform_v1.types.NotebookEucConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The euc configuration of NotebookRuntimeTemplate.

euc_disabled

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.

Type:

bool

bypass_actas_check

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.

Type:

bool

class google.cloud.aiplatform_v1.types.NotebookExecutionJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

NotebookExecutionJob represents an instance of a notebook execution.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

dataform_repository_source

The Dataform Repository pointing to a single file notebook repository.

This field is a member of oneof notebook_source.

Type:

google.cloud.aiplatform_v1.types.NotebookExecutionJob.DataformRepositorySource

gcs_notebook_source

The Cloud Storage url pointing to the ipynb file. Format: gs://bucket/notebook_file.ipynb

This field is a member of oneof notebook_source.

Type:

google.cloud.aiplatform_v1.types.NotebookExecutionJob.GcsNotebookSource

direct_notebook_source

The contents of an input notebook file.

This field is a member of oneof notebook_source.

Type:

google.cloud.aiplatform_v1.types.NotebookExecutionJob.DirectNotebookSource

notebook_runtime_template_resource_name

The NotebookRuntimeTemplate to source compute configuration from.

This field is a member of oneof environment_spec.

Type:

str

custom_environment_spec

The custom compute configuration for an execution job.

This field is a member of oneof environment_spec.

Type:

google.cloud.aiplatform_v1.types.NotebookExecutionJob.CustomEnvironmentSpec

gcs_output_uri

The Cloud Storage location to upload the result to. Format: gs://bucket-name

This field is a member of oneof execution_sink.

Type:

str

execution_user

The user email to run the execution as. Only supported by Colab runtimes.

This field is a member of oneof execution_identity.

Type:

str

service_account

The service account to run the execution as.

This field is a member of oneof execution_identity.

Type:

str

name

Output only. The resource name of this NotebookExecutionJob. Format: projects/{project_id}/locations/{location}/notebookExecutionJobs/{job_id}

Type:

str

display_name

The display name of the NotebookExecutionJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

execution_timeout

Max running time of the execution job in seconds (default 86400s / 24 hrs).

Type:

google.protobuf.duration_pb2.Duration

schedule_resource_name

Output only. The Schedule resource name if this job is triggered by one. Format: projects/{project_id}/locations/{location}/schedules/{schedule_id}

Type:

str

job_state

Output only. The state of the NotebookExecutionJob.

Type:

google.cloud.aiplatform_v1.types.JobState

status

Output only. Populated when the NotebookExecutionJob is completed. When there is an error during notebook execution, the error details are populated.

Type:

google.rpc.status_pb2.Status

create_time

Output only. Timestamp when this NotebookExecutionJob was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this NotebookExecutionJob was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

labels

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.

Type:

MutableMapping[str, str]

encryption_spec

Customer-managed encryption key spec for the notebook execution job. This field is auto-populated if the [NotebookService.NotebookRuntimeTemplate][] has an encryption spec.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

class CustomEnvironmentSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Compute configuration to use for an execution job.

machine_spec

The specification of a single machine for the execution job.

Type:

google.cloud.aiplatform_v1.types.MachineSpec

persistent_disk_spec

The specification of a persistent disk to attach for the execution job.

Type:

google.cloud.aiplatform_v1.types.PersistentDiskSpec

network_spec

The network configuration to use for the execution job.

Type:

google.cloud.aiplatform_v1.types.NetworkSpec

class DataformRepositorySource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The Dataform Repository containing the input notebook.

dataform_repository_resource_name

The resource name of the Dataform Repository. Format: projects/{project_id}/locations/{location}/repositories/{repository_id}

Type:

str

commit_sha

The commit SHA to read repository with. If unset, the file will be read at HEAD.

Type:

str

class DirectNotebookSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The content of the input notebook in ipynb format.

content

The base64-encoded contents of the input notebook file.

Type:

bytes

class GcsNotebookSource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The Cloud Storage uri for the input notebook.

uri

The Cloud Storage uri pointing to the ipynb file. Format: gs://bucket/notebook_file.ipynb

Type:

str

generation

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.

Type:

str

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.NotebookExecutionJobView(value)

Bases: Enum

Views for Get/List NotebookExecutionJob

Values:

NOTEBOOK_EXECUTION_JOB_VIEW_UNSPECIFIED (0):

When unspecified, the API defaults to the BASIC view.

NOTEBOOK_EXECUTION_JOB_VIEW_BASIC (1):

Includes all fields except for direct notebook inputs.

NOTEBOOK_EXECUTION_JOB_VIEW_FULL (2):

Includes all fields.

class google.cloud.aiplatform_v1.types.NotebookIdleShutdownConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

idle_timeout

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

Type:

google.protobuf.duration_pb2.Duration

idle_shutdown_disabled

Whether Idle Shutdown is disabled in this NotebookRuntimeTemplate.

Type:

bool

class google.cloud.aiplatform_v1.types.NotebookRuntime(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

name

Output only. The resource name of the NotebookRuntime.

Type:

str

runtime_user

Required. The user email of the NotebookRuntime.

Type:

str

notebook_runtime_template_ref

Output only. The pointer to NotebookRuntimeTemplate this NotebookRuntime is created from.

Type:

google.cloud.aiplatform_v1.types.NotebookRuntimeTemplateRef

proxy_uri

Output only. The proxy endpoint used to access the NotebookRuntime.

Type:

str

create_time

Output only. Timestamp when this NotebookRuntime was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this NotebookRuntime was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

health_state

Output only. The health state of the NotebookRuntime.

Type:

google.cloud.aiplatform_v1.types.NotebookRuntime.HealthState

display_name

Required. The display name of the NotebookRuntime. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

description

The description of the NotebookRuntime.

Type:

str

service_account

Output only. The service account that the NotebookRuntime workload runs as.

Type:

str

runtime_state

Output only. The runtime (instance) state of the NotebookRuntime.

Type:

google.cloud.aiplatform_v1.types.NotebookRuntime.RuntimeState

is_upgradable

Output only. Whether NotebookRuntime is upgradable.

Type:

bool

labels

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.

Type:

MutableMapping[str, str]

expiration_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

version

Output only. The VM os image version of NotebookRuntime.

Type:

str

notebook_runtime_type

Output only. The type of the notebook runtime.

Type:

google.cloud.aiplatform_v1.types.NotebookRuntimeType

idle_shutdown_config

Output only. The idle shutdown configuration of the notebook runtime.

Type:

google.cloud.aiplatform_v1.types.NotebookIdleShutdownConfig

network_tags

Optional. The Compute Engine tags to add to runtime (see Tagging instances).

Type:

MutableSequence[str]

encryption_spec

Output only. Customer-managed encryption key spec for the notebook runtime.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class HealthState(value)

Bases: Enum

The substate of the NotebookRuntime to display health information.

Values:

HEALTH_STATE_UNSPECIFIED (0):

Unspecified health state.

HEALTHY (1):

NotebookRuntime is in healthy state. Applies to ACTIVE state.

UNHEALTHY (2):

NotebookRuntime is in unhealthy state. Applies to ACTIVE state.

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class RuntimeState(value)

Bases: Enum

The substate of the NotebookRuntime to display state of runtime. The resource of NotebookRuntime is in ACTIVE state for these sub state.

Values:

RUNTIME_STATE_UNSPECIFIED (0):

Unspecified runtime state.

RUNNING (1):

NotebookRuntime is in running state.

BEING_STARTED (2):

NotebookRuntime is in starting state.

BEING_STOPPED (3):

NotebookRuntime is in stopping state.

STOPPED (4):

NotebookRuntime is in stopped state.

BEING_UPGRADED (5):

NotebookRuntime is in upgrading state. It is in the middle of upgrading process.

ERROR (100):

NotebookRuntime was unable to start/stop properly.

INVALID (101):

NotebookRuntime is in invalid state. Cannot be recovered.

class google.cloud.aiplatform_v1.types.NotebookRuntimeTemplate(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

The resource name of the NotebookRuntimeTemplate.

Type:

str

display_name

Required. The display name of the NotebookRuntimeTemplate. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

description

The description of the NotebookRuntimeTemplate.

Type:

str

is_default

Output only. The default template to use if not specified.

Type:

bool

machine_spec

Optional. Immutable. The specification of a single machine for the template.

Type:

google.cloud.aiplatform_v1.types.MachineSpec

data_persistent_disk_spec

Optional. The specification of [persistent disk][https://cloud.google.com/compute/docs/disks/persistent-disks] attached to the runtime as data disk storage.

Type:

google.cloud.aiplatform_v1.types.PersistentDiskSpec

network_spec

Optional. Network spec.

Type:

google.cloud.aiplatform_v1.types.NetworkSpec

service_account

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.

Type:

str

etag

Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

labels

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.

Type:

MutableMapping[str, str]

idle_shutdown_config

The idle shutdown configuration of NotebookRuntimeTemplate. This config will only be set when idle shutdown is enabled.

Type:

google.cloud.aiplatform_v1.types.NotebookIdleShutdownConfig

euc_config

EUC configuration of the NotebookRuntimeTemplate.

Type:

google.cloud.aiplatform_v1.types.NotebookEucConfig

create_time

Output only. Timestamp when this NotebookRuntimeTemplate was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this NotebookRuntimeTemplate was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

notebook_runtime_type

Optional. Immutable. The type of the notebook runtime template.

Type:

google.cloud.aiplatform_v1.types.NotebookRuntimeType

shielded_vm_config

Optional. Immutable. Runtime Shielded VM spec.

Type:

google.cloud.aiplatform_v1.types.ShieldedVmConfig

network_tags

Optional. The Compute Engine tags to add to runtime (see Tagging instances).

Type:

MutableSequence[str]

encryption_spec

Customer-managed encryption key spec for the notebook runtime.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.NotebookRuntimeTemplateRef(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Points to a NotebookRuntimeTemplateRef.

notebook_runtime_template

Immutable. A resource name of the NotebookRuntimeTemplate.

Type:

str

class google.cloud.aiplatform_v1.types.NotebookRuntimeType(value)

Bases: Enum

Represents a notebook runtime type.

Values:

NOTEBOOK_RUNTIME_TYPE_UNSPECIFIED (0):

Unspecified notebook runtime type, NotebookRuntimeType will default to USER_DEFINED.

USER_DEFINED (1):

runtime or template with coustomized configurations from user.

ONE_CLICK (2):

runtime or template with system defined configurations.

class google.cloud.aiplatform_v1.types.PSCAutomationConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

PSC config that is used to automatically create forwarding rule via ServiceConnectionMap.

project_id

Required. Project id used to create forwarding rule.

Type:

str

network

Required. The full name of the Google Compute Engine network. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in ‘12345’, and {network} is network name.

Type:

str

class google.cloud.aiplatform_v1.types.PairwiseChoice(value)

Bases: Enum

Pairwise prediction autorater preference.

Values:

PAIRWISE_CHOICE_UNSPECIFIED (0):

Unspecified prediction choice.

BASELINE (1):

Baseline prediction wins

CANDIDATE (2):

Candidate prediction wins

TIE (3):

Winner cannot be determined

class google.cloud.aiplatform_v1.types.PairwiseMetricInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for pairwise metric.

metric_spec

Required. Spec for pairwise metric.

Type:

google.cloud.aiplatform_v1.types.PairwiseMetricSpec

instance

Required. Pairwise metric instance.

Type:

google.cloud.aiplatform_v1.types.PairwiseMetricInstance

class google.cloud.aiplatform_v1.types.PairwiseMetricInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Pairwise metric instance. Usually one instance corresponds to one row in an evaluation dataset.

json_instance

Instance specified as a json string. String key-value pairs are expected in the json_instance to render PairwiseMetricSpec.instance_prompt_template.

This field is a member of oneof instance.

Type:

str

class google.cloud.aiplatform_v1.types.PairwiseMetricResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for pairwise metric result.

pairwise_choice

Output only. Pairwise metric choice.

Type:

google.cloud.aiplatform_v1.types.PairwiseChoice

explanation

Output only. Explanation for pairwise metric score.

Type:

str

class google.cloud.aiplatform_v1.types.PairwiseMetricSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for pairwise metric.

metric_prompt_template

Required. Metric prompt template for pairwise metric.

This field is a member of oneof _metric_prompt_template.

Type:

str

class google.cloud.aiplatform_v1.types.PairwiseQuestionAnsweringQualityInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for pairwise question answering quality metric.

metric_spec

Required. Spec for pairwise question answering quality score metric.

Type:

google.cloud.aiplatform_v1.types.PairwiseQuestionAnsweringQualitySpec

instance

Required. Pairwise question answering quality instance.

Type:

google.cloud.aiplatform_v1.types.PairwiseQuestionAnsweringQualityInstance

class google.cloud.aiplatform_v1.types.PairwiseQuestionAnsweringQualityInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for pairwise question answering quality instance.

prediction

Required. Output of the candidate model.

This field is a member of oneof _prediction.

Type:

str

baseline_prediction

Required. Output of the baseline model.

This field is a member of oneof _baseline_prediction.

Type:

str

reference

Optional. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

context

Required. Text to answer the question.

This field is a member of oneof _context.

Type:

str

instruction

Required. Question Answering prompt for LLM.

This field is a member of oneof _instruction.

Type:

str

class google.cloud.aiplatform_v1.types.PairwiseQuestionAnsweringQualityResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for pairwise question answering quality result.

pairwise_choice

Output only. Pairwise question answering prediction choice.

Type:

google.cloud.aiplatform_v1.types.PairwiseChoice

explanation

Output only. Explanation for question answering quality score.

Type:

str

confidence

Output only. Confidence for question answering quality score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.PairwiseQuestionAnsweringQualitySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for pairwise question answering quality score metric.

use_reference

Optional. Whether to use instance.reference to compute question answering quality.

Type:

bool

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.PairwiseSummarizationQualityInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for pairwise summarization quality metric.

metric_spec

Required. Spec for pairwise summarization quality score metric.

Type:

google.cloud.aiplatform_v1.types.PairwiseSummarizationQualitySpec

instance

Required. Pairwise summarization quality instance.

Type:

google.cloud.aiplatform_v1.types.PairwiseSummarizationQualityInstance

class google.cloud.aiplatform_v1.types.PairwiseSummarizationQualityInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for pairwise summarization quality instance.

prediction

Required. Output of the candidate model.

This field is a member of oneof _prediction.

Type:

str

baseline_prediction

Required. Output of the baseline model.

This field is a member of oneof _baseline_prediction.

Type:

str

reference

Optional. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

context

Required. Text to be summarized.

This field is a member of oneof _context.

Type:

str

instruction

Required. Summarization prompt for LLM.

This field is a member of oneof _instruction.

Type:

str

class google.cloud.aiplatform_v1.types.PairwiseSummarizationQualityResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for pairwise summarization quality result.

pairwise_choice

Output only. Pairwise summarization prediction choice.

Type:

google.cloud.aiplatform_v1.types.PairwiseChoice

explanation

Output only. Explanation for summarization quality score.

Type:

str

confidence

Output only. Confidence for summarization quality score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.PairwiseSummarizationQualitySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for pairwise summarization quality score metric.

use_reference

Optional. Whether to use instance.reference to compute pairwise summarization quality.

Type:

bool

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.Part(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

text

Optional. Text part (can be code).

This field is a member of oneof data.

Type:

str

inline_data

Optional. Inlined bytes data.

This field is a member of oneof data.

Type:

google.cloud.aiplatform_v1.types.Blob

file_data

Optional. URI based data.

This field is a member of oneof data.

Type:

google.cloud.aiplatform_v1.types.FileData

function_call

Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.

This field is a member of oneof data.

Type:

google.cloud.aiplatform_v1.types.FunctionCall

function_response

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.

This field is a member of oneof data.

Type:

google.cloud.aiplatform_v1.types.FunctionResponse

video_metadata

Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.

This field is a member of oneof metadata.

Type:

google.cloud.aiplatform_v1.types.VideoMetadata

class google.cloud.aiplatform_v1.types.PauseModelDeploymentMonitoringJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.PauseModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.PauseModelDeploymentMonitoringJob].

name

Required. The resource name of the ModelDeploymentMonitoringJob to pause. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

Type:

str

class google.cloud.aiplatform_v1.types.PauseScheduleRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ScheduleService.PauseSchedule][google.cloud.aiplatform.v1.ScheduleService.PauseSchedule].

name

Required. The name of the Schedule resource to be paused. Format: projects/{project}/locations/{location}/schedules/{schedule}

Type:

str

class google.cloud.aiplatform_v1.types.PersistentDiskSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents the spec of [persistent disk][https://cloud.google.com/compute/docs/disks/persistent-disks] options.

disk_type

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)

Type:

str

disk_size_gb

Size in GB of the disk (default is 100GB).

Type:

int

class google.cloud.aiplatform_v1.types.PersistentResource(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

name

Immutable. Resource name of a PersistentResource.

Type:

str

display_name

Optional. The display name of the PersistentResource. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

resource_pools

Required. The spec of the pools of different resources.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ResourcePool]

state

Output only. The detailed state of a Study.

Type:

google.cloud.aiplatform_v1.types.PersistentResource.State

error

Output only. Only populated when persistent resource’s state is STOPPING or ERROR.

Type:

google.rpc.status_pb2.Status

create_time

Output only. Time when the PersistentResource was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

start_time

Output only. Time when the PersistentResource for the first time entered the RUNNING state.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Time when the PersistentResource was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

labels

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.

Type:

MutableMapping[str, str]

network

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.

Type:

str

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

resource_runtime_spec

Optional. Persistent Resource runtime spec. For example, used for Ray cluster configuration.

Type:

google.cloud.aiplatform_v1.types.ResourceRuntimeSpec

resource_runtime

Output only. Runtime information of the Persistent Resource.

Type:

google.cloud.aiplatform_v1.types.ResourceRuntime

reserved_ip_ranges

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’].

Type:

MutableSequence[str]

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class State(value)

Bases: Enum

Describes the PersistentResource state.

Values:

STATE_UNSPECIFIED (0):

Not set.

PROVISIONING (1):

The PROVISIONING state indicates the persistent resources is being created.

RUNNING (3):

The RUNNING state indicates the persistent resource is healthy and fully usable.

STOPPING (4):

The STOPPING state indicates the persistent resource is being deleted.

ERROR (5):

The ERROR state indicates the persistent resource may be unusable. Details can be found in the error field.

REBOOTING (6):

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 (7):

The UPDATING state indicates the persistent resource is being updated.

class google.cloud.aiplatform_v1.types.PipelineFailurePolicy(value)

Bases: 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.

Values:

PIPELINE_FAILURE_POLICY_UNSPECIFIED (0):

Default value, and follows fail slow behavior.

PIPELINE_FAILURE_POLICY_FAIL_SLOW (1):

Indicates that the pipeline should continue to run until all possible tasks have been scheduled and completed.

PIPELINE_FAILURE_POLICY_FAIL_FAST (2):

Indicates that the pipeline should stop scheduling new tasks after a task has failed.

class google.cloud.aiplatform_v1.types.PipelineJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

An instance of a machine learning PipelineJob.

name

Output only. The resource name of the PipelineJob.

Type:

str

display_name

The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

create_time

Output only. Pipeline creation time.

Type:

google.protobuf.timestamp_pb2.Timestamp

start_time

Output only. Pipeline start time.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

Output only. Pipeline end time.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this PipelineJob was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

pipeline_spec

The spec of the pipeline.

Type:

google.protobuf.struct_pb2.Struct

state

Output only. The detailed state of the job.

Type:

google.cloud.aiplatform_v1.types.PipelineState

job_detail

Output only. The details of pipeline run. Not available in the list view.

Type:

google.cloud.aiplatform_v1.types.PipelineJobDetail

error

Output only. The error that occurred during pipeline execution. Only populated when the pipeline’s state is FAILED or CANCELLED.

Type:

google.rpc.status_pb2.Status

labels

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.

Type:

MutableMapping[str, str]

runtime_config

Runtime config of the pipeline.

Type:

google.cloud.aiplatform_v1.types.PipelineJob.RuntimeConfig

encryption_spec

Customer-managed encryption key spec for a pipelineJob. If set, this PipelineJob and all of its sub-resources will be secured by this key.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

service_account

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.

Type:

str

network

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.

Type:

str

reserved_ip_ranges

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’].

Type:

MutableSequence[str]

template_uri

A template uri from where the [PipelineJob.pipeline_spec][google.cloud.aiplatform.v1.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.

Type:

str

template_metadata

Output only. Pipeline template metadata. Will fill up fields if [PipelineJob.template_uri][google.cloud.aiplatform.v1.PipelineJob.template_uri] is from supported template registry.

Type:

google.cloud.aiplatform_v1.types.PipelineTemplateMetadata

schedule_name

Output only. The schedule resource name. Only returned if the Pipeline is created by Schedule API.

Type:

str

preflight_validations

Optional. Whether to do component level validations before job creation.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class RuntimeConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The runtime config of a PipelineJob.

parameters

Deprecated. Use [RuntimeConfig.parameter_values][google.cloud.aiplatform.v1.PipelineJob.RuntimeConfig.parameter_values] instead. The runtime parameters of the PipelineJob. The parameters will be passed into [PipelineJob.pipeline_spec][google.cloud.aiplatform.v1.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.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.Value]

gcs_output_directory

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.

Type:

str

parameter_values

The runtime parameters of the PipelineJob. The parameters will be passed into [PipelineJob.pipeline_spec][google.cloud.aiplatform.v1.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.

Type:

MutableMapping[str, google.protobuf.struct_pb2.Value]

failure_policy

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.

Type:

google.cloud.aiplatform_v1.types.PipelineFailurePolicy

input_artifacts

The runtime artifacts of the PipelineJob. The key will be the input artifact name and the value would be one of the InputArtifact.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.PipelineJob.RuntimeConfig.InputArtifact]

class InputArtifact(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The type of an input artifact.

artifact_id

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.

This field is a member of oneof kind.

Type:

str

class InputArtifactsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class ParameterValuesEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class ParametersEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.PipelineJobDetail(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The runtime detail of PipelineJob.

pipeline_context

Output only. The context of the pipeline.

Type:

google.cloud.aiplatform_v1.types.Context

pipeline_run_context

Output only. The context of the current pipeline run.

Type:

google.cloud.aiplatform_v1.types.Context

task_details

Output only. The runtime details of the tasks under the pipeline.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.PipelineTaskDetail]

class google.cloud.aiplatform_v1.types.PipelineState(value)

Bases: Enum

Describes the state of a pipeline.

Values:

PIPELINE_STATE_UNSPECIFIED (0):

The pipeline state is unspecified.

PIPELINE_STATE_QUEUED (1):

The pipeline has been created or resumed, and processing has not yet begun.

PIPELINE_STATE_PENDING (2):

The service is preparing to run the pipeline.

PIPELINE_STATE_RUNNING (3):

The pipeline is in progress.

PIPELINE_STATE_SUCCEEDED (4):

The pipeline completed successfully.

PIPELINE_STATE_FAILED (5):

The pipeline failed.

PIPELINE_STATE_CANCELLING (6):

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 (7):

The pipeline has been cancelled.

PIPELINE_STATE_PAUSED (8):

The pipeline has been stopped, and can be resumed.

class google.cloud.aiplatform_v1.types.PipelineTaskDetail(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The runtime detail of a task execution.

task_id

Output only. The system generated ID of the task.

Type:

int

parent_task_id

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.

Type:

int

task_name

Output only. The user specified name of the task that is defined in [pipeline_spec][google.cloud.aiplatform.v1.PipelineJob.pipeline_spec].

Type:

str

create_time

Output only. Task create time.

Type:

google.protobuf.timestamp_pb2.Timestamp

start_time

Output only. Task start time.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

Output only. Task end time.

Type:

google.protobuf.timestamp_pb2.Timestamp

executor_detail

Output only. The detailed execution info.

Type:

google.cloud.aiplatform_v1.types.PipelineTaskExecutorDetail

state

Output only. State of the task.

Type:

google.cloud.aiplatform_v1.types.PipelineTaskDetail.State

execution

Output only. The execution metadata of the task.

Type:

google.cloud.aiplatform_v1.types.Execution

error

Output only. The error that occurred during task execution. Only populated when the task’s state is FAILED or CANCELLED.

Type:

google.rpc.status_pb2.Status

pipeline_task_status

Output only. A list of task status. This field keeps a record of task status evolving over time.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.PipelineTaskDetail.PipelineTaskStatus]

inputs

Output only. The runtime input artifacts of the task.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.PipelineTaskDetail.ArtifactList]

outputs

Output only. The runtime output artifacts of the task.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.PipelineTaskDetail.ArtifactList]

class ArtifactList(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A list of artifact metadata.

artifacts

Output only. A list of artifact metadata.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Artifact]

class InputsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class OutputsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class PipelineTaskStatus(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A single record of the task status.

update_time

Output only. Update time of this status.

Type:

google.protobuf.timestamp_pb2.Timestamp

state

Output only. The state of the task.

Type:

google.cloud.aiplatform_v1.types.PipelineTaskDetail.State

error

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.

Type:

google.rpc.status_pb2.Status

class State(value)

Bases: Enum

Specifies state of TaskExecution

Values:

STATE_UNSPECIFIED (0):

Unspecified.

PENDING (1):

Specifies pending state for the task.

RUNNING (2):

Specifies task is being executed.

SUCCEEDED (3):

Specifies task completed successfully.

CANCEL_PENDING (4):

Specifies Task cancel is in pending state.

CANCELLING (5):

Specifies task is being cancelled.

CANCELLED (6):

Specifies task was cancelled.

FAILED (7):

Specifies task failed.

SKIPPED (8):

Specifies task was skipped due to cache hit.

NOT_TRIGGERED (9):

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][google.cloud.aiplatform.v1.PipelineJob.pipeline_spec].

class google.cloud.aiplatform_v1.types.PipelineTaskExecutorDetail(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The runtime detail of a pipeline executor.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

container_detail

Output only. The detailed info for a container executor.

This field is a member of oneof details.

Type:

google.cloud.aiplatform_v1.types.PipelineTaskExecutorDetail.ContainerDetail

custom_job_detail

Output only. The detailed info for a custom job executor.

This field is a member of oneof details.

Type:

google.cloud.aiplatform_v1.types.PipelineTaskExecutorDetail.CustomJobDetail

class ContainerDetail(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

main_job

Output only. The name of the [CustomJob][google.cloud.aiplatform.v1.CustomJob] for the main container execution.

Type:

str

pre_caching_check_job

Output only. The name of the [CustomJob][google.cloud.aiplatform.v1.CustomJob] for the pre-caching-check container execution. This job will be available if the [PipelineJob.pipeline_spec][google.cloud.aiplatform.v1.PipelineJob.pipeline_spec] specifies the pre_caching_check hook in the lifecycle events.

Type:

str

failed_main_jobs

Output only. The names of the previously failed [CustomJob][google.cloud.aiplatform.v1.CustomJob] for the main container executions. The list includes the all attempts in chronological order.

Type:

MutableSequence[str]

failed_pre_caching_check_jobs

Output only. The names of the previously failed [CustomJob][google.cloud.aiplatform.v1.CustomJob] for the pre-caching-check container executions. This job will be available if the [PipelineJob.pipeline_spec][google.cloud.aiplatform.v1.PipelineJob.pipeline_spec] specifies the pre_caching_check hook in the lifecycle events. The list includes the all attempts in chronological order.

Type:

MutableSequence[str]

class CustomJobDetail(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The detailed info for a custom job executor.

job

Output only. The name of the [CustomJob][google.cloud.aiplatform.v1.CustomJob].

Type:

str

failed_jobs

Output only. The names of the previously failed [CustomJob][google.cloud.aiplatform.v1.CustomJob]. The list includes the all attempts in chronological order.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.PipelineTemplateMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Pipeline template metadata if [PipelineJob.template_uri][google.cloud.aiplatform.v1.PipelineJob.template_uri] is from supported template registry. Currently, the only supported registry is Artifact Registry.

version

The version_name in artifact registry.

Will always be presented in output if the [PipelineJob.template_uri][google.cloud.aiplatform.v1.PipelineJob.template_uri] is from supported template registry.

Format is “sha256:abcdef123456…”.

Type:

str

class google.cloud.aiplatform_v1.types.PointwiseMetricInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for pointwise metric.

metric_spec

Required. Spec for pointwise metric.

Type:

google.cloud.aiplatform_v1.types.PointwiseMetricSpec

instance

Required. Pointwise metric instance.

Type:

google.cloud.aiplatform_v1.types.PointwiseMetricInstance

class google.cloud.aiplatform_v1.types.PointwiseMetricInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Pointwise metric instance. Usually one instance corresponds to one row in an evaluation dataset.

json_instance

Instance specified as a json string. String key-value pairs are expected in the json_instance to render PointwiseMetricSpec.instance_prompt_template.

This field is a member of oneof instance.

Type:

str

class google.cloud.aiplatform_v1.types.PointwiseMetricResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for pointwise metric result.

score

Output only. Pointwise metric score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for pointwise metric score.

Type:

str

class google.cloud.aiplatform_v1.types.PointwiseMetricSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for pointwise metric.

metric_prompt_template

Required. Metric prompt template for pointwise metric.

This field is a member of oneof _metric_prompt_template.

Type:

str

class google.cloud.aiplatform_v1.types.Port(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents a network port in a container.

container_port

The number of the port to expose on the pod’s IP address. Must be a valid port number, between 1 and 65535 inclusive.

Type:

int

class google.cloud.aiplatform_v1.types.PredefinedSplit(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Assigns input data to training, validation, and test sets based on the value of a provided key.

Supported only for tabular Datasets.

key

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.

Type:

str

class google.cloud.aiplatform_v1.types.PredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict].

endpoint

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

instances

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][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata’s][google.cloud.aiplatform.v1.Model.predict_schemata] [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

Type:

MutableSequence[google.protobuf.struct_pb2.Value]

parameters

The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint’s DeployedModels’ [Model’s ][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata’s][google.cloud.aiplatform.v1.Model.predict_schemata] [parameters_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri].

Type:

google.protobuf.struct_pb2.Value

class google.cloud.aiplatform_v1.types.PredictRequestResponseLoggingConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configuration for logging request-response to a BigQuery table.

enabled

If logging is enabled or not.

Type:

bool

sampling_rate

Percentage of requests to be logged, expressed as a fraction in range(0,1].

Type:

float

bigquery_destination

BigQuery table for logging. If only given a project, a new dataset will be created with name logging_<endpoint-display-name>_<endpoint-id> 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

Type:

google.cloud.aiplatform_v1.types.BigQueryDestination

class google.cloud.aiplatform_v1.types.PredictResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict].

predictions

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 ][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata’s][google.cloud.aiplatform.v1.Model.predict_schemata] [prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri].

Type:

MutableSequence[google.protobuf.struct_pb2.Value]

deployed_model_id

ID of the Endpoint’s DeployedModel that served this prediction.

Type:

str

model

Output only. The resource name of the Model which is deployed as the DeployedModel that this prediction hits.

Type:

str

model_version_id

Output only. The version ID of the Model which is deployed as the DeployedModel that this prediction hits.

Type:

str

model_display_name

Output only. The [display name][google.cloud.aiplatform.v1.Model.display_name] of the Model which is deployed as the DeployedModel that this prediction hits.

Type:

str

metadata

Output only. Request-level metadata returned by the model. The metadata type will be dependent upon the model implementation.

Type:

google.protobuf.struct_pb2.Value

class google.cloud.aiplatform_v1.types.PredictSchemata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Contains the schemata used in Model’s predictions and explanations via [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict], [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain] and [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

instance_schema_uri

Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in [PredictRequest.instances][google.cloud.aiplatform.v1.PredictRequest.instances], [ExplainRequest.instances][google.cloud.aiplatform.v1.ExplainRequest.instances] and [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.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.

Type:

str

parameters_schema_uri

Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via [PredictRequest.parameters][google.cloud.aiplatform.v1.PredictRequest.parameters], [ExplainRequest.parameters][google.cloud.aiplatform.v1.ExplainRequest.parameters] and [BatchPredictionJob.model_parameters][google.cloud.aiplatform.v1.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.

Type:

str

prediction_schema_uri

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][google.cloud.aiplatform.v1.PredictResponse.predictions], [ExplainResponse.explanations][google.cloud.aiplatform.v1.ExplainResponse.explanations], and [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.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.

Type:

str

class google.cloud.aiplatform_v1.types.Presets(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Preset configuration for example-based explanations

query

Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

This field is a member of oneof _query.

Type:

google.cloud.aiplatform_v1.types.Presets.Query

modality

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.

Type:

google.cloud.aiplatform_v1.types.Presets.Modality

class Modality(value)

Bases: Enum

Preset option controlling parameters for different modalities

Values:

MODALITY_UNSPECIFIED (0):

Should not be set. Added as a recommended best practice for enums

IMAGE (1):

IMAGE modality

TEXT (2):

TEXT modality

TABULAR (3):

TABULAR modality

class Query(value)

Bases: Enum

Preset option controlling parameters for query speed-precision trade-off

Values:

PRECISE (0):

More precise neighbors as a trade-off against slower response.

FAST (1):

Faster response as a trade-off against less precise neighbors.

class google.cloud.aiplatform_v1.types.PrivateEndpoints(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

predict_http_uri

Output only. Http(s) path to send prediction requests.

Type:

str

explain_http_uri

Output only. Http(s) path to send explain requests.

Type:

str

health_http_uri

Output only. Http(s) path to send health check requests.

Type:

str

service_attachment

Output only. The name of the service attachment resource. Populated if private service connect is enabled.

Type:

str

class google.cloud.aiplatform_v1.types.PrivateServiceConnectConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents configuration for private service connect.

enable_private_service_connect

Required. If true, expose the IndexEndpoint via private service connect.

Type:

bool

project_allowlist

A list of Projects from which the forwarding rule will target the service attachment.

Type:

MutableSequence[str]

service_attachment

Output only. The name of the generated service attachment resource. This is only populated if the endpoint is deployed with PrivateServiceConnect.

Type:

str

class google.cloud.aiplatform_v1.types.Probe(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

exec_

ExecAction probes the health of a container by executing a command.

This field is a member of oneof probe_type.

Type:

google.cloud.aiplatform_v1.types.Probe.ExecAction

period_seconds

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’.

Type:

int

timeout_seconds

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’.

Type:

int

class ExecAction(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

ExecAction specifies a command to execute.

command

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.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.PscAutomatedEndpoints(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

project_id

Corresponding project_id in pscAutomationConfigs

Type:

str

network

Corresponding network in pscAutomationConfigs.

Type:

str

match_address

Ip Address created by the automated forwarding rule.

Type:

str

class google.cloud.aiplatform_v1.types.PublisherModel(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A Model Garden Publisher Model.

name

Output only. The resource name of the PublisherModel.

Type:

str

version_id

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.

Type:

str

open_source_category

Required. Indicates the open source category of the publisher model.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.OpenSourceCategory

supported_actions

Optional. Supported call-to-action options.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction

frameworks

Optional. Additional information about the model’s Frameworks.

Type:

MutableSequence[str]

launch_stage

Optional. Indicates the launch stage of the model.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.LaunchStage

version_state

Optional. Indicates the state of the model version.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.VersionState

publisher_model_template

Optional. Output only. Immutable. Used to indicate this model has a publisher model and provide the template of the publisher model resource name.

Type:

str

predict_schemata

Optional. The schemata that describes formats of the PublisherModel’s predictions and explanations as given and returned via [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict].

Type:

google.cloud.aiplatform_v1.types.PredictSchemata

class CallToAction(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Actions could take on this Publisher Model.

view_rest_api

Optional. To view Rest API docs.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.ViewRestApi

open_notebook

Optional. Open notebook of the PublisherModel.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.RegionalResourceReferences

open_notebooks

Optional. Open notebooks of the PublisherModel.

This field is a member of oneof _open_notebooks.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.OpenNotebooks

create_application

Optional. Create application using the PublisherModel.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.RegionalResourceReferences

open_fine_tuning_pipeline

Optional. Open fine-tuning pipeline of the PublisherModel.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.RegionalResourceReferences

open_fine_tuning_pipelines

Optional. Open fine-tuning pipelines of the PublisherModel.

This field is a member of oneof _open_fine_tuning_pipelines.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.OpenFineTuningPipelines

open_prompt_tuning_pipeline

Optional. Open prompt-tuning pipeline of the PublisherModel.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.RegionalResourceReferences

open_genie

Optional. Open Genie / Playground.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.RegionalResourceReferences

deploy

Optional. Deploy the PublisherModel to Vertex Endpoint.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.Deploy

deploy_gke

Optional. Deploy PublisherModel to Google Kubernetes Engine.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.DeployGke

open_generation_ai_studio

Optional. Open in Generation AI Studio.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.RegionalResourceReferences

request_access

Optional. Request for access.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.RegionalResourceReferences

open_evaluation_pipeline

Optional. Open evaluation pipeline of the PublisherModel.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.RegionalResourceReferences

class Deploy(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

dedicated_resources

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

This field is a member of oneof prediction_resources.

Type:

google.cloud.aiplatform_v1.types.DedicatedResources

automatic_resources

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.

This field is a member of oneof prediction_resources.

Type:

google.cloud.aiplatform_v1.types.AutomaticResources

shared_resources

The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

This field is a member of oneof prediction_resources.

Type:

str

model_display_name

Optional. Default model display name.

Type:

str

large_model_reference

Optional. Large model reference. When this is set, model_artifact_spec is not needed.

Type:

google.cloud.aiplatform_v1.types.LargeModelReference

container_spec

Optional. The specification of the container that is to be used when deploying this Model in Vertex AI. Not present for Large Models.

Type:

google.cloud.aiplatform_v1.types.ModelContainerSpec

artifact_uri

Optional. The path to the directory containing the Model artifact and any of its supporting files.

Type:

str

deploy_task_name

Optional. The name of the deploy task (e.g., “text to image generation”).

This field is a member of oneof _deploy_task_name.

Type:

str

deploy_metadata

Optional. Metadata information about this deployment config.

This field is a member of oneof _deploy_metadata.

Type:

google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.Deploy.DeployMetadata

title

Required. The title of the regional resource reference.

Type:

str

public_artifact_uri

Optional. The signed URI for ephemeral Cloud Storage access to model artifact.

Type:

str

class DeployMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata information about the deployment for managing deployment config.

labels

Optional. Labels for the deployment. For managing deployment config like verifying, source of deployment config, etc.

Type:

MutableMapping[str, str]

sample_request

Optional. Sample request for deployed endpoint.

Type:

str

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class DeployGke(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configurations for PublisherModel GKE deployment

gke_yaml_configs

Optional. GKE deployment configuration in yaml format.

Type:

MutableSequence[str]

class OpenFineTuningPipelines(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Open fine tuning pipelines.

fine_tuning_pipelines

Required. Regional resource references to fine tuning pipelines.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.RegionalResourceReferences]

class OpenNotebooks(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Open notebooks.

notebooks

Required. Regional resource references to notebooks.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.PublisherModel.CallToAction.RegionalResourceReferences]

class RegionalResourceReferences(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

references

Required.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.PublisherModel.ResourceReference]

title

Required.

Type:

str

resource_title

Optional. Title of the resource.

This field is a member of oneof _resource_title.

Type:

str

resource_use_case

Optional. Use case (CUJ) of the resource.

This field is a member of oneof _resource_use_case.

Type:

str

resource_description

Optional. Description of the resource.

This field is a member of oneof _resource_description.

Type:

str

class ReferencesEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class ViewRestApi(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Rest API docs.

documentations

Required.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.PublisherModel.Documentation]

title

Required. The title of the view rest API.

Type:

str

class Documentation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A named piece of documentation.

title

Required. E.g., OVERVIEW, USE CASES, DOCUMENTATION, SDK & SAMPLES, JAVA, NODE.JS, etc..

Type:

str

content

Required. Content of this piece of document (in Markdown format).

Type:

str

class LaunchStage(value)

Bases: Enum

An enum representing the launch stage of a PublisherModel.

Values:

LAUNCH_STAGE_UNSPECIFIED (0):

The model launch stage is unspecified.

EXPERIMENTAL (1):

Used to indicate the PublisherModel is at Experimental launch stage, available to a small set of customers.

PRIVATE_PREVIEW (2):

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 (3):

Used to indicate the PublisherModel is at Public Preview launch stage, available to all customers, although not supported for production workloads.

GA (4):

Used to indicate the PublisherModel is at GA launch stage, available to all customers and ready for production workload.

class OpenSourceCategory(value)

Bases: Enum

An enum representing the open source category of a PublisherModel.

Values:

OPEN_SOURCE_CATEGORY_UNSPECIFIED (0):

The open source category is unspecified, which should not be used.

PROPRIETARY (1):

Used to indicate the PublisherModel is not open sourced.

GOOGLE_OWNED_OSS_WITH_GOOGLE_CHECKPOINT (2):

Used to indicate the PublisherModel is a Google-owned open source model w/ Google checkpoint.

THIRD_PARTY_OWNED_OSS_WITH_GOOGLE_CHECKPOINT (3):

Used to indicate the PublisherModel is a 3p-owned open source model w/ Google checkpoint.

GOOGLE_OWNED_OSS (4):

Used to indicate the PublisherModel is a Google-owned pure open source model.

THIRD_PARTY_OWNED_OSS (5):

Used to indicate the PublisherModel is a 3p-owned pure open source model.

class ResourceReference(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Reference to a resource.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

uri

The URI of the resource.

This field is a member of oneof reference.

Type:

str

resource_name

The resource name of the Google Cloud resource.

This field is a member of oneof reference.

Type:

str

use_case

Use case (CUJ) of the resource.

This field is a member of oneof reference.

Type:

str

description

Description of the resource.

This field is a member of oneof reference.

Type:

str

class VersionState(value)

Bases: Enum

An enum representing the state of the PublicModelVersion.

Values:

VERSION_STATE_UNSPECIFIED (0):

The version state is unspecified.

VERSION_STATE_STABLE (1):

Used to indicate the version is stable.

VERSION_STATE_UNSTABLE (2):

Used to indicate the version is unstable.

class google.cloud.aiplatform_v1.types.PublisherModelView(value)

Bases: Enum

View enumeration of PublisherModel.

Values:

PUBLISHER_MODEL_VIEW_UNSPECIFIED (0):

The default / unset value. The API will default to the BASIC view.

PUBLISHER_MODEL_VIEW_BASIC (1):

Include basic metadata about the publisher model, but not the full contents.

PUBLISHER_MODEL_VIEW_FULL (2):

Include everything.

PUBLISHER_MODEL_VERSION_VIEW_BASIC (3):

Include: VersionId, ModelVersionExternalName, and SupportedActions.

class google.cloud.aiplatform_v1.types.PurgeArtifactsMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform [MetadataService.PurgeArtifacts][google.cloud.aiplatform.v1.MetadataService.PurgeArtifacts].

generic_metadata

Operation metadata for purging Artifacts.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.PurgeArtifactsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.PurgeArtifacts][google.cloud.aiplatform.v1.MetadataService.PurgeArtifacts].

parent

Required. The metadata store to purge Artifacts from. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

filter

Required. A required filter matching the Artifacts to be purged. E.g., update_time <= 2020-11-19T11:30:00-04:00.

Type:

str

force

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.

Type:

bool

class google.cloud.aiplatform_v1.types.PurgeArtifactsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.PurgeArtifacts][google.cloud.aiplatform.v1.MetadataService.PurgeArtifacts].

purge_count

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.

Type:

int

purge_sample

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).

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.PurgeContextsMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform [MetadataService.PurgeContexts][google.cloud.aiplatform.v1.MetadataService.PurgeContexts].

generic_metadata

Operation metadata for purging Contexts.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.PurgeContextsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.PurgeContexts][google.cloud.aiplatform.v1.MetadataService.PurgeContexts].

parent

Required. The metadata store to purge Contexts from. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

filter

Required. A required filter matching the Contexts to be purged. E.g., update_time <= 2020-11-19T11:30:00-04:00.

Type:

str

force

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.

Type:

bool

class google.cloud.aiplatform_v1.types.PurgeContextsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.PurgeContexts][google.cloud.aiplatform.v1.MetadataService.PurgeContexts].

purge_count

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.

Type:

int

purge_sample

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).

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.PurgeExecutionsMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform [MetadataService.PurgeExecutions][google.cloud.aiplatform.v1.MetadataService.PurgeExecutions].

generic_metadata

Operation metadata for purging Executions.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.PurgeExecutionsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.PurgeExecutions][google.cloud.aiplatform.v1.MetadataService.PurgeExecutions].

parent

Required. The metadata store to purge Executions from. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

Type:

str

filter

Required. A required filter matching the Executions to be purged. E.g., update_time <= 2020-11-19T11:30:00-04:00.

Type:

str

force

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.

Type:

bool

class google.cloud.aiplatform_v1.types.PurgeExecutionsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.PurgeExecutions][google.cloud.aiplatform.v1.MetadataService.PurgeExecutions].

purge_count

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.

Type:

int

purge_sample

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).

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.PythonPackageSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The spec of a Python packaged code.

executor_image_uri

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.

Type:

str

package_uris

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.

Type:

MutableSequence[str]

python_module

Required. The Python module name to run after installing the packages.

Type:

str

args

Command line arguments to be passed to the Python task.

Type:

MutableSequence[str]

env

Environment variables to be passed to the python module. Maximum limit is 100.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.EnvVar]

class google.cloud.aiplatform_v1.types.QueryArtifactLineageSubgraphRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.QueryArtifactLineageSubgraph][google.cloud.aiplatform.v1.MetadataService.QueryArtifactLineageSubgraph].

artifact

Required. The resource name of the Artifact whose Lineage needs to be retrieved as a LineageSubgraph. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}

The request may error with FAILED_PRECONDITION if the number of Artifacts, the number of Executions, or the number of Events that would be returned for the Context exceeds 1000.

Type:

str

max_hops

Specifies the size of the lineage graph in terms of number of hops from the specified artifact. Negative Value: INVALID_ARGUMENT error is returned 0: Only input artifact is returned. No value: Transitive closure is performed to return the complete graph.

Type:

int

filter

Filter specifying the boolean condition for the Artifacts to satisfy in order to be part of the Lineage Subgraph. The syntax to define filter query is based on https://google.aip.dev/160. The supported set of filters include the following:

• Attribute filtering: For example: display_name = "test" Supported fields include: name, display_name, uri, state, schema_title, create_time, and update_time. Time fields, such as create_time and update_time, require values specified in RFC-3339 format. For example: create_time = "2020-11-19T11:30:00-04:00"

• Metadata field: To filter on metadata fields use traversal operation as follows: metadata.<field_name>.<type_value>. For example: metadata.field_1.number_value = 10.0 In case the field name contains special characters (such as colon), one can embed it inside double quote. For example: metadata."field:1".number_value = 10.0

Each of the above supported filter types can be combined together using logical operators (AND & OR). Maximum nested expression depth allowed is 5.

For example: display_name = "test" AND metadata.field1.bool_value = true.

Type:

str

class google.cloud.aiplatform_v1.types.QueryContextLineageSubgraphRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.QueryContextLineageSubgraph][google.cloud.aiplatform.v1.MetadataService.QueryContextLineageSubgraph].

context

Required. The resource name of the Context whose Artifacts and Executions should be retrieved as a LineageSubgraph. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}

The request may error with FAILED_PRECONDITION if the number of Artifacts, the number of Executions, or the number of Events that would be returned for the Context exceeds 1000.

Type:

str

class google.cloud.aiplatform_v1.types.QueryDeployedModelsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for QueryDeployedModels method.

deployment_resource_pool

Required. The name of the target DeploymentResourcePool to query. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

Type:

str

page_size

The maximum number of DeployedModels to return. The service may return fewer than this value.

Type:

int

page_token

A page token, received from a previous QueryDeployedModels call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to QueryDeployedModels must match the call that provided the page token.

Type:

str

class google.cloud.aiplatform_v1.types.QueryDeployedModelsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for QueryDeployedModels method.

deployed_models

DEPRECATED Use deployed_model_refs instead.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DeployedModel]

next_page_token

A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

deployed_model_refs

References to the DeployedModels that share the specified deploymentResourcePool.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DeployedModelRef]

total_deployed_model_count

The total number of DeployedModels on this DeploymentResourcePool.

Type:

int

total_endpoint_count

The total number of Endpoints that have DeployedModels on this DeploymentResourcePool.

Type:

int

class google.cloud.aiplatform_v1.types.QueryExecutionInputsAndOutputsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.QueryExecutionInputsAndOutputs][google.cloud.aiplatform.v1.MetadataService.QueryExecutionInputsAndOutputs].

execution

Required. The resource name of the Execution whose input and output Artifacts should be retrieved as a LineageSubgraph. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}

Type:

str

class google.cloud.aiplatform_v1.types.QuestionAnsweringCorrectnessInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for question answering correctness metric.

metric_spec

Required. Spec for question answering correctness score metric.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringCorrectnessSpec

instance

Required. Question answering correctness instance.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringCorrectnessInstance

class google.cloud.aiplatform_v1.types.QuestionAnsweringCorrectnessInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering correctness instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Optional. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

context

Optional. Text provided as context to answer the question.

This field is a member of oneof _context.

Type:

str

instruction

Required. The question asked and other instruction in the inference prompt.

This field is a member of oneof _instruction.

Type:

str

class google.cloud.aiplatform_v1.types.QuestionAnsweringCorrectnessResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering correctness result.

score

Output only. Question Answering Correctness score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for question answering correctness score.

Type:

str

confidence

Output only. Confidence for question answering correctness score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.QuestionAnsweringCorrectnessSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering correctness metric.

use_reference

Optional. Whether to use instance.reference to compute question answering correctness.

Type:

bool

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.QuestionAnsweringHelpfulnessInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for question answering helpfulness metric.

metric_spec

Required. Spec for question answering helpfulness score metric.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringHelpfulnessSpec

instance

Required. Question answering helpfulness instance.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringHelpfulnessInstance

class google.cloud.aiplatform_v1.types.QuestionAnsweringHelpfulnessInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering helpfulness instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Optional. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

context

Optional. Text provided as context to answer the question.

This field is a member of oneof _context.

Type:

str

instruction

Required. The question asked and other instruction in the inference prompt.

This field is a member of oneof _instruction.

Type:

str

class google.cloud.aiplatform_v1.types.QuestionAnsweringHelpfulnessResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering helpfulness result.

score

Output only. Question Answering Helpfulness score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for question answering helpfulness score.

Type:

str

confidence

Output only. Confidence for question answering helpfulness score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.QuestionAnsweringHelpfulnessSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering helpfulness metric.

use_reference

Optional. Whether to use instance.reference to compute question answering helpfulness.

Type:

bool

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.QuestionAnsweringQualityInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for question answering quality metric.

metric_spec

Required. Spec for question answering quality score metric.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringQualitySpec

instance

Required. Question answering quality instance.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringQualityInstance

class google.cloud.aiplatform_v1.types.QuestionAnsweringQualityInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering quality instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Optional. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

context

Required. Text to answer the question.

This field is a member of oneof _context.

Type:

str

instruction

Required. Question Answering prompt for LLM.

This field is a member of oneof _instruction.

Type:

str

class google.cloud.aiplatform_v1.types.QuestionAnsweringQualityResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering quality result.

score

Output only. Question Answering Quality score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for question answering quality score.

Type:

str

confidence

Output only. Confidence for question answering quality score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.QuestionAnsweringQualitySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering quality score metric.

use_reference

Optional. Whether to use instance.reference to compute question answering quality.

Type:

bool

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.QuestionAnsweringRelevanceInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for question answering relevance metric.

metric_spec

Required. Spec for question answering relevance score metric.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringRelevanceSpec

instance

Required. Question answering relevance instance.

Type:

google.cloud.aiplatform_v1.types.QuestionAnsweringRelevanceInstance

class google.cloud.aiplatform_v1.types.QuestionAnsweringRelevanceInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering relevance instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Optional. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

context

Optional. Text provided as context to answer the question.

This field is a member of oneof _context.

Type:

str

instruction

Required. The question asked and other instruction in the inference prompt.

This field is a member of oneof _instruction.

Type:

str

class google.cloud.aiplatform_v1.types.QuestionAnsweringRelevanceResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering relevance result.

score

Output only. Question Answering Relevance score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for question answering relevance score.

Type:

str

confidence

Output only. Confidence for question answering relevance score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.QuestionAnsweringRelevanceSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for question answering relevance metric.

use_reference

Optional. Whether to use instance.reference to compute question answering relevance.

Type:

bool

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.RawPredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.RawPredict][google.cloud.aiplatform.v1.PredictionService.RawPredict].

endpoint

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

http_body

The prediction input. Supports HTTP headers and arbitrary data payload.

A [DeployedModel][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.PredictionService.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][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] field when you create a [Model][google.cloud.aiplatform.v1.Model]. This schema applies when you deploy the Model as a DeployedModel to an [Endpoint][google.cloud.aiplatform.v1.Endpoint] and use the RawPredict method.

Type:

google.api.httpbody_pb2.HttpBody

class google.cloud.aiplatform_v1.types.RayLogsSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configuration for the Ray OSS Logs.

disabled

Optional. Flag to disable the export of Ray OSS logs to Cloud Logging.

Type:

bool

class google.cloud.aiplatform_v1.types.RayMetricSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configuration for the Ray metrics.

disabled

Optional. Flag to disable the Ray metrics collection.

Type:

bool

class google.cloud.aiplatform_v1.types.RaySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

image_uri

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.

Type:

str

resource_pool_images

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” }

Type:

MutableMapping[str, str]

head_node_resource_pool_id

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.

Type:

str

ray_metric_spec

Optional. Ray metrics configurations.

Type:

google.cloud.aiplatform_v1.types.RayMetricSpec

ray_logs_spec

Optional. OSS Ray logging configurations.

Type:

google.cloud.aiplatform_v1.types.RayLogsSpec

class ResourcePoolImagesEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.ReadFeatureValuesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreOnlineServingService.ReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreOnlineServingService.ReadFeatureValues].

entity_type

Required. The resource name of the EntityType for the entity being read. Value format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}. For example, for a machine learning model predicting user clicks on a website, an EntityType ID could be user.

Type:

str

entity_id

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.

Type:

str

feature_selector

Required. Selector choosing Features of the target EntityType.

Type:

google.cloud.aiplatform_v1.types.FeatureSelector

class google.cloud.aiplatform_v1.types.ReadFeatureValuesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeaturestoreOnlineServingService.ReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreOnlineServingService.ReadFeatureValues].

header

Response header.

Type:

google.cloud.aiplatform_v1.types.ReadFeatureValuesResponse.Header

entity_view

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.

Type:

google.cloud.aiplatform_v1.types.ReadFeatureValuesResponse.EntityView

class EntityView(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Entity view with Feature values.

entity_id

ID of the requested entity.

Type:

str

data

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][google.cloud.aiplatform.v1.ReadFeatureValuesResponse.header].

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ReadFeatureValuesResponse.EntityView.Data]

class Data(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

value

Feature value if a single value is requested.

This field is a member of oneof data.

Type:

google.cloud.aiplatform_v1.types.FeatureValue

values

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.

This field is a member of oneof data.

Type:

google.cloud.aiplatform_v1.types.FeatureValueList

class FeatureDescriptor(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata for requested Features.

id

Feature ID.

Type:

str

class Header(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response header with metadata for the requested [ReadFeatureValuesRequest.entity_type][google.cloud.aiplatform.v1.ReadFeatureValuesRequest.entity_type] and Features.

entity_type

The resource name of the EntityType from the [ReadFeatureValuesRequest][google.cloud.aiplatform.v1.ReadFeatureValuesRequest]. Value format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}.

Type:

str

feature_descriptors

List of Feature metadata corresponding to each piece of [ReadFeatureValuesResponse.EntityView.data][google.cloud.aiplatform.v1.ReadFeatureValuesResponse.EntityView.data].

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ReadFeatureValuesResponse.FeatureDescriptor]

class google.cloud.aiplatform_v1.types.ReadIndexDatapointsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The request message for [MatchService.ReadIndexDatapoints][google.cloud.aiplatform.v1.MatchService.ReadIndexDatapoints].

index_endpoint

Required. The name of the index endpoint. Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

Type:

str

deployed_index_id

The ID of the DeployedIndex that will serve the request.

Type:

str

ids

IDs of the datapoints to be searched for.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.ReadIndexDatapointsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The response message for [MatchService.ReadIndexDatapoints][google.cloud.aiplatform.v1.MatchService.ReadIndexDatapoints].

datapoints

The result list of datapoints.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.IndexDatapoint]

class google.cloud.aiplatform_v1.types.ReadTensorboardBlobDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.ReadTensorboardBlobData][google.cloud.aiplatform.v1.TensorboardService.ReadTensorboardBlobData].

time_series

Required. The resource name of the TensorboardTimeSeries to list Blobs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}

Type:

str

blob_ids

IDs of the blobs to read.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.ReadTensorboardBlobDataResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.ReadTensorboardBlobData][google.cloud.aiplatform.v1.TensorboardService.ReadTensorboardBlobData].

blobs

Blob messages containing blob bytes.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TensorboardBlob]

class google.cloud.aiplatform_v1.types.ReadTensorboardSizeRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.ReadTensorboardSize][google.cloud.aiplatform.v1.TensorboardService.ReadTensorboardSize].

tensorboard

Required. The name of the Tensorboard resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

Type:

str

class google.cloud.aiplatform_v1.types.ReadTensorboardSizeResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.ReadTensorboardSize][google.cloud.aiplatform.v1.TensorboardService.ReadTensorboardSize].

storage_size_byte

Payload storage size for the TensorBoard

Type:

int

class google.cloud.aiplatform_v1.types.ReadTensorboardTimeSeriesDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.ReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.ReadTensorboardTimeSeriesData].

tensorboard_time_series

Required. The resource name of the TensorboardTimeSeries to read data from. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}

Type:

str

max_data_points

The maximum number of TensorboardTimeSeries’ data to return. This value should be a positive integer. This value can be set to -1 to return all data.

Type:

int

filter

Reads the TensorboardTimeSeries’ data that match the filter expression.

Type:

str

class google.cloud.aiplatform_v1.types.ReadTensorboardTimeSeriesDataResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.ReadTensorboardTimeSeriesData][google.cloud.aiplatform.v1.TensorboardService.ReadTensorboardTimeSeriesData].

time_series_data

The returned time series data.

Type:

google.cloud.aiplatform_v1.types.TimeSeriesData

class google.cloud.aiplatform_v1.types.ReadTensorboardUsageRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.ReadTensorboardUsage][google.cloud.aiplatform.v1.TensorboardService.ReadTensorboardUsage].

tensorboard

Required. The name of the Tensorboard resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

Type:

str

class google.cloud.aiplatform_v1.types.ReadTensorboardUsageResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.ReadTensorboardUsage][google.cloud.aiplatform.v1.TensorboardService.ReadTensorboardUsage].

monthly_usage_data

Maps year-month (YYYYMM) string to per month usage data.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.ReadTensorboardUsageResponse.PerMonthUsageData]

class MonthlyUsageDataEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class PerMonthUsageData(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Per month usage data

user_usage_data

Usage data for each user in the given month.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ReadTensorboardUsageResponse.PerUserUsageData]

class PerUserUsageData(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Per user usage data.

username

User’s username

Type:

str

view_count

Number of times the user has read data within the Tensorboard.

Type:

int

class google.cloud.aiplatform_v1.types.RebaseTunedModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [GenAiTuningService.RebaseTunedModel][google.cloud.aiplatform.v1.GenAiTuningService.RebaseTunedModel].

generic_metadata

The common part of the operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.RebaseTunedModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [GenAiTuningService.RebaseTunedModel][google.cloud.aiplatform.v1.GenAiTuningService.RebaseTunedModel].

parent

Required. The resource name of the Location into which to rebase the Model. Format: projects/{project}/locations/{location}

Type:

str

tuned_model_ref

Required. TunedModel reference to retrieve the legacy model information.

Type:

google.cloud.aiplatform_v1.types.TunedModelRef

tuning_job

Optional. The TuningJob to be updated. Users can use this TuningJob field to overwrite tuning configs.

Type:

google.cloud.aiplatform_v1.types.TuningJob

artifact_destination

Optional. The Google Cloud Storage location to write the artifacts.

Type:

google.cloud.aiplatform_v1.types.GcsDestination

deploy_to_same_endpoint

Optional. By default, bison to gemini migration will always create new model/endpoint, but for gemini-1.0 to gemini-1.5 migration, we default deploy to the same endpoint. See details in this Section.

Type:

bool

class google.cloud.aiplatform_v1.types.RebootPersistentResourceOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform reboot PersistentResource.

generic_metadata

Operation metadata for PersistentResource.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

progress_message

Progress Message for Reboot LRO

Type:

str

class google.cloud.aiplatform_v1.types.RebootPersistentResourceRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PersistentResourceService.RebootPersistentResource][google.cloud.aiplatform.v1.PersistentResourceService.RebootPersistentResource].

name

Required. The name of the PersistentResource resource. Format: projects/{project_id_or_number}/locations/{location_id}/persistentResources/{persistent_resource_id}

Type:

str

class google.cloud.aiplatform_v1.types.RemoveContextChildrenRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.DeleteContextChildrenRequest][].

context

Required. The resource name of the parent Context.

Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}

Type:

str

child_contexts

The resource names of the child Contexts.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.RemoveContextChildrenResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MetadataService.RemoveContextChildren][google.cloud.aiplatform.v1.MetadataService.RemoveContextChildren].

class google.cloud.aiplatform_v1.types.RemoveDatapointsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexService.RemoveDatapoints][google.cloud.aiplatform.v1.IndexService.RemoveDatapoints]

index

Required. The name of the Index resource to be updated. Format: projects/{project}/locations/{location}/indexes/{index}

Type:

str

datapoint_ids

A list of datapoint ids to be deleted.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.RemoveDatapointsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [IndexService.RemoveDatapoints][google.cloud.aiplatform.v1.IndexService.RemoveDatapoints]

class google.cloud.aiplatform_v1.types.ReservationAffinity(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A ReservationAffinity can be used to configure a Vertex AI resource (e.g., a DeployedModel) to draw its Compute Engine resources from a Shared Reservation, or exclusively from on-demand capacity.

reservation_affinity_type

Required. Specifies the reservation affinity type.

Type:

google.cloud.aiplatform_v1.types.ReservationAffinity.Type

key

Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.

Type:

str

values

Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation.

Type:

MutableSequence[str]

class Type(value)

Bases: Enum

Identifies a type of reservation affinity.

Values:

TYPE_UNSPECIFIED (0):

Default value. This should not be used.

NO_RESERVATION (1):

Do not consume from any reserved capacity, only use on-demand.

ANY_RESERVATION (2):

Consume any reservation available, falling back to on-demand.

SPECIFIC_RESERVATION (3):

Consume from a specific reservation. When chosen, the reservation must be identified via the key and values fields.

class google.cloud.aiplatform_v1.types.ResourcePool(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

id

Immutable. The unique ID in a PersistentResource for referring to this resource pool. User can specify it if necessary. Otherwise, it’s generated automatically.

Type:

str

machine_spec

Required. Immutable. The specification of a single machine.

Type:

google.cloud.aiplatform_v1.types.MachineSpec

replica_count

Optional. The total number of machines to use for this resource pool.

This field is a member of oneof _replica_count.

Type:

int

disk_spec

Optional. Disk spec for the machine in this node pool.

Type:

google.cloud.aiplatform_v1.types.DiskSpec

used_replica_count

Output only. The number of machines currently in use by training jobs for this resource pool. Will replace idle_replica_count.

Type:

int

autoscaling_spec

Optional. Optional spec to configure GKE or Ray-on-Vertex autoscaling

Type:

google.cloud.aiplatform_v1.types.ResourcePool.AutoscalingSpec

class AutoscalingSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The min/max number of replicas allowed if enabling autoscaling

min_replica_count

Optional. min replicas in the node pool, must be ≤ replica_count and < max_replica_count or will throw error. For autoscaling enabled Ray-on-Vertex, we allow min_replica_count of a resource_pool to be 0 to match the OSS Ray behavior(https://docs.ray.io/en/latest/cluster/vms/user-guides/configuring-autoscaling.html#cluster-config-parameters). As for Persistent Resource, the min_replica_count must be > 0, we added a corresponding validation inside CreatePersistentResourceRequestValidator.java.

This field is a member of oneof _min_replica_count.

Type:

int

max_replica_count

Optional. max replicas in the node pool, must be ≥ replica_count and > min_replica_count or will throw error

This field is a member of oneof _max_replica_count.

Type:

int

class google.cloud.aiplatform_v1.types.ResourceRuntime(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Persistent Cluster runtime information as output

access_uris

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” }

Type:

MutableMapping[str, str]

class AccessUrisEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.ResourceRuntimeSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

service_account_spec

Optional. Configure the use of workload identity on the PersistentResource

Type:

google.cloud.aiplatform_v1.types.ServiceAccountSpec

ray_spec

Optional. Ray cluster configuration. Required when creating a dedicated RayCluster on the PersistentResource.

Type:

google.cloud.aiplatform_v1.types.RaySpec

class google.cloud.aiplatform_v1.types.ResourcesConsumed(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Statistics information about resource consumption.

replica_hours

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.

Type:

float

class google.cloud.aiplatform_v1.types.RestoreDatasetVersionOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [DatasetService.RestoreDatasetVersion][google.cloud.aiplatform.v1.DatasetService.RestoreDatasetVersion].

generic_metadata

The common part of the operation metadata.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.RestoreDatasetVersionRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.RestoreDatasetVersion][google.cloud.aiplatform.v1.DatasetService.RestoreDatasetVersion].

name

Required. The name of the DatasetVersion resource. Format: projects/{project}/locations/{location}/datasets/{dataset}/datasetVersions/{dataset_version}

Type:

str

class google.cloud.aiplatform_v1.types.ResumeModelDeploymentMonitoringJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.ResumeModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.ResumeModelDeploymentMonitoringJob].

name

Required. The resource name of the ModelDeploymentMonitoringJob to resume. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

Type:

str

class google.cloud.aiplatform_v1.types.ResumeScheduleRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ScheduleService.ResumeSchedule][google.cloud.aiplatform.v1.ScheduleService.ResumeSchedule].

name

Required. The name of the Schedule resource to be resumed. Format: projects/{project}/locations/{location}/schedules/{schedule}

Type:

str

catch_up

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][google.cloud.aiplatform.v1.Schedule.catch_up] field. Default to false.

Type:

bool

class google.cloud.aiplatform_v1.types.Retrieval(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

vertex_ai_search

Set to use data source powered by Vertex AI Search.

This field is a member of oneof source.

Type:

google.cloud.aiplatform_v1.types.VertexAISearch

disable_attribution

Optional. Deprecated. This option is no longer supported.

Type:

bool

class google.cloud.aiplatform_v1.types.RetrievalMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata related to retrieval in the grounding flow.

google_search_dynamic_retrieval_score

Optional. Score indicating how likely information from Google Search could help answer the prompt. The score is in the range [0, 1], where 0 is the least likely and 1 is the most likely. This score is only populated when Google Search grounding and dynamic retrieval is enabled. It will be compared to the threshold to determine whether to trigger Google Search.

Type:

float

class google.cloud.aiplatform_v1.types.RougeInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for rouge metric.

metric_spec

Required. Spec for rouge score metric.

Type:

google.cloud.aiplatform_v1.types.RougeSpec

instances

Required. Repeated rouge instances.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.RougeInstance]

class google.cloud.aiplatform_v1.types.RougeInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for rouge instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Required. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

class google.cloud.aiplatform_v1.types.RougeMetricValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Rouge metric value for an instance.

score

Output only. Rouge score.

This field is a member of oneof _score.

Type:

float

class google.cloud.aiplatform_v1.types.RougeResults(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Results for rouge metric.

rouge_metric_values

Output only. Rouge metric values.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.RougeMetricValue]

class google.cloud.aiplatform_v1.types.RougeSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

rouge_type

Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.

Type:

str

use_stemmer

Optional. Whether to use stemmer to compute rouge score.

Type:

bool

split_summaries

Optional. Whether to split summaries while using rougeLsum.

Type:

bool

class google.cloud.aiplatform_v1.types.SafetyInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for safety metric.

metric_spec

Required. Spec for safety metric.

Type:

google.cloud.aiplatform_v1.types.SafetySpec

instance

Required. Safety instance.

Type:

google.cloud.aiplatform_v1.types.SafetyInstance

class google.cloud.aiplatform_v1.types.SafetyInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for safety instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

class google.cloud.aiplatform_v1.types.SafetyRating(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Safety rating corresponding to the generated content.

category

Output only. Harm category.

Type:

google.cloud.aiplatform_v1.types.HarmCategory

probability

Output only. Harm probability levels in the content.

Type:

google.cloud.aiplatform_v1.types.SafetyRating.HarmProbability

probability_score

Output only. Harm probability score.

Type:

float

severity

Output only. Harm severity levels in the content.

Type:

google.cloud.aiplatform_v1.types.SafetyRating.HarmSeverity

severity_score

Output only. Harm severity score.

Type:

float

blocked

Output only. Indicates whether the content was filtered out because of this rating.

Type:

bool

class HarmProbability(value)

Bases: Enum

Harm probability levels in the content.

Values:

HARM_PROBABILITY_UNSPECIFIED (0):

Harm probability unspecified.

NEGLIGIBLE (1):

Negligible level of harm.

LOW (2):

Low level of harm.

MEDIUM (3):

Medium level of harm.

HIGH (4):

High level of harm.

class HarmSeverity(value)

Bases: Enum

Harm severity levels.

Values:

HARM_SEVERITY_UNSPECIFIED (0):

Harm severity unspecified.

HARM_SEVERITY_NEGLIGIBLE (1):

Negligible level of harm severity.

HARM_SEVERITY_LOW (2):

Low level of harm severity.

HARM_SEVERITY_MEDIUM (3):

Medium level of harm severity.

HARM_SEVERITY_HIGH (4):

High level of harm severity.

class google.cloud.aiplatform_v1.types.SafetyResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for safety result.

score

Output only. Safety score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for safety score.

Type:

str

confidence

Output only. Confidence for safety score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.SafetySetting(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Safety settings.

category

Required. Harm category.

Type:

google.cloud.aiplatform_v1.types.HarmCategory

threshold

Required. The harm block threshold.

Type:

google.cloud.aiplatform_v1.types.SafetySetting.HarmBlockThreshold

method

Optional. Specify if the threshold is used for probability or severity score. If not specified, the threshold is used for probability score.

Type:

google.cloud.aiplatform_v1.types.SafetySetting.HarmBlockMethod

class HarmBlockMethod(value)

Bases: Enum

Probability vs severity.

Values:

HARM_BLOCK_METHOD_UNSPECIFIED (0):

The harm block method is unspecified.

SEVERITY (1):

The harm block method uses both probability and severity scores.

PROBABILITY (2):

The harm block method uses the probability score.

class HarmBlockThreshold(value)

Bases: Enum

Probability based thresholds levels for blocking.

Values:

HARM_BLOCK_THRESHOLD_UNSPECIFIED (0):

Unspecified harm block threshold.

BLOCK_LOW_AND_ABOVE (1):

Block low threshold and above (i.e. block more).

BLOCK_MEDIUM_AND_ABOVE (2):

Block medium threshold and above.

BLOCK_ONLY_HIGH (3):

Block only high threshold (i.e. block less).

BLOCK_NONE (4):

Block none.

OFF (5):

Turn off the safety filter.

class google.cloud.aiplatform_v1.types.SafetySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for safety metric.

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.SampleConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

initial_batch_sample_percentage

The percentage of data needed to be labeled in the first batch.

This field is a member of oneof initial_batch_sample_size.

Type:

int

following_batch_sample_percentage

The percentage of data needed to be labeled in each following batch (except the first batch).

This field is a member of oneof following_batch_sample_size.

Type:

int

sample_strategy

Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.

Type:

google.cloud.aiplatform_v1.types.SampleConfig.SampleStrategy

class SampleStrategy(value)

Bases: Enum

Sample strategy decides which subset of DataItems should be selected for human labeling in every batch.

Values:

SAMPLE_STRATEGY_UNSPECIFIED (0):

Default will be treated as UNCERTAINTY.

UNCERTAINTY (1):

Sample the most uncertain data to label.

class google.cloud.aiplatform_v1.types.SampledShapleyAttribution(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

path_count

Required. The number of feature permutations to consider when approximating the Shapley values.

Valid range of its value is [1, 50], inclusively.

Type:

int

class google.cloud.aiplatform_v1.types.SamplingStrategy(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

random_sample_config

Random sample config. Will support more sampling strategies later.

Type:

google.cloud.aiplatform_v1.types.SamplingStrategy.RandomSampleConfig

class RandomSampleConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Requests are randomly selected.

sample_rate

Sample rate (0, 1]

Type:

float

class google.cloud.aiplatform_v1.types.SavedQuery(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

name

Output only. Resource name of the SavedQuery.

Type:

str

display_name

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.

Type:

str

metadata

Some additional information about the SavedQuery.

Type:

google.protobuf.struct_pb2.Value

create_time

Output only. Timestamp when this SavedQuery was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when SavedQuery was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

annotation_filter

Output only. Filters on the Annotations in the dataset.

Type:

str

problem_type

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

Type:

str

annotation_spec_count

Output only. Number of AnnotationSpecs in the context of the SavedQuery.

Type:

int

etag

Used to perform a consistent read-modify-write update. If not set, a blind “overwrite” update happens.

Type:

str

support_automl_training

Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.

Type:

bool

class google.cloud.aiplatform_v1.types.Scalar(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

One point viewable on a scalar metric plot.

value

Value of the point at this step / timestamp.

Type:

float

class google.cloud.aiplatform_v1.types.Schedule(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

cron

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 * * * *”.

This field is a member of oneof time_specification.

Type:

str

create_pipeline_job_request

Request for [PipelineService.CreatePipelineJob][google.cloud.aiplatform.v1.PipelineService.CreatePipelineJob]. CreatePipelineJobRequest.parent field is required (format: projects/{project}/locations/{location}).

This field is a member of oneof request.

Type:

google.cloud.aiplatform_v1.types.CreatePipelineJobRequest

create_notebook_execution_job_request

Request for [NotebookService.CreateNotebookExecutionJob][google.cloud.aiplatform.v1.NotebookService.CreateNotebookExecutionJob].

This field is a member of oneof request.

Type:

google.cloud.aiplatform_v1.types.CreateNotebookExecutionJobRequest

name

Immutable. The resource name of the Schedule.

Type:

str

display_name

Required. User provided name of the Schedule. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

start_time

Optional. Timestamp after which the first run can be scheduled. Default to Schedule create time if not specified.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

max_run_count

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.

Type:

int

started_run_count

Output only. The number of runs started by this schedule.

Type:

int

state

Output only. The state of this Schedule.

Type:

google.cloud.aiplatform_v1.types.Schedule.State

create_time

Output only. Timestamp when this Schedule was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this Schedule was updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

next_run_time

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.

Type:

google.protobuf.timestamp_pb2.Timestamp

last_pause_time

Output only. Timestamp when this Schedule was last paused. Unset if never paused.

Type:

google.protobuf.timestamp_pb2.Timestamp

last_resume_time

Output only. Timestamp when this Schedule was last resumed. Unset if never resumed from pause.

Type:

google.protobuf.timestamp_pb2.Timestamp

max_concurrent_run_count

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).

Type:

int

allow_queueing

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.

Type:

bool

catch_up

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.

Type:

bool

last_scheduled_run_response

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.

Type:

google.cloud.aiplatform_v1.types.Schedule.RunResponse

class RunResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Status of a scheduled run.

scheduled_run_time

The scheduled run time based on the user-specified schedule.

Type:

google.protobuf.timestamp_pb2.Timestamp

run_response

The response of the scheduled run.

Type:

str

class State(value)

Bases: Enum

Possible state of the schedule.

Values:

STATE_UNSPECIFIED (0):

Unspecified.

ACTIVE (1):

The Schedule is active. Runs are being scheduled on the user-specified timespec.

PAUSED (2):

The schedule is paused. No new runs will be created until the schedule is resumed. Already started runs will be allowed to complete.

COMPLETED (3):

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.

class google.cloud.aiplatform_v1.types.Scheduling(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

All parameters related to queuing and scheduling of custom jobs.

timeout

Optional. The maximum job running time. The default is 7 days.

Type:

google.protobuf.duration_pb2.Duration

restart_job_on_worker_restart

Optional. 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.

Type:

bool

strategy

Optional. This determines which type of scheduling strategy to use.

Type:

google.cloud.aiplatform_v1.types.Scheduling.Strategy

disable_retries

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.

Type:

bool

max_wait_duration

Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours.

Type:

google.protobuf.duration_pb2.Duration

class Strategy(value)

Bases: Enum

Optional. This determines which type of scheduling strategy to use. Right now users have two options such as STANDARD which will use regular on demand resources to schedule the job, the other is SPOT which would leverage spot resources alongwith regular resources to schedule the job.

Values:

STRATEGY_UNSPECIFIED (0):

Strategy will default to STANDARD.

ON_DEMAND (1):

Deprecated. Regular on-demand provisioning strategy.

LOW_COST (2):

Deprecated. Low cost by making potential use of spot resources.

STANDARD (3):

Standard provisioning strategy uses regular on-demand resources.

SPOT (4):

Spot provisioning strategy uses spot resources.

FLEX_START (6):

Flex Start strategy uses DWS to queue for resources.

class google.cloud.aiplatform_v1.types.Schema(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

type_

Optional. The type of the data.

Type:

google.cloud.aiplatform_v1.types.Type

format_

Optional. The format of the data. Supported formats:

for NUMBER type: “float”, “double” for INTEGER type: “int32”, “int64” for STRING type: “email”, “byte”, etc

Type:

str

title

Optional. The title of the Schema.

Type:

str

description

Optional. The description of the data.

Type:

str

nullable

Optional. Indicates if the value may be null.

Type:

bool

default

Optional. Default value of the data.

Type:

google.protobuf.struct_pb2.Value

items

Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.

Type:

google.cloud.aiplatform_v1.types.Schema

min_items

Optional. Minimum number of the elements for Type.ARRAY.

Type:

int

max_items

Optional. Maximum number of the elements for Type.ARRAY.

Type:

int

enum

Optional. Possible values of the element of primitive type with enum format. Examples:

1. We can define direction as : {type:STRING, format:enum, enum:[“EAST”, NORTH”, “SOUTH”, “WEST”]}

2. We can define apartment number as : {type:INTEGER, format:enum, enum:[“101”, “201”, “301”]}

Type:

MutableSequence[str]

properties

Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.Schema]

property_ordering

Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.

Type:

MutableSequence[str]

required

Optional. Required properties of Type.OBJECT.

Type:

MutableSequence[str]

min_properties

Optional. Minimum number of the properties for Type.OBJECT.

Type:

int

max_properties

Optional. Maximum number of the properties for Type.OBJECT.

Type:

int

minimum

Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER

Type:

float

maximum

Optional. Maximum value of the Type.INTEGER and Type.NUMBER

Type:

float

min_length

Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING

Type:

int

max_length

Optional. Maximum length of the Type.STRING

Type:

int

pattern

Optional. Pattern of the Type.STRING to restrict a string to a regular expression.

Type:

str

example

Optional. Example of the object. Will only populated when the object is the root.

Type:

google.protobuf.struct_pb2.Value

any_of

Optional. The value should be validated against any (one or more) of the subschemas in the list.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Schema]

class PropertiesEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.SearchDataItemsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.SearchDataItems][google.cloud.aiplatform.v1.DatasetService.SearchDataItems].

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

order_by_data_item

A comma-separated list of data item fields to order by, sorted in ascending order. Use “desc” after a field name for descending.

This field is a member of oneof order.

Type:

str

order_by_annotation

Expression that allows ranking results based on annotation’s property.

This field is a member of oneof order.

Type:

google.cloud.aiplatform_v1.types.SearchDataItemsRequest.OrderByAnnotation

dataset

Required. The resource name of the Dataset from which to search DataItems. Format: projects/{project}/locations/{location}/datasets/{dataset}

Type:

str

saved_query

The resource name of a SavedQuery(annotation set in UI). Format: projects/{project}/locations/{location}/datasets/{dataset}/savedQueries/{saved_query} All of the search will be done in the context of this SavedQuery.

Type:

str

data_labeling_job

The resource name of a DataLabelingJob. Format: projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job} If this field is set, all of the search will be done in the context of this DataLabelingJob.

Type:

str

data_item_filter

An expression for filtering the DataItem that will be returned.

• data_item_id - for = or !=.

• labeled - for = or !=.

• has_annotation(ANNOTATION_SPEC_ID) - true only for DataItem that have at least one annotation with annotation_spec_id = ANNOTATION_SPEC_ID in the context of SavedQuery or DataLabelingJob.

For example:

• data_item=1

• has_annotation(5)

Type:

str

annotations_filter

An expression for filtering the Annotations that will be returned per DataItem.

• annotation_spec_id - for = or !=.

Type:

str

annotation_filters

An expression that specifies what Annotations will be returned per DataItem. Annotations satisfied either of the conditions will be returned.

• annotation_spec_id - for = or !=. Must specify saved_query_id= - saved query id that annotations should belong to.

Type:

MutableSequence[str]

field_mask

Mask specifying which fields of [DataItemView][google.cloud.aiplatform.v1.DataItemView] to read.

Type:

google.protobuf.field_mask_pb2.FieldMask

annotations_limit

If set, only up to this many of Annotations will be returned per DataItemView. The maximum value is 1000. If not set, the maximum value will be used.

Type:

int

page_size

Requested page size. Server may return fewer results than requested. Default and maximum page size is 100.

Type:

int

order_by

A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending.

Type:

str

page_token

A token identifying a page of results for the server to return Typically obtained via [SearchDataItemsResponse.next_page_token][google.cloud.aiplatform.v1.SearchDataItemsResponse.next_page_token] of the previous [DatasetService.SearchDataItems][google.cloud.aiplatform.v1.DatasetService.SearchDataItems] call.

Type:

str

class OrderByAnnotation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Expression that allows ranking results based on annotation’s property.

saved_query

Required. Saved query of the Annotation. Only Annotations belong to this saved query will be considered for ordering.

Type:

str

order_by

A comma-separated list of annotation fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Must also specify saved_query.

Type:

str

class google.cloud.aiplatform_v1.types.SearchDataItemsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [DatasetService.SearchDataItems][google.cloud.aiplatform.v1.DatasetService.SearchDataItems].

data_item_views

The DataItemViews read.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.DataItemView]

next_page_token

A token to retrieve next page of results. Pass to [SearchDataItemsRequest.page_token][google.cloud.aiplatform.v1.SearchDataItemsRequest.page_token] to obtain that page.

Type:

str

class google.cloud.aiplatform_v1.types.SearchEntryPoint(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Google search entry point.

rendered_content

Optional. Web content snippet that can be embedded in a web page or an app webview.

Type:

str

sdk_blob

Optional. Base64 encoded JSON representing array of <search term, search url> tuple.

Type:

bytes

class google.cloud.aiplatform_v1.types.SearchFeaturesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1.FeaturestoreService.SearchFeatures].

location

Required. The resource name of the Location to search Features. Format: projects/{project}/locations/{location}

Type:

str

query

Query string that is a conjunction of field-restricted queries and/or field-restricted filters. Field-restricted queries and filters can be combined using AND to form a conjunction.

A field query is in the form FIELD:QUERY. This implicitly checks if QUERY exists as a substring within Feature’s FIELD. The QUERY and the FIELD are converted to a sequence of words (i.e. tokens) for comparison. This is done by:

• Removing leading/trailing whitespace and tokenizing the search value. Characters that are not one of alphanumeric [a-zA-Z0-9], underscore _, or asterisk * are treated as delimiters for tokens. * is treated as a wildcard that matches characters within a token.

• Ignoring case.

• Prepending an asterisk to the first and appending an asterisk to the last token in QUERY.

A QUERY must be either a singular token or a phrase. A phrase is one or multiple words enclosed in double quotation marks (“). With phrases, the order of the words is important. Words in the phrase must be matching in order and consecutively.

Supported FIELDs for field-restricted queries:

• feature_id

• description

• entity_type_id

Examples:

• feature_id: foo –> Matches a Feature with ID containing the substring foo (eg. foo, foofeature, barfoo).

• feature_id: foo*feature –> Matches a Feature with ID containing the substring foo*feature (eg. foobarfeature).

• feature_id: foo AND description: bar –> Matches a Feature with ID containing the substring foo and description containing the substring bar.

Besides field queries, the following exact-match filters are supported. The exact-match filters do not support wildcards. Unlike field-restricted queries, exact-match filters are case-sensitive.

• feature_id: Supports = comparisons.

• description: Supports = comparisons. Multi-token filters should be enclosed in quotes.

• entity_type_id: Supports = comparisons.

• value_type: Supports = and != comparisons.

• labels: Supports key-value equality as well as key presence.

• featurestore_id: Supports = comparisons.

Examples:

• description = "foo bar" –> Any Feature with description exactly equal to foo bar

• value_type = DOUBLE –> Features whose type is DOUBLE.

• labels.active = yes AND labels.env = prod –> Features having both (active: yes) and (env: prod) labels.

• labels.env: * –> Any Feature which has a label with env as the key.

Type:

str

page_size

The maximum number of Features to return. The service may return fewer than this value. If unspecified, at most 100 Features will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100.

Type:

int

page_token

A page token, received from a previous [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1.FeaturestoreService.SearchFeatures] call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1.FeaturestoreService.SearchFeatures], except page_size, must match the call that provided the page token.

Type:

str

class google.cloud.aiplatform_v1.types.SearchFeaturesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeaturestoreService.SearchFeatures][google.cloud.aiplatform.v1.FeaturestoreService.SearchFeatures].

features

The Features matching the request.

Fields returned:

• name

• description

• labels

• create_time

• update_time

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Feature]

next_page_token

A token, which can be sent as [SearchFeaturesRequest.page_token][google.cloud.aiplatform.v1.SearchFeaturesRequest.page_token] to retrieve the next page. If this field is omitted, there are no subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.SearchMigratableResourcesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MigrationService.SearchMigratableResources][google.cloud.aiplatform.v1.MigrationService.SearchMigratableResources].

parent

Required. The location that the migratable resources should be searched from. It’s the Vertex AI location that the resources can be migrated to, not the resources’ original location. Format: projects/{project}/locations/{location}

Type:

str

page_size

The standard page size. The default and maximum value is 100.

Type:

int

page_token

The standard page token.

Type:

str

filter

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][google.cloud.aiplatform.v1.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.

Type:

str

class google.cloud.aiplatform_v1.types.SearchMigratableResourcesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [MigrationService.SearchMigratableResources][google.cloud.aiplatform.v1.MigrationService.SearchMigratableResources].

migratable_resources

All migratable resources that can be migrated to the location specified in the request.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.MigratableResource]

next_page_token

The standard next-page token. The migratable_resources may not fill page_size in SearchMigratableResourcesRequest even when there are subsequent pages.

Type:

str

class google.cloud.aiplatform_v1.types.SearchModelDeploymentMonitoringStatsAnomaliesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.SearchModelDeploymentMonitoringStatsAnomalies][google.cloud.aiplatform.v1.JobService.SearchModelDeploymentMonitoringStatsAnomalies].

model_deployment_monitoring_job

Required. ModelDeploymentMonitoring Job resource name. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

Type:

str

deployed_model_id

Required. The DeployedModel ID of the [ModelDeploymentMonitoringObjectiveConfig.deployed_model_id].

Type:

str

feature_display_name

The feature display name. If specified, only return the stats belonging to this feature. Format: [ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.feature_display_name][google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.feature_display_name], example: “user_destination”.

Type:

str

objectives

Required. Objectives of the stats to retrieve.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.SearchModelDeploymentMonitoringStatsAnomaliesRequest.StatsAnomaliesObjective]

page_size

The standard list page size.

Type:

int

page_token

A page token received from a previous [JobService.SearchModelDeploymentMonitoringStatsAnomalies][google.cloud.aiplatform.v1.JobService.SearchModelDeploymentMonitoringStatsAnomalies] call.

Type:

str

start_time

The earliest timestamp of stats being generated. If not set, indicates fetching stats till the earliest possible one.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

The latest timestamp of stats being generated. If not set, indicates feching stats till the latest possible one.

Type:

google.protobuf.timestamp_pb2.Timestamp

class StatsAnomaliesObjective(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Stats requested for specific objective.

type_

Type:

google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringObjectiveType

top_feature_count

If set, all attribution scores between [SearchModelDeploymentMonitoringStatsAnomaliesRequest.start_time][google.cloud.aiplatform.v1.SearchModelDeploymentMonitoringStatsAnomaliesRequest.start_time] and [SearchModelDeploymentMonitoringStatsAnomaliesRequest.end_time][google.cloud.aiplatform.v1.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:

int

class google.cloud.aiplatform_v1.types.SearchModelDeploymentMonitoringStatsAnomaliesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [JobService.SearchModelDeploymentMonitoringStatsAnomalies][google.cloud.aiplatform.v1.JobService.SearchModelDeploymentMonitoringStatsAnomalies].

monitoring_stats

Stats retrieved for requested objectives. There are at most 1000 [ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.prediction_stats][google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies.prediction_stats] in the response.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ModelMonitoringStatsAnomalies]

next_page_token

The page token that can be used by the next [JobService.SearchModelDeploymentMonitoringStatsAnomalies][google.cloud.aiplatform.v1.JobService.SearchModelDeploymentMonitoringStatsAnomalies] call.

Type:

str

class google.cloud.aiplatform_v1.types.SearchNearestEntitiesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The request message for [FeatureOnlineStoreService.SearchNearestEntities][google.cloud.aiplatform.v1.FeatureOnlineStoreService.SearchNearestEntities].

feature_view

Required. FeatureView resource format projects/{project}/locations/{location}/featureOnlineStores/{featureOnlineStore}/featureViews/{featureView}

Type:

str

query

Required. The query.

Type:

google.cloud.aiplatform_v1.types.NearestNeighborQuery

return_full_entity

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.

Type:

bool

class google.cloud.aiplatform_v1.types.SearchNearestEntitiesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeatureOnlineStoreService.SearchNearestEntities][google.cloud.aiplatform.v1.FeatureOnlineStoreService.SearchNearestEntities]

nearest_neighbors

The nearest neighbors of the query entity.

Type:

google.cloud.aiplatform_v1.types.NearestNeighbors

class google.cloud.aiplatform_v1.types.Segment(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Segment of the content.

part_index

Output only. The index of a Part object within its parent Content object.

Type:

int

start_index

Output only. Start index in the given Part, measured in bytes. Offset from the start of the Part, inclusive, starting at zero.

Type:

int

end_index

Output only. End index in the given Part, measured in bytes. Offset from the start of the Part, exclusive, starting at zero.

Type:

int

text

Output only. The text corresponding to the segment from the response.

Type:

str

class google.cloud.aiplatform_v1.types.ServiceAccountSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Configuration for the use of custom service account to run the workloads.

enable_custom_service_account

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.

Type:

bool

service_account

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.

Type:

str

class google.cloud.aiplatform_v1.types.ShieldedVmConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A set of Shielded Instance options. See Images using supported Shielded VM features.

enable_secure_boot

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.

Type:

bool

class google.cloud.aiplatform_v1.types.SmoothGradConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

noise_sigma

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][google.cloud.aiplatform.v1.SmoothGradConfig.feature_noise_sigma] instead for each feature.

This field is a member of oneof GradientNoiseSigma.

Type:

float

feature_noise_sigma

This is similar to [noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.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][google.cloud.aiplatform.v1.SmoothGradConfig.noise_sigma] will be used for all features.

This field is a member of oneof GradientNoiseSigma.

Type:

google.cloud.aiplatform_v1.types.FeatureNoiseSigma

noisy_sample_count

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.

Type:

int

class google.cloud.aiplatform_v1.types.SpecialistPool(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

name

Required. The resource name of the SpecialistPool.

Type:

str

display_name

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.

Type:

str

specialist_managers_count

Output only. The number of managers in this SpecialistPool.

Type:

int

specialist_manager_emails

The email addresses of the managers in the SpecialistPool.

Type:

MutableSequence[str]

pending_data_labeling_jobs

Output only. The resource name of the pending data labeling jobs.

Type:

MutableSequence[str]

specialist_worker_emails

The email addresses of workers in the SpecialistPool.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.StartNotebookRuntimeOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata information for [NotebookService.StartNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.StartNotebookRuntime].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

progress_message

A human-readable message that shows the intermediate progress details of NotebookRuntime.

Type:

str

class google.cloud.aiplatform_v1.types.StartNotebookRuntimeRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.StartNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.StartNotebookRuntime].

name

Required. The name of the NotebookRuntime resource to be started. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner.

Type:

str

class google.cloud.aiplatform_v1.types.StartNotebookRuntimeResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [NotebookService.StartNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.StartNotebookRuntime].

class google.cloud.aiplatform_v1.types.StopNotebookRuntimeOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata information for [NotebookService.StopNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.StopNotebookRuntime].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.StopNotebookRuntimeRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.StopNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.StopNotebookRuntime].

name

Required. The name of the NotebookRuntime resource to be stopped. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner.

Type:

str

class google.cloud.aiplatform_v1.types.StopNotebookRuntimeResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [NotebookService.StopNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.StopNotebookRuntime].

class google.cloud.aiplatform_v1.types.StopTrialRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.StopTrial][google.cloud.aiplatform.v1.VizierService.StopTrial].

name

Required. The Trial’s name. Format: projects/{project}/locations/{location}/studies/{study}/trials/{trial}

Type:

str

class google.cloud.aiplatform_v1.types.StratifiedSplit(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

training_fraction

The fraction of the input data that is to be used to train the Model.

Type:

float

validation_fraction

The fraction of the input data that is to be used to validate the Model.

Type:

float

test_fraction

The fraction of the input data that is to be used to evaluate the Model.

Type:

float

key

Required. The key is a name of one of the Dataset’s data columns. The key provided must be for a categorical column.

Type:

str

class google.cloud.aiplatform_v1.types.StreamDirectPredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.StreamDirectPredict][google.cloud.aiplatform.v1.PredictionService.StreamDirectPredict].

The first message must contain [endpoint][google.cloud.aiplatform.v1.StreamDirectPredictRequest.endpoint] field and optionally [input][]. The subsequent messages must contain [input][].

endpoint

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

inputs

Optional. The prediction input.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Tensor]

parameters

Optional. The parameters that govern the prediction.

Type:

google.cloud.aiplatform_v1.types.Tensor

class google.cloud.aiplatform_v1.types.StreamDirectPredictResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PredictionService.StreamDirectPredict][google.cloud.aiplatform.v1.PredictionService.StreamDirectPredict].

outputs

The prediction output.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Tensor]

parameters

The parameters that govern the prediction.

Type:

google.cloud.aiplatform_v1.types.Tensor

class google.cloud.aiplatform_v1.types.StreamDirectRawPredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.StreamDirectRawPredict][google.cloud.aiplatform.v1.PredictionService.StreamDirectRawPredict].

The first message must contain [endpoint][google.cloud.aiplatform.v1.StreamDirectRawPredictRequest.endpoint] and [method_name][google.cloud.aiplatform.v1.StreamDirectRawPredictRequest.method_name] fields and optionally [input][google.cloud.aiplatform.v1.StreamDirectRawPredictRequest.input]. The subsequent messages must contain [input][google.cloud.aiplatform.v1.StreamDirectRawPredictRequest.input]. [method_name][google.cloud.aiplatform.v1.StreamDirectRawPredictRequest.method_name] in the subsequent messages have no effect.

endpoint

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

method_name

Optional. Fully qualified name of the API method being invoked to perform predictions.

Format: /namespace.Service/Method/ Example: /tensorflow.serving.PredictionService/Predict

Type:

str

input

Optional. The prediction input.

Type:

bytes

class google.cloud.aiplatform_v1.types.StreamDirectRawPredictResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PredictionService.StreamDirectRawPredict][google.cloud.aiplatform.v1.PredictionService.StreamDirectRawPredict].

output

The prediction output.

Type:

bytes

class google.cloud.aiplatform_v1.types.StreamRawPredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.StreamRawPredict][google.cloud.aiplatform.v1.PredictionService.StreamRawPredict].

endpoint

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

http_body

The prediction input. Supports HTTP headers and arbitrary data payload.

Type:

google.api.httpbody_pb2.HttpBody

class google.cloud.aiplatform_v1.types.StreamingPredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.StreamingPredict][google.cloud.aiplatform.v1.PredictionService.StreamingPredict].

The first message must contain [endpoint][google.cloud.aiplatform.v1.StreamingPredictRequest.endpoint] field and optionally [input][]. The subsequent messages must contain [input][].

endpoint

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

inputs

The prediction input.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Tensor]

parameters

The parameters that govern the prediction.

Type:

google.cloud.aiplatform_v1.types.Tensor

class google.cloud.aiplatform_v1.types.StreamingPredictResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PredictionService.StreamingPredict][google.cloud.aiplatform.v1.PredictionService.StreamingPredict].

outputs

The prediction output.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Tensor]

parameters

The parameters that govern the prediction.

Type:

google.cloud.aiplatform_v1.types.Tensor

class google.cloud.aiplatform_v1.types.StreamingRawPredictRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [PredictionService.StreamingRawPredict][google.cloud.aiplatform.v1.PredictionService.StreamingRawPredict].

The first message must contain [endpoint][google.cloud.aiplatform.v1.StreamingRawPredictRequest.endpoint] and [method_name][google.cloud.aiplatform.v1.StreamingRawPredictRequest.method_name] fields and optionally [input][google.cloud.aiplatform.v1.StreamingRawPredictRequest.input]. The subsequent messages must contain [input][google.cloud.aiplatform.v1.StreamingRawPredictRequest.input]. [method_name][google.cloud.aiplatform.v1.StreamingRawPredictRequest.method_name] in the subsequent messages have no effect.

endpoint

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

method_name

Fully qualified name of the API method being invoked to perform predictions.

Format: /namespace.Service/Method/ Example: /tensorflow.serving.PredictionService/Predict

Type:

str

input

The prediction input.

Type:

bytes

class google.cloud.aiplatform_v1.types.StreamingRawPredictResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [PredictionService.StreamingRawPredict][google.cloud.aiplatform.v1.PredictionService.StreamingRawPredict].

output

The prediction output.

Type:

bytes

class google.cloud.aiplatform_v1.types.StreamingReadFeatureValuesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreOnlineServingService.StreamingReadFeatureValues][google.cloud.aiplatform.v1.FeaturestoreOnlineServingService.StreamingReadFeatureValues].

entity_type

Required. The resource name of the entities’ type. Value format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}. For example, for a machine learning model predicting user clicks on a website, an EntityType ID could be user.

Type:

str

entity_ids

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.

Type:

MutableSequence[str]

feature_selector

Required. Selector choosing Features of the target EntityType. Feature IDs will be deduplicated.

Type:

google.cloud.aiplatform_v1.types.FeatureSelector

class google.cloud.aiplatform_v1.types.StringArray(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A list of string values.

values

A list of string values.

Type:

MutableSequence[str]

class google.cloud.aiplatform_v1.types.StructFieldValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

One field of a Struct (or object) type feature value.

name

Name of the field in the struct feature.

Type:

str

value

The value for this field.

Type:

google.cloud.aiplatform_v1.types.FeatureValue

class google.cloud.aiplatform_v1.types.StructValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Struct (or object) type feature value.

values

A list of field values.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.StructFieldValue]

class google.cloud.aiplatform_v1.types.Study(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A message representing a Study.

name

Output only. The name of a study. The study’s globally unique identifier. Format: projects/{project}/locations/{location}/studies/{study}

Type:

str

display_name

Required. Describes the Study, default value is empty string.

Type:

str

study_spec

Required. Configuration of the Study.

Type:

google.cloud.aiplatform_v1.types.StudySpec

state

Output only. The detailed state of a Study.

Type:

google.cloud.aiplatform_v1.types.Study.State

create_time

Output only. Time at which the study was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

inactive_reason

Output only. A human readable reason why the Study is inactive. This should be empty if a study is ACTIVE or COMPLETED.

Type:

str

class State(value)

Bases: Enum

Describes the Study state.

Values:

STATE_UNSPECIFIED (0):

The study state is unspecified.

ACTIVE (1):

The study is active.

INACTIVE (2):

The study is stopped due to an internal error.

COMPLETED (3):

The study is done when the service exhausts the parameter search space or max_trial_count is reached.

class google.cloud.aiplatform_v1.types.StudySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents specification of a Study.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

decay_curve_stopping_spec

The automated early stopping spec using decay curve rule.

This field is a member of oneof automated_stopping_spec.

Type:

google.cloud.aiplatform_v1.types.StudySpec.DecayCurveAutomatedStoppingSpec

median_automated_stopping_spec

The automated early stopping spec using median rule.

This field is a member of oneof automated_stopping_spec.

Type:

google.cloud.aiplatform_v1.types.StudySpec.MedianAutomatedStoppingSpec

convex_automated_stopping_spec

The automated early stopping spec using convex stopping rule.

This field is a member of oneof automated_stopping_spec.

Type:

google.cloud.aiplatform_v1.types.StudySpec.ConvexAutomatedStoppingSpec

metrics

Required. Metric specs for the Study.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.StudySpec.MetricSpec]

parameters

Required. The set of parameters to tune.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec]

algorithm

The search algorithm specified for the Study.

Type:

google.cloud.aiplatform_v1.types.StudySpec.Algorithm

observation_noise

The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.

Type:

google.cloud.aiplatform_v1.types.StudySpec.ObservationNoise

measurement_selection_type

Describe which measurement selection type will be used

Type:

google.cloud.aiplatform_v1.types.StudySpec.MeasurementSelectionType

study_stopping_config

Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.

This field is a member of oneof _study_stopping_config.

Type:

google.cloud.aiplatform_v1.types.StudySpec.StudyStoppingConfig

class Algorithm(value)

Bases: Enum

The available search algorithms for the Study.

Values:

ALGORITHM_UNSPECIFIED (0):

The default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.

GRID_SEARCH (2):

Simple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE.

RANDOM_SEARCH (3):

Simple random search within the feasible space.

class ConvexAutomatedStoppingSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

max_step_count

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.

Type:

int

min_step_count

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.

Type:

int

min_measurement_count

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.

Type:

int

learning_rate_parameter_name

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.

Type:

str

use_elapsed_duration

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.

Type:

bool

update_all_stopped_trials

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.

This field is a member of oneof _update_all_stopped_trials.

Type:

bool

class DecayCurveAutomatedStoppingSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

use_elapsed_duration

True if [Measurement.elapsed_duration][google.cloud.aiplatform.v1.Measurement.elapsed_duration] is used as the x-axis of each Trials Decay Curve. Otherwise, [Measurement.step_count][google.cloud.aiplatform.v1.Measurement.step_count] will be used as the x-axis.

Type:

bool

class MeasurementSelectionType(value)

Bases: Enum

This indicates which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. Choose this based on two considerations: A) Do you expect your measurements to monotonically improve? If so, choose LAST_MEASUREMENT. On the other hand, if you’re in a situation where your system can “over-train” and you expect the performance to get better for a while but then start declining, choose BEST_MEASUREMENT. B) Are your measurements significantly noisy and/or irreproducible? If so, BEST_MEASUREMENT will tend to be over-optimistic, and it may be better to choose LAST_MEASUREMENT. If both or neither of (A) and (B) apply, it doesn’t matter which selection type is chosen.

Values:

MEASUREMENT_SELECTION_TYPE_UNSPECIFIED (0):

Will be treated as LAST_MEASUREMENT.

LAST_MEASUREMENT (1):

Use the last measurement reported.

BEST_MEASUREMENT (2):

Use the best measurement reported.

class MedianAutomatedStoppingSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

use_elapsed_duration

True if median automated stopping rule applies on [Measurement.elapsed_duration][google.cloud.aiplatform.v1.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.

Type:

bool

class MetricSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents a metric to optimize.

metric_id

Required. The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.

Type:

str

goal

Required. The optimization goal of the metric.

Type:

google.cloud.aiplatform_v1.types.StudySpec.MetricSpec.GoalType

safety_config

Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.

This field is a member of oneof _safety_config.

Type:

google.cloud.aiplatform_v1.types.StudySpec.MetricSpec.SafetyMetricConfig

class GoalType(value)

Bases: Enum

The available types of optimization goals.

Values:

GOAL_TYPE_UNSPECIFIED (0):

Goal Type will default to maximize.

MAXIMIZE (1):

Maximize the goal metric.

MINIMIZE (2):

Minimize the goal metric.

class SafetyMetricConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Used in safe optimization to specify threshold levels and risk tolerance.

safety_threshold

Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.

Type:

float

desired_min_safe_trials_fraction

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.

This field is a member of oneof _desired_min_safe_trials_fraction.

Type:

float

class ObservationNoise(value)

Bases: Enum

Describes the noise level of the repeated observations.

“Noisy” means that the repeated observations with the same Trial parameters may lead to different metric evaluations.

Values:

OBSERVATION_NOISE_UNSPECIFIED (0):

The default noise level chosen by Vertex AI.

LOW (1):

Vertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.

HIGH (2):

Vertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.

class ParameterSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents a single parameter to optimize.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

double_value_spec

The value spec for a ‘DOUBLE’ parameter.

This field is a member of oneof parameter_value_spec.

Type:

google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec.DoubleValueSpec

integer_value_spec

The value spec for an ‘INTEGER’ parameter.

This field is a member of oneof parameter_value_spec.

Type:

google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec.IntegerValueSpec

categorical_value_spec

The value spec for a ‘CATEGORICAL’ parameter.

This field is a member of oneof parameter_value_spec.

Type:

google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec.CategoricalValueSpec

discrete_value_spec

The value spec for a ‘DISCRETE’ parameter.

This field is a member of oneof parameter_value_spec.

Type:

google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec.DiscreteValueSpec

parameter_id

Required. The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.

Type:

str

scale_type

How the parameter should be scaled. Leave unset for CATEGORICAL parameters.

Type:

google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec.ScaleType

conditional_parameter_specs

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec.ConditionalParameterSpec]

class CategoricalValueSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Value specification for a parameter in CATEGORICAL type.

values

Required. The list of possible categories.

Type:

MutableSequence[str]

default_value

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.

This field is a member of oneof _default_value.

Type:

str

class ConditionalParameterSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents a parameter spec with condition from its parent parameter.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

parent_discrete_values

The spec for matching values from a parent parameter of DISCRETE type.

This field is a member of oneof parent_value_condition.

Type:

google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec.ConditionalParameterSpec.DiscreteValueCondition

parent_int_values

The spec for matching values from a parent parameter of INTEGER type.

This field is a member of oneof parent_value_condition.

Type:

google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec.ConditionalParameterSpec.IntValueCondition

parent_categorical_values

The spec for matching values from a parent parameter of CATEGORICAL type.

This field is a member of oneof parent_value_condition.

Type:

google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec.ConditionalParameterSpec.CategoricalValueCondition

parameter_spec

Required. The spec for a conditional parameter.

Type:

google.cloud.aiplatform_v1.types.StudySpec.ParameterSpec

class CategoricalValueCondition(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents the spec to match categorical values from parent parameter.

values

Required. Matches values of the parent parameter of ‘CATEGORICAL’ type. All values must exist in categorical_value_spec of parent parameter.

Type:

MutableSequence[str]

class DiscreteValueCondition(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents the spec to match discrete values from parent parameter.

values

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.

Type:

MutableSequence[float]

class IntValueCondition(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents the spec to match integer values from parent parameter.

values

Required. Matches values of the parent parameter of ‘INTEGER’ type. All values must lie in integer_value_spec of parent parameter.

Type:

MutableSequence[int]

class DiscreteValueSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Value specification for a parameter in DISCRETE type.

values

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.

Type:

MutableSequence[float]

default_value

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.

This field is a member of oneof _default_value.

Type:

float

class DoubleValueSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Value specification for a parameter in DOUBLE type.

min_value

Required. Inclusive minimum value of the parameter.

Type:

float

max_value

Required. Inclusive maximum value of the parameter.

Type:

float

default_value

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.

This field is a member of oneof _default_value.

Type:

float

class IntegerValueSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Value specification for a parameter in INTEGER type.

min_value

Required. Inclusive minimum value of the parameter.

Type:

int

max_value

Required. Inclusive maximum value of the parameter.

Type:

int

default_value

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.

This field is a member of oneof _default_value.

Type:

int

class ScaleType(value)

Bases: Enum

The type of scaling that should be applied to this parameter.

Values:

SCALE_TYPE_UNSPECIFIED (0):

By default, no scaling is applied.

UNIT_LINEAR_SCALE (1):

Scales the feasible space to (0, 1) linearly.

UNIT_LOG_SCALE (2):

Scales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.

UNIT_REVERSE_LOG_SCALE (3):

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.

class StudyStoppingConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection.

should_stop_asap

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).

Type:

google.protobuf.wrappers_pb2.BoolValue

minimum_runtime_constraint

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.

Type:

google.cloud.aiplatform_v1.types.StudyTimeConstraint

maximum_runtime_constraint

If the specified time or duration has passed, stop the study.

Type:

google.cloud.aiplatform_v1.types.StudyTimeConstraint

min_num_trials

If there are fewer than this many COMPLETED trials, do not stop the study.

Type:

google.protobuf.wrappers_pb2.Int32Value

max_num_trials

If there are more than this many trials, stop the study.

Type:

google.protobuf.wrappers_pb2.Int32Value

max_num_trials_no_progress

If the objective value has not improved for this many consecutive trials, stop the study.

WARNING: Effective only for single-objective studies.

Type:

google.protobuf.wrappers_pb2.Int32Value

max_duration_no_progress

If the objective value has not improved for this much time, stop the study.

WARNING: Effective only for single-objective studies.

Type:

google.protobuf.duration_pb2.Duration

class google.cloud.aiplatform_v1.types.StudyTimeConstraint(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Time-based Constraint for Study

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

max_duration

Counts the wallclock time passed since the creation of this Study.

This field is a member of oneof constraint.

Type:

google.protobuf.duration_pb2.Duration

end_time

Compares the wallclock time to this time. Must use UTC timezone.

This field is a member of oneof constraint.

Type:

google.protobuf.timestamp_pb2.Timestamp

class google.cloud.aiplatform_v1.types.SuggestTrialsMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform Trials suggestion.

generic_metadata

Operation metadata for suggesting Trials.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

client_id

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.

Type:

str

class google.cloud.aiplatform_v1.types.SuggestTrialsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [VizierService.SuggestTrials][google.cloud.aiplatform.v1.VizierService.SuggestTrials].

parent

Required. The project and location that the Study belongs to. Format: projects/{project}/locations/{location}/studies/{study}

Type:

str

suggestion_count

Required. The number of suggestions requested. It must be positive.

Type:

int

client_id

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.

Type:

str

contexts

Optional. This allows you to specify the “context” for a Trial; a context is a slice (a subspace) of the search space.

Typical uses for contexts:

1. You are using Vizier to tune a server for best performance, but there’s a strong weekly cycle. The context specifies the day-of-week. This allows Tuesday to generalize from Wednesday without assuming that everything is identical.

2. Imagine you’re optimizing some medical treatment for people. As they walk in the door, you know certain facts about them (e.g. sex, weight, height, blood-pressure). Put that information in the context, and Vizier will adapt its suggestions to the patient.

3. You want to do a fair A/B test efficiently. Specify the “A” and “B” conditions as contexts, and Vizier will generalize between “A” and “B” conditions. If they are similar, this will allow Vizier to converge to the optimum faster than if “A” and “B” were separate Studies. NOTE: You can also enter contexts as REQUESTED Trials, e.g. via the CreateTrial() RPC; that’s the asynchronous option where you don’t need a close association between contexts and suggestions.

NOTE: All the Parameters you set in a context MUST be defined in the Study. NOTE: You must supply 0 or $suggestion_count contexts. If you don’t supply any contexts, Vizier will make suggestions from the full search space specified in the StudySpec; if you supply a full set of context, each suggestion will match the corresponding context. NOTE: A Context with no features set matches anything, and allows suggestions from the full search space. NOTE: Contexts MUST lie within the search space specified in the StudySpec. It’s an error if they don’t. NOTE: Contexts preferentially match ACTIVE then REQUESTED trials before new suggestions are generated. NOTE: Generation of suggestions involves a match between a Context and (optionally) a REQUESTED trial; if that match is not fully specified, a suggestion will be geneated in the merged subspace.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TrialContext]

class google.cloud.aiplatform_v1.types.SuggestTrialsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [VizierService.SuggestTrials][google.cloud.aiplatform.v1.VizierService.SuggestTrials].

trials

A list of Trials.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Trial]

study_state

The state of the Study.

Type:

google.cloud.aiplatform_v1.types.Study.State

start_time

The time at which the operation was started.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

The time at which operation processing completed.

Type:

google.protobuf.timestamp_pb2.Timestamp

class google.cloud.aiplatform_v1.types.SummarizationHelpfulnessInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for summarization helpfulness metric.

metric_spec

Required. Spec for summarization helpfulness score metric.

Type:

google.cloud.aiplatform_v1.types.SummarizationHelpfulnessSpec

instance

Required. Summarization helpfulness instance.

Type:

google.cloud.aiplatform_v1.types.SummarizationHelpfulnessInstance

class google.cloud.aiplatform_v1.types.SummarizationHelpfulnessInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for summarization helpfulness instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Optional. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

context

Required. Text to be summarized.

This field is a member of oneof _context.

Type:

str

instruction

Optional. Summarization prompt for LLM.

This field is a member of oneof _instruction.

Type:

str

class google.cloud.aiplatform_v1.types.SummarizationHelpfulnessResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for summarization helpfulness result.

score

Output only. Summarization Helpfulness score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for summarization helpfulness score.

Type:

str

confidence

Output only. Confidence for summarization helpfulness score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.SummarizationHelpfulnessSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for summarization helpfulness score metric.

use_reference

Optional. Whether to use instance.reference to compute summarization helpfulness.

Type:

bool

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.SummarizationQualityInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for summarization quality metric.

metric_spec

Required. Spec for summarization quality score metric.

Type:

google.cloud.aiplatform_v1.types.SummarizationQualitySpec

instance

Required. Summarization quality instance.

Type:

google.cloud.aiplatform_v1.types.SummarizationQualityInstance

class google.cloud.aiplatform_v1.types.SummarizationQualityInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for summarization quality instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Optional. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

context

Required. Text to be summarized.

This field is a member of oneof _context.

Type:

str

instruction

Required. Summarization prompt for LLM.

This field is a member of oneof _instruction.

Type:

str

class google.cloud.aiplatform_v1.types.SummarizationQualityResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for summarization quality result.

score

Output only. Summarization Quality score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for summarization quality score.

Type:

str

confidence

Output only. Confidence for summarization quality score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.SummarizationQualitySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for summarization quality score metric.

use_reference

Optional. Whether to use instance.reference to compute summarization quality.

Type:

bool

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.SummarizationVerbosityInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for summarization verbosity metric.

metric_spec

Required. Spec for summarization verbosity score metric.

Type:

google.cloud.aiplatform_v1.types.SummarizationVerbositySpec

instance

Required. Summarization verbosity instance.

Type:

google.cloud.aiplatform_v1.types.SummarizationVerbosityInstance

class google.cloud.aiplatform_v1.types.SummarizationVerbosityInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for summarization verbosity instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Optional. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

context

Required. Text to be summarized.

This field is a member of oneof _context.

Type:

str

instruction

Optional. Summarization prompt for LLM.

This field is a member of oneof _instruction.

Type:

str

class google.cloud.aiplatform_v1.types.SummarizationVerbosityResult(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for summarization verbosity result.

score

Output only. Summarization Verbosity score.

This field is a member of oneof _score.

Type:

float

explanation

Output only. Explanation for summarization verbosity score.

Type:

str

confidence

Output only. Confidence for summarization verbosity score.

This field is a member of oneof _confidence.

Type:

float

class google.cloud.aiplatform_v1.types.SummarizationVerbositySpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for summarization verbosity score metric.

use_reference

Optional. Whether to use instance.reference to compute summarization verbosity.

Type:

bool

version

Optional. Which version to use for evaluation.

Type:

int

class google.cloud.aiplatform_v1.types.SupervisedHyperParameters(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Hyperparameters for SFT.

epoch_count

Optional. Number of complete passes the model makes over the entire training dataset during training.

Type:

int

learning_rate_multiplier

Optional. Multiplier for adjusting the default learning rate.

Type:

float

adapter_size

Optional. Adapter size for tuning.

Type:

google.cloud.aiplatform_v1.types.SupervisedHyperParameters.AdapterSize

class AdapterSize(value)

Bases: Enum

Supported adapter sizes for tuning.

Values:

ADAPTER_SIZE_UNSPECIFIED (0):

Adapter size is unspecified.

ADAPTER_SIZE_ONE (1):

Adapter size 1.

ADAPTER_SIZE_FOUR (2):

Adapter size 4.

ADAPTER_SIZE_EIGHT (3):

Adapter size 8.

ADAPTER_SIZE_SIXTEEN (4):

Adapter size 16.

class google.cloud.aiplatform_v1.types.SupervisedTuningDataStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Tuning data statistics for Supervised Tuning.

tuning_dataset_example_count

Output only. Number of examples in the tuning dataset.

Type:

int

total_tuning_character_count

Output only. Number of tuning characters in the tuning dataset.

Type:

int

total_billable_character_count

Output only. Number of billable characters in the tuning dataset.

Type:

int

total_billable_token_count

Output only. Number of billable tokens in the tuning dataset.

Type:

int

tuning_step_count

Output only. Number of tuning steps for this Tuning Job.

Type:

int

user_input_token_distribution

Output only. Dataset distributions for the user input tokens.

Type:

google.cloud.aiplatform_v1.types.SupervisedTuningDatasetDistribution

user_output_token_distribution

Output only. Dataset distributions for the user output tokens.

Type:

google.cloud.aiplatform_v1.types.SupervisedTuningDatasetDistribution

user_message_per_example_distribution

Output only. Dataset distributions for the messages per example.

Type:

google.cloud.aiplatform_v1.types.SupervisedTuningDatasetDistribution

user_dataset_examples

Output only. Sample user messages in the training dataset uri.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Content]

total_truncated_example_count

The number of examples in the dataset that have been truncated by any amount.

Type:

int

truncated_example_indices

A partial sample of the indices (starting from 1) of the truncated examples.

Type:

MutableSequence[int]

class google.cloud.aiplatform_v1.types.SupervisedTuningDatasetDistribution(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Dataset distribution for Supervised Tuning.

sum

Output only. Sum of a given population of values.

Type:

int

billable_sum

Output only. Sum of a given population of values that are billable.

Type:

int

min_

Output only. The minimum of the population values.

Type:

float

max_

Output only. The maximum of the population values.

Type:

float

mean

Output only. The arithmetic mean of the values in the population.

Type:

float

median

Output only. The median of the values in the population.

Type:

float

p5

Output only. The 5th percentile of the values in the population.

Type:

float

p95

Output only. The 95th percentile of the values in the population.

Type:

float

buckets

Output only. Defines the histogram bucket.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.SupervisedTuningDatasetDistribution.DatasetBucket]

class DatasetBucket(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Dataset bucket used to create a histogram for the distribution given a population of values.

count

Output only. Number of values in the bucket.

Type:

float

left

Output only. Left bound of the bucket.

Type:

float

right

Output only. Right bound of the bucket.

Type:

float

class google.cloud.aiplatform_v1.types.SupervisedTuningSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Tuning Spec for Supervised Tuning for first party models.

training_dataset_uri

Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.

Type:

str

validation_dataset_uri

Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.

Type:

str

hyper_parameters

Optional. Hyperparameters for SFT.

Type:

google.cloud.aiplatform_v1.types.SupervisedHyperParameters

class google.cloud.aiplatform_v1.types.SyncFeatureViewRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.SyncFeatureView][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.SyncFeatureView].

feature_view

Required. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}

Type:

str

class google.cloud.aiplatform_v1.types.SyncFeatureViewResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeatureOnlineStoreAdminService.SyncFeatureView][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.SyncFeatureView].

feature_view_sync

Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}/featureViewSyncs/{feature_view_sync}

Type:

str

class google.cloud.aiplatform_v1.types.TFRecordDestination(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The storage details for TFRecord output content.

gcs_destination

Required. Google Cloud Storage location.

Type:

google.cloud.aiplatform_v1.types.GcsDestination

class google.cloud.aiplatform_v1.types.Tensor(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A tensor value type.

dtype

The data type of tensor.

Type:

google.cloud.aiplatform_v1.types.Tensor.DataType

shape

Shape of the tensor.

Type:

MutableSequence[int]

bool_val

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][google.cloud.aiplatform.v1.Tensor.DataType.BOOL]

Type:

MutableSequence[bool]

string_val

[STRING][google.cloud.aiplatform.v1.Tensor.DataType.STRING]

Type:

MutableSequence[str]

bytes_val

[STRING][google.cloud.aiplatform.v1.Tensor.DataType.STRING]

Type:

MutableSequence[bytes]

float_val

[FLOAT][google.cloud.aiplatform.v1.Tensor.DataType.FLOAT]

Type:

MutableSequence[float]

double_val

[DOUBLE][google.cloud.aiplatform.v1.Tensor.DataType.DOUBLE]

Type:

MutableSequence[float]

int_val

[INT_8][google.cloud.aiplatform.v1.Tensor.DataType.INT8] [INT_16][google.cloud.aiplatform.v1.Tensor.DataType.INT16] [INT_32][google.cloud.aiplatform.v1.Tensor.DataType.INT32]

Type:

MutableSequence[int]

int64_val

[INT64][google.cloud.aiplatform.v1.Tensor.DataType.INT64]

Type:

MutableSequence[int]

uint_val

[UINT8][google.cloud.aiplatform.v1.Tensor.DataType.UINT8] [UINT16][google.cloud.aiplatform.v1.Tensor.DataType.UINT16] [UINT32][google.cloud.aiplatform.v1.Tensor.DataType.UINT32]

Type:

MutableSequence[int]

uint64_val

[UINT64][google.cloud.aiplatform.v1.Tensor.DataType.UINT64]

Type:

MutableSequence[int]

list_val

A list of tensor values.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Tensor]

struct_val

A map of string to tensor.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.Tensor]

tensor_val

Serialized raw tensor content.

Type:

bytes

class DataType(value)

Bases: Enum

Data type of the tensor.

Values:

DATA_TYPE_UNSPECIFIED (0):

Not a legal value for DataType. Used to indicate a DataType field has not been set.

BOOL (1):

Data types that all computation devices are expected to be capable to support.

STRING (2):

No description available.

FLOAT (3):

No description available.

DOUBLE (4):

No description available.

INT8 (5):

No description available.

INT16 (6):

No description available.

INT32 (7):

No description available.

INT64 (8):

No description available.

UINT8 (9):

No description available.

UINT16 (10):

No description available.

UINT32 (11):

No description available.

UINT64 (12):

No description available.

class StructValEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.Tensorboard(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

name

Output only. Name of the Tensorboard. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

Type:

str

display_name

Required. User provided name of this Tensorboard.

Type:

str

description

Description of this Tensorboard.

Type:

str

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

blob_storage_path_prefix

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 ‘/’.

Type:

str

run_count

Output only. The number of Runs stored in this Tensorboard.

Type:

int

create_time

Output only. Timestamp when this Tensorboard was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this Tensorboard was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

labels

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.

Type:

MutableMapping[str, str]

etag

Used to perform a consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

is_default

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.

Type:

bool

satisfies_pzs

Output only. Reserved for future use.

Type:

bool

satisfies_pzi

Output only. Reserved for future use.

Type:

bool

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.TensorboardBlob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

One blob (e.g, image, graph) viewable on a blob metric plot.

id

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.

Type:

str

data

Optional. The bytes of the blob is not present unless it’s returned by the ReadTensorboardBlobData endpoint.

Type:

bytes

class google.cloud.aiplatform_v1.types.TensorboardBlobSequence(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

values

List of blobs contained within the sequence.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TensorboardBlob]

class google.cloud.aiplatform_v1.types.TensorboardExperiment(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

name

Output only. Name of the TensorboardExperiment. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}

Type:

str

display_name

User provided name of this TensorboardExperiment.

Type:

str

description

Description of this TensorboardExperiment.

Type:

str

create_time

Output only. Timestamp when this TensorboardExperiment was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this TensorboardExperiment was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

labels

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][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] title.

Type:

MutableMapping[str, str]

etag

Used to perform consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

source

Immutable. Source of the TensorboardExperiment. Example: a custom training job.

Type:

str

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.TensorboardRun(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

name

Output only. Name of the TensorboardRun. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}

Type:

str

display_name

Required. User provided name of this TensorboardRun. This value must be unique among all TensorboardRuns belonging to the same parent TensorboardExperiment.

Type:

str

description

Description of this TensorboardRun.

Type:

str

create_time

Output only. Timestamp when this TensorboardRun was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this TensorboardRun was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

labels

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.

Type:

MutableMapping[str, str]

etag

Used to perform a consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.TensorboardTensor(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

One point viewable on a tensor metric plot.

value

Required. Serialized form of https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.proto

Type:

bytes

version_number

Optional. Version number of TensorProto used to serialize [value][google.cloud.aiplatform.v1.TensorboardTensor.value].

Type:

int

class google.cloud.aiplatform_v1.types.TensorboardTimeSeries(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

TensorboardTimeSeries maps to times series produced in training runs

name

Output only. Name of the TensorboardTimeSeries.

Type:

str

display_name

Required. User provided name of this TensorboardTimeSeries. This value should be unique among all TensorboardTimeSeries resources belonging to the same TensorboardRun resource (parent resource).

Type:

str

description

Description of this TensorboardTimeSeries.

Type:

str

value_type

Required. Immutable. Type of TensorboardTimeSeries value.

Type:

google.cloud.aiplatform_v1.types.TensorboardTimeSeries.ValueType

create_time

Output only. Timestamp when this TensorboardTimeSeries was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Timestamp when this TensorboardTimeSeries was last updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

etag

Used to perform a consistent read-modify-write updates. If not set, a blind “overwrite” update happens.

Type:

str

plugin_name

Immutable. Name of the plugin this time series pertain to. Such as Scalar, Tensor, Blob

Type:

str

plugin_data

Data of the current plugin, with the size limited to 65KB.

Type:

bytes

metadata

Output only. Scalar, Tensor, or Blob metadata for this TensorboardTimeSeries.

Type:

google.cloud.aiplatform_v1.types.TensorboardTimeSeries.Metadata

class Metadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Describes metadata for a TensorboardTimeSeries.

max_step

Output only. Max step index of all data points within a TensorboardTimeSeries.

Type:

int

max_wall_time

Output only. Max wall clock timestamp of all data points within a TensorboardTimeSeries.

Type:

google.protobuf.timestamp_pb2.Timestamp

max_blob_sequence_length

Output only. The largest blob sequence length (number of blobs) of all data points in this time series, if its ValueType is BLOB_SEQUENCE.

Type:

int

class ValueType(value)

Bases: Enum

An enum representing the value type of a TensorboardTimeSeries.

Values:

VALUE_TYPE_UNSPECIFIED (0):

The value type is unspecified.

SCALAR (1):

Used for TensorboardTimeSeries that is a list of scalars. E.g. accuracy of a model over epochs/time.

TENSOR (2):

Used for TensorboardTimeSeries that is a list of tensors. E.g. histograms of weights of layer in a model over epoch/time.

BLOB_SEQUENCE (3):

Used for TensorboardTimeSeries that is a list of blob sequences. E.g. set of sample images with labels over epochs/time.

class google.cloud.aiplatform_v1.types.ThresholdConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The config for feature monitoring threshold.

value

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.

This field is a member of oneof threshold.

Type:

float

class google.cloud.aiplatform_v1.types.TimeSeriesData(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

All the data stored in a TensorboardTimeSeries.

tensorboard_time_series_id

Required. The ID of the TensorboardTimeSeries, which will become the final component of the TensorboardTimeSeries’ resource name

Type:

str

value_type

Required. Immutable. The value type of this time series. All the values in this time series data must match this value type.

Type:

google.cloud.aiplatform_v1.types.TensorboardTimeSeries.ValueType

values

Required. Data points in this time series.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TimeSeriesDataPoint]

class google.cloud.aiplatform_v1.types.TimeSeriesDataPoint(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A TensorboardTimeSeries data point.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

scalar

A scalar value.

This field is a member of oneof value.

Type:

google.cloud.aiplatform_v1.types.Scalar

tensor

A tensor value.

This field is a member of oneof value.

Type:

google.cloud.aiplatform_v1.types.TensorboardTensor

blobs

A blob sequence value.

This field is a member of oneof value.

Type:

google.cloud.aiplatform_v1.types.TensorboardBlobSequence

wall_time

Wall clock timestamp when this data point is generated by the end user.

Type:

google.protobuf.timestamp_pb2.Timestamp

step

Step index of this data point within the run.

Type:

int

class google.cloud.aiplatform_v1.types.TimestampSplit(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

training_fraction

The fraction of the input data that is to be used to train the Model.

Type:

float

validation_fraction

The fraction of the input data that is to be used to validate the Model.

Type:

float

test_fraction

The fraction of the input data that is to be used to evaluate the Model.

Type:

float

key

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.

Type:

str

class google.cloud.aiplatform_v1.types.TokensInfo(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Tokens info with a list of tokens and the corresponding list of token ids.

tokens

A list of tokens from the input.

Type:

MutableSequence[bytes]

token_ids

A list of token ids from the input.

Type:

MutableSequence[int]

role

Optional. Optional fields for the role from the corresponding Content.

Type:

str

class google.cloud.aiplatform_v1.types.Tool(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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).

function_declarations

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][content.part.function_call] in the response. User should provide a [FunctionResponse][content.part.function_response] for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 128 function declarations can be provided.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.FunctionDeclaration]

retrieval

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.

Type:

google.cloud.aiplatform_v1.types.Retrieval

google_search_retrieval

Optional. GoogleSearchRetrieval tool type. Specialized retrieval tool that is powered by Google search.

Type:

google.cloud.aiplatform_v1.types.GoogleSearchRetrieval

class google.cloud.aiplatform_v1.types.ToolCallValidInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for tool call valid metric.

metric_spec

Required. Spec for tool call valid metric.

Type:

google.cloud.aiplatform_v1.types.ToolCallValidSpec

instances

Required. Repeated tool call valid instances.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ToolCallValidInstance]

class google.cloud.aiplatform_v1.types.ToolCallValidInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for tool call valid instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Required. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

class google.cloud.aiplatform_v1.types.ToolCallValidMetricValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Tool call valid metric value for an instance.

score

Output only. Tool call valid score.

This field is a member of oneof _score.

Type:

float

class google.cloud.aiplatform_v1.types.ToolCallValidResults(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Results for tool call valid metric.

tool_call_valid_metric_values

Output only. Tool call valid metric values.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ToolCallValidMetricValue]

class google.cloud.aiplatform_v1.types.ToolCallValidSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for tool call valid metric.

class google.cloud.aiplatform_v1.types.ToolConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Tool config. This config is shared for all tools provided in the request.

function_calling_config

Optional. Function calling config.

Type:

google.cloud.aiplatform_v1.types.FunctionCallingConfig

class google.cloud.aiplatform_v1.types.ToolNameMatchInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for tool name match metric.

metric_spec

Required. Spec for tool name match metric.

Type:

google.cloud.aiplatform_v1.types.ToolNameMatchSpec

instances

Required. Repeated tool name match instances.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ToolNameMatchInstance]

class google.cloud.aiplatform_v1.types.ToolNameMatchInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for tool name match instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Required. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

class google.cloud.aiplatform_v1.types.ToolNameMatchMetricValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Tool name match metric value for an instance.

score

Output only. Tool name match score.

This field is a member of oneof _score.

Type:

float

class google.cloud.aiplatform_v1.types.ToolNameMatchResults(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Results for tool name match metric.

tool_name_match_metric_values

Output only. Tool name match metric values.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ToolNameMatchMetricValue]

class google.cloud.aiplatform_v1.types.ToolNameMatchSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for tool name match metric.

class google.cloud.aiplatform_v1.types.ToolParameterKVMatchInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for tool parameter key value match metric.

metric_spec

Required. Spec for tool parameter key value match metric.

Type:

google.cloud.aiplatform_v1.types.ToolParameterKVMatchSpec

instances

Required. Repeated tool parameter key value match instances.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ToolParameterKVMatchInstance]

class google.cloud.aiplatform_v1.types.ToolParameterKVMatchInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for tool parameter key value match instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Required. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

class google.cloud.aiplatform_v1.types.ToolParameterKVMatchMetricValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Tool parameter key value match metric value for an instance.

score

Output only. Tool parameter key value match score.

This field is a member of oneof _score.

Type:

float

class google.cloud.aiplatform_v1.types.ToolParameterKVMatchResults(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Results for tool parameter key value match metric.

tool_parameter_kv_match_metric_values

Output only. Tool parameter key value match metric values.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ToolParameterKVMatchMetricValue]

class google.cloud.aiplatform_v1.types.ToolParameterKVMatchSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for tool parameter key value match metric.

use_strict_string_match

Optional. Whether to use STRICT string match on parameter values.

Type:

bool

class google.cloud.aiplatform_v1.types.ToolParameterKeyMatchInput(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Input for tool parameter key match metric.

metric_spec

Required. Spec for tool parameter key match metric.

Type:

google.cloud.aiplatform_v1.types.ToolParameterKeyMatchSpec

instances

Required. Repeated tool parameter key match instances.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ToolParameterKeyMatchInstance]

class google.cloud.aiplatform_v1.types.ToolParameterKeyMatchInstance(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for tool parameter key match instance.

prediction

Required. Output of the evaluated model.

This field is a member of oneof _prediction.

Type:

str

reference

Required. Ground truth used to compare against the prediction.

This field is a member of oneof _reference.

Type:

str

class google.cloud.aiplatform_v1.types.ToolParameterKeyMatchMetricValue(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Tool parameter key match metric value for an instance.

score

Output only. Tool parameter key match score.

This field is a member of oneof _score.

Type:

float

class google.cloud.aiplatform_v1.types.ToolParameterKeyMatchResults(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Results for tool parameter key match metric.

tool_parameter_key_match_metric_values

Output only. Tool parameter key match metric values.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.ToolParameterKeyMatchMetricValue]

class google.cloud.aiplatform_v1.types.ToolParameterKeyMatchSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Spec for tool parameter key match metric.

class google.cloud.aiplatform_v1.types.TrainingConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

timeout_training_milli_hours

The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.

Type:

int

class google.cloud.aiplatform_v1.types.TrainingPipeline(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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][google.cloud.aiplatform.v1.ModelService.UploadModel] the Model to Vertex AI, and evaluate the Model.

name

Output only. Resource name of the TrainingPipeline.

Type:

str

display_name

Required. The user-defined name of this TrainingPipeline.

Type:

str

input_data_config

Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline’s [training_task_definition][google.cloud.aiplatform.v1.TrainingPipeline.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][google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition], then it should be assumed that the TrainingPipeline does not depend on this configuration.

Type:

google.cloud.aiplatform_v1.types.InputDataConfig

training_task_definition

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.

Type:

str

training_task_inputs

Required. The training task’s parameter(s), as specified in the [training_task_definition][google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]’s inputs.

Type:

google.protobuf.struct_pb2.Value

training_task_metadata

Output only. The metadata information as specified in the [training_task_definition][google.cloud.aiplatform.v1.TrainingPipeline.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][google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition] contains metadata object.

Type:

google.protobuf.struct_pb2.Value

model_to_upload

Describes the Model that may be uploaded (via [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel]) by this TrainingPipeline. The TrainingPipeline’s [training_task_definition][google.cloud.aiplatform.v1.TrainingPipeline.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][google.cloud.aiplatform.v1.TrainingPipeline.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][google.cloud.aiplatform.v1.Model.name] is populated. The Model is always uploaded into the Project and Location in which this pipeline is.

Type:

google.cloud.aiplatform_v1.types.Model

model_id

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.

Type:

str

parent_model

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.

Type:

str

state

Output only. The detailed state of the pipeline.

Type:

google.cloud.aiplatform_v1.types.PipelineState

error

Output only. Only populated when the pipeline’s state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

Type:

google.rpc.status_pb2.Status

create_time

Output only. Time when the TrainingPipeline was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

start_time

Output only. Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

Output only. Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Time when the TrainingPipeline was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

labels

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.

Type:

MutableMapping[str, str]

encryption_spec

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][google.cloud.aiplatform.v1.TrainingPipeline.encryption_spec] is not set separately.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.Trial(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

name

Output only. Resource name of the Trial assigned by the service.

Type:

str

id

Output only. The identifier of the Trial assigned by the service.

Type:

str

state

Output only. The detailed state of the Trial.

Type:

google.cloud.aiplatform_v1.types.Trial.State

parameters

Output only. The parameters of the Trial.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Trial.Parameter]

final_measurement

Output only. The final measurement containing the objective value.

Type:

google.cloud.aiplatform_v1.types.Measurement

measurements

Output only. A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Measurement]

start_time

Output only. Time when the Trial was started.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

Output only. Time when the Trial’s status changed to SUCCEEDED or INFEASIBLE.

Type:

google.protobuf.timestamp_pb2.Timestamp

client_id

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.

Type:

str

infeasible_reason

Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is INFEASIBLE.

Type:

str

custom_job

Output only. The CustomJob name linked to the Trial. It’s set for a HyperparameterTuningJob’s Trial.

Type:

str

web_access_uris

Output only. URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a [HyperparameterTuningJob][google.cloud.aiplatform.v1.HyperparameterTuningJob] and the job’s [trial_job_spec.enable_web_access][google.cloud.aiplatform.v1.CustomJobSpec.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.

Type:

MutableMapping[str, str]

class Parameter(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

A message representing a parameter to be tuned.

parameter_id

Output only. The ID of the parameter. The parameter should be defined in [StudySpec’s Parameters][google.cloud.aiplatform.v1.StudySpec.parameters].

Type:

str

value

Output only. The value of the parameter. number_value will be set if a parameter defined in StudySpec is in type ‘INTEGER’, ‘DOUBLE’ or ‘DISCRETE’. string_value will be set if a parameter defined in StudySpec is in type ‘CATEGORICAL’.

Type:

google.protobuf.struct_pb2.Value

class State(value)

Bases: Enum

Describes a Trial state.

Values:

STATE_UNSPECIFIED (0):

The Trial state is unspecified.

REQUESTED (1):

Indicates that a specific Trial has been requested, but it has not yet been suggested by the service.

ACTIVE (2):

Indicates that the Trial has been suggested.

STOPPING (3):

Indicates that the Trial should stop according to the service.

SUCCEEDED (4):

Indicates that the Trial is completed successfully.

INFEASIBLE (5):

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.

class WebAccessUrisEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.TrialContext(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

description

A human-readable field which can store a description of this context. This will become part of the resulting Trial’s description field.

Type:

str

parameters

If/when a Trial is generated or selected from this Context, its Parameters will match any parameters specified here. (I.e. if this context specifies parameter name:’a’ int_value:3, then a resulting Trial will have int_value:3 for its parameter named ‘a’.) Note that we first attempt to match existing REQUESTED Trials with contexts, and if there are no matches, we generate suggestions in the subspace defined by the parameters specified here. NOTE: a Context without any Parameters matches the entire feasible search space.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.Trial.Parameter]

class google.cloud.aiplatform_v1.types.TunedModel(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The Model Registry Model and Online Prediction Endpoint assiociated with this [TuningJob][google.cloud.aiplatform.v1.TuningJob].

model

Output only. The resource name of the TunedModel. Format: projects/{project}/locations/{location}/models/{model}.

Type:

str

endpoint

Output only. A resource name of an Endpoint. Format: projects/{project}/locations/{location}/endpoints/{endpoint}.

Type:

str

class google.cloud.aiplatform_v1.types.TunedModelRef(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

TunedModel Reference for legacy model migration.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

tuned_model

Support migration from model registry.

This field is a member of oneof tuned_model_ref.

Type:

str

tuning_job

Support migration from tuning job list page, from gemini-1.0-pro-002 to 1.5 and above.

This field is a member of oneof tuned_model_ref.

Type:

str

pipeline_job

Support migration from tuning job list page, from bison model to gemini model.

This field is a member of oneof tuned_model_ref.

Type:

str

class google.cloud.aiplatform_v1.types.TuningDataStats(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

The tuning data statistic values for [TuningJob][google.cloud.aiplatform.v1.TuningJob].

supervised_tuning_data_stats

The SFT Tuning data stats.

This field is a member of oneof tuning_data_stats.

Type:

google.cloud.aiplatform_v1.types.SupervisedTuningDataStats

class google.cloud.aiplatform_v1.types.TuningJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents a TuningJob that runs with Google owned models.

base_model

The base model that is being tuned, e.g., “gemini-1.0-pro-002”.

This field is a member of oneof source_model.

Type:

str

supervised_tuning_spec

Tuning Spec for Supervised Fine Tuning.

This field is a member of oneof tuning_spec.

Type:

google.cloud.aiplatform_v1.types.SupervisedTuningSpec

name

Output only. Identifier. Resource name of a TuningJob. Format: projects/{project}/locations/{location}/tuningJobs/{tuning_job}

Type:

str

tuned_model_display_name

Optional. The display name of the [TunedModel][google.cloud.aiplatform.v1.Model]. The name can be up to 128 characters long and can consist of any UTF-8 characters.

Type:

str

description

Optional. The description of the [TuningJob][google.cloud.aiplatform.v1.TuningJob].

Type:

str

state

Output only. The detailed state of the job.

Type:

google.cloud.aiplatform_v1.types.JobState

create_time

Output only. Time when the [TuningJob][google.cloud.aiplatform.v1.TuningJob] was created.

Type:

google.protobuf.timestamp_pb2.Timestamp

start_time

Output only. Time when the [TuningJob][google.cloud.aiplatform.v1.TuningJob] for the first time entered the JOB_STATE_RUNNING state.

Type:

google.protobuf.timestamp_pb2.Timestamp

end_time

Output only. Time when the TuningJob entered any of the following [JobStates][google.cloud.aiplatform.v1.JobState]: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED, JOB_STATE_EXPIRED.

Type:

google.protobuf.timestamp_pb2.Timestamp

update_time

Output only. Time when the [TuningJob][google.cloud.aiplatform.v1.TuningJob] was most recently updated.

Type:

google.protobuf.timestamp_pb2.Timestamp

error

Output only. Only populated when job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

Type:

google.rpc.status_pb2.Status

labels

Optional. The labels with user-defined metadata to organize [TuningJob][google.cloud.aiplatform.v1.TuningJob] and generated resources such as [Model][google.cloud.aiplatform.v1.Model] and [Endpoint][google.cloud.aiplatform.v1.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.

Type:

MutableMapping[str, str]

experiment

Output only. The Experiment associated with this [TuningJob][google.cloud.aiplatform.v1.TuningJob].

Type:

str

tuned_model

Output only. The tuned model resources assiociated with this [TuningJob][google.cloud.aiplatform.v1.TuningJob].

Type:

google.cloud.aiplatform_v1.types.TunedModel

tuning_data_stats

Output only. The tuning data statistics associated with this [TuningJob][google.cloud.aiplatform.v1.TuningJob].

Type:

google.cloud.aiplatform_v1.types.TuningDataStats

encryption_spec

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.

Type:

google.cloud.aiplatform_v1.types.EncryptionSpec

service_account

The service account that the tuningJob workload runs as. If not specified, the Vertex AI Secure Fine-Tuned Service Agent in the project will be used. See https://cloud.google.com/iam/docs/service-agents#vertex-ai-secure-fine-tuning-service-agent

Users starting the pipeline must have the iam.serviceAccounts.actAs permission on this service account.

Type:

str

class LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.Type(value)

Bases: Enum

Type contains the list of OpenAPI data types as defined by https://swagger.io/docs/specification/data-models/data-types/

Values:

TYPE_UNSPECIFIED (0):

Not specified, should not be used.

STRING (1):

OpenAPI string type

NUMBER (2):

OpenAPI number type

INTEGER (3):

OpenAPI integer type

BOOLEAN (4):

OpenAPI boolean type

ARRAY (5):

OpenAPI array type

OBJECT (6):

OpenAPI object type

class google.cloud.aiplatform_v1.types.UndeployIndexOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [IndexEndpointService.UndeployIndex][google.cloud.aiplatform.v1.IndexEndpointService.UndeployIndex].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UndeployIndexRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexEndpointService.UndeployIndex][google.cloud.aiplatform.v1.IndexEndpointService.UndeployIndex].

index_endpoint

Required. The name of the IndexEndpoint resource from which to undeploy an Index. Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

Type:

str

deployed_index_id

Required. The ID of the DeployedIndex to be undeployed from the IndexEndpoint.

Type:

str

class google.cloud.aiplatform_v1.types.UndeployIndexResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [IndexEndpointService.UndeployIndex][google.cloud.aiplatform.v1.IndexEndpointService.UndeployIndex].

class google.cloud.aiplatform_v1.types.UndeployModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [EndpointService.UndeployModel][google.cloud.aiplatform.v1.EndpointService.UndeployModel].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UndeployModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [EndpointService.UndeployModel][google.cloud.aiplatform.v1.EndpointService.UndeployModel].

endpoint

Required. The name of the Endpoint resource from which to undeploy a Model. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Type:

str

deployed_model_id

Required. The ID of the DeployedModel to be undeployed from the Endpoint.

Type:

str

traffic_split

If this field is provided, then the Endpoint’s [traffic_split][google.cloud.aiplatform.v1.Endpoint.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.

Type:

MutableMapping[str, int]

class TrafficSplitEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.UndeployModelResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [EndpointService.UndeployModel][google.cloud.aiplatform.v1.EndpointService.UndeployModel].

class google.cloud.aiplatform_v1.types.UnmanagedContainerModel(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Contains model information necessary to perform batch prediction without requiring a full model import.

artifact_uri

The path to the directory containing the Model artifact and any of its supporting files.

Type:

str

predict_schemata

Contains the schemata used in Model’s predictions and explanations

Type:

google.cloud.aiplatform_v1.types.PredictSchemata

container_spec

Input only. The specification of the container that is to be used when deploying this Model.

Type:

google.cloud.aiplatform_v1.types.ModelContainerSpec

class google.cloud.aiplatform_v1.types.UpdateArtifactRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.UpdateArtifact][google.cloud.aiplatform.v1.MetadataService.UpdateArtifact].

artifact

Required. The Artifact containing updates. The Artifact’s [Artifact.name][google.cloud.aiplatform.v1.Artifact.name] field is used to identify the Artifact to be updated. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}

Type:

google.cloud.aiplatform_v1.types.Artifact

update_mask

Optional. A FieldMask indicating which fields should be updated.

Type:

google.protobuf.field_mask_pb2.FieldMask

allow_missing

If set to true, and the [Artifact][google.cloud.aiplatform.v1.Artifact] is not found, a new [Artifact][google.cloud.aiplatform.v1.Artifact] is created.

Type:

bool

class google.cloud.aiplatform_v1.types.UpdateContextRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.UpdateContext][google.cloud.aiplatform.v1.MetadataService.UpdateContext].

context

Required. The Context containing updates. The Context’s [Context.name][google.cloud.aiplatform.v1.Context.name] field is used to identify the Context to be updated. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}

Type:

google.cloud.aiplatform_v1.types.Context

update_mask

Optional. A FieldMask indicating which fields should be updated.

Type:

google.protobuf.field_mask_pb2.FieldMask

allow_missing

If set to true, and the [Context][google.cloud.aiplatform.v1.Context] is not found, a new [Context][google.cloud.aiplatform.v1.Context] is created.

Type:

bool

class google.cloud.aiplatform_v1.types.UpdateDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.UpdateDataset][google.cloud.aiplatform.v1.DatasetService.UpdateDataset].

dataset

Required. The Dataset which replaces the resource on the server.

Type:

google.cloud.aiplatform_v1.types.Dataset

update_mask

Required. The update mask applies to the resource. For the FieldMask definition, see [google.protobuf.FieldMask][google.protobuf.FieldMask]. Updatable fields:

• display_name

• description

• labels

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateDatasetVersionRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [DatasetService.UpdateDatasetVersion][google.cloud.aiplatform.v1.DatasetService.UpdateDatasetVersion].

dataset_version

Required. The DatasetVersion which replaces the resource on the server.

Type:

google.cloud.aiplatform_v1.types.DatasetVersion

update_mask

Required. The update mask applies to the resource. For the FieldMask definition, see [google.protobuf.FieldMask][google.protobuf.FieldMask]. Updatable fields:

• display_name

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateDeploymentResourcePoolOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for UpdateDeploymentResourcePool method.

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateDeploymentResourcePoolRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for UpdateDeploymentResourcePool method.

deployment_resource_pool

Required. The DeploymentResourcePool to update.

The DeploymentResourcePool’s name field is used to identify the DeploymentResourcePool to update. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

Type:

google.cloud.aiplatform_v1.types.DeploymentResourcePool

update_mask

Required. The list of fields to update.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateEndpointLongRunningRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [EndpointService.UpdateEndpointLongRunning][google.cloud.aiplatform.v1.EndpointService.UpdateEndpointLongRunning].

endpoint

Required. The Endpoint which replaces the resource on the server. Currently we only support updating the client_connection_config field, all the other fields’ update will be blocked.

Type:

google.cloud.aiplatform_v1.types.Endpoint

class google.cloud.aiplatform_v1.types.UpdateEndpointOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [EndpointService.UpdateEndpointLongRunning][google.cloud.aiplatform.v1.EndpointService.UpdateEndpointLongRunning].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateEndpointRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [EndpointService.UpdateEndpoint][google.cloud.aiplatform.v1.EndpointService.UpdateEndpoint].

endpoint

Required. The Endpoint which replaces the resource on the server.

Type:

google.cloud.aiplatform_v1.types.Endpoint

update_mask

Required. The update mask applies to the resource. See [google.protobuf.FieldMask][google.protobuf.FieldMask].

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateEntityTypeRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.UpdateEntityType][google.cloud.aiplatform.v1.FeaturestoreService.UpdateEntityType].

entity_type

Required. The EntityType’s name field is used to identify the EntityType to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

Type:

google.cloud.aiplatform_v1.types.EntityType

update_mask

Field mask is used to specify the fields to be overwritten in the EntityType resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:

• description

• labels

• monitoring_config.snapshot_analysis.disabled

• monitoring_config.snapshot_analysis.monitoring_interval_days

• monitoring_config.snapshot_analysis.staleness_days

• monitoring_config.import_features_analysis.state

• monitoring_config.import_features_analysis.anomaly_detection_baseline

• monitoring_config.numerical_threshold_config.value

• monitoring_config.categorical_threshold_config.value

• offline_storage_ttl_days

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateExecutionRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [MetadataService.UpdateExecution][google.cloud.aiplatform.v1.MetadataService.UpdateExecution].

execution

Required. The Execution containing updates. The Execution’s [Execution.name][google.cloud.aiplatform.v1.Execution.name] field is used to identify the Execution to be updated. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}

Type:

google.cloud.aiplatform_v1.types.Execution

update_mask

Optional. A FieldMask indicating which fields should be updated.

Type:

google.protobuf.field_mask_pb2.FieldMask

allow_missing

If set to true, and the [Execution][google.cloud.aiplatform.v1.Execution] is not found, a new [Execution][google.cloud.aiplatform.v1.Execution] is created.

Type:

bool

class google.cloud.aiplatform_v1.types.UpdateExplanationDatasetOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1.ModelService.UpdateExplanationDataset].

generic_metadata

The common part of the operation metadata.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateExplanationDatasetRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1.ModelService.UpdateExplanationDataset].

model

Required. The resource name of the Model to update. Format: projects/{project}/locations/{location}/models/{model}

Type:

str

examples

The example config containing the location of the dataset.

Type:

google.cloud.aiplatform_v1.types.Examples

class google.cloud.aiplatform_v1.types.UpdateExplanationDatasetResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message of [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1.ModelService.UpdateExplanationDataset] operation.

class google.cloud.aiplatform_v1.types.UpdateFeatureGroupOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform update FeatureGroup.

generic_metadata

Operation metadata for FeatureGroup.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateFeatureGroupRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureRegistryService.UpdateFeatureGroup][google.cloud.aiplatform.v1.FeatureRegistryService.UpdateFeatureGroup].

feature_group

Required. The FeatureGroup’s name field is used to identify the FeatureGroup to be updated. Format: projects/{project}/locations/{location}/featureGroups/{feature_group}

Type:

google.cloud.aiplatform_v1.types.FeatureGroup

update_mask

Field mask is used to specify the fields to be overwritten in the FeatureGroup resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:

• labels

• description

• big_query

• big_query.entity_id_columns

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateFeatureOnlineStoreOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform update FeatureOnlineStore.

generic_metadata

Operation metadata for FeatureOnlineStore.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateFeatureOnlineStoreRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.UpdateFeatureOnlineStore][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.UpdateFeatureOnlineStore].

feature_online_store

Required. The FeatureOnlineStore’s name field is used to identify the FeatureOnlineStore to be updated. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}

Type:

google.cloud.aiplatform_v1.types.FeatureOnlineStore

update_mask

Field mask is used to specify the fields to be overwritten in the FeatureOnlineStore resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:

• labels

• description

• bigtable

• bigtable.auto_scaling

• bigtable.enable_multi_region_replica

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateFeatureOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform update Feature.

generic_metadata

Operation metadata for Feature Update.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateFeatureRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.UpdateFeature][google.cloud.aiplatform.v1.FeaturestoreService.UpdateFeature]. Request message for [FeatureRegistryService.UpdateFeature][google.cloud.aiplatform.v1.FeatureRegistryService.UpdateFeature].

feature

Required. The Feature’s name field is used to identify the Feature to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature}

Type:

google.cloud.aiplatform_v1.types.Feature

update_mask

Field mask is used to specify the fields to be overwritten in the Features resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:

• description

• labels

• disable_monitoring (Not supported for FeatureRegistryService Feature)

• point_of_contact (Not supported for FeaturestoreService FeatureStore)

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateFeatureViewOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform update FeatureView.

generic_metadata

Operation metadata for FeatureView Update.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateFeatureViewRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeatureOnlineStoreAdminService.UpdateFeatureView][google.cloud.aiplatform.v1.FeatureOnlineStoreAdminService.UpdateFeatureView].

feature_view

Required. The FeatureView’s name field is used to identify the FeatureView to be updated. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}

Type:

google.cloud.aiplatform_v1.types.FeatureView

update_mask

Field mask is used to specify the fields to be overwritten in the FeatureView resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:

• labels

• service_agent_type

• big_query_source

• big_query_source.uri

• big_query_source.entity_id_columns

• feature_registry_source

• feature_registry_source.feature_groups

• sync_config

• sync_config.cron

• optimized_config.automatic_resources

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateFeaturestoreOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform update Featurestore.

generic_metadata

Operation metadata for Featurestore.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateFeaturestoreRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreService.UpdateFeaturestore][google.cloud.aiplatform.v1.FeaturestoreService.UpdateFeaturestore].

featurestore

Required. The Featurestore’s name field is used to identify the Featurestore to be updated. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

Type:

google.cloud.aiplatform_v1.types.Featurestore

update_mask

Field mask is used to specify the fields to be overwritten in the Featurestore resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields.

Updatable fields:

• labels

• online_serving_config.fixed_node_count

• online_serving_config.scaling

• online_storage_ttl_days

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateIndexEndpointRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexEndpointService.UpdateIndexEndpoint][google.cloud.aiplatform.v1.IndexEndpointService.UpdateIndexEndpoint].

index_endpoint

Required. The IndexEndpoint which replaces the resource on the server.

Type:

google.cloud.aiplatform_v1.types.IndexEndpoint

update_mask

Required. The update mask applies to the resource. See [google.protobuf.FieldMask][google.protobuf.FieldMask].

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateIndexOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [IndexService.UpdateIndex][google.cloud.aiplatform.v1.IndexService.UpdateIndex].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

nearest_neighbor_search_operation_metadata

The operation metadata with regard to Matching Engine Index operation.

Type:

google.cloud.aiplatform_v1.types.NearestNeighborSearchOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateIndexRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexService.UpdateIndex][google.cloud.aiplatform.v1.IndexService.UpdateIndex].

index

Required. The Index which updates the resource on the server.

Type:

google.cloud.aiplatform_v1.types.Index

update_mask

The update mask applies to the resource. For the FieldMask definition, see [google.protobuf.FieldMask][google.protobuf.FieldMask].

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateModelDeploymentMonitoringJobOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation information for [JobService.UpdateModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.UpdateModelDeploymentMonitoringJob].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateModelDeploymentMonitoringJobRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [JobService.UpdateModelDeploymentMonitoringJob][google.cloud.aiplatform.v1.JobService.UpdateModelDeploymentMonitoringJob].

model_deployment_monitoring_job

Required. The model monitoring configuration which replaces the resource on the server.

Type:

google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringJob

update_mask

Required. The update mask is used to specify the fields to be overwritten in the ModelDeploymentMonitoringJob resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields. For the objective config, the user can either provide the update mask for model_deployment_monitoring_objective_configs or any combination of its nested fields, such as: model_deployment_monitoring_objective_configs.objective_config.training_dataset.

Updatable fields:

• display_name

• model_deployment_monitoring_schedule_config

• model_monitoring_alert_config

• logging_sampling_strategy

• labels

• log_ttl

• enable_monitoring_pipeline_logs . and

• model_deployment_monitoring_objective_configs . or

• model_deployment_monitoring_objective_configs.objective_config.training_dataset

• model_deployment_monitoring_objective_configs.objective_config.training_prediction_skew_detection_config

• model_deployment_monitoring_objective_configs.objective_config.prediction_drift_detection_config

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.UpdateModel][google.cloud.aiplatform.v1.ModelService.UpdateModel].

model

Required. The Model which replaces the resource on the server. When Model Versioning is enabled, the model.name will be used to determine whether to update the model or model version.

1. model.name with the @ value, e.g. models/123@1, refers to a version specific update.

2. model.name without the @ value, e.g. models/123, refers to a model update.

3. model.name with @-, e.g. models/123@-, refers to a model update.

4. Supported model fields: display_name, description; supported version-specific fields: version_description. Labels are supported in both scenarios. Both the model labels and the version labels are merged when a model is returned. When updating labels, if the request is for model-specific update, model label gets updated. Otherwise, version labels get updated.

5. A model name or model version name fields update mismatch will cause a precondition error.

6. One request cannot update both the model and the version fields. You must update them separately.

Type:

google.cloud.aiplatform_v1.types.Model

update_mask

Required. The update mask applies to the resource. For the FieldMask definition, see [google.protobuf.FieldMask][google.protobuf.FieldMask].

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateNotebookRuntimeTemplateRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.UpdateNotebookRuntimeTemplate][google.cloud.aiplatform.v1.NotebookService.UpdateNotebookRuntimeTemplate].

notebook_runtime_template

Required. The NotebookRuntimeTemplate to update.

Type:

google.cloud.aiplatform_v1.types.NotebookRuntimeTemplate

update_mask

Required. The update mask applies to the resource. For the FieldMask definition, see [google.protobuf.FieldMask][google.protobuf.FieldMask]. Input format: {paths: "${updated_filed}"} Updatable fields:

• encryption_spec.kms_key_name

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdatePersistentResourceOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform update PersistentResource.

generic_metadata

Operation metadata for PersistentResource.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

progress_message

Progress Message for Update LRO

Type:

str

class google.cloud.aiplatform_v1.types.UpdatePersistentResourceRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for UpdatePersistentResource method.

persistent_resource

Required. The PersistentResource to update.

The PersistentResource’s name field is used to identify the PersistentResource to update. Format: projects/{project}/locations/{location}/persistentResources/{persistent_resource}

Type:

google.cloud.aiplatform_v1.types.PersistentResource

update_mask

Required. Specify the fields to be overwritten in the PersistentResource by the update method.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateScheduleRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ScheduleService.UpdateSchedule][google.cloud.aiplatform.v1.ScheduleService.UpdateSchedule].

schedule

Required. The Schedule which replaces the resource on the server. The following restrictions will be applied:

• The scheduled request type cannot be changed.

• The non-empty fields cannot be unset.

• The output_only fields will be ignored if specified.

Type:

google.cloud.aiplatform_v1.types.Schedule

update_mask

Required. The update mask applies to the resource. See [google.protobuf.FieldMask][google.protobuf.FieldMask].

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateSpecialistPoolOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Runtime operation metadata for [SpecialistPoolService.UpdateSpecialistPool][google.cloud.aiplatform.v1.SpecialistPoolService.UpdateSpecialistPool].

specialist_pool

Output only. The name of the SpecialistPool to which the specialists are being added. Format: projects/{project_id}/locations/{location_id}/specialistPools/{specialist_pool}

Type:

str

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateSpecialistPoolRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [SpecialistPoolService.UpdateSpecialistPool][google.cloud.aiplatform.v1.SpecialistPoolService.UpdateSpecialistPool].

specialist_pool

Required. The SpecialistPool which replaces the resource on the server.

Type:

google.cloud.aiplatform_v1.types.SpecialistPool

update_mask

Required. The update mask applies to the resource.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpdateTensorboardExperimentRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.UpdateTensorboardExperiment][google.cloud.aiplatform.v1.TensorboardService.UpdateTensorboardExperiment].

update_mask

Required. Field mask is used to specify the fields to be overwritten in the TensorboardExperiment resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field is overwritten if it’s in the mask. If the user does not provide a mask then all fields are overwritten if new values are specified.

Type:

google.protobuf.field_mask_pb2.FieldMask

tensorboard_experiment

Required. The TensorboardExperiment’s name field is used to identify the TensorboardExperiment to be updated. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}

Type:

google.cloud.aiplatform_v1.types.TensorboardExperiment

class google.cloud.aiplatform_v1.types.UpdateTensorboardOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of operations that perform update Tensorboard.

generic_metadata

Operation metadata for Tensorboard.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UpdateTensorboardRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.UpdateTensorboard][google.cloud.aiplatform.v1.TensorboardService.UpdateTensorboard].

update_mask

Required. Field mask is used to specify the fields to be overwritten in the Tensorboard resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field is overwritten if it’s in the mask. If the user does not provide a mask then all fields are overwritten if new values are specified.

Type:

google.protobuf.field_mask_pb2.FieldMask

tensorboard

Required. The Tensorboard’s name field is used to identify the Tensorboard to be updated. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

Type:

google.cloud.aiplatform_v1.types.Tensorboard

class google.cloud.aiplatform_v1.types.UpdateTensorboardRunRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.UpdateTensorboardRun][google.cloud.aiplatform.v1.TensorboardService.UpdateTensorboardRun].

update_mask

Required. Field mask is used to specify the fields to be overwritten in the TensorboardRun resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field is overwritten if it’s in the mask. If the user does not provide a mask then all fields are overwritten if new values are specified.

Type:

google.protobuf.field_mask_pb2.FieldMask

tensorboard_run

Required. The TensorboardRun’s name field is used to identify the TensorboardRun to be updated. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}

Type:

google.cloud.aiplatform_v1.types.TensorboardRun

class google.cloud.aiplatform_v1.types.UpdateTensorboardTimeSeriesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.UpdateTensorboardTimeSeries][google.cloud.aiplatform.v1.TensorboardService.UpdateTensorboardTimeSeries].

update_mask

Required. Field mask is used to specify the fields to be overwritten in the TensorboardTimeSeries resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field is overwritten if it’s in the mask. If the user does not provide a mask then all fields are overwritten if new values are specified.

Type:

google.protobuf.field_mask_pb2.FieldMask

tensorboard_time_series

Required. The TensorboardTimeSeries’ name field is used to identify the TensorboardTimeSeries to be updated. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}

Type:

google.cloud.aiplatform_v1.types.TensorboardTimeSeries

class google.cloud.aiplatform_v1.types.UpgradeNotebookRuntimeOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata information for [NotebookService.UpgradeNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.UpgradeNotebookRuntime].

generic_metadata

The operation generic information.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

progress_message

A human-readable message that shows the intermediate progress details of NotebookRuntime.

Type:

str

class google.cloud.aiplatform_v1.types.UpgradeNotebookRuntimeRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [NotebookService.UpgradeNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.UpgradeNotebookRuntime].

name

Required. The name of the NotebookRuntime resource to be upgrade. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner.

Type:

str

class google.cloud.aiplatform_v1.types.UpgradeNotebookRuntimeResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [NotebookService.UpgradeNotebookRuntime][google.cloud.aiplatform.v1.NotebookService.UpgradeNotebookRuntime].

class google.cloud.aiplatform_v1.types.UploadModelOperationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Details of [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel] operation.

generic_metadata

The common part of the operation metadata.

Type:

google.cloud.aiplatform_v1.types.GenericOperationMetadata

class google.cloud.aiplatform_v1.types.UploadModelRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel].

parent

Required. The resource name of the Location into which to upload the Model. Format: projects/{project}/locations/{location}

Type:

str

parent_model

Optional. The resource name of the model into which to upload the version. Only specify this field when uploading a new version.

Type:

str

model_id

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.

Type:

str

model

Required. The Model to create.

Type:

google.cloud.aiplatform_v1.types.Model

service_account

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.).

Type:

str

class google.cloud.aiplatform_v1.types.UploadModelResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message of [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel] operation.

model

The name of the uploaded Model resource. Format: projects/{project}/locations/{location}/models/{model}

Type:

str

model_version_id

Output only. The version ID of the model that is uploaded.

Type:

str

class google.cloud.aiplatform_v1.types.UpsertDatapointsRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [IndexService.UpsertDatapoints][google.cloud.aiplatform.v1.IndexService.UpsertDatapoints]

index

Required. The name of the Index resource to be updated. Format: projects/{project}/locations/{location}/indexes/{index}

Type:

str

datapoints

A list of datapoints to be created/updated.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.IndexDatapoint]

update_mask

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.

Type:

google.protobuf.field_mask_pb2.FieldMask

class google.cloud.aiplatform_v1.types.UpsertDatapointsResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [IndexService.UpsertDatapoints][google.cloud.aiplatform.v1.IndexService.UpsertDatapoints]

class google.cloud.aiplatform_v1.types.UserActionReference(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

References an API call. It contains more information about long running operation and Jobs that are triggered by the API call.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

operation

For API calls that return a long running operation. Resource name of the long running operation. Format: projects/{project}/locations/{location}/operations/{operation}

This field is a member of oneof reference.

Type:

str

data_labeling_job

For API calls that start a LabelingJob. Resource name of the LabelingJob. Format: projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}

This field is a member of oneof reference.

Type:

str

method

The method name of the API RPC call. For example, “/google.cloud.aiplatform.{apiVersion}.DatasetService.CreateDataset”.

Type:

str

class google.cloud.aiplatform_v1.types.Value(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Value is the value of the field.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

int_value

An integer value.

This field is a member of oneof value.

Type:

int

double_value

A double value.

This field is a member of oneof value.

Type:

float

string_value

A string value.

This field is a member of oneof value.

Type:

str

class google.cloud.aiplatform_v1.types.VertexAISearch(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/products/agent-builder

datastore

Required. Fully-qualified Vertex AI Search data store resource ID. Format: projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}

Type:

str

class google.cloud.aiplatform_v1.types.VideoMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Metadata describes the input video content.

start_offset

Optional. The start offset of the video.

Type:

google.protobuf.duration_pb2.Duration

end_offset

Optional. The end offset of the video.

Type:

google.protobuf.duration_pb2.Duration

class google.cloud.aiplatform_v1.types.WorkerPoolSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Represents the spec of a worker pool in a job.

This message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

container_spec

The custom container task.

This field is a member of oneof task.

Type:

google.cloud.aiplatform_v1.types.ContainerSpec

python_package_spec

The Python packaged task.

This field is a member of oneof task.

Type:

google.cloud.aiplatform_v1.types.PythonPackageSpec

machine_spec

Optional. Immutable. The specification of a single machine.

Type:

google.cloud.aiplatform_v1.types.MachineSpec

replica_count

Optional. The number of worker replicas to use for this worker pool.

Type:

int

nfs_mounts

Optional. List of NFS mount spec.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.NfsMount]

disk_spec

Disk spec.

Type:

google.cloud.aiplatform_v1.types.DiskSpec

class google.cloud.aiplatform_v1.types.WriteFeatureValuesPayload(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Contains Feature values to be written for a specific entity.

entity_id

Required. The ID of the entity.

Type:

str

feature_values

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.

Type:

MutableMapping[str, google.cloud.aiplatform_v1.types.FeatureValue]

class FeatureValuesEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

class google.cloud.aiplatform_v1.types.WriteFeatureValuesRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [FeaturestoreOnlineServingService.WriteFeatureValues][google.cloud.aiplatform.v1.FeaturestoreOnlineServingService.WriteFeatureValues].

entity_type

Required. The resource name of the EntityType for the entities being written. Value format: projects/{project}/locations/{location}/featurestores/ {featurestore}/entityTypes/{entityType}. For example, for a machine learning model predicting user clicks on a website, an EntityType ID could be user.

Type:

str

payloads

Required. The entities to be written. Up to 100,000 feature values can be written across all payloads.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.WriteFeatureValuesPayload]

class google.cloud.aiplatform_v1.types.WriteFeatureValuesResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [FeaturestoreOnlineServingService.WriteFeatureValues][google.cloud.aiplatform.v1.FeaturestoreOnlineServingService.WriteFeatureValues].

class google.cloud.aiplatform_v1.types.WriteTensorboardExperimentDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.WriteTensorboardExperimentData][google.cloud.aiplatform.v1.TensorboardService.WriteTensorboardExperimentData].

tensorboard_experiment

Required. The resource name of the TensorboardExperiment to write data to. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}

Type:

str

write_run_data_requests

Required. Requests containing per-run TensorboardTimeSeries data to write.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.WriteTensorboardRunDataRequest]

class google.cloud.aiplatform_v1.types.WriteTensorboardExperimentDataResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.WriteTensorboardExperimentData][google.cloud.aiplatform.v1.TensorboardService.WriteTensorboardExperimentData].

class google.cloud.aiplatform_v1.types.WriteTensorboardRunDataRequest(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Request message for [TensorboardService.WriteTensorboardRunData][google.cloud.aiplatform.v1.TensorboardService.WriteTensorboardRunData].

tensorboard_run

Required. The resource name of the TensorboardRun to write data to. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}

Type:

str

time_series_data

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.

Type:

MutableSequence[google.cloud.aiplatform_v1.types.TimeSeriesData]

class google.cloud.aiplatform_v1.types.WriteTensorboardRunDataResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

Response message for [TensorboardService.WriteTensorboardRunData][google.cloud.aiplatform.v1.TensorboardService.WriteTensorboardRunData].

class google.cloud.aiplatform_v1.types.XraiAttribution(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: Message

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.

step_count

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.

Type:

int

smooth_grad_config

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

Type:

google.cloud.aiplatform_v1.types.SmoothGradConfig

blur_baseline_config

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

Type:

google.cloud.aiplatform_v1.types.BlurBaselineConfig