Google Cloud Native v0.32.0, Nov 29 23

Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

pulumi/pulumi-google-native

google-native.aiplatform/v1beta1.getEndpoint

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Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

pulumi/pulumi-google-native

Using getEndpoint

Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

function getEndpoint(args: GetEndpointArgs, opts?: InvokeOptions): Promise<GetEndpointResult>
function getEndpointOutput(args: GetEndpointOutputArgs, opts?: InvokeOptions): Output<GetEndpointResult>

def get_endpoint(endpoint_id: Optional[str] = None,
                 location: Optional[str] = None,
                 project: Optional[str] = None,
                 opts: Optional[InvokeOptions] = None) -> GetEndpointResult
def get_endpoint_output(endpoint_id: Optional[pulumi.Input[str]] = None,
                 location: Optional[pulumi.Input[str]] = None,
                 project: Optional[pulumi.Input[str]] = None,
                 opts: Optional[InvokeOptions] = None) -> Output[GetEndpointResult]

func LookupEndpoint(ctx *Context, args *LookupEndpointArgs, opts ...InvokeOption) (*LookupEndpointResult, error)
func LookupEndpointOutput(ctx *Context, args *LookupEndpointOutputArgs, opts ...InvokeOption) LookupEndpointResultOutput

> Note: This function is named LookupEndpoint in the Go SDK.

public static class GetEndpoint 
{
    public static Task<GetEndpointResult> InvokeAsync(GetEndpointArgs args, InvokeOptions? opts = null)
    public static Output<GetEndpointResult> Invoke(GetEndpointInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetEndpointResult> getEndpoint(GetEndpointArgs args, InvokeOptions options)
public static Output<GetEndpointResult> getEndpoint(GetEndpointArgs args, InvokeOptions options)

fn::invoke:
  function: google-native:aiplatform/v1beta1:getEndpoint
  arguments:
    # arguments dictionary

The following arguments are supported:

EndpointId string
Location string
Project string

EndpointId string
Location string
Project string

endpointId String
location String
project String

endpointId string
location string
project string

endpoint_id str
location str
project str

endpointId String
location String
project String

getEndpoint Result

The following output properties are available:

CreateTime string: Timestamp when this Endpoint was created.
DeployedModels List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1DeployedModelResponse>: The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
Description string: The description of the Endpoint.
DisplayName string: The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
EnablePrivateServiceConnect bool: Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
Deprecated: Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1EncryptionSpecResponse: Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key.
Etag string: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
Labels Dictionary<string, string>: The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
ModelDeploymentMonitoringJob string: Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}
Name string: The resource name of the Endpoint.
Network string: Optional. The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.
PredictRequestResponseLoggingConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1PredictRequestResponseLoggingConfigResponse: Configures the request-response logging for online prediction.
TrafficSplit Dictionary<string, string>: A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment.
UpdateTime string: Timestamp when this Endpoint was last updated.

CreateTime string: Timestamp when this Endpoint was created.
DeployedModels []GoogleCloudAiplatformV1beta1DeployedModelResponse: The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
Description string: The description of the Endpoint.
DisplayName string: The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
EnablePrivateServiceConnect bool: Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
Deprecated: Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
EncryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse: Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key.
Etag string: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
Labels map[string]string: The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
ModelDeploymentMonitoringJob string: Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}
Name string: The resource name of the Endpoint.
Network string: Optional. The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.
PredictRequestResponseLoggingConfig GoogleCloudAiplatformV1beta1PredictRequestResponseLoggingConfigResponse: Configures the request-response logging for online prediction.
TrafficSplit map[string]string: A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment.
UpdateTime string: Timestamp when this Endpoint was last updated.

createTime String: Timestamp when this Endpoint was created.
deployedModels List<GoogleCloudAiplatformV1beta1DeployedModelResponse>: The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
description String: The description of the Endpoint.
displayName String: The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
enablePrivateServiceConnect Boolean: Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
Deprecated: Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse: Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key.
etag String: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels Map<String,String>: The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
modelDeploymentMonitoringJob String: Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}
name String: The resource name of the Endpoint.
network String: Optional. The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.
predictRequestResponseLoggingConfig GoogleCloudAiplatformV1beta1PredictRequestResponseLoggingConfigResponse: Configures the request-response logging for online prediction.
trafficSplit Map<String,String>: A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment.
updateTime String: Timestamp when this Endpoint was last updated.

createTime string: Timestamp when this Endpoint was created.
deployedModels GoogleCloudAiplatformV1beta1DeployedModelResponse[]: The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
description string: The description of the Endpoint.
displayName string: The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
enablePrivateServiceConnect boolean: Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
Deprecated: Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse: Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key.
etag string: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels {[key: string]: string}: The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
modelDeploymentMonitoringJob string: Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}
name string: The resource name of the Endpoint.
network string: Optional. The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.
predictRequestResponseLoggingConfig GoogleCloudAiplatformV1beta1PredictRequestResponseLoggingConfigResponse: Configures the request-response logging for online prediction.
trafficSplit {[key: string]: string}: A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment.
updateTime string: Timestamp when this Endpoint was last updated.

create_time str: Timestamp when this Endpoint was created.
deployed_models Sequence[GoogleCloudAiplatformV1beta1DeployedModelResponse]: The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
description str: The description of the Endpoint.
display_name str: The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
enable_private_service_connect bool: Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
Deprecated: Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
encryption_spec GoogleCloudAiplatformV1beta1EncryptionSpecResponse: Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key.
etag str: Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels Mapping[str, str]: 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.
model_deployment_monitoring_job str: Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}
name str: The resource name of the Endpoint.
network str: Optional. The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.
predict_request_response_logging_config GoogleCloudAiplatformV1beta1PredictRequestResponseLoggingConfigResponse: Configures the request-response logging for online prediction.
traffic_split Mapping[str, str]: 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.
update_time str: Timestamp when this Endpoint was last updated.

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

Supporting Types

GoogleCloudAiplatformV1beta1AutomaticResourcesResponse

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

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

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

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

max_replica_count int: 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.
min_replica_count int: Immutable. The minimum number of replicas this DeployedModel will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.

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

GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse

MetricName string: The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
Target int: 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.

MetricName string: The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
Target int: 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.

metricName String: The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
target Integer: 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.

metricName string: The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
target number: 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.

metric_name str: The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
target int: 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.

metricName String: The resource metric name. Supported metrics: * For Online Prediction: * aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle * aiplatform.googleapis.com/prediction/online/cpu/utilization
target Number: 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.

GoogleCloudAiplatformV1beta1BigQueryDestinationResponse

OutputUri string: 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.

OutputUri string: 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.

outputUri String: 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.

outputUri string: 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.

output_uri str: 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.

outputUri String: 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.

GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse

MaxBlurSigma double: 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.

MaxBlurSigma float64: 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.

maxBlurSigma Double: 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.

maxBlurSigma number: 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.

max_blur_sigma float: 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.

maxBlurSigma Number: 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.

GoogleCloudAiplatformV1beta1DedicatedResourcesResponse

AutoscalingMetricSpecs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse>: Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
MachineSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine used by the prediction.
MaxReplicaCount int: Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
MinReplicaCount int: 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.

AutoscalingMetricSpecs []GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse: Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
MachineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine used by the prediction.
MaxReplicaCount int: Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
MinReplicaCount int: 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.

autoscalingMetricSpecs List<GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse>: Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine used by the prediction.
maxReplicaCount Integer: Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
minReplicaCount Integer: 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.

autoscalingMetricSpecs GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse[]: Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine used by the prediction.
maxReplicaCount number: Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
minReplicaCount number: 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.

autoscaling_metric_specs Sequence[GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse]: Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
machine_spec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine used by the prediction.
max_replica_count int: Immutable. The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
min_replica_count int: 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.

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

GoogleCloudAiplatformV1beta1DeployedModelResponse

AutomaticResources Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1AutomaticResourcesResponse: A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
CreateTime string: Timestamp when the DeployedModel was created.
DedicatedResources Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1DedicatedResourcesResponse: A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
DisableExplanations bool: If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec.
DisplayName string: The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
EnableAccessLogging bool: If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.
EnableContainerLogging bool: If true, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging. Only supported for custom-trained Models and AutoML Tabular Models.
ExplanationSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationSpecResponse: Explanation configuration for this DeployedModel. When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
Model string: The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint. The resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.
ModelVersionId string: The version ID of the model that is deployed.
PrivateEndpoints Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PrivateEndpointsResponse: Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.
ServiceAccount string: The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
SharedResources string: The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

AutomaticResources GoogleCloudAiplatformV1beta1AutomaticResourcesResponse: A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
CreateTime string: Timestamp when the DeployedModel was created.
DedicatedResources GoogleCloudAiplatformV1beta1DedicatedResourcesResponse: A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
DisableExplanations bool: If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec.
DisplayName string: The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
EnableAccessLogging bool: If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.
EnableContainerLogging bool: If true, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging. Only supported for custom-trained Models and AutoML Tabular Models.
ExplanationSpec GoogleCloudAiplatformV1beta1ExplanationSpecResponse: Explanation configuration for this DeployedModel. When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
Model string: The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint. The resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.
ModelVersionId string: The version ID of the model that is deployed.
PrivateEndpoints GoogleCloudAiplatformV1beta1PrivateEndpointsResponse: Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.
ServiceAccount string: The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
SharedResources string: The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

automaticResources GoogleCloudAiplatformV1beta1AutomaticResourcesResponse: A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
createTime String: Timestamp when the DeployedModel was created.
dedicatedResources GoogleCloudAiplatformV1beta1DedicatedResourcesResponse: A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
disableExplanations Boolean: If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec.
displayName String: The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
enableAccessLogging Boolean: If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.
enableContainerLogging Boolean: If true, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging. Only supported for custom-trained Models and AutoML Tabular Models.
explanationSpec GoogleCloudAiplatformV1beta1ExplanationSpecResponse: Explanation configuration for this DeployedModel. When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
model String: The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint. The resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.
modelVersionId String: The version ID of the model that is deployed.
privateEndpoints GoogleCloudAiplatformV1beta1PrivateEndpointsResponse: Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.
serviceAccount String: The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
sharedResources String: The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

automaticResources GoogleCloudAiplatformV1beta1AutomaticResourcesResponse: A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
createTime string: Timestamp when the DeployedModel was created.
dedicatedResources GoogleCloudAiplatformV1beta1DedicatedResourcesResponse: A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
disableExplanations boolean: If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec.
displayName string: The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
enableAccessLogging boolean: If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.
enableContainerLogging boolean: If true, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging. Only supported for custom-trained Models and AutoML Tabular Models.
explanationSpec GoogleCloudAiplatformV1beta1ExplanationSpecResponse: Explanation configuration for this DeployedModel. When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
model string: The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint. The resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.
modelVersionId string: The version ID of the model that is deployed.
privateEndpoints GoogleCloudAiplatformV1beta1PrivateEndpointsResponse: Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.
serviceAccount string: The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
sharedResources string: The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

automatic_resources GoogleCloudAiplatformV1beta1AutomaticResourcesResponse: A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.
create_time str: Timestamp when the DeployedModel was created.
dedicated_resources GoogleCloudAiplatformV1beta1DedicatedResourcesResponse: A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
disable_explanations bool: If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec.
display_name str: The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
enable_access_logging bool: 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.
enable_container_logging bool: If true, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging. Only supported for custom-trained Models and AutoML Tabular Models.
explanation_spec GoogleCloudAiplatformV1beta1ExplanationSpecResponse: Explanation configuration for this DeployedModel. When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
model str: 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.
model_version_id str: The version ID of the model that is deployed.
private_endpoints GoogleCloudAiplatformV1beta1PrivateEndpointsResponse: Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.
service_account str: 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.
shared_resources str: The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

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

GoogleCloudAiplatformV1beta1EncryptionSpecResponse

KmsKeyName string: 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.

KmsKeyName string: 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.

kmsKeyName String: 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.

kmsKeyName string: 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.

kms_key_name str: 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.

kmsKeyName String: 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.

GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse

DataFormat string: The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage location for the input instances.

DataFormat string: The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
GcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage location for the input instances.

dataFormat String: The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage location for the input instances.

dataFormat string: The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage location for the input instances.

data_format str: The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcs_source GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage location for the input instances.

dataFormat String: The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource Property Map: The Cloud Storage location for the input instances.

GoogleCloudAiplatformV1beta1ExamplesResponse

ExampleGcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse: The Cloud Storage input instances.
GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
NearestNeighborSearchConfig object: The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
NeighborCount int: The number of neighbors to return when querying for examples.
Presets Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PresetsResponse: Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

ExampleGcsSource GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse: The Cloud Storage input instances.
GcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
NearestNeighborSearchConfig interface{}: The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
NeighborCount int: The number of neighbors to return when querying for examples.
Presets GoogleCloudAiplatformV1beta1PresetsResponse: Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

exampleGcsSource GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse: The Cloud Storage input instances.
gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
nearestNeighborSearchConfig Object: The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighborCount Integer: The number of neighbors to return when querying for examples.
presets GoogleCloudAiplatformV1beta1PresetsResponse: Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

exampleGcsSource GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse: The Cloud Storage input instances.
gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
nearestNeighborSearchConfig any: The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighborCount number: The number of neighbors to return when querying for examples.
presets GoogleCloudAiplatformV1beta1PresetsResponse: Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

example_gcs_source GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse: The Cloud Storage input instances.
gcs_source GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
nearest_neighbor_search_config Any: The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighbor_count int: The number of neighbors to return when querying for examples.
presets GoogleCloudAiplatformV1beta1PresetsResponse: Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

exampleGcsSource Property Map: The Cloud Storage input instances.
gcsSource Property Map: The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
nearestNeighborSearchConfig Any: The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
neighborCount Number: The number of neighbors to return when querying for examples.
presets Property Map: Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

GoogleCloudAiplatformV1beta1ExplanationMetadataResponse

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

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

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

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

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

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

GoogleCloudAiplatformV1beta1ExplanationParametersResponse

Examples Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExamplesResponse: Example-based explanations that returns the nearest neighbors from the provided dataset.
IntegratedGradientsAttribution Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse: An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
OutputIndices List<object>: If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
SampledShapleyAttribution Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse: An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
TopK int: If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
XraiAttribution Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1XraiAttributionResponse: 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.

Examples GoogleCloudAiplatformV1beta1ExamplesResponse: Example-based explanations that returns the nearest neighbors from the provided dataset.
IntegratedGradientsAttribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse: An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
OutputIndices []interface{}: If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
SampledShapleyAttribution GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse: An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
TopK int: If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
XraiAttribution GoogleCloudAiplatformV1beta1XraiAttributionResponse: 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.

examples GoogleCloudAiplatformV1beta1ExamplesResponse: Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse: An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
outputIndices List<Object>: If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampledShapleyAttribution GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse: An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
topK Integer: If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xraiAttribution GoogleCloudAiplatformV1beta1XraiAttributionResponse: 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.

examples GoogleCloudAiplatformV1beta1ExamplesResponse: Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse: An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
outputIndices any[]: If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampledShapleyAttribution GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse: An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
topK number: If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xraiAttribution GoogleCloudAiplatformV1beta1XraiAttributionResponse: 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.

examples GoogleCloudAiplatformV1beta1ExamplesResponse: Example-based explanations that returns the nearest neighbors from the provided dataset.
integrated_gradients_attribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse: 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
output_indices Sequence[Any]: If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampled_shapley_attribution GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse: 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.
top_k int: 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.
xrai_attribution GoogleCloudAiplatformV1beta1XraiAttributionResponse: 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.

examples Property Map: Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution Property Map: An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
outputIndices List<Any>: If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
sampledShapleyAttribution Property Map: An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
topK Number: If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
xraiAttribution Property Map: 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.

GoogleCloudAiplatformV1beta1ExplanationSpecResponse

Metadata Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationMetadataResponse: Optional. Metadata describing the Model's input and output for explanation.
Parameters Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationParametersResponse: Parameters that configure explaining of the Model's predictions.

Metadata GoogleCloudAiplatformV1beta1ExplanationMetadataResponse: Optional. Metadata describing the Model's input and output for explanation.
Parameters GoogleCloudAiplatformV1beta1ExplanationParametersResponse: Parameters that configure explaining of the Model's predictions.

metadata GoogleCloudAiplatformV1beta1ExplanationMetadataResponse: Optional. Metadata describing the Model's input and output for explanation.
parameters GoogleCloudAiplatformV1beta1ExplanationParametersResponse: Parameters that configure explaining of the Model's predictions.

metadata GoogleCloudAiplatformV1beta1ExplanationMetadataResponse: Optional. Metadata describing the Model's input and output for explanation.
parameters GoogleCloudAiplatformV1beta1ExplanationParametersResponse: Parameters that configure explaining of the Model's predictions.

metadata GoogleCloudAiplatformV1beta1ExplanationMetadataResponse: Optional. Metadata describing the Model's input and output for explanation.
parameters GoogleCloudAiplatformV1beta1ExplanationParametersResponse: Parameters that configure explaining of the Model's predictions.

metadata Property Map: Optional. Metadata describing the Model's input and output for explanation.
parameters Property Map: Parameters that configure explaining of the Model's predictions.

GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse

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

Name string: The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
Sigma float64: This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.

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

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

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

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

GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse

NoiseSigma List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse>: Noise sigma per feature. No noise is added to features that are not set.

NoiseSigma []GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse: Noise sigma per feature. No noise is added to features that are not set.

noiseSigma List<GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse>: Noise sigma per feature. No noise is added to features that are not set.

noiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse[]: Noise sigma per feature. No noise is added to features that are not set.

noise_sigma Sequence[GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse]: Noise sigma per feature. No noise is added to features that are not set.

noiseSigma List<Property Map>: Noise sigma per feature. No noise is added to features that are not set.

GoogleCloudAiplatformV1beta1GcsSourceResponse

Uris List<string>: 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.

Uris []string: 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.

uris List<String>: 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.

uris string[]: 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.

uris Sequence[str]: 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.

uris List<String>: 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.

GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse

BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse: Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SmoothGradConfigResponse: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
StepCount int: 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.

BlurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse: Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
StepCount int: 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.

blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse: Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount Integer: 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.

blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse: Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount number: 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.

blur_baseline_config GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse: 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
smooth_grad_config GoogleCloudAiplatformV1beta1SmoothGradConfigResponse: 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
step_count int: 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.

blurBaselineConfig Property Map: Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig Property Map: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount Number: 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.

GoogleCloudAiplatformV1beta1MachineSpecResponse

AcceleratorCount int: The number of accelerators to attach to the machine.
AcceleratorType string: Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
MachineType string: Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
TpuTopology string: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

AcceleratorCount int: The number of accelerators to attach to the machine.
AcceleratorType string: Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
MachineType string: Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
TpuTopology string: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

acceleratorCount Integer: The number of accelerators to attach to the machine.
acceleratorType String: Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machineType String: Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpuTopology String: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

acceleratorCount number: The number of accelerators to attach to the machine.
acceleratorType string: Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machineType string: Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpuTopology string: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

accelerator_count int: The number of accelerators to attach to the machine.
accelerator_type str: Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machine_type str: Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpu_topology str: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

acceleratorCount Number: The number of accelerators to attach to the machine.
acceleratorType String: Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
machineType String: Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
tpuTopology String: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

GoogleCloudAiplatformV1beta1PredictRequestResponseLoggingConfigResponse

BigqueryDestination Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery table for logging. If only given a project, a new dataset will be created with name logging__ where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores). If no table name is given, a new table will be created with name request_response_logging
Enabled bool: If logging is enabled or not.
SamplingRate double: Percentage of requests to be logged, expressed as a fraction in range(0,1].

BigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery table for logging. If only given a project, a new dataset will be created with name logging__ where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores). If no table name is given, a new table will be created with name request_response_logging
Enabled bool: If logging is enabled or not.
SamplingRate float64: Percentage of requests to be logged, expressed as a fraction in range(0,1].

bigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery table for logging. If only given a project, a new dataset will be created with name logging__ where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores). If no table name is given, a new table will be created with name request_response_logging
enabled Boolean: If logging is enabled or not.
samplingRate Double: Percentage of requests to be logged, expressed as a fraction in range(0,1].

bigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery table for logging. If only given a project, a new dataset will be created with name logging__ where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores). If no table name is given, a new table will be created with name request_response_logging
enabled boolean: If logging is enabled or not.
samplingRate number: Percentage of requests to be logged, expressed as a fraction in range(0,1].

bigquery_destination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery table for logging. If only given a project, a new dataset will be created with name logging__ where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores). If no table name is given, a new table will be created with name request_response_logging
enabled bool: If logging is enabled or not.
sampling_rate float: Percentage of requests to be logged, expressed as a fraction in range(0,1].

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

GoogleCloudAiplatformV1beta1PresetsResponse

Modality string: 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.
Query string: Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

Modality string: 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.
Query string: Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

modality String: 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.
query String: Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

modality string: 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.
query string: Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

modality str: 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.
query str: Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

modality String: 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.
query String: Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

GoogleCloudAiplatformV1beta1PrivateEndpointsResponse

ExplainHttpUri string: Http(s) path to send explain requests.
HealthHttpUri string: Http(s) path to send health check requests.
PredictHttpUri string: Http(s) path to send prediction requests.
ServiceAttachment string: The name of the service attachment resource. Populated if private service connect is enabled.

ExplainHttpUri string: Http(s) path to send explain requests.
HealthHttpUri string: Http(s) path to send health check requests.
PredictHttpUri string: Http(s) path to send prediction requests.
ServiceAttachment string: The name of the service attachment resource. Populated if private service connect is enabled.

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

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

explain_http_uri str: Http(s) path to send explain requests.
health_http_uri str: Http(s) path to send health check requests.
predict_http_uri str: Http(s) path to send prediction requests.
service_attachment str: The name of the service attachment resource. Populated if private service connect is enabled.

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

GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse

PathCount int: The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

PathCount int: The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

pathCount Integer: The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

pathCount number: The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

path_count int: The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

pathCount Number: The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

GoogleCloudAiplatformV1beta1SmoothGradConfigResponse

FeatureNoiseSigma Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse: This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
NoiseSigma double: This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
NoisySampleCount int: 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.

FeatureNoiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse: This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
NoiseSigma float64: This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
NoisySampleCount int: 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.

featureNoiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse: This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noiseSigma Double: This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisySampleCount Integer: 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.

featureNoiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse: This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noiseSigma number: This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisySampleCount number: 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.

feature_noise_sigma GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse: This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noise_sigma float: This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisy_sample_count int: 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.

featureNoiseSigma Property Map: This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
noiseSigma Number: This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
noisySampleCount Number: 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.

GoogleCloudAiplatformV1beta1XraiAttributionResponse

BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse: Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SmoothGradConfigResponse: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
StepCount int: 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.

BlurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse: Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
SmoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
StepCount int: 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.

blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse: Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount Integer: 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.

blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse: Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount number: 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.

blur_baseline_config GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse: 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
smooth_grad_config GoogleCloudAiplatformV1beta1SmoothGradConfigResponse: 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
step_count int: 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.

blurBaselineConfig Property Map: Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
smoothGradConfig Property Map: Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
stepCount Number: 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.

Package Details

Repository: Google Cloud Native pulumi/pulumi-google-native
License: Apache-2.0

Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

pulumi/pulumi-google-native

google-native.aiplatform/v1beta1.getEndpoint

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Using getEndpoint

getEndpoint Result

Supporting Types

GoogleCloudAiplatformV1beta1AutomaticResourcesResponse

GoogleCloudAiplatformV1beta1AutoscalingMetricSpecResponse

GoogleCloudAiplatformV1beta1BigQueryDestinationResponse

GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse

GoogleCloudAiplatformV1beta1DedicatedResourcesResponse

GoogleCloudAiplatformV1beta1DeployedModelResponse

GoogleCloudAiplatformV1beta1EncryptionSpecResponse

GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse

GoogleCloudAiplatformV1beta1ExamplesResponse

GoogleCloudAiplatformV1beta1ExplanationMetadataResponse

GoogleCloudAiplatformV1beta1ExplanationParametersResponse

GoogleCloudAiplatformV1beta1ExplanationSpecResponse

GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse

GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse

GoogleCloudAiplatformV1beta1GcsSourceResponse

GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse

GoogleCloudAiplatformV1beta1MachineSpecResponse

GoogleCloudAiplatformV1beta1PredictRequestResponseLoggingConfigResponse

GoogleCloudAiplatformV1beta1PresetsResponse

GoogleCloudAiplatformV1beta1PrivateEndpointsResponse

GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse

GoogleCloudAiplatformV1beta1SmoothGradConfigResponse

GoogleCloudAiplatformV1beta1XraiAttributionResponse

Package Details

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