Google Cloud Native v0.32.0, Nov 29 23

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

pulumi/pulumi-google-native

google-native.aiplatform/v1beta1.getBatchPredictionJob

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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

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Using getBatchPredictionJob

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 getBatchPredictionJob(args: GetBatchPredictionJobArgs, opts?: InvokeOptions): Promise<GetBatchPredictionJobResult>
function getBatchPredictionJobOutput(args: GetBatchPredictionJobOutputArgs, opts?: InvokeOptions): Output<GetBatchPredictionJobResult>

def get_batch_prediction_job(batch_prediction_job_id: Optional[str] = None,
                             location: Optional[str] = None,
                             project: Optional[str] = None,
                             opts: Optional[InvokeOptions] = None) -> GetBatchPredictionJobResult
def get_batch_prediction_job_output(batch_prediction_job_id: Optional[pulumi.Input[str]] = None,
                             location: Optional[pulumi.Input[str]] = None,
                             project: Optional[pulumi.Input[str]] = None,
                             opts: Optional[InvokeOptions] = None) -> Output[GetBatchPredictionJobResult]

func LookupBatchPredictionJob(ctx *Context, args *LookupBatchPredictionJobArgs, opts ...InvokeOption) (*LookupBatchPredictionJobResult, error)
func LookupBatchPredictionJobOutput(ctx *Context, args *LookupBatchPredictionJobOutputArgs, opts ...InvokeOption) LookupBatchPredictionJobResultOutput

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

public static class GetBatchPredictionJob 
{
    public static Task<GetBatchPredictionJobResult> InvokeAsync(GetBatchPredictionJobArgs args, InvokeOptions? opts = null)
    public static Output<GetBatchPredictionJobResult> Invoke(GetBatchPredictionJobInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetBatchPredictionJobResult> getBatchPredictionJob(GetBatchPredictionJobArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

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

The following arguments are supported:

BatchPredictionJobId string
Location string
Project string

BatchPredictionJobId string
Location string
Project string

batchPredictionJobId String
location String
project String

batchPredictionJobId string
location string
project string

batch_prediction_job_id str
location str
project str

batchPredictionJobId String
location String
project String

getBatchPredictionJob Result

The following output properties are available:

CompletionStats Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1CompletionStatsResponse: Statistics on completed and failed prediction instances.
CreateTime string: Time when the BatchPredictionJob was created.
DedicatedResources Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1BatchDedicatedResourcesResponse: The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
DisableContainerLogging bool: For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
DisplayName string: The user-defined name of this BatchPredictionJob.
EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1EncryptionSpecResponse: Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
EndTime string: Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
Error Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleRpcStatusResponse: Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
ExplanationSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ExplanationSpecResponse: Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
GenerateExplanation bool: Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
InputConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigResponse: Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
InstanceConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigResponse: Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
Labels Dictionary<string, string>: The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
ManualBatchTuningParameters Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ManualBatchTuningParametersResponse: Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
Model string: The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
ModelMonitoringConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ModelMonitoringConfigResponse: Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
ModelMonitoringStatsAnomalies List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesResponse>: Get batch prediction job monitoring statistics.
ModelMonitoringStatus Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleRpcStatusResponse: The running status of the model monitoring pipeline.
ModelParameters object: The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
ModelVersionId string: The version ID of the Model that produces the predictions via this job.
Name string: Resource name of the BatchPredictionJob.
OutputConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigResponse: The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
OutputInfo Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse: Information further describing the output of this job.
PartialFailures List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleRpcStatusResponse>: Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
ResourcesConsumed Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ResourcesConsumedResponse: Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
ServiceAccount string: The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
StartTime string: Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
State string: The detailed state of the job.
UnmanagedContainerModel Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1UnmanagedContainerModelResponse: Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
UpdateTime string: Time when the BatchPredictionJob was most recently updated.

CompletionStats GoogleCloudAiplatformV1beta1CompletionStatsResponse: Statistics on completed and failed prediction instances.
CreateTime string: Time when the BatchPredictionJob was created.
DedicatedResources GoogleCloudAiplatformV1beta1BatchDedicatedResourcesResponse: The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
DisableContainerLogging bool: For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
DisplayName string: The user-defined name of this BatchPredictionJob.
EncryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse: Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
EndTime string: Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
Error GoogleRpcStatusResponse: Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
ExplanationSpec GoogleCloudAiplatformV1beta1ExplanationSpecResponse: Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
GenerateExplanation bool: Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
InputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigResponse: Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
InstanceConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigResponse: Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
Labels map[string]string: The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
ManualBatchTuningParameters GoogleCloudAiplatformV1beta1ManualBatchTuningParametersResponse: Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
Model string: The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
ModelMonitoringConfig GoogleCloudAiplatformV1beta1ModelMonitoringConfigResponse: Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
ModelMonitoringStatsAnomalies []GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesResponse: Get batch prediction job monitoring statistics.
ModelMonitoringStatus GoogleRpcStatusResponse: The running status of the model monitoring pipeline.
ModelParameters interface{}: The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
ModelVersionId string: The version ID of the Model that produces the predictions via this job.
Name string: Resource name of the BatchPredictionJob.
OutputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigResponse: The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
OutputInfo GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse: Information further describing the output of this job.
PartialFailures []GoogleRpcStatusResponse: Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
ResourcesConsumed GoogleCloudAiplatformV1beta1ResourcesConsumedResponse: Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
ServiceAccount string: The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
StartTime string: Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
State string: The detailed state of the job.
UnmanagedContainerModel GoogleCloudAiplatformV1beta1UnmanagedContainerModelResponse: Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
UpdateTime string: Time when the BatchPredictionJob was most recently updated.

completionStats GoogleCloudAiplatformV1beta1CompletionStatsResponse: Statistics on completed and failed prediction instances.
createTime String: Time when the BatchPredictionJob was created.
dedicatedResources GoogleCloudAiplatformV1beta1BatchDedicatedResourcesResponse: The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
disableContainerLogging Boolean: For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
displayName String: The user-defined name of this BatchPredictionJob.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse: Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
endTime String: Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error GoogleRpcStatusResponse: Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
explanationSpec GoogleCloudAiplatformV1beta1ExplanationSpecResponse: Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
generateExplanation Boolean: Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
inputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigResponse: Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
instanceConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigResponse: Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
labels Map<String,String>: The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
manualBatchTuningParameters GoogleCloudAiplatformV1beta1ManualBatchTuningParametersResponse: Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
model String: The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
modelMonitoringConfig GoogleCloudAiplatformV1beta1ModelMonitoringConfigResponse: Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
modelMonitoringStatsAnomalies List<GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesResponse>: Get batch prediction job monitoring statistics.
modelMonitoringStatus GoogleRpcStatusResponse: The running status of the model monitoring pipeline.
modelParameters Object: The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
modelVersionId String: The version ID of the Model that produces the predictions via this job.
name String: Resource name of the BatchPredictionJob.
outputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigResponse: The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
outputInfo GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse: Information further describing the output of this job.
partialFailures List<GoogleRpcStatusResponse>: Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
resourcesConsumed GoogleCloudAiplatformV1beta1ResourcesConsumedResponse: Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
serviceAccount String: The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
startTime String: Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
state String: The detailed state of the job.
unmanagedContainerModel GoogleCloudAiplatformV1beta1UnmanagedContainerModelResponse: Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
updateTime String: Time when the BatchPredictionJob was most recently updated.

completionStats GoogleCloudAiplatformV1beta1CompletionStatsResponse: Statistics on completed and failed prediction instances.
createTime string: Time when the BatchPredictionJob was created.
dedicatedResources GoogleCloudAiplatformV1beta1BatchDedicatedResourcesResponse: The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
disableContainerLogging boolean: For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
displayName string: The user-defined name of this BatchPredictionJob.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse: Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
endTime string: Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error GoogleRpcStatusResponse: Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
explanationSpec GoogleCloudAiplatformV1beta1ExplanationSpecResponse: Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
generateExplanation boolean: Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
inputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigResponse: Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
instanceConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigResponse: Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
labels {[key: string]: string}: The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
manualBatchTuningParameters GoogleCloudAiplatformV1beta1ManualBatchTuningParametersResponse: Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
model string: The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
modelMonitoringConfig GoogleCloudAiplatformV1beta1ModelMonitoringConfigResponse: Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
modelMonitoringStatsAnomalies GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesResponse[]: Get batch prediction job monitoring statistics.
modelMonitoringStatus GoogleRpcStatusResponse: The running status of the model monitoring pipeline.
modelParameters any: The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
modelVersionId string: The version ID of the Model that produces the predictions via this job.
name string: Resource name of the BatchPredictionJob.
outputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigResponse: The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
outputInfo GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse: Information further describing the output of this job.
partialFailures GoogleRpcStatusResponse[]: Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
resourcesConsumed GoogleCloudAiplatformV1beta1ResourcesConsumedResponse: Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
serviceAccount string: The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
startTime string: Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
state string: The detailed state of the job.
unmanagedContainerModel GoogleCloudAiplatformV1beta1UnmanagedContainerModelResponse: Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
updateTime string: Time when the BatchPredictionJob was most recently updated.

completion_stats GoogleCloudAiplatformV1beta1CompletionStatsResponse: Statistics on completed and failed prediction instances.
create_time str: Time when the BatchPredictionJob was created.
dedicated_resources GoogleCloudAiplatformV1beta1BatchDedicatedResourcesResponse: The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
disable_container_logging bool: For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
display_name str: The user-defined name of this BatchPredictionJob.
encryption_spec GoogleCloudAiplatformV1beta1EncryptionSpecResponse: Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
end_time str: Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error GoogleRpcStatusResponse: Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
explanation_spec GoogleCloudAiplatformV1beta1ExplanationSpecResponse: Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
generate_explanation bool: Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
input_config GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigResponse: Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
instance_config GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigResponse: Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
labels Mapping[str, str]: The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
manual_batch_tuning_parameters GoogleCloudAiplatformV1beta1ManualBatchTuningParametersResponse: Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
model str: The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
model_monitoring_config GoogleCloudAiplatformV1beta1ModelMonitoringConfigResponse: Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
model_monitoring_stats_anomalies Sequence[GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesResponse]: Get batch prediction job monitoring statistics.
model_monitoring_status GoogleRpcStatusResponse: The running status of the model monitoring pipeline.
model_parameters Any: The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
model_version_id str: The version ID of the Model that produces the predictions via this job.
name str: Resource name of the BatchPredictionJob.
output_config GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigResponse: The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
output_info GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse: Information further describing the output of this job.
partial_failures Sequence[GoogleRpcStatusResponse]: Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
resources_consumed GoogleCloudAiplatformV1beta1ResourcesConsumedResponse: Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
service_account str: The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
start_time str: Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
state str: The detailed state of the job.
unmanaged_container_model GoogleCloudAiplatformV1beta1UnmanagedContainerModelResponse: Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
update_time str: Time when the BatchPredictionJob was most recently updated.

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

Supporting Types

GoogleCloudAiplatformV1beta1BatchDedicatedResourcesResponse

MachineSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine.
MaxReplicaCount int: Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
StartingReplicaCount int: Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

MachineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine.
MaxReplicaCount int: Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
StartingReplicaCount int: Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine.
maxReplicaCount Integer: Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
startingReplicaCount Integer: Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine.
maxReplicaCount number: Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
startingReplicaCount number: Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

machine_spec GoogleCloudAiplatformV1beta1MachineSpecResponse: Immutable. The specification of a single machine.
max_replica_count int: Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
starting_replica_count int: Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

machineSpec Property Map: Immutable. The specification of a single machine.
maxReplicaCount Number: Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
startingReplicaCount Number: Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigResponse

BigquerySource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySourceResponse: The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage location for the input instances.
InstancesFormat string: The format in which instances are given, must be one of the Model's supported_input_storage_formats.

BigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse: The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
GcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage location for the input instances.
InstancesFormat string: The format in which instances are given, must be one of the Model's supported_input_storage_formats.

bigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse: The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage location for the input instances.
instancesFormat String: The format in which instances are given, must be one of the Model's supported_input_storage_formats.

bigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse: The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage location for the input instances.
instancesFormat string: The format in which instances are given, must be one of the Model's supported_input_storage_formats.

bigquery_source GoogleCloudAiplatformV1beta1BigQuerySourceResponse: The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
gcs_source GoogleCloudAiplatformV1beta1GcsSourceResponse: The Cloud Storage location for the input instances.
instances_format str: The format in which instances are given, must be one of the Model's supported_input_storage_formats.

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

GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigResponse

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

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

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

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

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

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

GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigResponse

BigqueryDestination Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
GcsDestination Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse: The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
PredictionsFormat string: The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.

BigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
GcsDestination GoogleCloudAiplatformV1beta1GcsDestinationResponse: The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
PredictionsFormat string: The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.

bigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcsDestination GoogleCloudAiplatformV1beta1GcsDestinationResponse: The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictionsFormat String: The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.

bigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcsDestination GoogleCloudAiplatformV1beta1GcsDestinationResponse: The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictionsFormat string: The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.

bigquery_destination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcs_destination GoogleCloudAiplatformV1beta1GcsDestinationResponse: The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictions_format str: The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.

bigqueryDestination Property Map: The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
gcsDestination Property Map: The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
predictionsFormat String: The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.

GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse

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

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

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

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

bigquery_output_dataset str: The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
bigquery_output_table str: The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
gcs_output_directory str: The full path of the Cloud Storage directory created, into which the prediction output is written.

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

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.

GoogleCloudAiplatformV1beta1BigQuerySourceResponse

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

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

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

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

input_uri str: BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

inputUri String: BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: 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.

GoogleCloudAiplatformV1beta1CompletionStatsResponse

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

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

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

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

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

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

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.

GoogleCloudAiplatformV1beta1EnvVarResponse

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

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

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

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

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

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

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.

GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse

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

AnomalyDetectionThreshold float64: This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
AnomalyUri string: Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
DistributionDeviation float64: Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
EndTime string: The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
Score float64: Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
StartTime string: The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
StatsUri string: Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

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

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

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

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

GoogleCloudAiplatformV1beta1GcsDestinationResponse

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

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

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

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

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

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

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").

GoogleCloudAiplatformV1beta1ManualBatchTuningParametersResponse

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

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

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

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

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

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

GoogleCloudAiplatformV1beta1ModelContainerSpecResponse

Args List<string>: Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
Command List<string>: Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
DeploymentTimeout string: Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
Env List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EnvVarResponse>: Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
HealthProbe Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeResponse: Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
HealthRoute string: Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
ImageUri string: Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
Ports List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PortResponse>: Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
PredictRoute string: Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
SharedMemorySizeMb string: Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
StartupProbe Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeResponse: Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

Args []string: Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
Command []string: Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
DeploymentTimeout string: Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
Env []GoogleCloudAiplatformV1beta1EnvVarResponse: Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
HealthProbe GoogleCloudAiplatformV1beta1ProbeResponse: Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
HealthRoute string: Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
ImageUri string: Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
Ports []GoogleCloudAiplatformV1beta1PortResponse: Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
PredictRoute string: Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
SharedMemorySizeMb string: Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
StartupProbe GoogleCloudAiplatformV1beta1ProbeResponse: Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

args List<String>: Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command List<String>: Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deploymentTimeout String: Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env List<GoogleCloudAiplatformV1beta1EnvVarResponse>: Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
healthProbe GoogleCloudAiplatformV1beta1ProbeResponse: Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute String: Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
imageUri String: Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
ports List<GoogleCloudAiplatformV1beta1PortResponse>: Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute String: Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
sharedMemorySizeMb String: Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startupProbe GoogleCloudAiplatformV1beta1ProbeResponse: Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

args string[]: Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command string[]: Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deploymentTimeout string: Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env GoogleCloudAiplatformV1beta1EnvVarResponse[]: Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
healthProbe GoogleCloudAiplatformV1beta1ProbeResponse: Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute string: Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
imageUri string: Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
ports GoogleCloudAiplatformV1beta1PortResponse[]: Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute string: Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
sharedMemorySizeMb string: Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startupProbe GoogleCloudAiplatformV1beta1ProbeResponse: Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

args Sequence[str]: Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command Sequence[str]: Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deployment_timeout str: Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env Sequence[GoogleCloudAiplatformV1beta1EnvVarResponse]: Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
health_probe GoogleCloudAiplatformV1beta1ProbeResponse: Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
health_route str: Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
image_uri str: Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
ports Sequence[GoogleCloudAiplatformV1beta1PortResponse]: Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predict_route str: Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
shared_memory_size_mb str: Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startup_probe GoogleCloudAiplatformV1beta1ProbeResponse: Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

args List<String>: Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
command List<String>: Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
deploymentTimeout String: Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env List<Property Map>: Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
healthProbe Property Map: Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute String: Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
imageUri String: Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
ports List<Property Map>: Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
predictRoute String: Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
sharedMemorySizeMb String: Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
startupProbe Property Map: Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse

UserEmails List<string>: The email addresses to send the alert.

UserEmails []string: The email addresses to send the alert.

userEmails List<String>: The email addresses to send the alert.

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

user_emails Sequence[str]: The email addresses to send the alert.

userEmails List<String>: The email addresses to send the alert.

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse

EmailAlertConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse: Email alert config.
EnableLogging bool: Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
NotificationChannels List<string>: Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/

EmailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse: Email alert config.
EnableLogging bool: Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
NotificationChannels []string: Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/

emailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse: Email alert config.
enableLogging Boolean: Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notificationChannels List<String>: Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/

emailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse: Email alert config.
enableLogging boolean: Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notificationChannels string[]: Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/

email_alert_config GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse: Email alert config.
enable_logging bool: Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notification_channels Sequence[str]: Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/

emailAlertConfig Property Map: Email alert config.
enableLogging Boolean: Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
notificationChannels List<String>: Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/

GoogleCloudAiplatformV1beta1ModelMonitoringConfigResponse

AlertConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse: Model monitoring alert config.
AnalysisInstanceSchemaUri string: YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
ObjectiveConfigs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse>: Model monitoring objective config.
StatsAnomaliesBaseDirectory Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse: A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.

AlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse: Model monitoring alert config.
AnalysisInstanceSchemaUri string: YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
ObjectiveConfigs []GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse: Model monitoring objective config.
StatsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse: A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.

alertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse: Model monitoring alert config.
analysisInstanceSchemaUri String: YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
objectiveConfigs List<GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse>: Model monitoring objective config.
statsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse: A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.

alertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse: Model monitoring alert config.
analysisInstanceSchemaUri string: YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
objectiveConfigs GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse[]: Model monitoring objective config.
statsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse: A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.

alert_config GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse: Model monitoring alert config.
analysis_instance_schema_uri str: YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
objective_configs Sequence[GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse]: Model monitoring objective config.
stats_anomalies_base_directory GoogleCloudAiplatformV1beta1GcsDestinationResponse: A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.

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

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse

Bigquery Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery location for BatchExplain output.
Gcs Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse: Cloud Storage location for BatchExplain output.
PredictionFormat string: The storage format of the predictions generated BatchPrediction job.

Bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery location for BatchExplain output.
Gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse: Cloud Storage location for BatchExplain output.
PredictionFormat string: The storage format of the predictions generated BatchPrediction job.

bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery location for BatchExplain output.
gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse: Cloud Storage location for BatchExplain output.
predictionFormat String: The storage format of the predictions generated BatchPrediction job.

bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery location for BatchExplain output.
gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse: Cloud Storage location for BatchExplain output.
predictionFormat string: The storage format of the predictions generated BatchPrediction job.

bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse: BigQuery location for BatchExplain output.
gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse: Cloud Storage location for BatchExplain output.
prediction_format str: The storage format of the predictions generated BatchPrediction job.

bigquery Property Map: BigQuery location for BatchExplain output.
gcs Property Map: Cloud Storage location for BatchExplain output.
predictionFormat String: The storage format of the predictions generated BatchPrediction job.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse

EnableFeatureAttributes bool: If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
ExplanationBaseline Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse: Predictions generated by the BatchPredictionJob using baseline dataset.

EnableFeatureAttributes bool: If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
ExplanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse: Predictions generated by the BatchPredictionJob using baseline dataset.

enableFeatureAttributes Boolean: If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse: Predictions generated by the BatchPredictionJob using baseline dataset.

enableFeatureAttributes boolean: If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse: Predictions generated by the BatchPredictionJob using baseline dataset.

enable_feature_attributes bool: If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
explanation_baseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse: Predictions generated by the BatchPredictionJob using baseline dataset.

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

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse

AttributionScoreDriftThresholds Dictionary<string, string>: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
DefaultDriftThreshold Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
DriftThresholds Dictionary<string, string>: Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

AttributionScoreDriftThresholds map[string]string: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
DefaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
DriftThresholds map[string]string: Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

attributionScoreDriftThresholds Map<String,String>: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
defaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
driftThresholds Map<String,String>: Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

attributionScoreDriftThresholds {[key: string]: string}: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
defaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
driftThresholds {[key: string]: string}: Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

attribution_score_drift_thresholds Mapping[str, str]: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
default_drift_threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
drift_thresholds Mapping[str, str]: Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

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

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse

ExplanationConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse: The config for integrating with Vertex Explainable AI.
PredictionDriftDetectionConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse: The config for drift of prediction data.
TrainingDataset Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
TrainingPredictionSkewDetectionConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse: The config for skew between training data and prediction data.

ExplanationConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse: The config for integrating with Vertex Explainable AI.
PredictionDriftDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse: The config for drift of prediction data.
TrainingDataset GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
TrainingPredictionSkewDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse: The config for skew between training data and prediction data.

explanationConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse: The config for integrating with Vertex Explainable AI.
predictionDriftDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse: The config for drift of prediction data.
trainingDataset GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
trainingPredictionSkewDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse: The config for skew between training data and prediction data.

explanationConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse: The config for integrating with Vertex Explainable AI.
predictionDriftDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse: The config for drift of prediction data.
trainingDataset GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
trainingPredictionSkewDetectionConfig GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse: The config for skew between training data and prediction data.

explanation_config GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse: The config for integrating with Vertex Explainable AI.
prediction_drift_detection_config GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse: The config for drift of prediction data.
training_dataset GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
training_prediction_skew_detection_config GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse: The config for skew between training data and prediction data.

explanationConfig Property Map: The config for integrating with Vertex Explainable AI.
predictionDriftDetectionConfig Property Map: The config for drift of prediction data.
trainingDataset Property Map: Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
trainingPredictionSkewDetectionConfig Property Map: The config for skew between training data and prediction data.

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse

BigquerySource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySourceResponse: The BigQuery table of the unmanaged Dataset used to train this Model.
DataFormat string: Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
Dataset string: The resource name of the Dataset used to train this Model.
GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceResponse: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
LoggingSamplingStrategy Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyResponse: Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
TargetField string: The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

BigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse: The BigQuery table of the unmanaged Dataset used to train this Model.
DataFormat string: Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
Dataset string: The resource name of the Dataset used to train this Model.
GcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
LoggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse: Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
TargetField string: The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

bigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse: The BigQuery table of the unmanaged Dataset used to train this Model.
dataFormat String: Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset String: The resource name of the Dataset used to train this Model.
gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
loggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse: Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
targetField String: The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

bigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse: The BigQuery table of the unmanaged Dataset used to train this Model.
dataFormat string: Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset string: The resource name of the Dataset used to train this Model.
gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
loggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse: Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
targetField string: The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

bigquery_source GoogleCloudAiplatformV1beta1BigQuerySourceResponse: The BigQuery table of the unmanaged Dataset used to train this Model.
data_format str: Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
dataset str: The resource name of the Dataset used to train this Model.
gcs_source GoogleCloudAiplatformV1beta1GcsSourceResponse: The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
logging_sampling_strategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse: Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
target_field str: The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

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

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse

AttributionScoreSkewThresholds Dictionary<string, string>: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
DefaultSkewThreshold Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
SkewThresholds Dictionary<string, string>: Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

AttributionScoreSkewThresholds map[string]string: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
DefaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
SkewThresholds map[string]string: Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

attributionScoreSkewThresholds Map<String,String>: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
defaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skewThresholds Map<String,String>: Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

attributionScoreSkewThresholds {[key: string]: string}: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
defaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skewThresholds {[key: string]: string}: Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

attribution_score_skew_thresholds Mapping[str, str]: Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
default_skew_threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
skew_thresholds Mapping[str, str]: Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

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

GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse

FeatureDisplayName string: Display Name of the Feature.
PredictionStats List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse>: A list of historical stats generated by different time window's Prediction Dataset.
Threshold Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Threshold for anomaly detection.
TrainingStats Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse: Stats calculated for the Training Dataset.

FeatureDisplayName string: Display Name of the Feature.
PredictionStats []GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse: A list of historical stats generated by different time window's Prediction Dataset.
Threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Threshold for anomaly detection.
TrainingStats GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse: Stats calculated for the Training Dataset.

featureDisplayName String: Display Name of the Feature.
predictionStats List<GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse>: A list of historical stats generated by different time window's Prediction Dataset.
threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Threshold for anomaly detection.
trainingStats GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse: Stats calculated for the Training Dataset.

featureDisplayName string: Display Name of the Feature.
predictionStats GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse[]: A list of historical stats generated by different time window's Prediction Dataset.
threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Threshold for anomaly detection.
trainingStats GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse: Stats calculated for the Training Dataset.

feature_display_name str: Display Name of the Feature.
prediction_stats Sequence[GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse]: A list of historical stats generated by different time window's Prediction Dataset.
threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse: Threshold for anomaly detection.
training_stats GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse: Stats calculated for the Training Dataset.

featureDisplayName String: Display Name of the Feature.
predictionStats List<Property Map>: A list of historical stats generated by different time window's Prediction Dataset.
threshold Property Map: Threshold for anomaly detection.
trainingStats Property Map: Stats calculated for the Training Dataset.

GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesResponse

AnomalyCount int: Number of anomalies within all stats.
DeployedModelId string: Deployed Model ID.
FeatureStats List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse>: A list of historical Stats and Anomalies generated for all Features.
Objective string: Model Monitoring Objective those stats and anomalies belonging to.

AnomalyCount int: Number of anomalies within all stats.
DeployedModelId string: Deployed Model ID.
FeatureStats []GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse: A list of historical Stats and Anomalies generated for all Features.
Objective string: Model Monitoring Objective those stats and anomalies belonging to.

anomalyCount Integer: Number of anomalies within all stats.
deployedModelId String: Deployed Model ID.
featureStats List<GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse>: A list of historical Stats and Anomalies generated for all Features.
objective String: Model Monitoring Objective those stats and anomalies belonging to.

anomalyCount number: Number of anomalies within all stats.
deployedModelId string: Deployed Model ID.
featureStats GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse[]: A list of historical Stats and Anomalies generated for all Features.
objective string: Model Monitoring Objective those stats and anomalies belonging to.

anomaly_count int: Number of anomalies within all stats.
deployed_model_id str: Deployed Model ID.
feature_stats Sequence[GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse]: A list of historical Stats and Anomalies generated for all Features.
objective str: Model Monitoring Objective those stats and anomalies belonging to.

anomalyCount Number: Number of anomalies within all stats.
deployedModelId String: Deployed Model ID.
featureStats List<Property Map>: A list of historical Stats and Anomalies generated for all Features.
objective String: Model Monitoring Objective those stats and anomalies belonging to.

GoogleCloudAiplatformV1beta1PortResponse

ContainerPort int: The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

ContainerPort int: The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

containerPort Integer: The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

containerPort number: The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

container_port int: The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

containerPort Number: The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

GoogleCloudAiplatformV1beta1PredictSchemataResponse

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

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

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

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

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

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

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.

GoogleCloudAiplatformV1beta1ProbeExecActionResponse

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

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

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

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

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

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

GoogleCloudAiplatformV1beta1ProbeResponse

Exec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeExecActionResponse: Exec specifies the action to take.
PeriodSeconds int: How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
TimeoutSeconds int: Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.

Exec GoogleCloudAiplatformV1beta1ProbeExecActionResponse: Exec specifies the action to take.
PeriodSeconds int: How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
TimeoutSeconds int: Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.

exec GoogleCloudAiplatformV1beta1ProbeExecActionResponse: Exec specifies the action to take.
periodSeconds Integer: How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeoutSeconds Integer: Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.

exec GoogleCloudAiplatformV1beta1ProbeExecActionResponse: Exec specifies the action to take.
periodSeconds number: How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeoutSeconds number: Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.

exec_ GoogleCloudAiplatformV1beta1ProbeExecActionResponse: Exec specifies the action to take.
period_seconds int: How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
timeout_seconds int: Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.

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

GoogleCloudAiplatformV1beta1ResourcesConsumedResponse

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

ReplicaHours float64: The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.

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

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

replica_hours float: The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.

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

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.

GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse

SampleRate double: Sample rate (0, 1]

SampleRate float64: Sample rate (0, 1]

sampleRate Double: Sample rate (0, 1]

sampleRate number: Sample rate (0, 1]

sample_rate float: Sample rate (0, 1]

sampleRate Number: Sample rate (0, 1]

GoogleCloudAiplatformV1beta1SamplingStrategyResponse

RandomSampleConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse: Random sample config. Will support more sampling strategies later.

RandomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse: Random sample config. Will support more sampling strategies later.

randomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse: Random sample config. Will support more sampling strategies later.

randomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse: Random sample config. Will support more sampling strategies later.

random_sample_config GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse: Random sample config. Will support more sampling strategies later.

randomSampleConfig Property Map: Random sample config. Will support more sampling strategies later.

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.

GoogleCloudAiplatformV1beta1ThresholdConfigResponse

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

Value float64: Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

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

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

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

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

GoogleCloudAiplatformV1beta1UnmanagedContainerModelResponse

ArtifactUri string: The path to the directory containing the Model artifact and any of its supporting files.
ContainerSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelContainerSpecResponse: Input only. The specification of the container that is to be used when deploying this Model.
PredictSchemata Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredictSchemataResponse: Contains the schemata used in Model's predictions and explanations

ArtifactUri string: The path to the directory containing the Model artifact and any of its supporting files.
ContainerSpec GoogleCloudAiplatformV1beta1ModelContainerSpecResponse: Input only. The specification of the container that is to be used when deploying this Model.
PredictSchemata GoogleCloudAiplatformV1beta1PredictSchemataResponse: Contains the schemata used in Model's predictions and explanations

artifactUri String: The path to the directory containing the Model artifact and any of its supporting files.
containerSpec GoogleCloudAiplatformV1beta1ModelContainerSpecResponse: Input only. The specification of the container that is to be used when deploying this Model.
predictSchemata GoogleCloudAiplatformV1beta1PredictSchemataResponse: Contains the schemata used in Model's predictions and explanations

artifactUri string: The path to the directory containing the Model artifact and any of its supporting files.
containerSpec GoogleCloudAiplatformV1beta1ModelContainerSpecResponse: Input only. The specification of the container that is to be used when deploying this Model.
predictSchemata GoogleCloudAiplatformV1beta1PredictSchemataResponse: Contains the schemata used in Model's predictions and explanations

artifact_uri str: The path to the directory containing the Model artifact and any of its supporting files.
container_spec GoogleCloudAiplatformV1beta1ModelContainerSpecResponse: Input only. The specification of the container that is to be used when deploying this Model.
predict_schemata GoogleCloudAiplatformV1beta1PredictSchemataResponse: Contains the schemata used in Model's predictions and explanations

artifactUri String: The path to the directory containing the Model artifact and any of its supporting files.
containerSpec Property Map: Input only. The specification of the container that is to be used when deploying this Model.
predictSchemata Property Map: Contains the schemata used in Model's predictions and explanations

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.

GoogleRpcStatusResponse

Code int: The status code, which should be an enum value of google.rpc.Code.
Details List<ImmutableDictionary<string, string>>: A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

Code int: The status code, which should be an enum value of google.rpc.Code.
Details []map[string]string: A list of messages that carry the error details. There is a common set of message types for APIs to use.
Message string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code Integer: The status code, which should be an enum value of google.rpc.Code.
details List<Map<String,String>>: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message String: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code number: The status code, which should be an enum value of google.rpc.Code.
details {[key: string]: string}[]: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message string: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code int: The status code, which should be an enum value of google.rpc.Code.
details Sequence[Mapping[str, str]]: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message str: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code Number: The status code, which should be an enum value of google.rpc.Code.
details List<Map<String>>: A list of messages that carry the error details. There is a common set of message types for APIs to use.
message String: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

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.getBatchPredictionJob

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Using getBatchPredictionJob

getBatchPredictionJob Result

Supporting Types

GoogleCloudAiplatformV1beta1BatchDedicatedResourcesResponse

GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigResponse

GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigResponse

GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigResponse

GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse

GoogleCloudAiplatformV1beta1BigQueryDestinationResponse

GoogleCloudAiplatformV1beta1BigQuerySourceResponse

GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse

GoogleCloudAiplatformV1beta1CompletionStatsResponse

GoogleCloudAiplatformV1beta1EncryptionSpecResponse

GoogleCloudAiplatformV1beta1EnvVarResponse

GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse

GoogleCloudAiplatformV1beta1ExamplesResponse

GoogleCloudAiplatformV1beta1ExplanationMetadataResponse

GoogleCloudAiplatformV1beta1ExplanationParametersResponse

GoogleCloudAiplatformV1beta1ExplanationSpecResponse

GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse

GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse

GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse

GoogleCloudAiplatformV1beta1GcsDestinationResponse

GoogleCloudAiplatformV1beta1GcsSourceResponse

GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse

GoogleCloudAiplatformV1beta1MachineSpecResponse

GoogleCloudAiplatformV1beta1ManualBatchTuningParametersResponse

GoogleCloudAiplatformV1beta1ModelContainerSpecResponse

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse

GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse

GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse

GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesResponse

GoogleCloudAiplatformV1beta1PortResponse

GoogleCloudAiplatformV1beta1PredictSchemataResponse

GoogleCloudAiplatformV1beta1PresetsResponse

GoogleCloudAiplatformV1beta1ProbeExecActionResponse

GoogleCloudAiplatformV1beta1ProbeResponse

GoogleCloudAiplatformV1beta1ResourcesConsumedResponse

GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse

GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse

GoogleCloudAiplatformV1beta1SamplingStrategyResponse

GoogleCloudAiplatformV1beta1SmoothGradConfigResponse

GoogleCloudAiplatformV1beta1ThresholdConfigResponse

GoogleCloudAiplatformV1beta1UnmanagedContainerModelResponse

GoogleCloudAiplatformV1beta1XraiAttributionResponse

GoogleRpcStatusResponse

Package Details

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