BatchPredictionJobArgs class

The set of arguments for BatchPredictionJob. The set of arguments for BatchPredictionJob.

Constructors

BatchPredictionJobArgs({Input<GoogleCloudAiplatformV1BatchDedicatedResources>? dedicatedResources, Input<bool>? disableContainerLogging, required Input<String> displayName, Input<GoogleCloudAiplatformV1EncryptionSpec>? encryptionSpec, Input<GoogleCloudAiplatformV1ExplanationSpec>? explanationSpec, Input<bool>? generateExplanation, required Input<GoogleCloudAiplatformV1BatchPredictionJobInputConfig> inputConfig, Input<GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig>? instanceConfig, Input<Map<String, String>>? labels, Input<String>? location, Input<GoogleCloudAiplatformV1ManualBatchTuningParameters>? manualBatchTuningParameters, Input<String>? model, Input? modelParameters, required Input<GoogleCloudAiplatformV1BatchPredictionJobOutputConfig> outputConfig, Input<String>? project, Input<String>? serviceAccount, Input<GoogleCloudAiplatformV1UnmanagedContainerModel>? unmanagedContainerModel})
Creates a new BatchPredictionJobArgs. dedicatedResources 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 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 The user-defined name of this BatchPredictionJob. encryptionSpec 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. explanationSpec 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 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 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 Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model. labels The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. location Optional. manualBatchTuningParameters Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself). model The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model} modelParameters The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri. outputConfig 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. project Optional. serviceAccount 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. unmanagedContainerModel 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.
const
BatchPredictionJobArgs.fromMap(Map<String, dynamic> map)
factory

Properties

dedicatedResources → Input<GoogleCloudAiplatformV1BatchDedicatedResources>?
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.
final
disableContainerLogging → Input<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.
final
displayName → Input<String>
The user-defined name of this BatchPredictionJob.
final
encryptionSpec → Input<GoogleCloudAiplatformV1EncryptionSpec>?
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.
final
explanationSpec → Input<GoogleCloudAiplatformV1ExplanationSpec>?
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.
final
generateExplanation → Input<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.
final
hashCode → int
The hash code for this object.
no setterinherited
inputConfig → Input<GoogleCloudAiplatformV1BatchPredictionJobInputConfig>
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.
final
instanceConfig → Input<GoogleCloudAiplatformV1BatchPredictionJobInstanceConfig>?
Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
final
labels → Input<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.
final
location → Input<String>?
final
manualBatchTuningParameters → Input<GoogleCloudAiplatformV1ManualBatchTuningParameters>?
Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
final
model → Input<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}
final
modelParameters → Input?
The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
final
outputConfig → Input<GoogleCloudAiplatformV1BatchPredictionJobOutputConfig>
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.
final
project → Input<String>?
final
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited
serviceAccount → Input<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.
final
unmanagedContainerModel → Input<GoogleCloudAiplatformV1UnmanagedContainerModel>?
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.
final

Methods

noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toMap() → Map<String, dynamic>
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited