Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::BatchPredictionJob (v0.42.0)

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::BatchPredictionJob.

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

Inherits

Object

Extended By

Google::Protobuf::MessageExts::ClassMethods

Includes

Google::Protobuf::MessageExts

Methods

#completion_stats

def completion_stats() -> ::Google::Cloud::AIPlatform::V1::CompletionStats

Returns

(::Google::Cloud::AIPlatform::V1::CompletionStats) — Output only. Statistics on completed and failed prediction instances.

#create_time

def create_time() -> ::Google::Protobuf::Timestamp

Returns

(::Google::Protobuf::Timestamp) — Output only. Time when the BatchPredictionJob was created.

#dedicated_resources

def dedicated_resources() -> ::Google::Cloud::AIPlatform::V1::BatchDedicatedResources

Returns

(::Google::Cloud::AIPlatform::V1::BatchDedicatedResources) — 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.

#dedicated_resources=

def dedicated_resources=(value) -> ::Google::Cloud::AIPlatform::V1::BatchDedicatedResources

Parameter

value (::Google::Cloud::AIPlatform::V1::BatchDedicatedResources) — 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.

Returns

(::Google::Cloud::AIPlatform::V1::BatchDedicatedResources) — 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

def disable_container_logging() -> ::Boolean

Returns

(::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.

#disable_container_logging=

def disable_container_logging=(value) -> ::Boolean

Parameter

value (::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.

Returns

(::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.

#display_name

def display_name() -> ::String

Returns

(::String) — Required. The user-defined name of this BatchPredictionJob.

#display_name=

def display_name=(value) -> ::String

Parameter

value (::String) — Required. The user-defined name of this BatchPredictionJob.

Returns

(::String) — Required. The user-defined name of this BatchPredictionJob.

#encryption_spec

def encryption_spec() -> ::Google::Cloud::AIPlatform::V1::EncryptionSpec

Returns

(::Google::Cloud::AIPlatform::V1::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.

#encryption_spec=

def encryption_spec=(value) -> ::Google::Cloud::AIPlatform::V1::EncryptionSpec

Parameter

value (::Google::Cloud::AIPlatform::V1::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.

Returns

(::Google::Cloud::AIPlatform::V1::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.

#end_time

def end_time() -> ::Google::Protobuf::Timestamp

Returns

(::Google::Protobuf::Timestamp) — Output only. Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

#error

def error() -> ::Google::Rpc::Status

Returns

(::Google::Rpc::Status) — Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

#explanation_spec

def explanation_spec() -> ::Google::Cloud::AIPlatform::V1::ExplanationSpec

Returns

(::Google::Cloud::AIPlatform::V1::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.

#explanation_spec=

def explanation_spec=(value) -> ::Google::Cloud::AIPlatform::V1::ExplanationSpec

Parameter

value (::Google::Cloud::AIPlatform::V1::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.

Returns

(::Google::Cloud::AIPlatform::V1::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.

#generate_explanation

def generate_explanation() -> ::Boolean

Returns

(::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.

#generate_explanation=

def generate_explanation=(value) -> ::Boolean

Parameter

value (::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.

Returns

(::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.

#input_config

def input_config() -> ::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InputConfig

Returns

(::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InputConfig) — Required. Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri.

#input_config=

def input_config=(value) -> ::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InputConfig

Parameter

value (::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InputConfig) — Required. Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri.

Returns

(::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InputConfig) — Required. Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri.

#instance_config

def instance_config() -> ::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InstanceConfig

Returns

(::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InstanceConfig) — Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.

#instance_config=

def instance_config=(value) -> ::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InstanceConfig

Parameter

value (::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InstanceConfig) — Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.

Returns

(::Google::Cloud::AIPlatform::V1::BatchPredictionJob::InstanceConfig) — Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.

#labels

def labels() -> ::Google::Protobuf::Map{::String => ::String}

Returns

(::Google::Protobuf::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.

#labels=

def labels=(value) -> ::Google::Protobuf::Map{::String => ::String}

Parameter

value (::Google::Protobuf::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.

Returns

(::Google::Protobuf::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.

#manual_batch_tuning_parameters

def manual_batch_tuning_parameters() -> ::Google::Cloud::AIPlatform::V1::ManualBatchTuningParameters

Returns

(::Google::Cloud::AIPlatform::V1::ManualBatchTuningParameters) — Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).

#manual_batch_tuning_parameters=

def manual_batch_tuning_parameters=(value) -> ::Google::Cloud::AIPlatform::V1::ManualBatchTuningParameters

Parameter

value (::Google::Cloud::AIPlatform::V1::ManualBatchTuningParameters) — Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).

Returns

(::Google::Cloud::AIPlatform::V1::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

def model() -> ::String

Returns

(::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}

#model=

def model=(value) -> ::String

Parameter

value (::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}

Returns

(::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}

#model_parameters

def model_parameters() -> ::Google::Protobuf::Value

Returns

(::Google::Protobuf::Value) — The parameters that govern the predictions. The schema of the parameters may be specified via the [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] parameters_schema_uri.

#model_parameters=

def model_parameters=(value) -> ::Google::Protobuf::Value

Parameter

value (::Google::Protobuf::Value) — The parameters that govern the predictions. The schema of the parameters may be specified via the [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] parameters_schema_uri.

Returns

(::Google::Protobuf::Value) — The parameters that govern the predictions. The schema of the parameters may be specified via the [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] parameters_schema_uri.

#model_version_id

def model_version_id() -> ::String

Returns

(::String) — Output only. The version ID of the Model that produces the predictions via this job.

#name

def name() -> ::String

Returns

(::String) — Output only. Resource name of the BatchPredictionJob.

#output_config

def output_config() -> ::Google::Cloud::AIPlatform::V1::BatchPredictionJob::OutputConfig

Returns

(::Google::Cloud::AIPlatform::V1::BatchPredictionJob::OutputConfig) — Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri and prediction_schema_uri.

#output_config=

def output_config=(value) -> ::Google::Cloud::AIPlatform::V1::BatchPredictionJob::OutputConfig

Parameter

value (::Google::Cloud::AIPlatform::V1::BatchPredictionJob::OutputConfig) — Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri and prediction_schema_uri.

Returns

(::Google::Cloud::AIPlatform::V1::BatchPredictionJob::OutputConfig) — Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of [Model's][google.cloud.aiplatform.v1.BatchPredictionJob.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri and prediction_schema_uri.

#output_info

def output_info() -> ::Google::Cloud::AIPlatform::V1::BatchPredictionJob::OutputInfo

Returns

(::Google::Cloud::AIPlatform::V1::BatchPredictionJob::OutputInfo) — Output only. Information further describing the output of this job.

#partial_failures

def partial_failures() -> ::Array<::Google::Rpc::Status>

Returns

(::Array<::Google::Rpc::Status>) — Output only. Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.

#resources_consumed

def resources_consumed() -> ::Google::Cloud::AIPlatform::V1::ResourcesConsumed

Returns

(::Google::Cloud::AIPlatform::V1::ResourcesConsumed) — Output only. Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes.

Note: This field currently may be not populated for batch predictions that use AutoML Models.

#service_account

def service_account() -> ::String

Returns

(::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.

#service_account=

def service_account=(value) -> ::String

Parameter

value (::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.

Returns

(::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.

#start_time

def start_time() -> ::Google::Protobuf::Timestamp

Returns

(::Google::Protobuf::Timestamp) — Output only. Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.

#state

def state() -> ::Google::Cloud::AIPlatform::V1::JobState

Returns

(::Google::Cloud::AIPlatform::V1::JobState) — Output only. The detailed state of the job.

#unmanaged_container_model

def unmanaged_container_model() -> ::Google::Cloud::AIPlatform::V1::UnmanagedContainerModel

Returns

(::Google::Cloud::AIPlatform::V1::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.

#unmanaged_container_model=

def unmanaged_container_model=(value) -> ::Google::Cloud::AIPlatform::V1::UnmanagedContainerModel

Parameter

value (::Google::Cloud::AIPlatform::V1::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.

Returns

(::Google::Cloud::AIPlatform::V1::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.

#update_time

def update_time() -> ::Google::Protobuf::Timestamp

Returns

(::Google::Protobuf::Timestamp) — Output only. Time when the BatchPredictionJob was most recently updated.