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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. 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
- (::Google::Cloud::AIPlatform::V1::CompletionStats) — Output only. Statistics on completed and failed prediction instances.
#create_time
def create_time() -> ::Google::Protobuf::Timestamp
- (::Google::Protobuf::Timestamp) — Output only. Time when the BatchPredictionJob was created.
#dedicated_resources
def dedicated_resources() -> ::Google::Cloud::AIPlatform::V1::BatchDedicatedResources
- (::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
- 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.
- (::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.
#display_name
def display_name() -> ::String
- (::String) — Required. The user-defined name of this BatchPredictionJob.
#display_name=
def display_name=(value) -> ::String
- value (::String) — Required. The user-defined name of this BatchPredictionJob.
- (::String) — Required. The user-defined name of this BatchPredictionJob.
#encryption_spec
def encryption_spec() -> ::Google::Cloud::AIPlatform::V1::EncryptionSpec
- (::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
- 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.
- (::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
-
(::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
- (::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
-
(::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
-
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.
-
(::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
-
(::Boolean) — Generate explanation with the batch prediction results.
When set to
true
, the batch prediction output changes based on thepredictions_format
field of the BatchPredictionJob.output_config object:bigquery
: output includes a column namedexplanation
. The value is a struct that conforms to the Explanation object.jsonl
: The JSON objects on each line include an additional entry keyedexplanation
. 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
-
value (::Boolean) — Generate explanation with the batch prediction results.
When set to
true
, the batch prediction output changes based on thepredictions_format
field of the BatchPredictionJob.output_config object:bigquery
: output includes a column namedexplanation
. The value is a struct that conforms to the Explanation object.jsonl
: The JSON objects on each line include an additional entry keyedexplanation
. 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.
-
(::Boolean) — Generate explanation with the batch prediction results.
When set to
true
, the batch prediction output changes based on thepredictions_format
field of the BatchPredictionJob.output_config object:bigquery
: output includes a column namedexplanation
. The value is a struct that conforms to the Explanation object.jsonl
: The JSON objects on each line include an additional entry keyedexplanation
. 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
- (::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
- 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.
- (::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.
#labels
def labels() -> ::Google::Protobuf::Map{::String => ::String}
-
(::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}
-
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.
-
(::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
- (::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
- 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).
- (::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
-
(::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, if no version is specified, the default version will be used.
#model=
def model=(value) -> ::String
-
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, if no version is specified, the default version will be used.
-
(::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, if no version is specified, the default version will be used.
#model_parameters
def model_parameters() -> ::Google::Protobuf::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.
#model_parameters=
def model_parameters=(value) -> ::Google::Protobuf::Value
- 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.
- (::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
- (::String) — Output only. The version ID of the Model that produces the predictions via this job.
#name
def name() -> ::String
- (::String) — Output only. Resource name of the BatchPredictionJob.
#output_config
def output_config() -> ::Google::Cloud::AIPlatform::V1::BatchPredictionJob::OutputConfig
- (::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
- 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.
- (::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
- (::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>
- (::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 GCP error details.
#resources_consumed
def resources_consumed() -> ::Google::Cloud::AIPlatform::V1::ResourcesConsumed
-
(::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.
#start_time
def start_time() -> ::Google::Protobuf::Timestamp
-
(::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
- (::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
- (::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
- 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.
- (::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
- (::Google::Protobuf::Timestamp) — Output only. Time when the BatchPredictionJob was most recently updated.