Struct BatchPredictionJob

#[non_exhaustive]
pub struct BatchPredictionJob {

    pub name: String,
    pub display_name: String,
    pub model: String,
    pub model_version_id: String,
    pub unmanaged_container_model: Option<UnmanagedContainerModel>,
    pub input_config: Option<InputConfig>,
    pub instance_config: Option<InstanceConfig>,
    pub model_parameters: Option<Value>,
    pub output_config: Option<OutputConfig>,
    pub dedicated_resources: Option<BatchDedicatedResources>,
    pub service_account: String,
    pub manual_batch_tuning_parameters: Option<ManualBatchTuningParameters>,
    pub generate_explanation: bool,
    pub explanation_spec: Option<ExplanationSpec>,
    pub output_info: Option<OutputInfo>,
    pub state: JobState,
    pub error: Option<Status>,
    pub partial_failures: Vec<Status>,
    pub resources_consumed: Option<ResourcesConsumed>,
    pub completion_stats: Option<CompletionStats>,
    pub create_time: Option<Timestamp>,
    pub start_time: Option<Timestamp>,
    pub end_time: Option<Timestamp>,
    pub update_time: Option<Timestamp>,
    pub labels: HashMap<String, String>,
    pub encryption_spec: Option<EncryptionSpec>,
    pub disable_container_logging: bool,
    pub satisfies_pzs: bool,
    pub satisfies_pzi: bool,
    /* private fields */
}

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.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

name: String

Output only. Resource name of the BatchPredictionJob.

display_name: String

Required. The user-defined name of this BatchPredictionJob.

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}

model_version_id: String

Output only. The version ID of the Model that produces the predictions via this job.

unmanaged_container_model: Option<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.

input_config: Option<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 PredictSchemata’s instance_schema_uri.

instance_config: Option<InstanceConfig>

Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.

model_parameters: Option<Value>

The parameters that govern the predictions. The schema of the parameters may be specified via the Model’s PredictSchemata’s parameters_schema_uri.

output_config: Option<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 PredictSchemata’s instance_schema_uri and prediction_schema_uri.

dedicated_resources: Option<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.

service_account: 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.

manual_batch_tuning_parameters: Option<ManualBatchTuningParameters>

Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).

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.

explanation_spec: Option<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.

output_info: Option<OutputInfo>

Output only. Information further describing the output of this job.

state: JobState

Output only. The detailed state of the job.

error: Option<Status>

Output only. Only populated when the job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

partial_failures: Vec<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: Option<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.

completion_stats: Option<CompletionStats>

Output only. Statistics on completed and failed prediction instances.

create_time: Option<Timestamp>

Output only. Time when the BatchPredictionJob was created.

start_time: Option<Timestamp>

Output only. Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.

end_time: Option<Timestamp>

Output only. Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

update_time: Option<Timestamp>

Output only. Time when the BatchPredictionJob was most recently updated.

labels: HashMap<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.

encryption_spec: Option<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.

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.

satisfies_pzs: bool

Output only. Reserved for future use.

satisfies_pzi: bool

Output only. Reserved for future use.

Implementations

impl BatchPredictionJob

pub fn new() -> Self

pub fn set_name<T: Into<String>>(self, v: T) -> Self

Sets the value of name.

pub fn set_display_name<T: Into<String>>(self, v: T) -> Self

Sets the value of display_name.

pub fn set_model<T: Into<String>>(self, v: T) -> Self

Sets the value of model.

pub fn set_model_version_id<T: Into<String>>(self, v: T) -> Self

Sets the value of model_version_id.

pub fn set_unmanaged_container_model<T: Into<Option<UnmanagedContainerModel>>>( self, v: T, ) -> Self

Sets the value of unmanaged_container_model.

pub fn set_input_config<T: Into<Option<InputConfig>>>(self, v: T) -> Self

Sets the value of input_config.

pub fn set_instance_config<T: Into<Option<InstanceConfig>>>(self, v: T) -> Self

Sets the value of instance_config.

pub fn set_model_parameters<T: Into<Option<Value>>>(self, v: T) -> Self

Sets the value of model_parameters.

pub fn set_output_config<T: Into<Option<OutputConfig>>>(self, v: T) -> Self

Sets the value of output_config.

pub fn set_dedicated_resources<T: Into<Option<BatchDedicatedResources>>>( self, v: T, ) -> Self

Sets the value of dedicated_resources.

pub fn set_service_account<T: Into<String>>(self, v: T) -> Self

Sets the value of service_account.

pub fn set_manual_batch_tuning_parameters<T: Into<Option<ManualBatchTuningParameters>>>( self, v: T, ) -> Self

Sets the value of manual_batch_tuning_parameters.

pub fn set_generate_explanation<T: Into<bool>>(self, v: T) -> Self

Sets the value of generate_explanation.

pub fn set_explanation_spec<T: Into<Option<ExplanationSpec>>>( self, v: T, ) -> Self

Sets the value of explanation_spec.

pub fn set_output_info<T: Into<Option<OutputInfo>>>(self, v: T) -> Self

Sets the value of output_info.

pub fn set_state<T: Into<JobState>>(self, v: T) -> Self

Sets the value of state.

pub fn set_error<T: Into<Option<Status>>>(self, v: T) -> Self

Sets the value of error.

pub fn set_partial_failures<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<Status>,

Sets the value of partial_failures.

pub fn set_resources_consumed<T: Into<Option<ResourcesConsumed>>>( self, v: T, ) -> Self

Sets the value of resources_consumed.

pub fn set_completion_stats<T: Into<Option<CompletionStats>>>( self, v: T, ) -> Self

Sets the value of completion_stats.

pub fn set_create_time<T: Into<Option<Timestamp>>>(self, v: T) -> Self

Sets the value of create_time.

pub fn set_start_time<T: Into<Option<Timestamp>>>(self, v: T) -> Self

Sets the value of start_time.

pub fn set_end_time<T: Into<Option<Timestamp>>>(self, v: T) -> Self

Sets the value of end_time.

pub fn set_update_time<T: Into<Option<Timestamp>>>(self, v: T) -> Self

Sets the value of update_time.

pub fn set_labels<T, K, V>(self, v: T) -> Self

where T: IntoIterator<Item = (K, V)>, K: Into<String>, V: Into<String>,

Sets the value of labels.

pub fn set_encryption_spec<T: Into<Option<EncryptionSpec>>>(self, v: T) -> Self

Sets the value of encryption_spec.

pub fn set_disable_container_logging<T: Into<bool>>(self, v: T) -> Self

Sets the value of disable_container_logging.

pub fn set_satisfies_pzs<T: Into<bool>>(self, v: T) -> Self

Sets the value of satisfies_pzs.

pub fn set_satisfies_pzi<T: Into<bool>>(self, v: T) -> Self

Sets the value of satisfies_pzi.

Trait Implementations

impl Clone for BatchPredictionJob

fn clone(&self) -> BatchPredictionJob

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for BatchPredictionJob

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for BatchPredictionJob

fn default() -> BatchPredictionJob

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for BatchPredictionJob

where BatchPredictionJob: Default,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Message for BatchPredictionJob

fn typename() -> &'static str

The typename of this message.

impl PartialEq for BatchPredictionJob

fn eq(&self, other: &BatchPredictionJob) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for BatchPredictionJob

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for BatchPredictionJob