Struct gapi_grpc::google::cloud::aiplatform::v1beta1::BatchPredictionJob

pub struct BatchPredictionJob {
    pub name: String,
    pub display_name: String,
    pub model: String,
    pub input_config: Option<InputConfig>,
    pub model_parameters: Option<Value>,
    pub output_config: Option<OutputConfig>,
    pub dedicated_resources: Option<BatchDedicatedResources>,
    pub manual_batch_tuning_parameters: Option<ManualBatchTuningParameters>,
    pub generate_explanation: bool,
    pub explanation_spec: Option<ExplanationSpec>,
    pub output_info: Option<OutputInfo>,
    pub state: i32,
    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>,
}

A job that uses a [Model][google.cloud.aiplatform.v1beta1.BatchPredictionJob.model] to produce predictions on multiple [input instances][google.cloud.aiplatform.v1beta1.BatchPredictionJob.input_config]. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

Fields

name: String

Output only. Resource name of the BatchPredictionJob.

display_name: String

Required. The user-defined name of this BatchPredictionJob.

model: String

Required. The name of the Model 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.

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][google.cloud.aiplatform.v1beta1.BatchPredictionJob.model] [PredictSchemata’s][google.cloud.aiplatform.v1beta1.Model.predict_schemata] [instance_schema_uri][google.cloud.aiplatform.v1beta1.PredictSchemata.instance_schema_uri].

model_parameters: Option<Value>

The parameters that govern the predictions. The schema of the parameters may be specified via the [Model’s][google.cloud.aiplatform.v1beta1.BatchPredictionJob.model] [PredictSchemata’s][google.cloud.aiplatform.v1beta1.Model.predict_schemata] [parameters_schema_uri][google.cloud.aiplatform.v1beta1.PredictSchemata.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][google.cloud.aiplatform.v1beta1.BatchPredictionJob.model] [PredictSchemata’s][google.cloud.aiplatform.v1beta1.Model.predict_schemata] [instance_schema_uri][google.cloud.aiplatform.v1beta1.PredictSchemata.instance_schema_uri] and [prediction_schema_uri][google.cloud.aiplatform.v1beta1.PredictSchemata.prediction_schema_uri].

dedicated_resources: Option<BatchDedicatedResources>

The config of resources used by the Model during the batch prediction. If the Model [supports][google.cloud.aiplatform.v1beta1.Model.supported_deployment_resources_types] 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.

manual_batch_tuning_parameters: Option<ManualBatchTuningParameters>

Immutable. Parameters configuring the batch behavior. Currently only applicable when [dedicated_resources][google.cloud.aiplatform.v1beta1.BatchPredictionJob.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][google.cloud.aiplatform.v1beta1.BatchPredictionJob.output_config] object:

• bigquery: output includes a column named explanation. The value is a struct that conforms to the [Explanation][google.cloud.aiplatform.v1beta1.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][google.cloud.aiplatform.v1beta1.Explanation] object.
• csv: Generating explanations for CSV format is not supported.

If this field is set to true, either the [Model.explanation_spec][google.cloud.aiplatform.v1beta1.Model.explanation_spec] or [explanation_spec][google.cloud.aiplatform.v1beta1.BatchPredictionJob.explanation_spec] must be populated.

explanation_spec: Option<ExplanationSpec>

Explanation configuration for this BatchPredictionJob. Can be specified only if [generate_explanation][google.cloud.aiplatform.v1beta1.BatchPredictionJob.generate_explanation] is set to true.

This value overrides the value of [Model.explanation_spec][google.cloud.aiplatform.v1beta1.Model.explanation_spec]. All fields of [explanation_spec][google.cloud.aiplatform.v1beta1.BatchPredictionJob.explanation_spec] are optional in the request. If a field of the [explanation_spec][google.cloud.aiplatform.v1beta1.BatchPredictionJob.explanation_spec] object is not populated, the corresponding field of the [Model.explanation_spec][google.cloud.aiplatform.v1beta1.Model.explanation_spec] object is inherited.

output_info: Option<OutputInfo>

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

state: i32

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 GCP 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.

Implementations

impl BatchPredictionJob

pub fn state(&self) -> JobState

Returns the enum value of state, or the default if the field is set to an invalid enum value.

pub fn set_state(&mut self, value: JobState)

Sets state to the provided enum value.

Trait Implementations

impl Clone for BatchPredictionJob

fn clone(&self) -> BatchPredictionJob

Returns a copy of the value.

pub 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 Message for BatchPredictionJob

fn encode_raw<B>(&self, buf: &mut B) where
    B: BufMut, 

fn merge_field<B>(
    &mut self,
    tag: u32,
    wire_type: WireType,
    buf: &mut B,
    ctx: DecodeContext
) -> Result<(), DecodeError> where
    B: Buf, 

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

pub fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
    B: BufMut, 

Encodes the message to a buffer.

pub fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
    B: BufMut, 

Encodes the message with a length-delimiter to a buffer.

pub fn decode<B>(buf: B) -> Result<Self, DecodeError> where
    Self: Default,
    B: Buf, 

Decodes an instance of the message from a buffer.

pub fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError> where
    Self: Default,
    B: Buf, 

Decodes a length-delimited instance of the message from the buffer.

pub fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError> where
    B: Buf, 

Decodes an instance of the message from a buffer, and merges it into self.

pub fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError> where
    B: Buf, 

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq<BatchPredictionJob> for BatchPredictionJob

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

This method tests for self and other values to be equal, and is used by ==.

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

This method tests for !=.

impl StructuralPartialEq for BatchPredictionJob