Enum gapi_grpc::google::cloud::aiplatform::v1::batch_prediction_job::output_config::Destination

pub enum Destination {
    GcsDestination(GcsDestination),
    BigqueryDestination(BigQueryDestination),
}

Required. The destination of the output.

Variants

GcsDestination(GcsDestination)

The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction-<model-display-name>-<job-create-time>, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001.<extension>, predictions_0002.<extension>, …, predictions_N.<extension> are created where <extension> depends on chosen [predictions_format][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.predictions_format], and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both [instance][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] and [prediction][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri] schemata defined then each such file contains predictions as per the [predictions_format][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.predictions_format]. If prediction for any instance failed (partially or completely), then an additional errors_0001.<extension>, errors_0002.<extension>,…, errors_N.<extension> files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.

BigqueryDestination(BigQueryDestination)

The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction_<model-display-name>_<job-create-time> where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ “based on ISO-8601” format. In the dataset two tables will be created, predictions, and errors. If the Model has both [instance][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] and [prediction][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri] schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model’s instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single “errors” column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.

Implementations

impl Destination

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

pub fn merge<B>(
    field: &mut Option<Destination>,
    tag: u32,
    wire_type: WireType,
    buf: &mut B,
    ctx: DecodeContext
) -> Result<(), DecodeError> where
    B: Buf, 

pub fn encoded_len(&self) -> usize

Trait Implementations

impl Clone for Destination

fn clone(&self) -> Destination

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Destination

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

Formats the value using the given formatter.

impl PartialEq<Destination> for Destination

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

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

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

This method tests for !=.

impl StructuralPartialEq for Destination