Enum google_api_proto::google::cloud::aiplatform::v1::input_data_config::Destination

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

Only applicable to Custom and Hyperparameter Tuning TrainingPipelines.

The destination of the training data to be written to.

Supported destination file formats:

• For non-tabular data: “jsonl”.
• For tabular data: “csv” and “bigquery”.

The following Vertex AI environment variables are passed to containers or python modules of the training task when this field is set:

• AIP_DATA_FORMAT : Exported data format.
• AIP_TRAINING_DATA_URI : Sharded exported training data uris.
• AIP_VALIDATION_DATA_URI : Sharded exported validation data uris.
• AIP_TEST_DATA_URI : Sharded exported test data uris.

Variants

GcsDestination(GcsDestination)

The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call> where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory.

The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: “gs://…/training-*.jsonl”

• AIP_DATA_FORMAT = “jsonl” for non-tabular data, “csv” for tabular data

• AIP_TRAINING_DATA_URI = “gcs_destination/dataset---/training-*.${AIP_DATA_FORMAT}”

• AIP_VALIDATION_DATA_URI = “gcs_destination/dataset---/validation-*.${AIP_DATA_FORMAT}”

• AIP_TEST_DATA_URI = “gcs_destination/dataset---/test-*.${AIP_DATA_FORMAT}”

BigqueryDestination(BigQueryDestination)

Only applicable to custom training with tabular Dataset with BigQuery source.

The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call> where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test.

• AIP_DATA_FORMAT = “bigquery”.

• AIP_TRAINING_DATA_URI = “bigquery_destination.dataset_.training”

• AIP_VALIDATION_DATA_URI = “bigquery_destination.dataset_.validation”

• AIP_TEST_DATA_URI = “bigquery_destination.dataset_.test”

Implementations

impl Destination

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

where B: BufMut,

Encodes the message to a buffer.

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

where B: Buf,

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

pub fn encoded_len(&self) -> usize

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

Trait Implementations

impl Clone for Destination

fn clone(&self) -> Destination

Returns a copy of the value.

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 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: &Rhs) -> bool

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

impl StructuralPartialEq for Destination