Struct google_api_proto::google::cloud::aiplatform::v1::InputDataConfig

pub struct InputDataConfig {
    pub dataset_id: String,
    pub annotations_filter: String,
    pub annotation_schema_uri: String,
    pub saved_query_id: String,
    pub persist_ml_use_assignment: bool,
    pub split: Option<Split>,
    pub destination: Option<Destination>,
}

Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model.

Fields

dataset_id: String

Required. The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline’s [training_task_definition] [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. For tabular Datasets, all their data is exported to training, to pick and choose from.

annotations_filter: String

Applicable only to Datasets that have DataItems and Annotations.

A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.

annotation_schema_uri: String

Applicable only to custom training with Datasets that have DataItems and Annotations.

Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the Dataset specified by [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id].

Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on.

When used in conjunction with [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], the Annotations used for training are filtered by both [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] and [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri].

saved_query_id: String

Only applicable to Datasets that have SavedQueries.

The ID of a SavedQuery (annotation set) under the Dataset specified by [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used for filtering Annotations for training.

Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], the Annotations used for training are filtered by both [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] and [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter].

Only one of [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] and [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] should be specified as both of them represent the same thing: problem type.

persist_ml_use_assignment: bool

Whether to persist the ML use assignment to data item system labels.

split: Option<Split>

The instructions how the input data should be split between the training, validation and test sets. If no split type is provided, the [fraction_split][google.cloud.aiplatform.v1.InputDataConfig.fraction_split] is used by default.

destination: Option<Destination>

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.

Trait Implementations

impl Clone for InputDataConfig

fn clone(&self) -> InputDataConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for InputDataConfig

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

Formats the value using the given formatter.

impl Default for InputDataConfig

fn default() -> Self

Returns the “default value” for a type.

impl Message for InputDataConfig

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.

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

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

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

where Self: Sized,

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

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

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

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

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

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

where Self: Sized,

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

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

where Self: Sized,

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

impl PartialEq for InputDataConfig

fn eq(&self, other: &InputDataConfig) -> 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 InputDataConfig