Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::InputDataConfig (v0.4.0)

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::InputDataConfig.

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

Inherits

  • Object

Extended By

  • Google::Protobuf::MessageExts::ClassMethods

Includes

  • Google::Protobuf::MessageExts

Methods

#annotation_schema_uri

def annotation_schema_uri() -> ::String
Returns
  • (::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 of the Dataset specified by 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, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.

#annotation_schema_uri=

def annotation_schema_uri=(value) -> ::String
Parameter
  • value (::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 of the Dataset specified by 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, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.

Returns
  • (::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 of the Dataset specified by 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, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.

#annotations_filter

def annotations_filter() -> ::String
Returns
  • (::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 may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.

#annotations_filter=

def annotations_filter=(value) -> ::String
Parameter
  • value (::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 may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.

Returns
  • (::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 may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.

#bigquery_destination

def bigquery_destination() -> ::Google::Cloud::AIPlatform::V1::BigQueryDestination
Returns
  • (::Google::Cloud::AIPlatform::V1::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_

    • AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_

    • AIP_TEST_DATA_URI = "bigquery_destination.dataset_

#bigquery_destination=

def bigquery_destination=(value) -> ::Google::Cloud::AIPlatform::V1::BigQueryDestination
Parameter
  • value (::Google::Cloud::AIPlatform::V1::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_

    • AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_

    • AIP_TEST_DATA_URI = "bigquery_destination.dataset_

Returns
  • (::Google::Cloud::AIPlatform::V1::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_

    • AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_

    • AIP_TEST_DATA_URI = "bigquery_destination.dataset_

#dataset_id

def dataset_id() -> ::String
Returns
  • (::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.

#dataset_id=

def dataset_id=(value) -> ::String
Parameter
  • value (::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.
Returns
  • (::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.

#filter_split

def filter_split() -> ::Google::Cloud::AIPlatform::V1::FilterSplit
Returns

#filter_split=

def filter_split=(value) -> ::Google::Cloud::AIPlatform::V1::FilterSplit
Parameter
Returns

#fraction_split

def fraction_split() -> ::Google::Cloud::AIPlatform::V1::FractionSplit
Returns

#fraction_split=

def fraction_split=(value) -> ::Google::Cloud::AIPlatform::V1::FractionSplit
Parameter
Returns

#gcs_destination

def gcs_destination() -> ::Google::Cloud::AIPlatform::V1::GcsDestination
Returns
  • (::Google::Cloud::AIPlatform::V1::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-

    • AIP_VALIDATION_DATA_URI = "gcs_destination/dataset-

    • AIP_TEST_DATA_URI = "gcs_destination/dataset-

#gcs_destination=

def gcs_destination=(value) -> ::Google::Cloud::AIPlatform::V1::GcsDestination
Parameter
  • value (::Google::Cloud::AIPlatform::V1::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-

    • AIP_VALIDATION_DATA_URI = "gcs_destination/dataset-

    • AIP_TEST_DATA_URI = "gcs_destination/dataset-

Returns
  • (::Google::Cloud::AIPlatform::V1::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-

    • AIP_VALIDATION_DATA_URI = "gcs_destination/dataset-

    • AIP_TEST_DATA_URI = "gcs_destination/dataset-

#predefined_split

def predefined_split() -> ::Google::Cloud::AIPlatform::V1::PredefinedSplit
Returns

#predefined_split=

def predefined_split=(value) -> ::Google::Cloud::AIPlatform::V1::PredefinedSplit
Parameter
Returns

#stratified_split

def stratified_split() -> ::Google::Cloud::AIPlatform::V1::StratifiedSplit
Returns

#stratified_split=

def stratified_split=(value) -> ::Google::Cloud::AIPlatform::V1::StratifiedSplit
Parameter
Returns

#timestamp_split

def timestamp_split() -> ::Google::Cloud::AIPlatform::V1::TimestampSplit
Returns

#timestamp_split=

def timestamp_split=(value) -> ::Google::Cloud::AIPlatform::V1::TimestampSplit
Parameter
Returns