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public interface InputDataConfigOrBuilder extends MessageOrBuilderImplements
MessageOrBuilderMethods
getAnnotationSchemaUri()
public abstract String getAnnotationSchemaUri()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.
string annotation_schema_uri = 9;
| Type | Description |
| String | The annotationSchemaUri. |
getAnnotationSchemaUriBytes()
public abstract ByteString getAnnotationSchemaUriBytes()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.
string annotation_schema_uri = 9;
| Type | Description |
| ByteString | The bytes for annotationSchemaUri. |
getAnnotationsFilter()
public abstract String getAnnotationsFilter()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.
string annotations_filter = 6;
| Type | Description |
| String | The annotationsFilter. |
getAnnotationsFilterBytes()
public abstract ByteString getAnnotationsFilterBytes()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.
string annotations_filter = 6;
| Type | Description |
| ByteString | The bytes for annotationsFilter. |
getBigqueryDestination()
public abstract BigQueryDestination getBigqueryDestination() 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_<dataset-id><annotation-type><time>.training"
- AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_<dataset-id><annotation-type><time>.validation"
- AIP_TEST_DATA_URI = "bigquery_destination.dataset_<dataset-id><annotation-type><time>.test"
.google.cloud.aiplatform.v1beta1.BigQueryDestination bigquery_destination = 10;
| Type | Description |
| BigQueryDestination | The bigqueryDestination. |
getBigqueryDestinationOrBuilder()
public abstract BigQueryDestinationOrBuilder getBigqueryDestinationOrBuilder() 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_<dataset-id><annotation-type><time>.training"
- AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_<dataset-id><annotation-type><time>.validation"
- AIP_TEST_DATA_URI = "bigquery_destination.dataset_<dataset-id><annotation-type><time>.test"
.google.cloud.aiplatform.v1beta1.BigQueryDestination bigquery_destination = 10;
| Type | Description |
| BigQueryDestinationOrBuilder |
getDatasetId()
public abstract String getDatasetId()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.v1beta1.TrainingPipeline.training_task_definition]. For tabular Datasets, all their data is exported to training, to pick and choose from.
string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| String | The datasetId. |
getDatasetIdBytes()
public abstract ByteString getDatasetIdBytes()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.v1beta1.TrainingPipeline.training_task_definition]. For tabular Datasets, all their data is exported to training, to pick and choose from.
string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
| ByteString | The bytes for datasetId. |
getDestinationCase()
public abstract InputDataConfig.DestinationCase getDestinationCase()| Type | Description |
| InputDataConfig.DestinationCase |
getFilterSplit()
public abstract FilterSplit getFilterSplit()Split based on the provided filters for each set.
.google.cloud.aiplatform.v1beta1.FilterSplit filter_split = 3;
| Type | Description |
| FilterSplit | The filterSplit. |
getFilterSplitOrBuilder()
public abstract FilterSplitOrBuilder getFilterSplitOrBuilder()Split based on the provided filters for each set.
.google.cloud.aiplatform.v1beta1.FilterSplit filter_split = 3;
| Type | Description |
| FilterSplitOrBuilder |
getFractionSplit()
public abstract FractionSplit getFractionSplit()Split based on fractions defining the size of each set.
.google.cloud.aiplatform.v1beta1.FractionSplit fraction_split = 2;
| Type | Description |
| FractionSplit | The fractionSplit. |
getFractionSplitOrBuilder()
public abstract FractionSplitOrBuilder getFractionSplitOrBuilder()Split based on fractions defining the size of each set.
.google.cloud.aiplatform.v1beta1.FractionSplit fraction_split = 2;
| Type | Description |
| FractionSplitOrBuilder |
getGcsDestination()
public abstract GcsDestination getGcsDestination() 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-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}"
- AIP_VALIDATION_DATA_URI = "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}"
- AIP_TEST_DATA_URI = "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}"
.google.cloud.aiplatform.v1beta1.GcsDestination gcs_destination = 8;
| Type | Description |
| GcsDestination | The gcsDestination. |
getGcsDestinationOrBuilder()
public abstract GcsDestinationOrBuilder getGcsDestinationOrBuilder() The Cloud Storage location where the training data is to be
written to. In the given directory a new directory is created with
name:
dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>
where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format.
All training input data is written into that directory.
The Vertex AI environment variables representing Cloud Storage
data URIs are represented in the Cloud Storage wildcard
format to support sharded data. e.g.: "gs://.../training-*.jsonl"
- AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data
- AIP_TRAINING_DATA_URI = "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}"
- AIP_VALIDATION_DATA_URI = "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}"
- AIP_TEST_DATA_URI = "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}"
.google.cloud.aiplatform.v1beta1.GcsDestination gcs_destination = 8;
| Type | Description |
| GcsDestinationOrBuilder |
getPredefinedSplit()
public abstract PredefinedSplit getPredefinedSplit()Supported only for tabular Datasets. Split based on a predefined key.
.google.cloud.aiplatform.v1beta1.PredefinedSplit predefined_split = 4;
| Type | Description |
| PredefinedSplit | The predefinedSplit. |
getPredefinedSplitOrBuilder()
public abstract PredefinedSplitOrBuilder getPredefinedSplitOrBuilder()Supported only for tabular Datasets. Split based on a predefined key.
.google.cloud.aiplatform.v1beta1.PredefinedSplit predefined_split = 4;
| Type | Description |
| PredefinedSplitOrBuilder |
getSplitCase()
public abstract InputDataConfig.SplitCase getSplitCase()| Type | Description |
| InputDataConfig.SplitCase |
getStratifiedSplit()
public abstract StratifiedSplit getStratifiedSplit()Supported only for tabular Datasets. Split based on the distribution of the specified column.
.google.cloud.aiplatform.v1beta1.StratifiedSplit stratified_split = 12;
| Type | Description |
| StratifiedSplit | The stratifiedSplit. |
getStratifiedSplitOrBuilder()
public abstract StratifiedSplitOrBuilder getStratifiedSplitOrBuilder()Supported only for tabular Datasets. Split based on the distribution of the specified column.
.google.cloud.aiplatform.v1beta1.StratifiedSplit stratified_split = 12;
| Type | Description |
| StratifiedSplitOrBuilder |
getTimestampSplit()
public abstract TimestampSplit getTimestampSplit()Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
.google.cloud.aiplatform.v1beta1.TimestampSplit timestamp_split = 5;
| Type | Description |
| TimestampSplit | The timestampSplit. |
getTimestampSplitOrBuilder()
public abstract TimestampSplitOrBuilder getTimestampSplitOrBuilder()Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
.google.cloud.aiplatform.v1beta1.TimestampSplit timestamp_split = 5;
| Type | Description |
| TimestampSplitOrBuilder |
hasBigqueryDestination()
public abstract boolean hasBigqueryDestination() 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_<dataset-id><annotation-type><time>.training"
- AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_<dataset-id><annotation-type><time>.validation"
- AIP_TEST_DATA_URI = "bigquery_destination.dataset_<dataset-id><annotation-type><time>.test"
.google.cloud.aiplatform.v1beta1.BigQueryDestination bigquery_destination = 10;
| Type | Description |
| boolean | Whether the bigqueryDestination field is set. |
hasFilterSplit()
public abstract boolean hasFilterSplit()Split based on the provided filters for each set.
.google.cloud.aiplatform.v1beta1.FilterSplit filter_split = 3;
| Type | Description |
| boolean | Whether the filterSplit field is set. |
hasFractionSplit()
public abstract boolean hasFractionSplit()Split based on fractions defining the size of each set.
.google.cloud.aiplatform.v1beta1.FractionSplit fraction_split = 2;
| Type | Description |
| boolean | Whether the fractionSplit field is set. |
hasGcsDestination()
public abstract boolean hasGcsDestination() 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-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}"
- AIP_VALIDATION_DATA_URI = "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}"
- AIP_TEST_DATA_URI = "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}"
.google.cloud.aiplatform.v1beta1.GcsDestination gcs_destination = 8;
| Type | Description |
| boolean | Whether the gcsDestination field is set. |
hasPredefinedSplit()
public abstract boolean hasPredefinedSplit()Supported only for tabular Datasets. Split based on a predefined key.
.google.cloud.aiplatform.v1beta1.PredefinedSplit predefined_split = 4;
| Type | Description |
| boolean | Whether the predefinedSplit field is set. |
hasStratifiedSplit()
public abstract boolean hasStratifiedSplit()Supported only for tabular Datasets. Split based on the distribution of the specified column.
.google.cloud.aiplatform.v1beta1.StratifiedSplit stratified_split = 12;
| Type | Description |
| boolean | Whether the stratifiedSplit field is set. |
hasTimestampSplit()
public abstract boolean hasTimestampSplit()Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
.google.cloud.aiplatform.v1beta1.TimestampSplit timestamp_split = 5;
| Type | Description |
| boolean | Whether the timestampSplit field is set. |