Package com.google.cloud.aiplatform.v1

Class InputDataConfig.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<InputDataConfig.Builder>
com.google.cloud.aiplatform.v1.InputDataConfig.Builder
All Implemented Interfaces:
InputDataConfigOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
InputDataConfig
public static final class InputDataConfig.Builder extends com.google.protobuf.GeneratedMessage.Builder<InputDataConfig.Builder> implements InputDataConfigOrBuilder
 Specifies Vertex AI owned input data to be used for training, and
 possibly evaluating, the Model.
Protobuf type google.cloud.aiplatform.v1.InputDataConfig

  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<InputDataConfig.Builder>

    • clear

      public InputDataConfig.Builder clear()
      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessage.Builder<InputDataConfig.Builder>

    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<InputDataConfig.Builder>

    • getDefaultInstanceForType

      public InputDataConfig getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

    • build

      public InputDataConfig build()
      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder

    • buildPartial

      public InputDataConfig buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder

    • mergeFrom

      public InputDataConfig.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<InputDataConfig.Builder>

    • mergeFrom

      public InputDataConfig.Builder mergeFrom(InputDataConfig other)

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessage.Builder<InputDataConfig.Builder>

    • mergeFrom

      public InputDataConfig.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<InputDataConfig.Builder>
      Throws:
      IOException

    • getSplitCase

      public InputDataConfig.SplitCase getSplitCase()
      Specified by:
      getSplitCase in interface InputDataConfigOrBuilder

    • clearSplit

      public InputDataConfig.Builder clearSplit()

    • getDestinationCase

      public InputDataConfig.DestinationCase getDestinationCase()
      Specified by:
      getDestinationCase in interface InputDataConfigOrBuilder

    • clearDestination

      public InputDataConfig.Builder clearDestination()

    • hasFractionSplit

      public boolean hasFractionSplit()
       Split based on fractions defining the size of each set.
      .google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;
      Specified by:
      hasFractionSplit in interface InputDataConfigOrBuilder
      Returns:
      Whether the fractionSplit field is set.

    • getFractionSplit

      public FractionSplit getFractionSplit()
       Split based on fractions defining the size of each set.
      .google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;
      Specified by:
      getFractionSplit in interface InputDataConfigOrBuilder
      Returns:
      The fractionSplit.

    • setFractionSplit

      public InputDataConfig.Builder setFractionSplit(FractionSplit value)
       Split based on fractions defining the size of each set.
      .google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;

    • setFractionSplit

      public InputDataConfig.Builder setFractionSplit(FractionSplit.Builder builderForValue)
       Split based on fractions defining the size of each set.
      .google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;

    • mergeFractionSplit

      public InputDataConfig.Builder mergeFractionSplit(FractionSplit value)
       Split based on fractions defining the size of each set.
      .google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;

    • clearFractionSplit

      public InputDataConfig.Builder clearFractionSplit()
       Split based on fractions defining the size of each set.
      .google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;

    • getFractionSplitBuilder

      public FractionSplit.Builder getFractionSplitBuilder()
       Split based on fractions defining the size of each set.
      .google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;

    • getFractionSplitOrBuilder

      public FractionSplitOrBuilder getFractionSplitOrBuilder()
       Split based on fractions defining the size of each set.
      .google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;
      Specified by:
      getFractionSplitOrBuilder in interface InputDataConfigOrBuilder

    • hasFilterSplit

      public boolean hasFilterSplit()
       Split based on the provided filters for each set.
      .google.cloud.aiplatform.v1.FilterSplit filter_split = 3;
      Specified by:
      hasFilterSplit in interface InputDataConfigOrBuilder
      Returns:
      Whether the filterSplit field is set.

    • getFilterSplit

      public FilterSplit getFilterSplit()
       Split based on the provided filters for each set.
      .google.cloud.aiplatform.v1.FilterSplit filter_split = 3;
      Specified by:
      getFilterSplit in interface InputDataConfigOrBuilder
      Returns:
      The filterSplit.

    • setFilterSplit

      public InputDataConfig.Builder setFilterSplit(FilterSplit value)
       Split based on the provided filters for each set.
      .google.cloud.aiplatform.v1.FilterSplit filter_split = 3;

    • setFilterSplit

      public InputDataConfig.Builder setFilterSplit(FilterSplit.Builder builderForValue)
       Split based on the provided filters for each set.
      .google.cloud.aiplatform.v1.FilterSplit filter_split = 3;

    • mergeFilterSplit

      public InputDataConfig.Builder mergeFilterSplit(FilterSplit value)
       Split based on the provided filters for each set.
      .google.cloud.aiplatform.v1.FilterSplit filter_split = 3;

    • clearFilterSplit

      public InputDataConfig.Builder clearFilterSplit()
       Split based on the provided filters for each set.
      .google.cloud.aiplatform.v1.FilterSplit filter_split = 3;

    • getFilterSplitBuilder

      public FilterSplit.Builder getFilterSplitBuilder()
       Split based on the provided filters for each set.
      .google.cloud.aiplatform.v1.FilterSplit filter_split = 3;

    • getFilterSplitOrBuilder

      public FilterSplitOrBuilder getFilterSplitOrBuilder()
       Split based on the provided filters for each set.
      .google.cloud.aiplatform.v1.FilterSplit filter_split = 3;
      Specified by:
      getFilterSplitOrBuilder in interface InputDataConfigOrBuilder

    • hasPredefinedSplit

      public boolean hasPredefinedSplit()
       Supported only for tabular Datasets.

 Split based on a predefined key.
      .google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;
      Specified by:
      hasPredefinedSplit in interface InputDataConfigOrBuilder
      Returns:
      Whether the predefinedSplit field is set.

    • getPredefinedSplit

      public PredefinedSplit getPredefinedSplit()
       Supported only for tabular Datasets.

 Split based on a predefined key.
      .google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;
      Specified by:
      getPredefinedSplit in interface InputDataConfigOrBuilder
      Returns:
      The predefinedSplit.

    • setPredefinedSplit

      public InputDataConfig.Builder setPredefinedSplit(PredefinedSplit value)
       Supported only for tabular Datasets.

 Split based on a predefined key.
      .google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;

    • setPredefinedSplit

      public InputDataConfig.Builder setPredefinedSplit(PredefinedSplit.Builder builderForValue)
       Supported only for tabular Datasets.

 Split based on a predefined key.
      .google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;

    • mergePredefinedSplit

      public InputDataConfig.Builder mergePredefinedSplit(PredefinedSplit value)
       Supported only for tabular Datasets.

 Split based on a predefined key.
      .google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;

    • clearPredefinedSplit

      public InputDataConfig.Builder clearPredefinedSplit()
       Supported only for tabular Datasets.

 Split based on a predefined key.
      .google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;

    • getPredefinedSplitBuilder

      public PredefinedSplit.Builder getPredefinedSplitBuilder()
       Supported only for tabular Datasets.

 Split based on a predefined key.
      .google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;

    • getPredefinedSplitOrBuilder

      public PredefinedSplitOrBuilder getPredefinedSplitOrBuilder()
       Supported only for tabular Datasets.

 Split based on a predefined key.
      .google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;
      Specified by:
      getPredefinedSplitOrBuilder in interface InputDataConfigOrBuilder

    • hasTimestampSplit

      public boolean hasTimestampSplit()
       Supported only for tabular Datasets.

 Split based on the timestamp of the input data pieces.
      .google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;
      Specified by:
      hasTimestampSplit in interface InputDataConfigOrBuilder
      Returns:
      Whether the timestampSplit field is set.

    • getTimestampSplit

      public TimestampSplit getTimestampSplit()
       Supported only for tabular Datasets.

 Split based on the timestamp of the input data pieces.
      .google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;
      Specified by:
      getTimestampSplit in interface InputDataConfigOrBuilder
      Returns:
      The timestampSplit.

    • setTimestampSplit

      public InputDataConfig.Builder setTimestampSplit(TimestampSplit value)
       Supported only for tabular Datasets.

 Split based on the timestamp of the input data pieces.
      .google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;

    • setTimestampSplit

      public InputDataConfig.Builder setTimestampSplit(TimestampSplit.Builder builderForValue)
       Supported only for tabular Datasets.

 Split based on the timestamp of the input data pieces.
      .google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;

    • mergeTimestampSplit

      public InputDataConfig.Builder mergeTimestampSplit(TimestampSplit value)
       Supported only for tabular Datasets.

 Split based on the timestamp of the input data pieces.
      .google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;

    • clearTimestampSplit

      public InputDataConfig.Builder clearTimestampSplit()
       Supported only for tabular Datasets.

 Split based on the timestamp of the input data pieces.
      .google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;

    • getTimestampSplitBuilder

      public TimestampSplit.Builder getTimestampSplitBuilder()
       Supported only for tabular Datasets.

 Split based on the timestamp of the input data pieces.
      .google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;

    • getTimestampSplitOrBuilder

      public TimestampSplitOrBuilder getTimestampSplitOrBuilder()
       Supported only for tabular Datasets.

 Split based on the timestamp of the input data pieces.
      .google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;
      Specified by:
      getTimestampSplitOrBuilder in interface InputDataConfigOrBuilder

    • hasStratifiedSplit

      public boolean hasStratifiedSplit()
       Supported only for tabular Datasets.

 Split based on the distribution of the specified column.
      .google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;
      Specified by:
      hasStratifiedSplit in interface InputDataConfigOrBuilder
      Returns:
      Whether the stratifiedSplit field is set.

    • getStratifiedSplit

      public StratifiedSplit getStratifiedSplit()
       Supported only for tabular Datasets.

 Split based on the distribution of the specified column.
      .google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;
      Specified by:
      getStratifiedSplit in interface InputDataConfigOrBuilder
      Returns:
      The stratifiedSplit.

    • setStratifiedSplit

      public InputDataConfig.Builder setStratifiedSplit(StratifiedSplit value)
       Supported only for tabular Datasets.

 Split based on the distribution of the specified column.
      .google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;

    • setStratifiedSplit

      public InputDataConfig.Builder setStratifiedSplit(StratifiedSplit.Builder builderForValue)
       Supported only for tabular Datasets.

 Split based on the distribution of the specified column.
      .google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;

    • mergeStratifiedSplit

      public InputDataConfig.Builder mergeStratifiedSplit(StratifiedSplit value)
       Supported only for tabular Datasets.

 Split based on the distribution of the specified column.
      .google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;

    • clearStratifiedSplit

      public InputDataConfig.Builder clearStratifiedSplit()
       Supported only for tabular Datasets.

 Split based on the distribution of the specified column.
      .google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;

    • getStratifiedSplitBuilder

      public StratifiedSplit.Builder getStratifiedSplitBuilder()
       Supported only for tabular Datasets.

 Split based on the distribution of the specified column.
      .google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;

    • getStratifiedSplitOrBuilder

      public StratifiedSplitOrBuilder getStratifiedSplitOrBuilder()
       Supported only for tabular Datasets.

 Split based on the distribution of the specified column.
      .google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;
      Specified by:
      getStratifiedSplitOrBuilder in interface InputDataConfigOrBuilder

    • hasGcsDestination

      public 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.v1.GcsDestination gcs_destination = 8;
      Specified by:
      hasGcsDestination in interface InputDataConfigOrBuilder
      Returns:
      Whether the gcsDestination field is set.

    • getGcsDestination

      public 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.v1.GcsDestination gcs_destination = 8;
      Specified by:
      getGcsDestination in interface InputDataConfigOrBuilder
      Returns:
      The gcsDestination.

    • setGcsDestination

      public InputDataConfig.Builder setGcsDestination(GcsDestination value)
       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.v1.GcsDestination gcs_destination = 8;

    • setGcsDestination

      public InputDataConfig.Builder setGcsDestination(GcsDestination.Builder builderForValue)
       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.v1.GcsDestination gcs_destination = 8;

    • mergeGcsDestination

      public InputDataConfig.Builder mergeGcsDestination(GcsDestination value)
       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.v1.GcsDestination gcs_destination = 8;

    • clearGcsDestination

      public InputDataConfig.Builder clearGcsDestination()
       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.v1.GcsDestination gcs_destination = 8;

    • getGcsDestinationBuilder

      public GcsDestination.Builder getGcsDestinationBuilder()
       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.v1.GcsDestination gcs_destination = 8;

    • getGcsDestinationOrBuilder

      public 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.v1.GcsDestination gcs_destination = 8;
      Specified by:
      getGcsDestinationOrBuilder in interface InputDataConfigOrBuilder

    • hasBigqueryDestination

      public 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.v1.BigQueryDestination bigquery_destination = 10;
      Specified by:
      hasBigqueryDestination in interface InputDataConfigOrBuilder
      Returns:
      Whether the bigqueryDestination field is set.

    • getBigqueryDestination

      public 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.v1.BigQueryDestination bigquery_destination = 10;
      Specified by:
      getBigqueryDestination in interface InputDataConfigOrBuilder
      Returns:
      The bigqueryDestination.

    • setBigqueryDestination

      public InputDataConfig.Builder setBigqueryDestination(BigQueryDestination value)
       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.v1.BigQueryDestination bigquery_destination = 10;

    • setBigqueryDestination

      public InputDataConfig.Builder setBigqueryDestination(BigQueryDestination.Builder builderForValue)
       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.v1.BigQueryDestination bigquery_destination = 10;

    • mergeBigqueryDestination

      public InputDataConfig.Builder mergeBigqueryDestination(BigQueryDestination value)
       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.v1.BigQueryDestination bigquery_destination = 10;

    • clearBigqueryDestination

      public InputDataConfig.Builder clearBigqueryDestination()
       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.v1.BigQueryDestination bigquery_destination = 10;

    • getBigqueryDestinationBuilder

      public BigQueryDestination.Builder getBigqueryDestinationBuilder()
       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.v1.BigQueryDestination bigquery_destination = 10;

    • getBigqueryDestinationOrBuilder

      public 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.v1.BigQueryDestination bigquery_destination = 10;
      Specified by:
      getBigqueryDestinationOrBuilder in interface InputDataConfigOrBuilder

    • getDatasetId

      public 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.v1.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];
      Specified by:
      getDatasetId in interface InputDataConfigOrBuilder
      Returns:
      The datasetId.

    • getDatasetIdBytes

      public com.google.protobuf.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.v1.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];
      Specified by:
      getDatasetIdBytes in interface InputDataConfigOrBuilder
      Returns:
      The bytes for datasetId.

    • setDatasetId

      public InputDataConfig.Builder setDatasetId(String value)
       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.
      string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];
      Parameters:
      value - The datasetId to set.
      Returns:
      This builder for chaining.

    • clearDatasetId

      public InputDataConfig.Builder clearDatasetId()
       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.
      string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];
      Returns:
      This builder for chaining.

    • setDatasetIdBytes

      public InputDataConfig.Builder setDatasetIdBytes(com.google.protobuf.ByteString value)
       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.
      string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];
      Parameters:
      value - The bytes for datasetId to set.
      Returns:
      This builder for chaining.

    • getAnnotationsFilter

      public 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][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.
      string annotations_filter = 6;
      Specified by:
      getAnnotationsFilter in interface InputDataConfigOrBuilder
      Returns:
      The annotationsFilter.

    • getAnnotationsFilterBytes

      public com.google.protobuf.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][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.
      string annotations_filter = 6;
      Specified by:
      getAnnotationsFilterBytes in interface InputDataConfigOrBuilder
      Returns:
      The bytes for annotationsFilter.

    • setAnnotationsFilter

      public InputDataConfig.Builder setAnnotationsFilter(String value)
       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.
      string annotations_filter = 6;
      Parameters:
      value - The annotationsFilter to set.
      Returns:
      This builder for chaining.

    • clearAnnotationsFilter

      public InputDataConfig.Builder clearAnnotationsFilter()
       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.
      string annotations_filter = 6;
      Returns:
      This builder for chaining.

    • setAnnotationsFilterBytes

      public InputDataConfig.Builder setAnnotationsFilterBytes(com.google.protobuf.ByteString value)
       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.
      string annotations_filter = 6;
      Parameters:
      value - The bytes for annotationsFilter to set.
      Returns:
      This builder for chaining.

    • getAnnotationSchemaUri

      public 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](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
 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].
      string annotation_schema_uri = 9;
      Specified by:
      getAnnotationSchemaUri in interface InputDataConfigOrBuilder
      Returns:
      The annotationSchemaUri.

    • getAnnotationSchemaUriBytes

      public com.google.protobuf.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](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
 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].
      string annotation_schema_uri = 9;
      Specified by:
      getAnnotationSchemaUriBytes in interface InputDataConfigOrBuilder
      Returns:
      The bytes for annotationSchemaUri.

    • setAnnotationSchemaUri

      public InputDataConfig.Builder setAnnotationSchemaUri(String value)
       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](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
 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].
      string annotation_schema_uri = 9;
      Parameters:
      value - The annotationSchemaUri to set.
      Returns:
      This builder for chaining.

    • clearAnnotationSchemaUri

      public InputDataConfig.Builder clearAnnotationSchemaUri()
       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](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
 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].
      string annotation_schema_uri = 9;
      Returns:
      This builder for chaining.

    • setAnnotationSchemaUriBytes

      public InputDataConfig.Builder setAnnotationSchemaUriBytes(com.google.protobuf.ByteString value)
       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](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
 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].
      string annotation_schema_uri = 9;
      Parameters:
      value - The bytes for annotationSchemaUri to set.
      Returns:
      This builder for chaining.

    • getSavedQueryId

      public String getSavedQueryId()
       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.
      string saved_query_id = 7;
      Specified by:
      getSavedQueryId in interface InputDataConfigOrBuilder
      Returns:
      The savedQueryId.

    • getSavedQueryIdBytes

      public com.google.protobuf.ByteString getSavedQueryIdBytes()
       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.
      string saved_query_id = 7;
      Specified by:
      getSavedQueryIdBytes in interface InputDataConfigOrBuilder
      Returns:
      The bytes for savedQueryId.

    • setSavedQueryId

      public InputDataConfig.Builder setSavedQueryId(String value)
       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.
      string saved_query_id = 7;
      Parameters:
      value - The savedQueryId to set.
      Returns:
      This builder for chaining.

    • clearSavedQueryId

      public InputDataConfig.Builder clearSavedQueryId()
       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.
      string saved_query_id = 7;
      Returns:
      This builder for chaining.

    • setSavedQueryIdBytes

      public InputDataConfig.Builder setSavedQueryIdBytes(com.google.protobuf.ByteString value)
       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.
      string saved_query_id = 7;
      Parameters:
      value - The bytes for savedQueryId to set.
      Returns:
      This builder for chaining.

    • getPersistMlUseAssignment

      public boolean getPersistMlUseAssignment()
       Whether to persist the ML use assignment to data item system labels.
      bool persist_ml_use_assignment = 11;
      Specified by:
      getPersistMlUseAssignment in interface InputDataConfigOrBuilder
      Returns:
      The persistMlUseAssignment.

    • setPersistMlUseAssignment

      public InputDataConfig.Builder setPersistMlUseAssignment(boolean value)
       Whether to persist the ML use assignment to data item system labels.
      bool persist_ml_use_assignment = 11;
      Parameters:
      value - The persistMlUseAssignment to set.
      Returns:
      This builder for chaining.

    • clearPersistMlUseAssignment

      public InputDataConfig.Builder clearPersistMlUseAssignment()
       Whether to persist the ML use assignment to data item system labels.
      bool persist_ml_use_assignment = 11;
      Returns:
      This builder for chaining.