com.google.cloud.aiplatform.v1beta1

Class InputDataConfig.Builder

java.lang.Object

com.google.protobuf.AbstractMessageLite.Builder

com.google.protobuf.AbstractMessage.Builder<BuilderType>

com.google.protobuf.GeneratedMessageV3.Builder<InputDataConfig.Builder>

com.google.cloud.aiplatform.v1beta1.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.GeneratedMessageV3.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.v1beta1.InputDataConfig

Method Summary

Modifier and Type	Method and Description
InputDataConfig.Builder	addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
InputDataConfig	build()
InputDataConfig	buildPartial()
InputDataConfig.Builder	clear()
InputDataConfig.Builder	clearAnnotationSchemaUri() Applicable only to custom training with Datasets that have DataItems and Annotations.
InputDataConfig.Builder	clearAnnotationsFilter() Applicable only to Datasets that have DataItems and Annotations.
InputDataConfig.Builder	clearBigqueryDestination() Only applicable to custom training with tabular Dataset with BigQuery source.
InputDataConfig.Builder	clearDatasetId() Required.
InputDataConfig.Builder	clearDestination()
InputDataConfig.Builder	clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
InputDataConfig.Builder	clearFilterSplit() Split based on the provided filters for each set.
InputDataConfig.Builder	clearFractionSplit() Split based on fractions defining the size of each set.
InputDataConfig.Builder	clearGcsDestination() The Cloud Storage location where the training data is to be written to.
InputDataConfig.Builder	clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
InputDataConfig.Builder	clearPredefinedSplit() Supported only for tabular Datasets.
InputDataConfig.Builder	clearSavedQueryId() Only applicable to Datasets that have SavedQueries.
InputDataConfig.Builder	clearSplit()
InputDataConfig.Builder	clearStratifiedSplit() Supported only for tabular Datasets.
InputDataConfig.Builder	clearTimestampSplit() Supported only for tabular Datasets.
InputDataConfig.Builder	clone()
String	getAnnotationSchemaUri() Applicable only to custom training with Datasets that have DataItems and Annotations.
com.google.protobuf.ByteString	getAnnotationSchemaUriBytes() Applicable only to custom training with Datasets that have DataItems and Annotations.
String	getAnnotationsFilter() Applicable only to Datasets that have DataItems and Annotations.
com.google.protobuf.ByteString	getAnnotationsFilterBytes() Applicable only to Datasets that have DataItems and Annotations.
BigQueryDestination	getBigqueryDestination() Only applicable to custom training with tabular Dataset with BigQuery source.
BigQueryDestination.Builder	getBigqueryDestinationBuilder() Only applicable to custom training with tabular Dataset with BigQuery source.
BigQueryDestinationOrBuilder	getBigqueryDestinationOrBuilder() Only applicable to custom training with tabular Dataset with BigQuery source.
String	getDatasetId() Required.
com.google.protobuf.ByteString	getDatasetIdBytes() Required.
InputDataConfig	getDefaultInstanceForType()
static com.google.protobuf.Descriptors.Descriptor	getDescriptor()
com.google.protobuf.Descriptors.Descriptor	getDescriptorForType()
InputDataConfig.DestinationCase	getDestinationCase()
FilterSplit	getFilterSplit() Split based on the provided filters for each set.
FilterSplit.Builder	getFilterSplitBuilder() Split based on the provided filters for each set.
FilterSplitOrBuilder	getFilterSplitOrBuilder() Split based on the provided filters for each set.
FractionSplit	getFractionSplit() Split based on fractions defining the size of each set.
FractionSplit.Builder	getFractionSplitBuilder() Split based on fractions defining the size of each set.
FractionSplitOrBuilder	getFractionSplitOrBuilder() Split based on fractions defining the size of each set.
GcsDestination	getGcsDestination() The Cloud Storage location where the training data is to be written to.
GcsDestination.Builder	getGcsDestinationBuilder() The Cloud Storage location where the training data is to be written to.
GcsDestinationOrBuilder	getGcsDestinationOrBuilder() The Cloud Storage location where the training data is to be written to.
PredefinedSplit	getPredefinedSplit() Supported only for tabular Datasets.
PredefinedSplit.Builder	getPredefinedSplitBuilder() Supported only for tabular Datasets.
PredefinedSplitOrBuilder	getPredefinedSplitOrBuilder() Supported only for tabular Datasets.
String	getSavedQueryId() Only applicable to Datasets that have SavedQueries.
com.google.protobuf.ByteString	getSavedQueryIdBytes() Only applicable to Datasets that have SavedQueries.
InputDataConfig.SplitCase	getSplitCase()
StratifiedSplit	getStratifiedSplit() Supported only for tabular Datasets.
StratifiedSplit.Builder	getStratifiedSplitBuilder() Supported only for tabular Datasets.
StratifiedSplitOrBuilder	getStratifiedSplitOrBuilder() Supported only for tabular Datasets.
TimestampSplit	getTimestampSplit() Supported only for tabular Datasets.
TimestampSplit.Builder	getTimestampSplitBuilder() Supported only for tabular Datasets.
TimestampSplitOrBuilder	getTimestampSplitOrBuilder() Supported only for tabular Datasets.
boolean	hasBigqueryDestination() Only applicable to custom training with tabular Dataset with BigQuery source.
boolean	hasFilterSplit() Split based on the provided filters for each set.
boolean	hasFractionSplit() Split based on fractions defining the size of each set.
boolean	hasGcsDestination() The Cloud Storage location where the training data is to be written to.
boolean	hasPredefinedSplit() Supported only for tabular Datasets.
boolean	hasStratifiedSplit() Supported only for tabular Datasets.
boolean	hasTimestampSplit() Supported only for tabular Datasets.
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable	internalGetFieldAccessorTable()
boolean	isInitialized()
InputDataConfig.Builder	mergeBigqueryDestination(BigQueryDestination value) Only applicable to custom training with tabular Dataset with BigQuery source.
InputDataConfig.Builder	mergeFilterSplit(FilterSplit value) Split based on the provided filters for each set.
InputDataConfig.Builder	mergeFractionSplit(FractionSplit value) Split based on fractions defining the size of each set.
InputDataConfig.Builder	mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
InputDataConfig.Builder	mergeFrom(InputDataConfig other)
InputDataConfig.Builder	mergeFrom(com.google.protobuf.Message other)
InputDataConfig.Builder	mergeGcsDestination(GcsDestination value) The Cloud Storage location where the training data is to be written to.
InputDataConfig.Builder	mergePredefinedSplit(PredefinedSplit value) Supported only for tabular Datasets.
InputDataConfig.Builder	mergeStratifiedSplit(StratifiedSplit value) Supported only for tabular Datasets.
InputDataConfig.Builder	mergeTimestampSplit(TimestampSplit value) Supported only for tabular Datasets.
InputDataConfig.Builder	mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
InputDataConfig.Builder	setAnnotationSchemaUri(String value) Applicable only to custom training with Datasets that have DataItems and Annotations.
InputDataConfig.Builder	setAnnotationSchemaUriBytes(com.google.protobuf.ByteString value) Applicable only to custom training with Datasets that have DataItems and Annotations.
InputDataConfig.Builder	setAnnotationsFilter(String value) Applicable only to Datasets that have DataItems and Annotations.
InputDataConfig.Builder	setAnnotationsFilterBytes(com.google.protobuf.ByteString value) Applicable only to Datasets that have DataItems and Annotations.
InputDataConfig.Builder	setBigqueryDestination(BigQueryDestination.Builder builderForValue) Only applicable to custom training with tabular Dataset with BigQuery source.
InputDataConfig.Builder	setBigqueryDestination(BigQueryDestination value) Only applicable to custom training with tabular Dataset with BigQuery source.
InputDataConfig.Builder	setDatasetId(String value) Required.
InputDataConfig.Builder	setDatasetIdBytes(com.google.protobuf.ByteString value) Required.
InputDataConfig.Builder	setField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
InputDataConfig.Builder	setFilterSplit(FilterSplit.Builder builderForValue) Split based on the provided filters for each set.
InputDataConfig.Builder	setFilterSplit(FilterSplit value) Split based on the provided filters for each set.
InputDataConfig.Builder	setFractionSplit(FractionSplit.Builder builderForValue) Split based on fractions defining the size of each set.
InputDataConfig.Builder	setFractionSplit(FractionSplit value) Split based on fractions defining the size of each set.
InputDataConfig.Builder	setGcsDestination(GcsDestination.Builder builderForValue) The Cloud Storage location where the training data is to be written to.
InputDataConfig.Builder	setGcsDestination(GcsDestination value) The Cloud Storage location where the training data is to be written to.
InputDataConfig.Builder	setPredefinedSplit(PredefinedSplit.Builder builderForValue) Supported only for tabular Datasets.
InputDataConfig.Builder	setPredefinedSplit(PredefinedSplit value) Supported only for tabular Datasets.
InputDataConfig.Builder	setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value)
InputDataConfig.Builder	setSavedQueryId(String value) Only applicable to Datasets that have SavedQueries.
InputDataConfig.Builder	setSavedQueryIdBytes(com.google.protobuf.ByteString value) Only applicable to Datasets that have SavedQueries.
InputDataConfig.Builder	setStratifiedSplit(StratifiedSplit.Builder builderForValue) Supported only for tabular Datasets.
InputDataConfig.Builder	setStratifiedSplit(StratifiedSplit value) Supported only for tabular Datasets.
InputDataConfig.Builder	setTimestampSplit(TimestampSplit.Builder builderForValue) Supported only for tabular Datasets.
InputDataConfig.Builder	setTimestampSplit(TimestampSplit value) Supported only for tabular Datasets.
InputDataConfig.Builder	setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)

Methods inherited from class com.google.protobuf.GeneratedMessageV3.Builder

getAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof, internalGetMapField, internalGetMutableMapField, isClean, markClean, newBuilderForField, onBuilt, onChanged, setUnknownFieldsProto3

Methods inherited from class com.google.protobuf.AbstractMessage.Builder

findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toString

Methods inherited from class com.google.protobuf.AbstractMessageLite.Builder

addAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, mergeFrom, newUninitializedMessageException

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.Message.Builder

mergeDelimitedFrom, mergeDelimitedFrom

Methods inherited from interface com.google.protobuf.MessageLite.Builder

mergeFrom

Method Detail

getDescriptor

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

internalGetFieldAccessorTable

protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Specified by:

internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.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.GeneratedMessageV3.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.GeneratedMessageV3.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

clone

public InputDataConfig.Builder clone()

Specified by:

clone in interface com.google.protobuf.Message.Builder

Specified by:

clone in interface com.google.protobuf.MessageLite.Builder

Overrides:

clone in class com.google.protobuf.GeneratedMessageV3.Builder<InputDataConfig.Builder>

setField

public InputDataConfig.Builder setField(com.google.protobuf.Descriptors.FieldDescriptor field,
                                        Object value)

Specified by:

setField in interface com.google.protobuf.Message.Builder

Overrides:

setField in class com.google.protobuf.GeneratedMessageV3.Builder<InputDataConfig.Builder>

clearField

public InputDataConfig.Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field)

Specified by:

clearField in interface com.google.protobuf.Message.Builder

Overrides:

clearField in class com.google.protobuf.GeneratedMessageV3.Builder<InputDataConfig.Builder>

clearOneof

public InputDataConfig.Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)

Specified by:

clearOneof in interface com.google.protobuf.Message.Builder

Overrides:

clearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<InputDataConfig.Builder>

setRepeatedField

public InputDataConfig.Builder setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                int index,
                                                Object value)

Specified by:

setRepeatedField in interface com.google.protobuf.Message.Builder

Overrides:

setRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<InputDataConfig.Builder>

addRepeatedField

public InputDataConfig.Builder addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                Object value)

Specified by:

addRepeatedField in interface com.google.protobuf.Message.Builder

Overrides:

addRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<InputDataConfig.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.GeneratedMessageV3.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.FractionSplit fraction_split = 2;

mergeFractionSplit

public InputDataConfig.Builder mergeFractionSplit(FractionSplit value)

 Split based on fractions defining the size of each set.
 

.google.cloud.aiplatform.v1beta1.FractionSplit fraction_split = 2;

clearFractionSplit

public InputDataConfig.Builder clearFractionSplit()

 Split based on fractions defining the size of each set.
 

.google.cloud.aiplatform.v1beta1.FractionSplit fraction_split = 2;

getFractionSplitBuilder

public FractionSplit.Builder getFractionSplitBuilder()

 Split based on fractions defining the size of each set.
 

.google.cloud.aiplatform.v1beta1.FractionSplit fraction_split = 2;

getFractionSplitOrBuilder

public FractionSplitOrBuilder getFractionSplitOrBuilder()

 Split based on fractions defining the size of each set.
 

.google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.FilterSplit filter_split = 3;

setFilterSplit

public InputDataConfig.Builder setFilterSplit(FilterSplit.Builder builderForValue)

 Split based on the provided filters for each set.
 

.google.cloud.aiplatform.v1beta1.FilterSplit filter_split = 3;

mergeFilterSplit

public InputDataConfig.Builder mergeFilterSplit(FilterSplit value)

 Split based on the provided filters for each set.
 

.google.cloud.aiplatform.v1beta1.FilterSplit filter_split = 3;

clearFilterSplit

public InputDataConfig.Builder clearFilterSplit()

 Split based on the provided filters for each set.
 

.google.cloud.aiplatform.v1beta1.FilterSplit filter_split = 3;

getFilterSplitBuilder

public FilterSplit.Builder getFilterSplitBuilder()

 Split based on the provided filters for each set.
 

.google.cloud.aiplatform.v1beta1.FilterSplit filter_split = 3;

getFilterSplitOrBuilder

public FilterSplitOrBuilder getFilterSplitOrBuilder()

 Split based on the provided filters for each set.
 

.google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.PredefinedSplit predefined_split = 4;

clearPredefinedSplit

public InputDataConfig.Builder clearPredefinedSplit()

 Supported only for tabular Datasets.
 Split based on a predefined key.
 

.google.cloud.aiplatform.v1beta1.PredefinedSplit predefined_split = 4;

getPredefinedSplitBuilder

public PredefinedSplit.Builder getPredefinedSplitBuilder()

 Supported only for tabular Datasets.
 Split based on a predefined key.
 

.google.cloud.aiplatform.v1beta1.PredefinedSplit predefined_split = 4;

getPredefinedSplitOrBuilder

public PredefinedSplitOrBuilder getPredefinedSplitOrBuilder()

 Supported only for tabular Datasets.
 Split based on a predefined key.
 

.google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.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];

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.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];

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.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];

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.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];

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.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];

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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.Dataset.metadata_schema_uri] of the Dataset specified by
 [dataset_id][google.cloud.aiplatform.v1beta1.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.v1beta1.InputDataConfig.annotations_filter], the Annotations used
 for training are filtered by both [annotations_filter][google.cloud.aiplatform.v1beta1.InputDataConfig.annotations_filter] and
 [annotation_schema_uri][google.cloud.aiplatform.v1beta1.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.v1beta1.Dataset.metadata_schema_uri] of the Dataset specified by
 [dataset_id][google.cloud.aiplatform.v1beta1.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.v1beta1.InputDataConfig.annotations_filter], the Annotations used
 for training are filtered by both [annotations_filter][google.cloud.aiplatform.v1beta1.InputDataConfig.annotations_filter] and
 [annotation_schema_uri][google.cloud.aiplatform.v1beta1.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.v1beta1.Dataset.metadata_schema_uri] of the Dataset specified by
 [dataset_id][google.cloud.aiplatform.v1beta1.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.v1beta1.InputDataConfig.annotations_filter], the Annotations used
 for training are filtered by both [annotations_filter][google.cloud.aiplatform.v1beta1.InputDataConfig.annotations_filter] and
 [annotation_schema_uri][google.cloud.aiplatform.v1beta1.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.v1beta1.Dataset.metadata_schema_uri] of the Dataset specified by
 [dataset_id][google.cloud.aiplatform.v1beta1.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.v1beta1.InputDataConfig.annotations_filter], the Annotations used
 for training are filtered by both [annotations_filter][google.cloud.aiplatform.v1beta1.InputDataConfig.annotations_filter] and
 [annotation_schema_uri][google.cloud.aiplatform.v1beta1.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.v1beta1.Dataset.metadata_schema_uri] of the Dataset specified by
 [dataset_id][google.cloud.aiplatform.v1beta1.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.v1beta1.InputDataConfig.annotations_filter], the Annotations used
 for training are filtered by both [annotations_filter][google.cloud.aiplatform.v1beta1.InputDataConfig.annotations_filter] and
 [annotation_schema_uri][google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.InputDataConfig.annotations_filter], the Annotations used for training are filtered by
 both [saved_query_id][google.cloud.aiplatform.v1beta1.InputDataConfig.saved_query_id] and [annotations_filter][google.cloud.aiplatform.v1beta1.InputDataConfig.annotations_filter].
 Only one of [saved_query_id][google.cloud.aiplatform.v1beta1.InputDataConfig.saved_query_id] and [annotation_schema_uri][google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.InputDataConfig.annotations_filter], the Annotations used for training are filtered by
 both [saved_query_id][google.cloud.aiplatform.v1beta1.InputDataConfig.saved_query_id] and [annotations_filter][google.cloud.aiplatform.v1beta1.InputDataConfig.annotations_filter].
 Only one of [saved_query_id][google.cloud.aiplatform.v1beta1.InputDataConfig.saved_query_id] and [annotation_schema_uri][google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.InputDataConfig.annotations_filter], the Annotations used for training are filtered by
 both [saved_query_id][google.cloud.aiplatform.v1beta1.InputDataConfig.saved_query_id] and [annotations_filter][google.cloud.aiplatform.v1beta1.InputDataConfig.annotations_filter].
 Only one of [saved_query_id][google.cloud.aiplatform.v1beta1.InputDataConfig.saved_query_id] and [annotation_schema_uri][google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.InputDataConfig.annotations_filter], the Annotations used for training are filtered by
 both [saved_query_id][google.cloud.aiplatform.v1beta1.InputDataConfig.saved_query_id] and [annotations_filter][google.cloud.aiplatform.v1beta1.InputDataConfig.annotations_filter].
 Only one of [saved_query_id][google.cloud.aiplatform.v1beta1.InputDataConfig.saved_query_id] and [annotation_schema_uri][google.cloud.aiplatform.v1beta1.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.v1beta1.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.v1beta1.InputDataConfig.annotations_filter], the Annotations used for training are filtered by
 both [saved_query_id][google.cloud.aiplatform.v1beta1.InputDataConfig.saved_query_id] and [annotations_filter][google.cloud.aiplatform.v1beta1.InputDataConfig.annotations_filter].
 Only one of [saved_query_id][google.cloud.aiplatform.v1beta1.InputDataConfig.saved_query_id] and [annotation_schema_uri][google.cloud.aiplatform.v1beta1.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.

setUnknownFields

public final InputDataConfig.Builder setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)

Specified by:

setUnknownFields in interface com.google.protobuf.Message.Builder

Overrides:

setUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<InputDataConfig.Builder>

mergeUnknownFields

public final InputDataConfig.Builder mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)

Specified by:

mergeUnknownFields in interface com.google.protobuf.Message.Builder

Overrides:

mergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<InputDataConfig.Builder>