Package com.google.cloud.aiplatform.v1

Class Model.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<Model.Builder>
com.google.cloud.aiplatform.v1.Model.Builder
All Implemented Interfaces:
ModelOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
Model
public static final class Model.Builder extends com.google.protobuf.GeneratedMessage.Builder<Model.Builder> implements ModelOrBuilder
 A trained machine learning Model.
Protobuf type google.cloud.aiplatform.v1.Model

  • Method Details

    • getDescriptor

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

    • internalGetMapFieldReflection

      protected com.google.protobuf.MapFieldReflectionAccessor internalGetMapFieldReflection(int number)
      Overrides:
      internalGetMapFieldReflection in class com.google.protobuf.GeneratedMessage.Builder<Model.Builder>

    • internalGetMutableMapFieldReflection

      protected com.google.protobuf.MapFieldReflectionAccessor internalGetMutableMapFieldReflection(int number)
      Overrides:
      internalGetMutableMapFieldReflection in class com.google.protobuf.GeneratedMessage.Builder<Model.Builder>

    • internalGetFieldAccessorTable

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

    • clear

      public Model.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<Model.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<Model.Builder>

    • getDefaultInstanceForType

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

    • build

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

    • buildPartial

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

    • mergeFrom

      public Model.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<Model.Builder>

    • mergeFrom

      public Model.Builder mergeFrom(Model other)

    • isInitialized

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

    • mergeFrom

      public Model.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<Model.Builder>
      Throws:
      IOException

    • getName

      public String getName()
       The resource name of the Model.
      string name = 1;
      Specified by:
      getName in interface ModelOrBuilder
      Returns:
      The name.

    • getNameBytes

      public com.google.protobuf.ByteString getNameBytes()
       The resource name of the Model.
      string name = 1;
      Specified by:
      getNameBytes in interface ModelOrBuilder
      Returns:
      The bytes for name.

    • setName

      public Model.Builder setName(String value)
       The resource name of the Model.
      string name = 1;
      Parameters:
      value - The name to set.
      Returns:
      This builder for chaining.

    • clearName

      public Model.Builder clearName()
       The resource name of the Model.
      string name = 1;
      Returns:
      This builder for chaining.

    • setNameBytes

      public Model.Builder setNameBytes(com.google.protobuf.ByteString value)
       The resource name of the Model.
      string name = 1;
      Parameters:
      value - The bytes for name to set.
      Returns:
      This builder for chaining.

    • getVersionId

      public String getVersionId()
       Output only. Immutable. The version ID of the model.
 A new version is committed when a new model version is uploaded or
 trained under an existing model id. It is an auto-incrementing decimal
 number in string representation.
      string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getVersionId in interface ModelOrBuilder
      Returns:
      The versionId.

    • getVersionIdBytes

      public com.google.protobuf.ByteString getVersionIdBytes()
       Output only. Immutable. The version ID of the model.
 A new version is committed when a new model version is uploaded or
 trained under an existing model id. It is an auto-incrementing decimal
 number in string representation.
      string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getVersionIdBytes in interface ModelOrBuilder
      Returns:
      The bytes for versionId.

    • setVersionId

      public Model.Builder setVersionId(String value)
       Output only. Immutable. The version ID of the model.
 A new version is committed when a new model version is uploaded or
 trained under an existing model id. It is an auto-incrementing decimal
 number in string representation.
      string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The versionId to set.
      Returns:
      This builder for chaining.

    • clearVersionId

      public Model.Builder clearVersionId()
       Output only. Immutable. The version ID of the model.
 A new version is committed when a new model version is uploaded or
 trained under an existing model id. It is an auto-incrementing decimal
 number in string representation.
      string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      This builder for chaining.

    • setVersionIdBytes

      public Model.Builder setVersionIdBytes(com.google.protobuf.ByteString value)
       Output only. Immutable. The version ID of the model.
 A new version is committed when a new model version is uploaded or
 trained under an existing model id. It is an auto-incrementing decimal
 number in string representation.
      string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The bytes for versionId to set.
      Returns:
      This builder for chaining.

    • getVersionAliasesList

      public com.google.protobuf.ProtocolStringList getVersionAliasesList()
       User provided version aliases so that a model version can be referenced via
 alias (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_alias}`
 instead of auto-generated version id (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_id})`.
 The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
 version_id. A default version alias will be created for the first version
 of the model, and there must be exactly one default version alias for a
 model.
      repeated string version_aliases = 29;
      Specified by:
      getVersionAliasesList in interface ModelOrBuilder
      Returns:
      A list containing the versionAliases.

    • getVersionAliasesCount

      public int getVersionAliasesCount()
       User provided version aliases so that a model version can be referenced via
 alias (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_alias}`
 instead of auto-generated version id (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_id})`.
 The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
 version_id. A default version alias will be created for the first version
 of the model, and there must be exactly one default version alias for a
 model.
      repeated string version_aliases = 29;
      Specified by:
      getVersionAliasesCount in interface ModelOrBuilder
      Returns:
      The count of versionAliases.

    • getVersionAliases

      public String getVersionAliases(int index)
       User provided version aliases so that a model version can be referenced via
 alias (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_alias}`
 instead of auto-generated version id (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_id})`.
 The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
 version_id. A default version alias will be created for the first version
 of the model, and there must be exactly one default version alias for a
 model.
      repeated string version_aliases = 29;
      Specified by:
      getVersionAliases in interface ModelOrBuilder
      Parameters:
      index - The index of the element to return.
      Returns:
      The versionAliases at the given index.

    • getVersionAliasesBytes

      public com.google.protobuf.ByteString getVersionAliasesBytes(int index)
       User provided version aliases so that a model version can be referenced via
 alias (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_alias}`
 instead of auto-generated version id (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_id})`.
 The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
 version_id. A default version alias will be created for the first version
 of the model, and there must be exactly one default version alias for a
 model.
      repeated string version_aliases = 29;
      Specified by:
      getVersionAliasesBytes in interface ModelOrBuilder
      Parameters:
      index - The index of the value to return.
      Returns:
      The bytes of the versionAliases at the given index.

    • setVersionAliases

      public Model.Builder setVersionAliases(int index, String value)
       User provided version aliases so that a model version can be referenced via
 alias (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_alias}`
 instead of auto-generated version id (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_id})`.
 The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
 version_id. A default version alias will be created for the first version
 of the model, and there must be exactly one default version alias for a
 model.
      repeated string version_aliases = 29;
      Parameters:
      index - The index to set the value at.
      value - The versionAliases to set.
      Returns:
      This builder for chaining.

    • addVersionAliases

      public Model.Builder addVersionAliases(String value)
       User provided version aliases so that a model version can be referenced via
 alias (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_alias}`
 instead of auto-generated version id (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_id})`.
 The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
 version_id. A default version alias will be created for the first version
 of the model, and there must be exactly one default version alias for a
 model.
      repeated string version_aliases = 29;
      Parameters:
      value - The versionAliases to add.
      Returns:
      This builder for chaining.

    • addAllVersionAliases

      public Model.Builder addAllVersionAliases(Iterable<String> values)
       User provided version aliases so that a model version can be referenced via
 alias (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_alias}`
 instead of auto-generated version id (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_id})`.
 The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
 version_id. A default version alias will be created for the first version
 of the model, and there must be exactly one default version alias for a
 model.
      repeated string version_aliases = 29;
      Parameters:
      values - The versionAliases to add.
      Returns:
      This builder for chaining.

    • clearVersionAliases

      public Model.Builder clearVersionAliases()
       User provided version aliases so that a model version can be referenced via
 alias (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_alias}`
 instead of auto-generated version id (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_id})`.
 The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
 version_id. A default version alias will be created for the first version
 of the model, and there must be exactly one default version alias for a
 model.
      repeated string version_aliases = 29;
      Returns:
      This builder for chaining.

    • addVersionAliasesBytes

      public Model.Builder addVersionAliasesBytes(com.google.protobuf.ByteString value)
       User provided version aliases so that a model version can be referenced via
 alias (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_alias}`
 instead of auto-generated version id (i.e.
 `projects/{project}/locations/{location}/models/{model_id}@{version_id})`.
 The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
 version_id. A default version alias will be created for the first version
 of the model, and there must be exactly one default version alias for a
 model.
      repeated string version_aliases = 29;
      Parameters:
      value - The bytes of the versionAliases to add.
      Returns:
      This builder for chaining.

    • hasVersionCreateTime

      public boolean hasVersionCreateTime()
       Output only. Timestamp when this version was created.
      .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      hasVersionCreateTime in interface ModelOrBuilder
      Returns:
      Whether the versionCreateTime field is set.

    • getVersionCreateTime

      public com.google.protobuf.Timestamp getVersionCreateTime()
       Output only. Timestamp when this version was created.
      .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getVersionCreateTime in interface ModelOrBuilder
      Returns:
      The versionCreateTime.

    • setVersionCreateTime

      public Model.Builder setVersionCreateTime(com.google.protobuf.Timestamp value)
       Output only. Timestamp when this version was created.
      .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • setVersionCreateTime

      public Model.Builder setVersionCreateTime(com.google.protobuf.Timestamp.Builder builderForValue)
       Output only. Timestamp when this version was created.
      .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • mergeVersionCreateTime

      public Model.Builder mergeVersionCreateTime(com.google.protobuf.Timestamp value)
       Output only. Timestamp when this version was created.
      .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • clearVersionCreateTime

      public Model.Builder clearVersionCreateTime()
       Output only. Timestamp when this version was created.
      .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getVersionCreateTimeBuilder

      public com.google.protobuf.Timestamp.Builder getVersionCreateTimeBuilder()
       Output only. Timestamp when this version was created.
      .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getVersionCreateTimeOrBuilder

      public com.google.protobuf.TimestampOrBuilder getVersionCreateTimeOrBuilder()
       Output only. Timestamp when this version was created.
      .google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getVersionCreateTimeOrBuilder in interface ModelOrBuilder

    • hasVersionUpdateTime

      public boolean hasVersionUpdateTime()
       Output only. Timestamp when this version was most recently updated.
      .google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      hasVersionUpdateTime in interface ModelOrBuilder
      Returns:
      Whether the versionUpdateTime field is set.

    • getVersionUpdateTime

      public com.google.protobuf.Timestamp getVersionUpdateTime()
       Output only. Timestamp when this version was most recently updated.
      .google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getVersionUpdateTime in interface ModelOrBuilder
      Returns:
      The versionUpdateTime.

    • setVersionUpdateTime

      public Model.Builder setVersionUpdateTime(com.google.protobuf.Timestamp value)
       Output only. Timestamp when this version was most recently updated.
      .google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • setVersionUpdateTime

      public Model.Builder setVersionUpdateTime(com.google.protobuf.Timestamp.Builder builderForValue)
       Output only. Timestamp when this version was most recently updated.
      .google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • mergeVersionUpdateTime

      public Model.Builder mergeVersionUpdateTime(com.google.protobuf.Timestamp value)
       Output only. Timestamp when this version was most recently updated.
      .google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • clearVersionUpdateTime

      public Model.Builder clearVersionUpdateTime()
       Output only. Timestamp when this version was most recently updated.
      .google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getVersionUpdateTimeBuilder

      public com.google.protobuf.Timestamp.Builder getVersionUpdateTimeBuilder()
       Output only. Timestamp when this version was most recently updated.
      .google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getVersionUpdateTimeOrBuilder

      public com.google.protobuf.TimestampOrBuilder getVersionUpdateTimeOrBuilder()
       Output only. Timestamp when this version was most recently updated.
      .google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getVersionUpdateTimeOrBuilder in interface ModelOrBuilder

    • getDisplayName

      public String getDisplayName()
       Required. The display name of the Model.
 The name can be up to 128 characters long and can consist of any UTF-8
 characters.
      string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
      Specified by:
      getDisplayName in interface ModelOrBuilder
      Returns:
      The displayName.

    • getDisplayNameBytes

      public com.google.protobuf.ByteString getDisplayNameBytes()
       Required. The display name of the Model.
 The name can be up to 128 characters long and can consist of any UTF-8
 characters.
      string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
      Specified by:
      getDisplayNameBytes in interface ModelOrBuilder
      Returns:
      The bytes for displayName.

    • setDisplayName

      public Model.Builder setDisplayName(String value)
       Required. The display name of the Model.
 The name can be up to 128 characters long and can consist of any UTF-8
 characters.
      string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
      Parameters:
      value - The displayName to set.
      Returns:
      This builder for chaining.

    • clearDisplayName

      public Model.Builder clearDisplayName()
       Required. The display name of the Model.
 The name can be up to 128 characters long and can consist of any UTF-8
 characters.
      string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
      Returns:
      This builder for chaining.

    • setDisplayNameBytes

      public Model.Builder setDisplayNameBytes(com.google.protobuf.ByteString value)
       Required. The display name of the Model.
 The name can be up to 128 characters long and can consist of any UTF-8
 characters.
      string display_name = 2 [(.google.api.field_behavior) = REQUIRED];
      Parameters:
      value - The bytes for displayName to set.
      Returns:
      This builder for chaining.

    • getDescription

      public String getDescription()
       The description of the Model.
      string description = 3;
      Specified by:
      getDescription in interface ModelOrBuilder
      Returns:
      The description.

    • getDescriptionBytes

      public com.google.protobuf.ByteString getDescriptionBytes()
       The description of the Model.
      string description = 3;
      Specified by:
      getDescriptionBytes in interface ModelOrBuilder
      Returns:
      The bytes for description.

    • setDescription

      public Model.Builder setDescription(String value)
       The description of the Model.
      string description = 3;
      Parameters:
      value - The description to set.
      Returns:
      This builder for chaining.

    • clearDescription

      public Model.Builder clearDescription()
       The description of the Model.
      string description = 3;
      Returns:
      This builder for chaining.

    • setDescriptionBytes

      public Model.Builder setDescriptionBytes(com.google.protobuf.ByteString value)
       The description of the Model.
      string description = 3;
      Parameters:
      value - The bytes for description to set.
      Returns:
      This builder for chaining.

    • getVersionDescription

      public String getVersionDescription()
       The description of this version.
      string version_description = 30;
      Specified by:
      getVersionDescription in interface ModelOrBuilder
      Returns:
      The versionDescription.

    • getVersionDescriptionBytes

      public com.google.protobuf.ByteString getVersionDescriptionBytes()
       The description of this version.
      string version_description = 30;
      Specified by:
      getVersionDescriptionBytes in interface ModelOrBuilder
      Returns:
      The bytes for versionDescription.

    • setVersionDescription

      public Model.Builder setVersionDescription(String value)
       The description of this version.
      string version_description = 30;
      Parameters:
      value - The versionDescription to set.
      Returns:
      This builder for chaining.

    • clearVersionDescription

      public Model.Builder clearVersionDescription()
       The description of this version.
      string version_description = 30;
      Returns:
      This builder for chaining.

    • setVersionDescriptionBytes

      public Model.Builder setVersionDescriptionBytes(com.google.protobuf.ByteString value)
       The description of this version.
      string version_description = 30;
      Parameters:
      value - The bytes for versionDescription to set.
      Returns:
      This builder for chaining.

    • getDefaultCheckpointId

      public String getDefaultCheckpointId()
       The default checkpoint id of a model version.
      string default_checkpoint_id = 53;
      Specified by:
      getDefaultCheckpointId in interface ModelOrBuilder
      Returns:
      The defaultCheckpointId.

    • getDefaultCheckpointIdBytes

      public com.google.protobuf.ByteString getDefaultCheckpointIdBytes()
       The default checkpoint id of a model version.
      string default_checkpoint_id = 53;
      Specified by:
      getDefaultCheckpointIdBytes in interface ModelOrBuilder
      Returns:
      The bytes for defaultCheckpointId.

    • setDefaultCheckpointId

      public Model.Builder setDefaultCheckpointId(String value)
       The default checkpoint id of a model version.
      string default_checkpoint_id = 53;
      Parameters:
      value - The defaultCheckpointId to set.
      Returns:
      This builder for chaining.

    • clearDefaultCheckpointId

      public Model.Builder clearDefaultCheckpointId()
       The default checkpoint id of a model version.
      string default_checkpoint_id = 53;
      Returns:
      This builder for chaining.

    • setDefaultCheckpointIdBytes

      public Model.Builder setDefaultCheckpointIdBytes(com.google.protobuf.ByteString value)
       The default checkpoint id of a model version.
      string default_checkpoint_id = 53;
      Parameters:
      value - The bytes for defaultCheckpointId to set.
      Returns:
      This builder for chaining.

    • hasPredictSchemata

      public boolean hasPredictSchemata()
       The schemata that describe formats of the Model's predictions and
 explanations as given and returned via
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 and
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;
      Specified by:
      hasPredictSchemata in interface ModelOrBuilder
      Returns:
      Whether the predictSchemata field is set.

    • getPredictSchemata

      public PredictSchemata getPredictSchemata()
       The schemata that describe formats of the Model's predictions and
 explanations as given and returned via
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 and
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;
      Specified by:
      getPredictSchemata in interface ModelOrBuilder
      Returns:
      The predictSchemata.

    • setPredictSchemata

      public Model.Builder setPredictSchemata(PredictSchemata value)
       The schemata that describe formats of the Model's predictions and
 explanations as given and returned via
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 and
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

    • setPredictSchemata

      public Model.Builder setPredictSchemata(PredictSchemata.Builder builderForValue)
       The schemata that describe formats of the Model's predictions and
 explanations as given and returned via
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 and
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

    • mergePredictSchemata

      public Model.Builder mergePredictSchemata(PredictSchemata value)
       The schemata that describe formats of the Model's predictions and
 explanations as given and returned via
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 and
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

    • clearPredictSchemata

      public Model.Builder clearPredictSchemata()
       The schemata that describe formats of the Model's predictions and
 explanations as given and returned via
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 and
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

    • getPredictSchemataBuilder

      public PredictSchemata.Builder getPredictSchemataBuilder()
       The schemata that describe formats of the Model's predictions and
 explanations as given and returned via
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 and
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;

    • getPredictSchemataOrBuilder

      public PredictSchemataOrBuilder getPredictSchemataOrBuilder()
       The schemata that describe formats of the Model's predictions and
 explanations as given and returned via
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 and
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      .google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;
      Specified by:
      getPredictSchemataOrBuilder in interface ModelOrBuilder

    • getMetadataSchemaUri

      public String getMetadataSchemaUri()
       Immutable. Points to a YAML file stored on Google Cloud Storage describing
 additional information about the Model, that is specific to it. Unset if
 the Model does not have any additional information. 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).
 AutoML Models always have this field populated by Vertex AI, if no
 additional metadata is needed, this field is set to an empty string.
 Note: The URI given on output will be immutable and probably different,
 including the URI scheme, than the one given on input. The output URI will
 point to a location where the user only has a read access.
      string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];
      Specified by:
      getMetadataSchemaUri in interface ModelOrBuilder
      Returns:
      The metadataSchemaUri.

    • getMetadataSchemaUriBytes

      public com.google.protobuf.ByteString getMetadataSchemaUriBytes()
       Immutable. Points to a YAML file stored on Google Cloud Storage describing
 additional information about the Model, that is specific to it. Unset if
 the Model does not have any additional information. 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).
 AutoML Models always have this field populated by Vertex AI, if no
 additional metadata is needed, this field is set to an empty string.
 Note: The URI given on output will be immutable and probably different,
 including the URI scheme, than the one given on input. The output URI will
 point to a location where the user only has a read access.
      string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];
      Specified by:
      getMetadataSchemaUriBytes in interface ModelOrBuilder
      Returns:
      The bytes for metadataSchemaUri.

    • setMetadataSchemaUri

      public Model.Builder setMetadataSchemaUri(String value)
       Immutable. Points to a YAML file stored on Google Cloud Storage describing
 additional information about the Model, that is specific to it. Unset if
 the Model does not have any additional information. 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).
 AutoML Models always have this field populated by Vertex AI, if no
 additional metadata is needed, this field is set to an empty string.
 Note: The URI given on output will be immutable and probably different,
 including the URI scheme, than the one given on input. The output URI will
 point to a location where the user only has a read access.
      string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];
      Parameters:
      value - The metadataSchemaUri to set.
      Returns:
      This builder for chaining.

    • clearMetadataSchemaUri

      public Model.Builder clearMetadataSchemaUri()
       Immutable. Points to a YAML file stored on Google Cloud Storage describing
 additional information about the Model, that is specific to it. Unset if
 the Model does not have any additional information. 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).
 AutoML Models always have this field populated by Vertex AI, if no
 additional metadata is needed, this field is set to an empty string.
 Note: The URI given on output will be immutable and probably different,
 including the URI scheme, than the one given on input. The output URI will
 point to a location where the user only has a read access.
      string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];
      Returns:
      This builder for chaining.

    • setMetadataSchemaUriBytes

      public Model.Builder setMetadataSchemaUriBytes(com.google.protobuf.ByteString value)
       Immutable. Points to a YAML file stored on Google Cloud Storage describing
 additional information about the Model, that is specific to it. Unset if
 the Model does not have any additional information. 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).
 AutoML Models always have this field populated by Vertex AI, if no
 additional metadata is needed, this field is set to an empty string.
 Note: The URI given on output will be immutable and probably different,
 including the URI scheme, than the one given on input. The output URI will
 point to a location where the user only has a read access.
      string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];
      Parameters:
      value - The bytes for metadataSchemaUri to set.
      Returns:
      This builder for chaining.

    • hasMetadata

      public boolean hasMetadata()
       Immutable. An additional information about the Model; the schema of the
 metadata can be found in
 [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
 Unset if the Model does not have any additional information.
      .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];
      Specified by:
      hasMetadata in interface ModelOrBuilder
      Returns:
      Whether the metadata field is set.

    • getMetadata

      public com.google.protobuf.Value getMetadata()
       Immutable. An additional information about the Model; the schema of the
 metadata can be found in
 [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
 Unset if the Model does not have any additional information.
      .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];
      Specified by:
      getMetadata in interface ModelOrBuilder
      Returns:
      The metadata.

    • setMetadata

      public Model.Builder setMetadata(com.google.protobuf.Value value)
       Immutable. An additional information about the Model; the schema of the
 metadata can be found in
 [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
 Unset if the Model does not have any additional information.
      .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

    • setMetadata

      public Model.Builder setMetadata(com.google.protobuf.Value.Builder builderForValue)
       Immutable. An additional information about the Model; the schema of the
 metadata can be found in
 [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
 Unset if the Model does not have any additional information.
      .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

    • mergeMetadata

      public Model.Builder mergeMetadata(com.google.protobuf.Value value)
       Immutable. An additional information about the Model; the schema of the
 metadata can be found in
 [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
 Unset if the Model does not have any additional information.
      .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

    • clearMetadata

      public Model.Builder clearMetadata()
       Immutable. An additional information about the Model; the schema of the
 metadata can be found in
 [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
 Unset if the Model does not have any additional information.
      .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

    • getMetadataBuilder

      public com.google.protobuf.Value.Builder getMetadataBuilder()
       Immutable. An additional information about the Model; the schema of the
 metadata can be found in
 [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
 Unset if the Model does not have any additional information.
      .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];

    • getMetadataOrBuilder

      public com.google.protobuf.ValueOrBuilder getMetadataOrBuilder()
       Immutable. An additional information about the Model; the schema of the
 metadata can be found in
 [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
 Unset if the Model does not have any additional information.
      .google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];
      Specified by:
      getMetadataOrBuilder in interface ModelOrBuilder

    • getSupportedExportFormatsList

      public List<Model.ExportFormat> getSupportedExportFormatsList()
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedExportFormatsList in interface ModelOrBuilder

    • getSupportedExportFormatsCount

      public int getSupportedExportFormatsCount()
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedExportFormatsCount in interface ModelOrBuilder

    • getSupportedExportFormats

      public Model.ExportFormat getSupportedExportFormats(int index)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedExportFormats in interface ModelOrBuilder

    • setSupportedExportFormats

      public Model.Builder setSupportedExportFormats(int index, Model.ExportFormat value)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • setSupportedExportFormats

      public Model.Builder setSupportedExportFormats(int index, Model.ExportFormat.Builder builderForValue)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addSupportedExportFormats

      public Model.Builder addSupportedExportFormats(Model.ExportFormat value)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addSupportedExportFormats

      public Model.Builder addSupportedExportFormats(int index, Model.ExportFormat value)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addSupportedExportFormats

      public Model.Builder addSupportedExportFormats(Model.ExportFormat.Builder builderForValue)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addSupportedExportFormats

      public Model.Builder addSupportedExportFormats(int index, Model.ExportFormat.Builder builderForValue)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addAllSupportedExportFormats

      public Model.Builder addAllSupportedExportFormats(Iterable<? extends Model.ExportFormat> values)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • clearSupportedExportFormats

      public Model.Builder clearSupportedExportFormats()
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • removeSupportedExportFormats

      public Model.Builder removeSupportedExportFormats(int index)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getSupportedExportFormatsBuilder

      public Model.ExportFormat.Builder getSupportedExportFormatsBuilder(int index)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getSupportedExportFormatsOrBuilder

      public Model.ExportFormatOrBuilder getSupportedExportFormatsOrBuilder(int index)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedExportFormatsOrBuilder in interface ModelOrBuilder

    • getSupportedExportFormatsOrBuilderList

      public List<? extends Model.ExportFormatOrBuilder> getSupportedExportFormatsOrBuilderList()
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedExportFormatsOrBuilderList in interface ModelOrBuilder

    • addSupportedExportFormatsBuilder

      public Model.ExportFormat.Builder addSupportedExportFormatsBuilder()
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addSupportedExportFormatsBuilder

      public Model.ExportFormat.Builder addSupportedExportFormatsBuilder(int index)
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getSupportedExportFormatsBuilderList

      public List<Model.ExportFormat.Builder> getSupportedExportFormatsBuilderList()
       Output only. The formats in which this Model may be exported. If empty,
 this Model is not available for export.
      repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getTrainingPipeline

      public String getTrainingPipeline()
       Output only. The resource name of the TrainingPipeline that uploaded this
 Model, if any.
      string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = { ... }
      Specified by:
      getTrainingPipeline in interface ModelOrBuilder
      Returns:
      The trainingPipeline.

    • getTrainingPipelineBytes

      public com.google.protobuf.ByteString getTrainingPipelineBytes()
       Output only. The resource name of the TrainingPipeline that uploaded this
 Model, if any.
      string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = { ... }
      Specified by:
      getTrainingPipelineBytes in interface ModelOrBuilder
      Returns:
      The bytes for trainingPipeline.

    • setTrainingPipeline

      public Model.Builder setTrainingPipeline(String value)
       Output only. The resource name of the TrainingPipeline that uploaded this
 Model, if any.
      string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = { ... }
      Parameters:
      value - The trainingPipeline to set.
      Returns:
      This builder for chaining.

    • clearTrainingPipeline

      public Model.Builder clearTrainingPipeline()
       Output only. The resource name of the TrainingPipeline that uploaded this
 Model, if any.
      string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = { ... }
      Returns:
      This builder for chaining.

    • setTrainingPipelineBytes

      public Model.Builder setTrainingPipelineBytes(com.google.protobuf.ByteString value)
       Output only. The resource name of the TrainingPipeline that uploaded this
 Model, if any.
      string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = { ... }
      Parameters:
      value - The bytes for trainingPipeline to set.
      Returns:
      This builder for chaining.

    • getPipelineJob

      public String getPipelineJob()
       Optional. This field is populated if the model is produced by a pipeline
 job.
      string pipeline_job = 47 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }
      Specified by:
      getPipelineJob in interface ModelOrBuilder
      Returns:
      The pipelineJob.

    • getPipelineJobBytes

      public com.google.protobuf.ByteString getPipelineJobBytes()
       Optional. This field is populated if the model is produced by a pipeline
 job.
      string pipeline_job = 47 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }
      Specified by:
      getPipelineJobBytes in interface ModelOrBuilder
      Returns:
      The bytes for pipelineJob.

    • setPipelineJob

      public Model.Builder setPipelineJob(String value)
       Optional. This field is populated if the model is produced by a pipeline
 job.
      string pipeline_job = 47 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }
      Parameters:
      value - The pipelineJob to set.
      Returns:
      This builder for chaining.

    • clearPipelineJob

      public Model.Builder clearPipelineJob()
       Optional. This field is populated if the model is produced by a pipeline
 job.
      string pipeline_job = 47 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }
      Returns:
      This builder for chaining.

    • setPipelineJobBytes

      public Model.Builder setPipelineJobBytes(com.google.protobuf.ByteString value)
       Optional. This field is populated if the model is produced by a pipeline
 job.
      string pipeline_job = 47 [(.google.api.field_behavior) = OPTIONAL, (.google.api.resource_reference) = { ... }
      Parameters:
      value - The bytes for pipelineJob to set.
      Returns:
      This builder for chaining.

    • hasContainerSpec

      public boolean hasContainerSpec()
       Input only. The specification of the container that is to be used when
 deploying this Model. The specification is ingested upon
 [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
 and all binaries it contains are copied and stored internally by Vertex AI.
 Not required for AutoML Models.
      .google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];
      Specified by:
      hasContainerSpec in interface ModelOrBuilder
      Returns:
      Whether the containerSpec field is set.

    • getContainerSpec

      public ModelContainerSpec getContainerSpec()
       Input only. The specification of the container that is to be used when
 deploying this Model. The specification is ingested upon
 [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
 and all binaries it contains are copied and stored internally by Vertex AI.
 Not required for AutoML Models.
      .google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];
      Specified by:
      getContainerSpec in interface ModelOrBuilder
      Returns:
      The containerSpec.

    • setContainerSpec

      public Model.Builder setContainerSpec(ModelContainerSpec value)
       Input only. The specification of the container that is to be used when
 deploying this Model. The specification is ingested upon
 [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
 and all binaries it contains are copied and stored internally by Vertex AI.
 Not required for AutoML Models.
      .google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

    • setContainerSpec

      public Model.Builder setContainerSpec(ModelContainerSpec.Builder builderForValue)
       Input only. The specification of the container that is to be used when
 deploying this Model. The specification is ingested upon
 [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
 and all binaries it contains are copied and stored internally by Vertex AI.
 Not required for AutoML Models.
      .google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

    • mergeContainerSpec

      public Model.Builder mergeContainerSpec(ModelContainerSpec value)
       Input only. The specification of the container that is to be used when
 deploying this Model. The specification is ingested upon
 [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
 and all binaries it contains are copied and stored internally by Vertex AI.
 Not required for AutoML Models.
      .google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

    • clearContainerSpec

      public Model.Builder clearContainerSpec()
       Input only. The specification of the container that is to be used when
 deploying this Model. The specification is ingested upon
 [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
 and all binaries it contains are copied and stored internally by Vertex AI.
 Not required for AutoML Models.
      .google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

    • getContainerSpecBuilder

      public ModelContainerSpec.Builder getContainerSpecBuilder()
       Input only. The specification of the container that is to be used when
 deploying this Model. The specification is ingested upon
 [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
 and all binaries it contains are copied and stored internally by Vertex AI.
 Not required for AutoML Models.
      .google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];

    • getContainerSpecOrBuilder

      public ModelContainerSpecOrBuilder getContainerSpecOrBuilder()
       Input only. The specification of the container that is to be used when
 deploying this Model. The specification is ingested upon
 [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
 and all binaries it contains are copied and stored internally by Vertex AI.
 Not required for AutoML Models.
      .google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];
      Specified by:
      getContainerSpecOrBuilder in interface ModelOrBuilder

    • getArtifactUri

      public String getArtifactUri()
       Immutable. The path to the directory containing the Model artifact and any
 of its supporting files. Not required for AutoML Models.
      string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];
      Specified by:
      getArtifactUri in interface ModelOrBuilder
      Returns:
      The artifactUri.

    • getArtifactUriBytes

      public com.google.protobuf.ByteString getArtifactUriBytes()
       Immutable. The path to the directory containing the Model artifact and any
 of its supporting files. Not required for AutoML Models.
      string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];
      Specified by:
      getArtifactUriBytes in interface ModelOrBuilder
      Returns:
      The bytes for artifactUri.

    • setArtifactUri

      public Model.Builder setArtifactUri(String value)
       Immutable. The path to the directory containing the Model artifact and any
 of its supporting files. Not required for AutoML Models.
      string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];
      Parameters:
      value - The artifactUri to set.
      Returns:
      This builder for chaining.

    • clearArtifactUri

      public Model.Builder clearArtifactUri()
       Immutable. The path to the directory containing the Model artifact and any
 of its supporting files. Not required for AutoML Models.
      string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];
      Returns:
      This builder for chaining.

    • setArtifactUriBytes

      public Model.Builder setArtifactUriBytes(com.google.protobuf.ByteString value)
       Immutable. The path to the directory containing the Model artifact and any
 of its supporting files. Not required for AutoML Models.
      string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];
      Parameters:
      value - The bytes for artifactUri to set.
      Returns:
      This builder for chaining.

    • getSupportedDeploymentResourcesTypesList

      public List<Model.DeploymentResourcesType> getSupportedDeploymentResourcesTypesList()
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedDeploymentResourcesTypesList in interface ModelOrBuilder
      Returns:
      A list containing the supportedDeploymentResourcesTypes.

    • getSupportedDeploymentResourcesTypesCount

      public int getSupportedDeploymentResourcesTypesCount()
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedDeploymentResourcesTypesCount in interface ModelOrBuilder
      Returns:
      The count of supportedDeploymentResourcesTypes.

    • getSupportedDeploymentResourcesTypes

      public Model.DeploymentResourcesType getSupportedDeploymentResourcesTypes(int index)
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedDeploymentResourcesTypes in interface ModelOrBuilder
      Parameters:
      index - The index of the element to return.
      Returns:
      The supportedDeploymentResourcesTypes at the given index.

    • setSupportedDeploymentResourcesTypes

      public Model.Builder setSupportedDeploymentResourcesTypes(int index, Model.DeploymentResourcesType value)
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      index - The index to set the value at.
      value - The supportedDeploymentResourcesTypes to set.
      Returns:
      This builder for chaining.

    • addSupportedDeploymentResourcesTypes

      public Model.Builder addSupportedDeploymentResourcesTypes(Model.DeploymentResourcesType value)
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The supportedDeploymentResourcesTypes to add.
      Returns:
      This builder for chaining.

    • addAllSupportedDeploymentResourcesTypes

      public Model.Builder addAllSupportedDeploymentResourcesTypes(Iterable<? extends Model.DeploymentResourcesType> values)
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      values - The supportedDeploymentResourcesTypes to add.
      Returns:
      This builder for chaining.

    • clearSupportedDeploymentResourcesTypes

      public Model.Builder clearSupportedDeploymentResourcesTypes()
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      This builder for chaining.

    • getSupportedDeploymentResourcesTypesValueList

      public List<Integer> getSupportedDeploymentResourcesTypesValueList()
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedDeploymentResourcesTypesValueList in interface ModelOrBuilder
      Returns:
      A list containing the enum numeric values on the wire for supportedDeploymentResourcesTypes.

    • getSupportedDeploymentResourcesTypesValue

      public int getSupportedDeploymentResourcesTypesValue(int index)
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedDeploymentResourcesTypesValue in interface ModelOrBuilder
      Parameters:
      index - The index of the value to return.
      Returns:
      The enum numeric value on the wire of supportedDeploymentResourcesTypes at the given index.

    • setSupportedDeploymentResourcesTypesValue

      public Model.Builder setSupportedDeploymentResourcesTypesValue(int index, int value)
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      index - The index to set the value at.
      value - The enum numeric value on the wire for supportedDeploymentResourcesTypes to set.
      Returns:
      This builder for chaining.

    • addSupportedDeploymentResourcesTypesValue

      public Model.Builder addSupportedDeploymentResourcesTypesValue(int value)
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The enum numeric value on the wire for supportedDeploymentResourcesTypes to add.
      Returns:
      This builder for chaining.

    • addAllSupportedDeploymentResourcesTypesValue

      public Model.Builder addAllSupportedDeploymentResourcesTypesValue(Iterable<Integer> values)
       Output only. When this Model is deployed, its prediction resources are
 described by the `prediction_resources` field of the
 [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
 object. Because not all Models support all resource configuration types,
 the configuration types this Model supports are listed here. If no
 configuration types are listed, the Model cannot be deployed to an
 [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
 online predictions
 ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
 Such a Model can serve predictions by using a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
 has at least one entry each in
 [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
 and
 [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
      repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      values - The enum numeric values on the wire for supportedDeploymentResourcesTypes to add.
      Returns:
      This builder for chaining.

    • getSupportedInputStorageFormatsList

      public com.google.protobuf.ProtocolStringList getSupportedInputStorageFormatsList()
       Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 exists, the instances should be given as per that schema.

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each instance is a single line. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `csv`
 The CSV format, where each instance is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedInputStorageFormatsList in interface ModelOrBuilder
      Returns:
      A list containing the supportedInputStorageFormats.

    • getSupportedInputStorageFormatsCount

      public int getSupportedInputStorageFormatsCount()
       Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 exists, the instances should be given as per that schema.

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each instance is a single line. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `csv`
 The CSV format, where each instance is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedInputStorageFormatsCount in interface ModelOrBuilder
      Returns:
      The count of supportedInputStorageFormats.

    • getSupportedInputStorageFormats

      public String getSupportedInputStorageFormats(int index)
       Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 exists, the instances should be given as per that schema.

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each instance is a single line. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `csv`
 The CSV format, where each instance is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedInputStorageFormats in interface ModelOrBuilder
      Parameters:
      index - The index of the element to return.
      Returns:
      The supportedInputStorageFormats at the given index.

    • getSupportedInputStorageFormatsBytes

      public com.google.protobuf.ByteString getSupportedInputStorageFormatsBytes(int index)
       Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 exists, the instances should be given as per that schema.

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each instance is a single line. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `csv`
 The CSV format, where each instance is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedInputStorageFormatsBytes in interface ModelOrBuilder
      Parameters:
      index - The index of the value to return.
      Returns:
      The bytes of the supportedInputStorageFormats at the given index.

    • setSupportedInputStorageFormats

      public Model.Builder setSupportedInputStorageFormats(int index, String value)
       Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 exists, the instances should be given as per that schema.

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each instance is a single line. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `csv`
 The CSV format, where each instance is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      index - The index to set the value at.
      value - The supportedInputStorageFormats to set.
      Returns:
      This builder for chaining.

    • addSupportedInputStorageFormats

      public Model.Builder addSupportedInputStorageFormats(String value)
       Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 exists, the instances should be given as per that schema.

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each instance is a single line. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `csv`
 The CSV format, where each instance is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The supportedInputStorageFormats to add.
      Returns:
      This builder for chaining.

    • addAllSupportedInputStorageFormats

      public Model.Builder addAllSupportedInputStorageFormats(Iterable<String> values)
       Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 exists, the instances should be given as per that schema.

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each instance is a single line. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `csv`
 The CSV format, where each instance is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      values - The supportedInputStorageFormats to add.
      Returns:
      This builder for chaining.

    • clearSupportedInputStorageFormats

      public Model.Builder clearSupportedInputStorageFormats()
       Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 exists, the instances should be given as per that schema.

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each instance is a single line. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `csv`
 The CSV format, where each instance is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      This builder for chaining.

    • addSupportedInputStorageFormatsBytes

      public Model.Builder addSupportedInputStorageFormatsBytes(com.google.protobuf.ByteString value)
       Output only. The formats this Model supports in
 [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
 If
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 exists, the instances should be given as per that schema.

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each instance is a single line. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `csv`
 The CSV format, where each instance is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record`
 The TFRecord format, where each instance is a single record in tfrecord
 syntax. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `tf-record-gzip`
 Similar to `tf-record`, but the file is gzipped. Uses
 [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].

 * `bigquery`
 Each instance is a single row in BigQuery. Uses
 [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].

 * `file-list`
 Each line of the file is the location of an instance to process, uses
 `gcs_source` field of the
 [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
 object.


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The bytes of the supportedInputStorageFormats to add.
      Returns:
      This builder for chaining.

    • getSupportedOutputStorageFormatsList

      public com.google.protobuf.ProtocolStringList getSupportedOutputStorageFormatsList()
       Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
 exist, the predictions are returned together with their instances. In other
 words, the prediction has the original instance data first, followed by the
 actual prediction content (as per the schema).

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each prediction is a single line. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `csv`
 The CSV format, where each prediction is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedOutputStorageFormatsList in interface ModelOrBuilder
      Returns:
      A list containing the supportedOutputStorageFormats.

    • getSupportedOutputStorageFormatsCount

      public int getSupportedOutputStorageFormatsCount()
       Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
 exist, the predictions are returned together with their instances. In other
 words, the prediction has the original instance data first, followed by the
 actual prediction content (as per the schema).

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each prediction is a single line. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `csv`
 The CSV format, where each prediction is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedOutputStorageFormatsCount in interface ModelOrBuilder
      Returns:
      The count of supportedOutputStorageFormats.

    • getSupportedOutputStorageFormats

      public String getSupportedOutputStorageFormats(int index)
       Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
 exist, the predictions are returned together with their instances. In other
 words, the prediction has the original instance data first, followed by the
 actual prediction content (as per the schema).

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each prediction is a single line. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `csv`
 The CSV format, where each prediction is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedOutputStorageFormats in interface ModelOrBuilder
      Parameters:
      index - The index of the element to return.
      Returns:
      The supportedOutputStorageFormats at the given index.

    • getSupportedOutputStorageFormatsBytes

      public com.google.protobuf.ByteString getSupportedOutputStorageFormatsBytes(int index)
       Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
 exist, the predictions are returned together with their instances. In other
 words, the prediction has the original instance data first, followed by the
 actual prediction content (as per the schema).

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each prediction is a single line. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `csv`
 The CSV format, where each prediction is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSupportedOutputStorageFormatsBytes in interface ModelOrBuilder
      Parameters:
      index - The index of the value to return.
      Returns:
      The bytes of the supportedOutputStorageFormats at the given index.

    • setSupportedOutputStorageFormats

      public Model.Builder setSupportedOutputStorageFormats(int index, String value)
       Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
 exist, the predictions are returned together with their instances. In other
 words, the prediction has the original instance data first, followed by the
 actual prediction content (as per the schema).

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each prediction is a single line. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `csv`
 The CSV format, where each prediction is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      index - The index to set the value at.
      value - The supportedOutputStorageFormats to set.
      Returns:
      This builder for chaining.

    • addSupportedOutputStorageFormats

      public Model.Builder addSupportedOutputStorageFormats(String value)
       Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
 exist, the predictions are returned together with their instances. In other
 words, the prediction has the original instance data first, followed by the
 actual prediction content (as per the schema).

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each prediction is a single line. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `csv`
 The CSV format, where each prediction is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The supportedOutputStorageFormats to add.
      Returns:
      This builder for chaining.

    • addAllSupportedOutputStorageFormats

      public Model.Builder addAllSupportedOutputStorageFormats(Iterable<String> values)
       Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
 exist, the predictions are returned together with their instances. In other
 words, the prediction has the original instance data first, followed by the
 actual prediction content (as per the schema).

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each prediction is a single line. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `csv`
 The CSV format, where each prediction is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      values - The supportedOutputStorageFormats to add.
      Returns:
      This builder for chaining.

    • clearSupportedOutputStorageFormats

      public Model.Builder clearSupportedOutputStorageFormats()
       Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
 exist, the predictions are returned together with their instances. In other
 words, the prediction has the original instance data first, followed by the
 actual prediction content (as per the schema).

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each prediction is a single line. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `csv`
 The CSV format, where each prediction is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      This builder for chaining.

    • addSupportedOutputStorageFormatsBytes

      public Model.Builder addSupportedOutputStorageFormatsBytes(com.google.protobuf.ByteString value)
       Output only. The formats this Model supports in
 [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
 If both
 [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
 and
 [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
 exist, the predictions are returned together with their instances. In other
 words, the prediction has the original instance data first, followed by the
 actual prediction content (as per the schema).

 The possible formats are:

 * `jsonl`
 The JSON Lines format, where each prediction is a single line. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `csv`
 The CSV format, where each prediction is a single comma-separated line.
 The first line in the file is the header, containing comma-separated field
 names. Uses
 [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].

 * `bigquery`
 Each prediction is a single row in a BigQuery table, uses
 [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
 .


 If this Model doesn't support any of these formats it means it cannot be
 used with a
 [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
 However, if it has
 [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
 it could serve online predictions by using
 [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
 or
 [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
      repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The bytes of the supportedOutputStorageFormats to add.
      Returns:
      This builder for chaining.

    • hasCreateTime

      public boolean hasCreateTime()
       Output only. Timestamp when this Model was uploaded into Vertex AI.
      .google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      hasCreateTime in interface ModelOrBuilder
      Returns:
      Whether the createTime field is set.

    • getCreateTime

      public com.google.protobuf.Timestamp getCreateTime()
       Output only. Timestamp when this Model was uploaded into Vertex AI.
      .google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getCreateTime in interface ModelOrBuilder
      Returns:
      The createTime.

    • setCreateTime

      public Model.Builder setCreateTime(com.google.protobuf.Timestamp value)
       Output only. Timestamp when this Model was uploaded into Vertex AI.
      .google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • setCreateTime

      public Model.Builder setCreateTime(com.google.protobuf.Timestamp.Builder builderForValue)
       Output only. Timestamp when this Model was uploaded into Vertex AI.
      .google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • mergeCreateTime

      public Model.Builder mergeCreateTime(com.google.protobuf.Timestamp value)
       Output only. Timestamp when this Model was uploaded into Vertex AI.
      .google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • clearCreateTime

      public Model.Builder clearCreateTime()
       Output only. Timestamp when this Model was uploaded into Vertex AI.
      .google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getCreateTimeBuilder

      public com.google.protobuf.Timestamp.Builder getCreateTimeBuilder()
       Output only. Timestamp when this Model was uploaded into Vertex AI.
      .google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getCreateTimeOrBuilder

      public com.google.protobuf.TimestampOrBuilder getCreateTimeOrBuilder()
       Output only. Timestamp when this Model was uploaded into Vertex AI.
      .google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getCreateTimeOrBuilder in interface ModelOrBuilder

    • hasUpdateTime

      public boolean hasUpdateTime()
       Output only. Timestamp when this Model was most recently updated.
      .google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      hasUpdateTime in interface ModelOrBuilder
      Returns:
      Whether the updateTime field is set.

    • getUpdateTime

      public com.google.protobuf.Timestamp getUpdateTime()
       Output only. Timestamp when this Model was most recently updated.
      .google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getUpdateTime in interface ModelOrBuilder
      Returns:
      The updateTime.

    • setUpdateTime

      public Model.Builder setUpdateTime(com.google.protobuf.Timestamp value)
       Output only. Timestamp when this Model was most recently updated.
      .google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • setUpdateTime

      public Model.Builder setUpdateTime(com.google.protobuf.Timestamp.Builder builderForValue)
       Output only. Timestamp when this Model was most recently updated.
      .google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • mergeUpdateTime

      public Model.Builder mergeUpdateTime(com.google.protobuf.Timestamp value)
       Output only. Timestamp when this Model was most recently updated.
      .google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • clearUpdateTime

      public Model.Builder clearUpdateTime()
       Output only. Timestamp when this Model was most recently updated.
      .google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getUpdateTimeBuilder

      public com.google.protobuf.Timestamp.Builder getUpdateTimeBuilder()
       Output only. Timestamp when this Model was most recently updated.
      .google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getUpdateTimeOrBuilder

      public com.google.protobuf.TimestampOrBuilder getUpdateTimeOrBuilder()
       Output only. Timestamp when this Model was most recently updated.
      .google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getUpdateTimeOrBuilder in interface ModelOrBuilder

    • getDeployedModelsList

      public List<DeployedModelRef> getDeployedModelsList()
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getDeployedModelsList in interface ModelOrBuilder

    • getDeployedModelsCount

      public int getDeployedModelsCount()
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getDeployedModelsCount in interface ModelOrBuilder

    • getDeployedModels

      public DeployedModelRef getDeployedModels(int index)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getDeployedModels in interface ModelOrBuilder

    • setDeployedModels

      public Model.Builder setDeployedModels(int index, DeployedModelRef value)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • setDeployedModels

      public Model.Builder setDeployedModels(int index, DeployedModelRef.Builder builderForValue)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addDeployedModels

      public Model.Builder addDeployedModels(DeployedModelRef value)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addDeployedModels

      public Model.Builder addDeployedModels(int index, DeployedModelRef value)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addDeployedModels

      public Model.Builder addDeployedModels(DeployedModelRef.Builder builderForValue)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addDeployedModels

      public Model.Builder addDeployedModels(int index, DeployedModelRef.Builder builderForValue)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addAllDeployedModels

      public Model.Builder addAllDeployedModels(Iterable<? extends DeployedModelRef> values)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • clearDeployedModels

      public Model.Builder clearDeployedModels()
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • removeDeployedModels

      public Model.Builder removeDeployedModels(int index)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getDeployedModelsBuilder

      public DeployedModelRef.Builder getDeployedModelsBuilder(int index)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getDeployedModelsOrBuilder

      public DeployedModelRefOrBuilder getDeployedModelsOrBuilder(int index)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getDeployedModelsOrBuilder in interface ModelOrBuilder

    • getDeployedModelsOrBuilderList

      public List<? extends DeployedModelRefOrBuilder> getDeployedModelsOrBuilderList()
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getDeployedModelsOrBuilderList in interface ModelOrBuilder

    • addDeployedModelsBuilder

      public DeployedModelRef.Builder addDeployedModelsBuilder()
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • addDeployedModelsBuilder

      public DeployedModelRef.Builder addDeployedModelsBuilder(int index)
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getDeployedModelsBuilderList

      public List<DeployedModelRef.Builder> getDeployedModelsBuilderList()
       Output only. The pointers to DeployedModels created from this Model. Note
 that Model could have been deployed to Endpoints in different Locations.
      repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • hasExplanationSpec

      public boolean hasExplanationSpec()
       The default explanation specification for this Model.

 The Model can be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
 being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if
 it is populated. The Model can be used for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 if it is populated.

 All fields of the explanation_spec can be overridden by
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
 or
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

 If the default explanation specification is not set for this Model, this
 Model can still be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
 setting
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
 and for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 by setting
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
      .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;
      Specified by:
      hasExplanationSpec in interface ModelOrBuilder
      Returns:
      Whether the explanationSpec field is set.

    • getExplanationSpec

      public ExplanationSpec getExplanationSpec()
       The default explanation specification for this Model.

 The Model can be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
 being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if
 it is populated. The Model can be used for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 if it is populated.

 All fields of the explanation_spec can be overridden by
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
 or
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

 If the default explanation specification is not set for this Model, this
 Model can still be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
 setting
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
 and for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 by setting
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
      .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;
      Specified by:
      getExplanationSpec in interface ModelOrBuilder
      Returns:
      The explanationSpec.

    • setExplanationSpec

      public Model.Builder setExplanationSpec(ExplanationSpec value)
       The default explanation specification for this Model.

 The Model can be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
 being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if
 it is populated. The Model can be used for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 if it is populated.

 All fields of the explanation_spec can be overridden by
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
 or
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

 If the default explanation specification is not set for this Model, this
 Model can still be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
 setting
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
 and for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 by setting
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
      .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

    • setExplanationSpec

      public Model.Builder setExplanationSpec(ExplanationSpec.Builder builderForValue)
       The default explanation specification for this Model.

 The Model can be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
 being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if
 it is populated. The Model can be used for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 if it is populated.

 All fields of the explanation_spec can be overridden by
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
 or
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

 If the default explanation specification is not set for this Model, this
 Model can still be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
 setting
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
 and for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 by setting
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
      .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

    • mergeExplanationSpec

      public Model.Builder mergeExplanationSpec(ExplanationSpec value)
       The default explanation specification for this Model.

 The Model can be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
 being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if
 it is populated. The Model can be used for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 if it is populated.

 All fields of the explanation_spec can be overridden by
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
 or
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

 If the default explanation specification is not set for this Model, this
 Model can still be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
 setting
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
 and for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 by setting
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
      .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

    • clearExplanationSpec

      public Model.Builder clearExplanationSpec()
       The default explanation specification for this Model.

 The Model can be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
 being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if
 it is populated. The Model can be used for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 if it is populated.

 All fields of the explanation_spec can be overridden by
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
 or
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

 If the default explanation specification is not set for this Model, this
 Model can still be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
 setting
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
 and for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 by setting
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
      .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

    • getExplanationSpecBuilder

      public ExplanationSpec.Builder getExplanationSpecBuilder()
       The default explanation specification for this Model.

 The Model can be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
 being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if
 it is populated. The Model can be used for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 if it is populated.

 All fields of the explanation_spec can be overridden by
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
 or
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

 If the default explanation specification is not set for this Model, this
 Model can still be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
 setting
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
 and for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 by setting
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
      .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;

    • getExplanationSpecOrBuilder

      public ExplanationSpecOrBuilder getExplanationSpecOrBuilder()
       The default explanation specification for this Model.

 The Model can be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] after
 being [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if
 it is populated. The Model can be used for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 if it is populated.

 All fields of the explanation_spec can be overridden by
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
 or
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].

 If the default explanation specification is not set for this Model, this
 Model can still be used for
 [requesting
 explanation][google.cloud.aiplatform.v1.PredictionService.Explain] by
 setting
 [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
 of
 [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
 and for [batch
 explanation][google.cloud.aiplatform.v1.BatchPredictionJob.generate_explanation]
 by setting
 [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
 of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
      .google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;
      Specified by:
      getExplanationSpecOrBuilder in interface ModelOrBuilder

    • getEtag

      public String getEtag()
       Used to perform consistent read-modify-write updates. If not set, a blind
 "overwrite" update happens.
      string etag = 16;
      Specified by:
      getEtag in interface ModelOrBuilder
      Returns:
      The etag.

    • getEtagBytes

      public com.google.protobuf.ByteString getEtagBytes()
       Used to perform consistent read-modify-write updates. If not set, a blind
 "overwrite" update happens.
      string etag = 16;
      Specified by:
      getEtagBytes in interface ModelOrBuilder
      Returns:
      The bytes for etag.

    • setEtag

      public Model.Builder setEtag(String value)
       Used to perform consistent read-modify-write updates. If not set, a blind
 "overwrite" update happens.
      string etag = 16;
      Parameters:
      value - The etag to set.
      Returns:
      This builder for chaining.

    • clearEtag

      public Model.Builder clearEtag()
       Used to perform consistent read-modify-write updates. If not set, a blind
 "overwrite" update happens.
      string etag = 16;
      Returns:
      This builder for chaining.

    • setEtagBytes

      public Model.Builder setEtagBytes(com.google.protobuf.ByteString value)
       Used to perform consistent read-modify-write updates. If not set, a blind
 "overwrite" update happens.
      string etag = 16;
      Parameters:
      value - The bytes for etag to set.
      Returns:
      This builder for chaining.

    • getLabelsCount

      public int getLabelsCount()
      Description copied from interface: ModelOrBuilder
       The labels with user-defined metadata to organize your Models.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
      map<string, string> labels = 17;
      Specified by:
      getLabelsCount in interface ModelOrBuilder

    • containsLabels

      public boolean containsLabels(String key)
       The labels with user-defined metadata to organize your Models.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
      map<string, string> labels = 17;
      Specified by:
      containsLabels in interface ModelOrBuilder

    • getLabels

      @Deprecated public Map<String,String> getLabels()
      Deprecated.
      Use getLabelsMap() instead.
      Specified by:
      getLabels in interface ModelOrBuilder

    • getLabelsMap

      public Map<String,String> getLabelsMap()
       The labels with user-defined metadata to organize your Models.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
      map<string, string> labels = 17;
      Specified by:
      getLabelsMap in interface ModelOrBuilder

    • getLabelsOrDefault

      public String getLabelsOrDefault(String key, String defaultValue)
       The labels with user-defined metadata to organize your Models.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
      map<string, string> labels = 17;
      Specified by:
      getLabelsOrDefault in interface ModelOrBuilder

    • getLabelsOrThrow

      public String getLabelsOrThrow(String key)
       The labels with user-defined metadata to organize your Models.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
      map<string, string> labels = 17;
      Specified by:
      getLabelsOrThrow in interface ModelOrBuilder

    • clearLabels

      public Model.Builder clearLabels()

    • removeLabels

      public Model.Builder removeLabels(String key)
       The labels with user-defined metadata to organize your Models.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
      map<string, string> labels = 17;

    • getMutableLabels

      @Deprecated public Map<String,String> getMutableLabels()
      Deprecated.
      Use alternate mutation accessors instead.

    • putLabels

      public Model.Builder putLabels(String key, String value)
       The labels with user-defined metadata to organize your Models.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
      map<string, string> labels = 17;

    • putAllLabels

      public Model.Builder putAllLabels(Map<String,String> values)
       The labels with user-defined metadata to organize your Models.

 Label keys and values can be no longer than 64 characters
 (Unicode codepoints), can only contain lowercase letters, numeric
 characters, underscores and dashes. International characters are allowed.

 See https://goo.gl/xmQnxf for more information and examples of labels.
      map<string, string> labels = 17;

    • hasDataStats

      public boolean hasDataStats()
       Stats of data used for training or evaluating the Model.

 Only populated when the Model is trained by a TrainingPipeline with
 [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].
      .google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;
      Specified by:
      hasDataStats in interface ModelOrBuilder
      Returns:
      Whether the dataStats field is set.

    • getDataStats

      public Model.DataStats getDataStats()
       Stats of data used for training or evaluating the Model.

 Only populated when the Model is trained by a TrainingPipeline with
 [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].
      .google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;
      Specified by:
      getDataStats in interface ModelOrBuilder
      Returns:
      The dataStats.

    • setDataStats

      public Model.Builder setDataStats(Model.DataStats value)
       Stats of data used for training or evaluating the Model.

 Only populated when the Model is trained by a TrainingPipeline with
 [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].
      .google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

    • setDataStats

      public Model.Builder setDataStats(Model.DataStats.Builder builderForValue)
       Stats of data used for training or evaluating the Model.

 Only populated when the Model is trained by a TrainingPipeline with
 [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].
      .google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

    • mergeDataStats

      public Model.Builder mergeDataStats(Model.DataStats value)
       Stats of data used for training or evaluating the Model.

 Only populated when the Model is trained by a TrainingPipeline with
 [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].
      .google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

    • clearDataStats

      public Model.Builder clearDataStats()
       Stats of data used for training or evaluating the Model.

 Only populated when the Model is trained by a TrainingPipeline with
 [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].
      .google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

    • getDataStatsBuilder

      public Model.DataStats.Builder getDataStatsBuilder()
       Stats of data used for training or evaluating the Model.

 Only populated when the Model is trained by a TrainingPipeline with
 [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].
      .google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;

    • getDataStatsOrBuilder

      public Model.DataStatsOrBuilder getDataStatsOrBuilder()
       Stats of data used for training or evaluating the Model.

 Only populated when the Model is trained by a TrainingPipeline with
 [data_input_config][google.cloud.aiplatform.v1.TrainingPipeline.input_data_config].
      .google.cloud.aiplatform.v1.Model.DataStats data_stats = 21;
      Specified by:
      getDataStatsOrBuilder in interface ModelOrBuilder

    • hasEncryptionSpec

      public boolean hasEncryptionSpec()
       Customer-managed encryption key spec for a Model. If set, this
 Model and all sub-resources of this Model will be secured by this key.
      .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;
      Specified by:
      hasEncryptionSpec in interface ModelOrBuilder
      Returns:
      Whether the encryptionSpec field is set.

    • getEncryptionSpec

      public EncryptionSpec getEncryptionSpec()
       Customer-managed encryption key spec for a Model. If set, this
 Model and all sub-resources of this Model will be secured by this key.
      .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;
      Specified by:
      getEncryptionSpec in interface ModelOrBuilder
      Returns:
      The encryptionSpec.

    • setEncryptionSpec

      public Model.Builder setEncryptionSpec(EncryptionSpec value)
       Customer-managed encryption key spec for a Model. If set, this
 Model and all sub-resources of this Model will be secured by this key.
      .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

    • setEncryptionSpec

      public Model.Builder setEncryptionSpec(EncryptionSpec.Builder builderForValue)
       Customer-managed encryption key spec for a Model. If set, this
 Model and all sub-resources of this Model will be secured by this key.
      .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

    • mergeEncryptionSpec

      public Model.Builder mergeEncryptionSpec(EncryptionSpec value)
       Customer-managed encryption key spec for a Model. If set, this
 Model and all sub-resources of this Model will be secured by this key.
      .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

    • clearEncryptionSpec

      public Model.Builder clearEncryptionSpec()
       Customer-managed encryption key spec for a Model. If set, this
 Model and all sub-resources of this Model will be secured by this key.
      .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

    • getEncryptionSpecBuilder

      public EncryptionSpec.Builder getEncryptionSpecBuilder()
       Customer-managed encryption key spec for a Model. If set, this
 Model and all sub-resources of this Model will be secured by this key.
      .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;

    • getEncryptionSpecOrBuilder

      public EncryptionSpecOrBuilder getEncryptionSpecOrBuilder()
       Customer-managed encryption key spec for a Model. If set, this
 Model and all sub-resources of this Model will be secured by this key.
      .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;
      Specified by:
      getEncryptionSpecOrBuilder in interface ModelOrBuilder

    • hasModelSourceInfo

      public boolean hasModelSourceInfo()
       Output only. Source of a model. It can either be automl training pipeline,
 custom training pipeline, BigQuery ML, or saved and tuned from Genie or
 Model Garden.
      .google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      hasModelSourceInfo in interface ModelOrBuilder
      Returns:
      Whether the modelSourceInfo field is set.

    • getModelSourceInfo

      public ModelSourceInfo getModelSourceInfo()
       Output only. Source of a model. It can either be automl training pipeline,
 custom training pipeline, BigQuery ML, or saved and tuned from Genie or
 Model Garden.
      .google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getModelSourceInfo in interface ModelOrBuilder
      Returns:
      The modelSourceInfo.

    • setModelSourceInfo

      public Model.Builder setModelSourceInfo(ModelSourceInfo value)
       Output only. Source of a model. It can either be automl training pipeline,
 custom training pipeline, BigQuery ML, or saved and tuned from Genie or
 Model Garden.
      .google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • setModelSourceInfo

      public Model.Builder setModelSourceInfo(ModelSourceInfo.Builder builderForValue)
       Output only. Source of a model. It can either be automl training pipeline,
 custom training pipeline, BigQuery ML, or saved and tuned from Genie or
 Model Garden.
      .google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • mergeModelSourceInfo

      public Model.Builder mergeModelSourceInfo(ModelSourceInfo value)
       Output only. Source of a model. It can either be automl training pipeline,
 custom training pipeline, BigQuery ML, or saved and tuned from Genie or
 Model Garden.
      .google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • clearModelSourceInfo

      public Model.Builder clearModelSourceInfo()
       Output only. Source of a model. It can either be automl training pipeline,
 custom training pipeline, BigQuery ML, or saved and tuned from Genie or
 Model Garden.
      .google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getModelSourceInfoBuilder

      public ModelSourceInfo.Builder getModelSourceInfoBuilder()
       Output only. Source of a model. It can either be automl training pipeline,
 custom training pipeline, BigQuery ML, or saved and tuned from Genie or
 Model Garden.
      .google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getModelSourceInfoOrBuilder

      public ModelSourceInfoOrBuilder getModelSourceInfoOrBuilder()
       Output only. Source of a model. It can either be automl training pipeline,
 custom training pipeline, BigQuery ML, or saved and tuned from Genie or
 Model Garden.
      .google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getModelSourceInfoOrBuilder in interface ModelOrBuilder

    • hasOriginalModelInfo

      public boolean hasOriginalModelInfo()
       Output only. If this Model is a copy of another Model, this contains info
 about the original.
      .google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      hasOriginalModelInfo in interface ModelOrBuilder
      Returns:
      Whether the originalModelInfo field is set.

    • getOriginalModelInfo

      public Model.OriginalModelInfo getOriginalModelInfo()
       Output only. If this Model is a copy of another Model, this contains info
 about the original.
      .google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getOriginalModelInfo in interface ModelOrBuilder
      Returns:
      The originalModelInfo.

    • setOriginalModelInfo

      public Model.Builder setOriginalModelInfo(Model.OriginalModelInfo value)
       Output only. If this Model is a copy of another Model, this contains info
 about the original.
      .google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • setOriginalModelInfo

      public Model.Builder setOriginalModelInfo(Model.OriginalModelInfo.Builder builderForValue)
       Output only. If this Model is a copy of another Model, this contains info
 about the original.
      .google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • mergeOriginalModelInfo

      public Model.Builder mergeOriginalModelInfo(Model.OriginalModelInfo value)
       Output only. If this Model is a copy of another Model, this contains info
 about the original.
      .google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • clearOriginalModelInfo

      public Model.Builder clearOriginalModelInfo()
       Output only. If this Model is a copy of another Model, this contains info
 about the original.
      .google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getOriginalModelInfoBuilder

      public Model.OriginalModelInfo.Builder getOriginalModelInfoBuilder()
       Output only. If this Model is a copy of another Model, this contains info
 about the original.
      .google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];

    • getOriginalModelInfoOrBuilder

      public Model.OriginalModelInfoOrBuilder getOriginalModelInfoOrBuilder()
       Output only. If this Model is a copy of another Model, this contains info
 about the original.
      .google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getOriginalModelInfoOrBuilder in interface ModelOrBuilder

    • getMetadataArtifact

      public String getMetadataArtifact()
       Output only. The resource name of the Artifact that was created in
 MetadataStore when creating the Model. The Artifact resource name pattern
 is
 `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
      string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getMetadataArtifact in interface ModelOrBuilder
      Returns:
      The metadataArtifact.

    • getMetadataArtifactBytes

      public com.google.protobuf.ByteString getMetadataArtifactBytes()
       Output only. The resource name of the Artifact that was created in
 MetadataStore when creating the Model. The Artifact resource name pattern
 is
 `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
      string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getMetadataArtifactBytes in interface ModelOrBuilder
      Returns:
      The bytes for metadataArtifact.

    • setMetadataArtifact

      public Model.Builder setMetadataArtifact(String value)
       Output only. The resource name of the Artifact that was created in
 MetadataStore when creating the Model. The Artifact resource name pattern
 is
 `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
      string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The metadataArtifact to set.
      Returns:
      This builder for chaining.

    • clearMetadataArtifact

      public Model.Builder clearMetadataArtifact()
       Output only. The resource name of the Artifact that was created in
 MetadataStore when creating the Model. The Artifact resource name pattern
 is
 `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
      string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      This builder for chaining.

    • setMetadataArtifactBytes

      public Model.Builder setMetadataArtifactBytes(com.google.protobuf.ByteString value)
       Output only. The resource name of the Artifact that was created in
 MetadataStore when creating the Model. The Artifact resource name pattern
 is
 `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
      string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The bytes for metadataArtifact to set.
      Returns:
      This builder for chaining.

    • hasBaseModelSource

      public boolean hasBaseModelSource()
       Optional. User input field to specify the base model source. Currently it
 only supports specifing the Model Garden models and Genie models.
      .google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];
      Specified by:
      hasBaseModelSource in interface ModelOrBuilder
      Returns:
      Whether the baseModelSource field is set.

    • getBaseModelSource

      public Model.BaseModelSource getBaseModelSource()
       Optional. User input field to specify the base model source. Currently it
 only supports specifing the Model Garden models and Genie models.
      .google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];
      Specified by:
      getBaseModelSource in interface ModelOrBuilder
      Returns:
      The baseModelSource.

    • setBaseModelSource

      public Model.Builder setBaseModelSource(Model.BaseModelSource value)
       Optional. User input field to specify the base model source. Currently it
 only supports specifing the Model Garden models and Genie models.
      .google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

    • setBaseModelSource

      public Model.Builder setBaseModelSource(Model.BaseModelSource.Builder builderForValue)
       Optional. User input field to specify the base model source. Currently it
 only supports specifing the Model Garden models and Genie models.
      .google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

    • mergeBaseModelSource

      public Model.Builder mergeBaseModelSource(Model.BaseModelSource value)
       Optional. User input field to specify the base model source. Currently it
 only supports specifing the Model Garden models and Genie models.
      .google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

    • clearBaseModelSource

      public Model.Builder clearBaseModelSource()
       Optional. User input field to specify the base model source. Currently it
 only supports specifing the Model Garden models and Genie models.
      .google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

    • getBaseModelSourceBuilder

      public Model.BaseModelSource.Builder getBaseModelSourceBuilder()
       Optional. User input field to specify the base model source. Currently it
 only supports specifing the Model Garden models and Genie models.
      .google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];

    • getBaseModelSourceOrBuilder

      public Model.BaseModelSourceOrBuilder getBaseModelSourceOrBuilder()
       Optional. User input field to specify the base model source. Currently it
 only supports specifing the Model Garden models and Genie models.
      .google.cloud.aiplatform.v1.Model.BaseModelSource base_model_source = 50 [(.google.api.field_behavior) = OPTIONAL];
      Specified by:
      getBaseModelSourceOrBuilder in interface ModelOrBuilder

    • getSatisfiesPzs

      public boolean getSatisfiesPzs()
       Output only. Reserved for future use.
      bool satisfies_pzs = 51 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSatisfiesPzs in interface ModelOrBuilder
      Returns:
      The satisfiesPzs.

    • setSatisfiesPzs

      public Model.Builder setSatisfiesPzs(boolean value)
       Output only. Reserved for future use.
      bool satisfies_pzs = 51 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The satisfiesPzs to set.
      Returns:
      This builder for chaining.

    • clearSatisfiesPzs

      public Model.Builder clearSatisfiesPzs()
       Output only. Reserved for future use.
      bool satisfies_pzs = 51 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      This builder for chaining.

    • getSatisfiesPzi

      public boolean getSatisfiesPzi()
       Output only. Reserved for future use.
      bool satisfies_pzi = 52 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getSatisfiesPzi in interface ModelOrBuilder
      Returns:
      The satisfiesPzi.

    • setSatisfiesPzi

      public Model.Builder setSatisfiesPzi(boolean value)
       Output only. Reserved for future use.
      bool satisfies_pzi = 52 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The satisfiesPzi to set.
      Returns:
      This builder for chaining.

    • clearSatisfiesPzi

      public Model.Builder clearSatisfiesPzi()
       Output only. Reserved for future use.
      bool satisfies_pzi = 52 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      This builder for chaining.

    • getCheckpointsList

      public List<Checkpoint> getCheckpointsList()
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];
      Specified by:
      getCheckpointsList in interface ModelOrBuilder

    • getCheckpointsCount

      public int getCheckpointsCount()
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];
      Specified by:
      getCheckpointsCount in interface ModelOrBuilder

    • getCheckpoints

      public Checkpoint getCheckpoints(int index)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];
      Specified by:
      getCheckpoints in interface ModelOrBuilder

    • setCheckpoints

      public Model.Builder setCheckpoints(int index, Checkpoint value)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • setCheckpoints

      public Model.Builder setCheckpoints(int index, Checkpoint.Builder builderForValue)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • addCheckpoints

      public Model.Builder addCheckpoints(Checkpoint value)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • addCheckpoints

      public Model.Builder addCheckpoints(int index, Checkpoint value)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • addCheckpoints

      public Model.Builder addCheckpoints(Checkpoint.Builder builderForValue)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • addCheckpoints

      public Model.Builder addCheckpoints(int index, Checkpoint.Builder builderForValue)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • addAllCheckpoints

      public Model.Builder addAllCheckpoints(Iterable<? extends Checkpoint> values)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • clearCheckpoints

      public Model.Builder clearCheckpoints()
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • removeCheckpoints

      public Model.Builder removeCheckpoints(int index)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • getCheckpointsBuilder

      public Checkpoint.Builder getCheckpointsBuilder(int index)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • getCheckpointsOrBuilder

      public CheckpointOrBuilder getCheckpointsOrBuilder(int index)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];
      Specified by:
      getCheckpointsOrBuilder in interface ModelOrBuilder

    • getCheckpointsOrBuilderList

      public List<? extends CheckpointOrBuilder> getCheckpointsOrBuilderList()
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];
      Specified by:
      getCheckpointsOrBuilderList in interface ModelOrBuilder

    • addCheckpointsBuilder

      public Checkpoint.Builder addCheckpointsBuilder()
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • addCheckpointsBuilder

      public Checkpoint.Builder addCheckpointsBuilder(int index)
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];

    • getCheckpointsBuilderList

      public List<Checkpoint.Builder> getCheckpointsBuilderList()
       Optional. Output only. The checkpoints of the model.
      repeated .google.cloud.aiplatform.v1.Checkpoint checkpoints = 57 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.field_behavior) = OPTIONAL];