Package com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition

Class AutoMlImageClassificationInputs.Builder

    • Method Detail

      • getDescriptor

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

      • internalGetFieldAccessorTable

        protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
        Specified by:
        internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlImageClassificationInputs.Builder>

      • getDescriptorForType

        public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
        Specified by:
        getDescriptorForType in interface com.google.protobuf.Message.Builder
        Specified by:
        getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
        Overrides:
        getDescriptorForType in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlImageClassificationInputs.Builder>

      • getDefaultInstanceForType

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

      • build

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

      • buildPartial

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

      • isInitialized

        public final boolean isInitialized()
        Specified by:
        isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
        Overrides:
        isInitialized in class com.google.protobuf.GeneratedMessageV3.Builder<AutoMlImageClassificationInputs.Builder>

      • getModelTypeValue

        public int getModelTypeValue()
        .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
        Specified by:
        getModelTypeValue in interface AutoMlImageClassificationInputsOrBuilder
        Returns:
        The enum numeric value on the wire for modelType.

      • setModelTypeValue

        public AutoMlImageClassificationInputs.Builder setModelTypeValue​(int value)
        .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
        Parameters:
        value - The enum numeric value on the wire for modelType to set.
        Returns:
        This builder for chaining.

      • clearModelType

        public AutoMlImageClassificationInputs.Builder clearModelType()
        .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
        Returns:
        This builder for chaining.

      • getBaseModelId

        public String getBaseModelId()
         The ID of the `base` model. If it is specified, the new model will be
 trained based on the `base` model. Otherwise, the new model will be
 trained from scratch. The `base` model must be in the same
 Project and Location as the new Model to train, and have the same
 modelType.
        string base_model_id = 2;
        Specified by:
        getBaseModelId in interface AutoMlImageClassificationInputsOrBuilder
        Returns:
        The baseModelId.

      • getBaseModelIdBytes

        public com.google.protobuf.ByteString getBaseModelIdBytes()
         The ID of the `base` model. If it is specified, the new model will be
 trained based on the `base` model. Otherwise, the new model will be
 trained from scratch. The `base` model must be in the same
 Project and Location as the new Model to train, and have the same
 modelType.
        string base_model_id = 2;
        Specified by:
        getBaseModelIdBytes in interface AutoMlImageClassificationInputsOrBuilder
        Returns:
        The bytes for baseModelId.

      • setBaseModelId

        public AutoMlImageClassificationInputs.Builder setBaseModelId​(String value)
         The ID of the `base` model. If it is specified, the new model will be
 trained based on the `base` model. Otherwise, the new model will be
 trained from scratch. The `base` model must be in the same
 Project and Location as the new Model to train, and have the same
 modelType.
        string base_model_id = 2;
        Parameters:
        value - The baseModelId to set.
        Returns:
        This builder for chaining.

      • clearBaseModelId

        public AutoMlImageClassificationInputs.Builder clearBaseModelId()
         The ID of the `base` model. If it is specified, the new model will be
 trained based on the `base` model. Otherwise, the new model will be
 trained from scratch. The `base` model must be in the same
 Project and Location as the new Model to train, and have the same
 modelType.
        string base_model_id = 2;
        Returns:
        This builder for chaining.

      • setBaseModelIdBytes

        public AutoMlImageClassificationInputs.Builder setBaseModelIdBytes​(com.google.protobuf.ByteString value)
         The ID of the `base` model. If it is specified, the new model will be
 trained based on the `base` model. Otherwise, the new model will be
 trained from scratch. The `base` model must be in the same
 Project and Location as the new Model to train, and have the same
 modelType.
        string base_model_id = 2;
        Parameters:
        value - The bytes for baseModelId to set.
        Returns:
        This builder for chaining.

      • getBudgetMilliNodeHours

        public long getBudgetMilliNodeHours()
         The training budget of creating this model, expressed in milli node
 hours i.e. 1,000 value in this field means 1 node hour. The actual
 metadata.costMilliNodeHours will be equal or less than this value.
 If further model training ceases to provide any improvements, it will
 stop without using the full budget and the metadata.successfulStopReason
 will be `model-converged`.
 Note, node_hour  = actual_hour * number_of_nodes_involved.
 For modelType `cloud`(default), the budget must be between 8,000
 and 800,000 milli node hours, inclusive. The default value is 192,000
 which represents one day in wall time, considering 8 nodes are used.
 For model types `mobile-tf-low-latency-1`, `mobile-tf-versatile-1`,
 `mobile-tf-high-accuracy-1`, the training budget must be between
 1,000 and 100,000 milli node hours, inclusive.
 The default value is 24,000 which represents one day in wall time on a
 single node that is used.
        int64 budget_milli_node_hours = 3;
        Specified by:
        getBudgetMilliNodeHours in interface AutoMlImageClassificationInputsOrBuilder
        Returns:
        The budgetMilliNodeHours.

      • setBudgetMilliNodeHours

        public AutoMlImageClassificationInputs.Builder setBudgetMilliNodeHours​(long value)
         The training budget of creating this model, expressed in milli node
 hours i.e. 1,000 value in this field means 1 node hour. The actual
 metadata.costMilliNodeHours will be equal or less than this value.
 If further model training ceases to provide any improvements, it will
 stop without using the full budget and the metadata.successfulStopReason
 will be `model-converged`.
 Note, node_hour  = actual_hour * number_of_nodes_involved.
 For modelType `cloud`(default), the budget must be between 8,000
 and 800,000 milli node hours, inclusive. The default value is 192,000
 which represents one day in wall time, considering 8 nodes are used.
 For model types `mobile-tf-low-latency-1`, `mobile-tf-versatile-1`,
 `mobile-tf-high-accuracy-1`, the training budget must be between
 1,000 and 100,000 milli node hours, inclusive.
 The default value is 24,000 which represents one day in wall time on a
 single node that is used.
        int64 budget_milli_node_hours = 3;
        Parameters:
        value - The budgetMilliNodeHours to set.
        Returns:
        This builder for chaining.

      • clearBudgetMilliNodeHours

        public AutoMlImageClassificationInputs.Builder clearBudgetMilliNodeHours()
         The training budget of creating this model, expressed in milli node
 hours i.e. 1,000 value in this field means 1 node hour. The actual
 metadata.costMilliNodeHours will be equal or less than this value.
 If further model training ceases to provide any improvements, it will
 stop without using the full budget and the metadata.successfulStopReason
 will be `model-converged`.
 Note, node_hour  = actual_hour * number_of_nodes_involved.
 For modelType `cloud`(default), the budget must be between 8,000
 and 800,000 milli node hours, inclusive. The default value is 192,000
 which represents one day in wall time, considering 8 nodes are used.
 For model types `mobile-tf-low-latency-1`, `mobile-tf-versatile-1`,
 `mobile-tf-high-accuracy-1`, the training budget must be between
 1,000 and 100,000 milli node hours, inclusive.
 The default value is 24,000 which represents one day in wall time on a
 single node that is used.
        int64 budget_milli_node_hours = 3;
        Returns:
        This builder for chaining.

      • getDisableEarlyStopping

        public boolean getDisableEarlyStopping()
         Use the entire training budget. This disables the early stopping feature.
 When false the early stopping feature is enabled, which means that
 AutoML Image Classification might stop training before the entire
 training budget has been used.
        bool disable_early_stopping = 4;
        Specified by:
        getDisableEarlyStopping in interface AutoMlImageClassificationInputsOrBuilder
        Returns:
        The disableEarlyStopping.

      • setDisableEarlyStopping

        public AutoMlImageClassificationInputs.Builder setDisableEarlyStopping​(boolean value)
         Use the entire training budget. This disables the early stopping feature.
 When false the early stopping feature is enabled, which means that
 AutoML Image Classification might stop training before the entire
 training budget has been used.
        bool disable_early_stopping = 4;
        Parameters:
        value - The disableEarlyStopping to set.
        Returns:
        This builder for chaining.

      • clearDisableEarlyStopping

        public AutoMlImageClassificationInputs.Builder clearDisableEarlyStopping()
         Use the entire training budget. This disables the early stopping feature.
 When false the early stopping feature is enabled, which means that
 AutoML Image Classification might stop training before the entire
 training budget has been used.
        bool disable_early_stopping = 4;
        Returns:
        This builder for chaining.

      • getMultiLabel

        public boolean getMultiLabel()
         If false, a single-label (multi-class) Model will be trained (i.e.
 assuming that for each image just up to one annotation may be
 applicable). If true, a multi-label Model will be trained (i.e.
 assuming that for each image multiple annotations may be applicable).
        bool multi_label = 5;
        Specified by:
        getMultiLabel in interface AutoMlImageClassificationInputsOrBuilder
        Returns:
        The multiLabel.

      • setMultiLabel

        public AutoMlImageClassificationInputs.Builder setMultiLabel​(boolean value)
         If false, a single-label (multi-class) Model will be trained (i.e.
 assuming that for each image just up to one annotation may be
 applicable). If true, a multi-label Model will be trained (i.e.
 assuming that for each image multiple annotations may be applicable).
        bool multi_label = 5;
        Parameters:
        value - The multiLabel to set.
        Returns:
        This builder for chaining.

      • clearMultiLabel

        public AutoMlImageClassificationInputs.Builder clearMultiLabel()
         If false, a single-label (multi-class) Model will be trained (i.e.
 assuming that for each image just up to one annotation may be
 applicable). If true, a multi-label Model will be trained (i.e.
 assuming that for each image multiple annotations may be applicable).
        bool multi_label = 5;
        Returns:
        This builder for chaining.