Package com.google.cloud.aiplatform.v1.schema.trainingjob.definition

Interface AutoMlImageClassificationInputsOrBuilder

    • Method Summary

      All Methods Instance Methods Abstract Methods 
      Modifier and Type Method Description
      String getBaseModelId()
      The ID of the `base` model.
      com.google.protobuf.ByteString getBaseModelIdBytes()
      The ID of the `base` model.
      long getBudgetMilliNodeHours()
      The training budget of creating this model, expressed in milli node hours i.e.
      boolean getDisableEarlyStopping()
      Use the entire training budget.
      AutoMlImageClassificationInputs.ModelType getModelType()
      .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
      int getModelTypeValue()
      .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
      boolean getMultiLabel()
      If false, a single-label (multi-class) Model will be trained (i.e.

      • Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

        isInitialized

      • Methods inherited from interface com.google.protobuf.MessageOrBuilder

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

    • Method Detail

      • getModelTypeValue

        int getModelTypeValue()
        .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
        Returns:
        The enum numeric value on the wire for modelType.

      • getModelType

        AutoMlImageClassificationInputs.ModelType getModelType()
        .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
        Returns:
        The modelType.

      • getBaseModelId

        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;
        Returns:
        The baseModelId.

      • getBaseModelIdBytes

        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;
        Returns:
        The bytes for baseModelId.

      • getBudgetMilliNodeHours

        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;
        Returns:
        The budgetMilliNodeHours.

      • getDisableEarlyStopping

        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;
        Returns:
        The disableEarlyStopping.

      • getMultiLabel

        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;
        Returns:
        The multiLabel.