Interface AutoMlImageClassificationInputsOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
AutoMlImageClassificationInputs, AutoMlImageClassificationInputs.Builder

@Generated public interface AutoMlImageClassificationInputsOrBuilder extends com.google.protobuf.MessageOrBuilder
  • Method Summary

    Modifier and Type
    Method
    Description
    The ID of the `base` model.
    com.google.protobuf.ByteString
    The ID of the `base` model.
    long
    The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.
    boolean
    Use the entire training budget.
    .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
    int
    .google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
    boolean
    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 Details

    • 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

      .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.