Package com.google.cloud.automl.v1

Interface ImageClassificationModelMetadataOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
ImageClassificationModelMetadata, ImageClassificationModelMetadata.Builder
@Generated public interface ImageClassificationModelMetadataOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Summary

    Modifier and Type
    Method
    Description
    String
    getBaseModelId()
    Optional.
    com.google.protobuf.ByteString
    getBaseModelIdBytes()
    Optional.
    String
    getModelType()
    Optional.
    com.google.protobuf.ByteString
    getModelTypeBytes()
    Optional.
    long
    getNodeCount()
    Output only.
    double
    getNodeQps()
    Output only.
    String
    getStopReason()
    Output only.
    com.google.protobuf.ByteString
    getStopReasonBytes()
    Output only.
    long
    getTrainBudgetMilliNodeHours()
    Optional.
    long
    getTrainCostMilliNodeHours()
    Output only.

    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

    • getBaseModelId

      String getBaseModelId()
       Optional. The ID of the `base` model. If it is specified, the new model
 will be created based on the `base` model. Otherwise, the new model will be
 created from scratch. The `base` model must be in the same
 `project` and `location` as the new model to create, and have the same
 `model_type`.
      string base_model_id = 1 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      The baseModelId.

    • getBaseModelIdBytes

      com.google.protobuf.ByteString getBaseModelIdBytes()
       Optional. The ID of the `base` model. If it is specified, the new model
 will be created based on the `base` model. Otherwise, the new model will be
 created from scratch. The `base` model must be in the same
 `project` and `location` as the new model to create, and have the same
 `model_type`.
      string base_model_id = 1 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      The bytes for baseModelId.

    • getTrainBudgetMilliNodeHours

      long getTrainBudgetMilliNodeHours()
       Optional. The train budget of creating this model, expressed in milli node
 hours i.e. 1,000 value in this field means 1 node hour. The actual
 `train_cost` will be equal or less than this value. If further model
 training ceases to provide any improvements, it will stop without using
 full budget and the stop_reason will be `MODEL_CONVERGED`.
 Note, node_hour  = actual_hour * number_of_nodes_invovled.
 For model type `cloud`(default), the train 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. For model type
 `mobile-low-latency-1`, `mobile-versatile-1`, `mobile-high-accuracy-1`,
 `mobile-core-ml-low-latency-1`, `mobile-core-ml-versatile-1`,
 `mobile-core-ml-high-accuracy-1`, the train 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.
      int64 train_budget_milli_node_hours = 16 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      The trainBudgetMilliNodeHours.

    • getTrainCostMilliNodeHours

      long getTrainCostMilliNodeHours()
       Output only. The actual train cost of creating this model, expressed in
 milli node hours, i.e. 1,000 value in this field means 1 node hour.
 Guaranteed to not exceed the train budget.
      int64 train_cost_milli_node_hours = 17 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      The trainCostMilliNodeHours.

    • getStopReason

      String getStopReason()
       Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
      string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      The stopReason.

    • getStopReasonBytes

      com.google.protobuf.ByteString getStopReasonBytes()
       Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
      string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      The bytes for stopReason.

    • getModelType

      String getModelType()
       Optional. Type of the model. The available values are:
 *   `cloud` - Model to be used via prediction calls to AutoML API.
 This is the default value.
 *   `mobile-low-latency-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards. Expected to have low latency, but
 may have lower prediction quality than other models.
 *   `mobile-versatile-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.
 *   `mobile-high-accuracy-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.  Expected to have a higher
 latency, but should also have a higher prediction quality
 than other models.
 *   `mobile-core-ml-low-latency-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
 ML afterwards. Expected to have low latency, but may have
 lower prediction quality than other models.
 *   `mobile-core-ml-versatile-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
 ML afterwards.
 *   `mobile-core-ml-high-accuracy-1` - A model that, in addition to
 providing prediction via AutoML API, can also be exported
 (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with
 Core ML afterwards.  Expected to have a higher latency, but
 should also have a higher prediction quality than other
 models.
      string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      The modelType.

    • getModelTypeBytes

      com.google.protobuf.ByteString getModelTypeBytes()
       Optional. Type of the model. The available values are:
 *   `cloud` - Model to be used via prediction calls to AutoML API.
 This is the default value.
 *   `mobile-low-latency-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards. Expected to have low latency, but
 may have lower prediction quality than other models.
 *   `mobile-versatile-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.
 *   `mobile-high-accuracy-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.  Expected to have a higher
 latency, but should also have a higher prediction quality
 than other models.
 *   `mobile-core-ml-low-latency-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
 ML afterwards. Expected to have low latency, but may have
 lower prediction quality than other models.
 *   `mobile-core-ml-versatile-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
 ML afterwards.
 *   `mobile-core-ml-high-accuracy-1` - A model that, in addition to
 providing prediction via AutoML API, can also be exported
 (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with
 Core ML afterwards.  Expected to have a higher latency, but
 should also have a higher prediction quality than other
 models.
      string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      The bytes for modelType.

    • getNodeQps

      double getNodeQps()
       Output only. An approximate number of online prediction QPS that can
 be supported by this model per each node on which it is deployed.
      double node_qps = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      The nodeQps.

    • getNodeCount

      long getNodeCount()
       Output only. The number of nodes this model is deployed on. A node is an
 abstraction of a machine resource, which can handle online prediction QPS
 as given in the node_qps field.
      int64 node_count = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      The nodeCount.