Package com.google.cloud.automl.v1

Interface ImageObjectDetectionModelMetadataOrBuilder

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

  • Method Summary

    Modifier and Type
    Method
    Description
    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

    • getModelType

      String getModelType()
       Optional. Type of the model. The available values are:
 *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
 calls to AutoML API. Expected to have a higher latency, but
 should also have a higher prediction quality than other
 models.
 *   `cloud-low-latency-1` -  A model to be used via prediction
 calls to AutoML API. Expected to have low latency, but may
 have lower prediction quality than other models.
 *   `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.
      string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      The modelType.

    • getModelTypeBytes

      com.google.protobuf.ByteString getModelTypeBytes()
       Optional. Type of the model. The available values are:
 *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
 calls to AutoML API. Expected to have a higher latency, but
 should also have a higher prediction quality than other
 models.
 *   `cloud-low-latency-1` -  A model to be used via prediction
 calls to AutoML API. Expected to have low latency, but may
 have lower prediction quality than other models.
 *   `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.
      string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      The bytes for modelType.

    • 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 qps_per_node field.
      int64 node_count = 3 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      The nodeCount.

    • 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 = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      The nodeQps.

    • 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.

    • 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-high-accuracy-1`(default) and `cloud-low-latency-1`,
 the train budget must be between 20,000 and 900,000 milli node hours,
 inclusive. The default value is 216, 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 = 6 [(.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 = 7 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      The trainCostMilliNodeHours.