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/*
* Copyright 2024 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: google/cloud/automl/v1/image.proto
// Protobuf Java Version: 3.25.4
package com.google.cloud.automl.v1;
public interface ImageObjectDetectionModelMetadataOrBuilder
extends
// @@protoc_insertion_point(interface_extends:google.cloud.automl.v1.ImageObjectDetectionModelMetadata)
com.google.protobuf.MessageOrBuilder {
/**
*
*
*
* 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];
*
* @return The modelType.
*/
java.lang.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];
*
* @return The bytes for modelType.
*/
com.google.protobuf.ByteString getModelTypeBytes();
/**
*
*
*
* 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];
*
* @return The nodeCount.
*/
long getNodeCount();
/**
*
*
*
* 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];
*
* @return The nodeQps.
*/
double getNodeQps();
/**
*
*
*
* 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];
*
* @return The stopReason.
*/
java.lang.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];
*
* @return The bytes for stopReason.
*/
com.google.protobuf.ByteString getStopReasonBytes();
/**
*
*
*
* 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];
*
* @return The trainBudgetMilliNodeHours.
*/
long getTrainBudgetMilliNodeHours();
/**
*
*
*
* 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];
*
*
* @return The trainCostMilliNodeHours.
*/
long getTrainCostMilliNodeHours();
}