# frozen_string_literal: true

# Copyright 2020 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.

# Auto-generated by gapic-generator-ruby. DO NOT EDIT!


module Google
  module Cloud
    module AutoML
      module V1
        # Dataset metadata that is specific to image classification.
        # @!attribute [rw] classification_type
        #   @return [::Google::Cloud::AutoML::V1::ClassificationType]
        #     Required. Type of the classification problem.
        class ImageClassificationDatasetMetadata
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # Dataset metadata specific to image object detection.
        class ImageObjectDetectionDatasetMetadata
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # Model metadata for image classification.
        # @!attribute [rw] base_model_id
        #   @return [::String]
        #     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`.
        # @!attribute [rw] train_budget_milli_node_hours
        #   @return [::Integer]
        #     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.
        # @!attribute [r] train_cost_milli_node_hours
        #   @return [::Integer]
        #     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.
        # @!attribute [r] stop_reason
        #   @return [::String]
        #     Output only. The reason that this create model operation stopped,
        #     e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
        # @!attribute [rw] model_type
        #   @return [::String]
        #     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
        #                   {::Google::Cloud::AutoML::V1::AutoML::Client#export_model 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
        #                   {::Google::Cloud::AutoML::V1::AutoML::Client#export_model 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
        #                   {::Google::Cloud::AutoML::V1::AutoML::Client#export_model 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
        #                   {::Google::Cloud::AutoML::V1::AutoML::Client#export_model 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
        #                   {::Google::Cloud::AutoML::V1::AutoML::Client#export_model 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 {::Google::Cloud::AutoML::V1::AutoML::Client#export_model 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.
        # @!attribute [r] node_qps
        #   @return [::Float]
        #     Output only. An approximate number of online prediction QPS that can
        #     be supported by this model per each node on which it is deployed.
        # @!attribute [r] node_count
        #   @return [::Integer]
        #     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.
        class ImageClassificationModelMetadata
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # Model metadata specific to image object detection.
        # @!attribute [rw] model_type
        #   @return [::String]
        #     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
        #                   {::Google::Cloud::AutoML::V1::AutoML::Client#export_model 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
        #                   {::Google::Cloud::AutoML::V1::AutoML::Client#export_model 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
        #                   {::Google::Cloud::AutoML::V1::AutoML::Client#export_model 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.
        # @!attribute [r] node_count
        #   @return [::Integer]
        #     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.
        # @!attribute [r] node_qps
        #   @return [::Float]
        #     Output only. An approximate number of online prediction QPS that can
        #     be supported by this model per each node on which it is deployed.
        # @!attribute [r] stop_reason
        #   @return [::String]
        #     Output only. The reason that this create model operation stopped,
        #     e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
        # @!attribute [rw] train_budget_milli_node_hours
        #   @return [::Integer]
        #     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.
        # @!attribute [r] train_cost_milli_node_hours
        #   @return [::Integer]
        #     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.
        class ImageObjectDetectionModelMetadata
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # Model deployment metadata specific to Image Classification.
        # @!attribute [rw] node_count
        #   @return [::Integer]
        #     Input only. The number of nodes to deploy the model on. A node is an
        #     abstraction of a machine resource, which can handle online prediction QPS
        #     as given in the model's
        #     {::Google::Cloud::AutoML::V1::ImageClassificationModelMetadata#node_qps node_qps}.
        #     Must be between 1 and 100, inclusive on both ends.
        class ImageClassificationModelDeploymentMetadata
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # Model deployment metadata specific to Image Object Detection.
        # @!attribute [rw] node_count
        #   @return [::Integer]
        #     Input only. The number of nodes to deploy the model on. A node is an
        #     abstraction of a machine resource, which can handle online prediction QPS
        #     as given in the model's
        #     [qps_per_node][google.cloud.automl.v1.ImageObjectDetectionModelMetadata.qps_per_node].
        #     Must be between 1 and 100, inclusive on both ends.
        class ImageObjectDetectionModelDeploymentMetadata
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end
      end
    end
  end
end
