# Copyright 2015 Google Inc. # # 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 # # http://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. require 'date' require 'google/apis/core/base_service' require 'google/apis/core/json_representation' require 'google/apis/core/hashable' require 'google/apis/errors' module Google module Apis module MlV1 # Response message for the ListModels method. class GoogleCloudMlV1ListModelsResponse include Google::Apis::Core::Hashable # The list of models. # Corresponds to the JSON property `models` # @return [Array] attr_accessor :models # Optional. Pass this token as the `page_token` field of the request for a # subsequent call. # Corresponds to the JSON property `nextPageToken` # @return [String] attr_accessor :next_page_token def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @models = args[:models] if args.key?(:models) @next_page_token = args[:next_page_token] if args.key?(:next_page_token) end end # Represents input parameters for a training job. class GoogleCloudMlV1TrainingInput include Google::Apis::Core::Hashable # Optional. The number of parameter server replicas to use for the training # job. Each replica in the cluster will be of the type specified in # `parameter_server_type`. # This value can only be used when `scale_tier` is set to `CUSTOM`.If you # set this value, you must also set `parameter_server_type`. # Corresponds to the JSON property `parameterServerCount` # @return [Fixnum] attr_accessor :parameter_server_count # Required. The Google Cloud Storage location of the packages with # the training program and any additional dependencies. # The maximum number of package URIs is 100. # Corresponds to the JSON property `packageUris` # @return [Array] attr_accessor :package_uris # Optional. The number of worker replicas to use for the training job. Each # replica in the cluster will be of the type specified in `worker_type`. # This value can only be used when `scale_tier` is set to `CUSTOM`. If you # set this value, you must also set `worker_type`. # Corresponds to the JSON property `workerCount` # @return [Fixnum] attr_accessor :worker_count # Optional. Specifies the type of virtual machine to use for your training # job's master worker. # The following types are supported: #
#
standard
#
# A basic machine configuration suitable for training simple models with # small to moderate datasets. #
#
large_model
#
# A machine with a lot of memory, specially suited for parameter servers # when your model is large (having many hidden layers or layers with very # large numbers of nodes). #
#
complex_model_s
#
# A machine suitable for the master and workers of the cluster when your # model requires more computation than the standard machine can handle # satisfactorily. #
#
complex_model_m
#
# A machine with roughly twice the number of cores and roughly double the # memory of complex_model_s. #
#
complex_model_l
#
# A machine with roughly twice the number of cores and roughly double the # memory of complex_model_m. #
#
standard_gpu
#
# A machine equivalent to standard that # also includes a # # GPU that you can use in your trainer. #
#
complex_model_m_gpu
#
# A machine equivalent to # coplex_model_m that also includes # four GPUs. #
#
# You must set this value when `scaleTier` is set to `CUSTOM`. # Corresponds to the JSON property `masterType` # @return [String] attr_accessor :master_type # Optional. The Google Cloud ML runtime version to use for training. If not # set, Google Cloud ML will choose the latest stable version. # Corresponds to the JSON property `runtimeVersion` # @return [String] attr_accessor :runtime_version # Required. The Python module name to run after installing the packages. # Corresponds to the JSON property `pythonModule` # @return [String] attr_accessor :python_module # Optional. Specifies the type of virtual machine to use for your training # job's worker nodes. # The supported values are the same as those described in the entry for # `masterType`. # This value must be present when `scaleTier` is set to `CUSTOM` and # `workerCount` is greater than zero. # Corresponds to the JSON property `workerType` # @return [String] attr_accessor :worker_type # Optional. Command line arguments to pass to the program. # Corresponds to the JSON property `args` # @return [Array] attr_accessor :args # Required. The Google Compute Engine region to run the training job in. # Corresponds to the JSON property `region` # @return [String] attr_accessor :region # Optional. Specifies the type of virtual machine to use for your training # job's parameter server. # The supported values are the same as those described in the entry for # `master_type`. # This value must be present when `scaleTier` is set to `CUSTOM` and # `parameter_server_count` is greater than zero. # Corresponds to the JSON property `parameterServerType` # @return [String] attr_accessor :parameter_server_type # Required. Specifies the machine types, the number of replicas for workers # and parameter servers. # Corresponds to the JSON property `scaleTier` # @return [String] attr_accessor :scale_tier # Optional. A Google Cloud Storage path in which to store training outputs # and other data needed for training. This path is passed to your TensorFlow # program as the 'job_dir' command-line argument. The benefit of specifying # this field is that Cloud ML validates the path for use in training. # Corresponds to the JSON property `jobDir` # @return [String] attr_accessor :job_dir # Represents a set of hyperparameters to optimize. # Corresponds to the JSON property `hyperparameters` # @return [Google::Apis::MlV1::GoogleCloudMlV1HyperparameterSpec] attr_accessor :hyperparameters def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @parameter_server_count = args[:parameter_server_count] if args.key?(:parameter_server_count) @package_uris = args[:package_uris] if args.key?(:package_uris) @worker_count = args[:worker_count] if args.key?(:worker_count) @master_type = args[:master_type] if args.key?(:master_type) @runtime_version = args[:runtime_version] if args.key?(:runtime_version) @python_module = args[:python_module] if args.key?(:python_module) @worker_type = args[:worker_type] if args.key?(:worker_type) @args = args[:args] if args.key?(:args) @region = args[:region] if args.key?(:region) @parameter_server_type = args[:parameter_server_type] if args.key?(:parameter_server_type) @scale_tier = args[:scale_tier] if args.key?(:scale_tier) @job_dir = args[:job_dir] if args.key?(:job_dir) @hyperparameters = args[:hyperparameters] if args.key?(:hyperparameters) end end # Represents a training or prediction job. class GoogleCloudMlV1Job include Google::Apis::Core::Hashable # Represents results of a training job. Output only. # Corresponds to the JSON property `trainingOutput` # @return [Google::Apis::MlV1::GoogleCloudMlV1TrainingOutput] attr_accessor :training_output # Output only. When the job was created. # Corresponds to the JSON property `createTime` # @return [String] attr_accessor :create_time # Represents input parameters for a training job. # Corresponds to the JSON property `trainingInput` # @return [Google::Apis::MlV1::GoogleCloudMlV1TrainingInput] attr_accessor :training_input # Output only. The detailed state of a job. # Corresponds to the JSON property `state` # @return [String] attr_accessor :state # Represents input parameters for a prediction job. # Corresponds to the JSON property `predictionInput` # @return [Google::Apis::MlV1::GoogleCloudMlV1PredictionInput] attr_accessor :prediction_input # Output only. The details of a failure or a cancellation. # Corresponds to the JSON property `errorMessage` # @return [String] attr_accessor :error_message # Required. The user-specified id of the job. # Corresponds to the JSON property `jobId` # @return [String] attr_accessor :job_id # Output only. When the job processing was completed. # Corresponds to the JSON property `endTime` # @return [String] attr_accessor :end_time # Output only. When the job processing was started. # Corresponds to the JSON property `startTime` # @return [String] attr_accessor :start_time # Represents results of a prediction job. # Corresponds to the JSON property `predictionOutput` # @return [Google::Apis::MlV1::GoogleCloudMlV1PredictionOutput] attr_accessor :prediction_output def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @training_output = args[:training_output] if args.key?(:training_output) @create_time = args[:create_time] if args.key?(:create_time) @training_input = args[:training_input] if args.key?(:training_input) @state = args[:state] if args.key?(:state) @prediction_input = args[:prediction_input] if args.key?(:prediction_input) @error_message = args[:error_message] if args.key?(:error_message) @job_id = args[:job_id] if args.key?(:job_id) @end_time = args[:end_time] if args.key?(:end_time) @start_time = args[:start_time] if args.key?(:start_time) @prediction_output = args[:prediction_output] if args.key?(:prediction_output) end end # Message that represents an arbitrary HTTP body. It should only be used for # payload formats that can't be represented as JSON, such as raw binary or # an HTML page. # This message can be used both in streaming and non-streaming API methods in # the request as well as the response. # It can be used as a top-level request field, which is convenient if one # wants to extract parameters from either the URL or HTTP template into the # request fields and also want access to the raw HTTP body. # Example: # message GetResourceRequest ` # // A unique request id. # string request_id = 1; # // The raw HTTP body is bound to this field. # google.api.HttpBody http_body = 2; # ` # service ResourceService ` # rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); # rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); # ` # Example with streaming methods: # service CaldavService ` # rpc GetCalendar(stream google.api.HttpBody) # returns (stream google.api.HttpBody); # rpc UpdateCalendar(stream google.api.HttpBody) # returns (stream google.api.HttpBody); # ` # Use of this type only changes how the request and response bodies are # handled, all other features will continue to work unchanged. class GoogleApiHttpBody include Google::Apis::Core::Hashable # HTTP body binary data. # Corresponds to the JSON property `data` # NOTE: Values are automatically base64 encoded/decoded in the client library. # @return [String] attr_accessor :data # The HTTP Content-Type string representing the content type of the body. # Corresponds to the JSON property `contentType` # @return [String] attr_accessor :content_type def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @data = args[:data] if args.key?(:data) @content_type = args[:content_type] if args.key?(:content_type) end end # Represents a version of the model. # Each version is a trained model deployed in the cloud, ready to handle # prediction requests. A model can have multiple versions. You can get # information about all of the versions of a given model by calling # [projects.models.versions.list](/ml-engine/reference/rest/v1beta1/projects. # models.versions/list). class GoogleCloudMlV1beta1Version include Google::Apis::Core::Hashable # Optional. The Google Cloud ML runtime version to use for this deployment. # If not set, Google Cloud ML will choose a version. # Corresponds to the JSON property `runtimeVersion` # @return [String] attr_accessor :runtime_version # Output only. The time the version was last used for prediction. # Corresponds to the JSON property `lastUseTime` # @return [String] attr_accessor :last_use_time # Optional. The description specified for the version when it was created. # Corresponds to the JSON property `description` # @return [String] attr_accessor :description # Required. The Google Cloud Storage location of the trained model used to # create the version. See the # [overview of model # deployment](/ml-engine/docs/concepts/deployment-overview) for more # informaiton. # When passing Version to # [projects.models.versions.create](/ml-engine/reference/rest/v1beta1/projects. # models.versions/create) # the model service uses the specified location as the source of the model. # Once deployed, the model version is hosted by the prediction service, so # this location is useful only as a historical record. # The total number of model files can't exceed 1000. # Corresponds to the JSON property `deploymentUri` # @return [String] attr_accessor :deployment_uri # Output only. If true, this version will be used to handle prediction # requests that do not specify a version. # You can change the default version by calling # [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1beta1/ # projects.models.versions/setDefault). # Corresponds to the JSON property `isDefault` # @return [Boolean] attr_accessor :is_default alias_method :is_default?, :is_default # Output only. The time the version was created. # Corresponds to the JSON property `createTime` # @return [String] attr_accessor :create_time # Options for manually scaling a model. # Corresponds to the JSON property `manualScaling` # @return [Google::Apis::MlV1::GoogleCloudMlV1beta1ManualScaling] attr_accessor :manual_scaling # Required.The name specified for the version when it was created. # The version name must be unique within the model it is created in. # Corresponds to the JSON property `name` # @return [String] attr_accessor :name def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @runtime_version = args[:runtime_version] if args.key?(:runtime_version) @last_use_time = args[:last_use_time] if args.key?(:last_use_time) @description = args[:description] if args.key?(:description) @deployment_uri = args[:deployment_uri] if args.key?(:deployment_uri) @is_default = args[:is_default] if args.key?(:is_default) @create_time = args[:create_time] if args.key?(:create_time) @manual_scaling = args[:manual_scaling] if args.key?(:manual_scaling) @name = args[:name] if args.key?(:name) end end # Returns service account information associated with a project. class GoogleCloudMlV1GetConfigResponse include Google::Apis::Core::Hashable # The project number for `service_account`. # Corresponds to the JSON property `serviceAccountProject` # @return [Fixnum] attr_accessor :service_account_project # The service account Cloud ML uses to access resources in the project. # Corresponds to the JSON property `serviceAccount` # @return [String] attr_accessor :service_account def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @service_account_project = args[:service_account_project] if args.key?(:service_account_project) @service_account = args[:service_account] if args.key?(:service_account) end end # Represents the result of a single hyperparameter tuning trial from a # training job. The TrainingOutput object that is returned on successful # completion of a training job with hyperparameter tuning includes a list # of HyperparameterOutput objects, one for each successful trial. class GoogleCloudMlV1HyperparameterOutput include Google::Apis::Core::Hashable # The hyperparameters given to this trial. # Corresponds to the JSON property `hyperparameters` # @return [Hash] attr_accessor :hyperparameters # The trial id for these results. # Corresponds to the JSON property `trialId` # @return [String] attr_accessor :trial_id # All recorded object metrics for this trial. # Corresponds to the JSON property `allMetrics` # @return [Array] attr_accessor :all_metrics # An observed value of a metric. # Corresponds to the JSON property `finalMetric` # @return [Google::Apis::MlV1::GoogleCloudMlV1HyperparameterOutputHyperparameterMetric] attr_accessor :final_metric def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @hyperparameters = args[:hyperparameters] if args.key?(:hyperparameters) @trial_id = args[:trial_id] if args.key?(:trial_id) @all_metrics = args[:all_metrics] if args.key?(:all_metrics) @final_metric = args[:final_metric] if args.key?(:final_metric) end end # Represents results of a prediction job. class GoogleCloudMlV1PredictionOutput include Google::Apis::Core::Hashable # The number of data instances which resulted in errors. # Corresponds to the JSON property `errorCount` # @return [Fixnum] attr_accessor :error_count # The output Google Cloud Storage location provided at the job creation time. # Corresponds to the JSON property `outputPath` # @return [String] attr_accessor :output_path # Node hours used by the batch prediction job. # Corresponds to the JSON property `nodeHours` # @return [Float] attr_accessor :node_hours # The number of generated predictions. # Corresponds to the JSON property `predictionCount` # @return [Fixnum] attr_accessor :prediction_count def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @error_count = args[:error_count] if args.key?(:error_count) @output_path = args[:output_path] if args.key?(:output_path) @node_hours = args[:node_hours] if args.key?(:node_hours) @prediction_count = args[:prediction_count] if args.key?(:prediction_count) end end # The response message for Operations.ListOperations. class GoogleLongrunningListOperationsResponse include Google::Apis::Core::Hashable # The standard List next-page token. # Corresponds to the JSON property `nextPageToken` # @return [String] attr_accessor :next_page_token # A list of operations that matches the specified filter in the request. # Corresponds to the JSON property `operations` # @return [Array] attr_accessor :operations def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @next_page_token = args[:next_page_token] if args.key?(:next_page_token) @operations = args[:operations] if args.key?(:operations) end end # Options for manually scaling a model. class GoogleCloudMlV1ManualScaling include Google::Apis::Core::Hashable # The number of nodes to allocate for this model. These nodes are always up, # starting from the time the model is deployed, so the cost of operating # this model will be proportional to nodes * number of hours since # deployment. # Corresponds to the JSON property `nodes` # @return [Fixnum] attr_accessor :nodes def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @nodes = args[:nodes] if args.key?(:nodes) end end # Represents results of a training job. Output only. class GoogleCloudMlV1TrainingOutput include Google::Apis::Core::Hashable # The number of hyperparameter tuning trials that completed successfully. # Only set for hyperparameter tuning jobs. # Corresponds to the JSON property `completedTrialCount` # @return [Fixnum] attr_accessor :completed_trial_count # Whether this job is a hyperparameter tuning job. # Corresponds to the JSON property `isHyperparameterTuningJob` # @return [Boolean] attr_accessor :is_hyperparameter_tuning_job alias_method :is_hyperparameter_tuning_job?, :is_hyperparameter_tuning_job # The amount of ML units consumed by the job. # Corresponds to the JSON property `consumedMLUnits` # @return [Float] attr_accessor :consumed_ml_units # Results for individual Hyperparameter trials. # Only set for hyperparameter tuning jobs. # Corresponds to the JSON property `trials` # @return [Array] attr_accessor :trials def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @completed_trial_count = args[:completed_trial_count] if args.key?(:completed_trial_count) @is_hyperparameter_tuning_job = args[:is_hyperparameter_tuning_job] if args.key?(:is_hyperparameter_tuning_job) @consumed_ml_units = args[:consumed_ml_units] if args.key?(:consumed_ml_units) @trials = args[:trials] if args.key?(:trials) end end # Request for predictions to be issued against a trained model. # The body of the request is a single JSON object with a single top-level # field: #
#
instances
#
A JSON array containing values representing the instances to use for # prediction.
#
# The structure of each element of the instances list is determined by your # model's input definition. Instances can include named inputs or can contain # only unlabeled values. # Not all data includes named inputs. Some instances will be simple # JSON values (boolean, number, or string). However, instances are often lists # of simple values, or complex nested lists. Here are some examples of request # bodies: # CSV data with each row encoded as a string value: #
      # `"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]`
      # 
# Plain text: #
      # `"instances": ["the quick brown fox", "la bruja le dio"]`
      # 
# Sentences encoded as lists of words (vectors of strings): #
      # `
      # "instances": [
      # ["the","quick","brown"],
      # ["la","bruja","le"],
      # ...
      # ]
      # `
      # 
# Floating point scalar values: #
      # `"instances": [0.0, 1.1, 2.2]`
      # 
# Vectors of integers: #
      # `
      # "instances": [
      # [0, 1, 2],
      # [3, 4, 5],
      # ...
      # ]
      # `
      # 
# Tensors (in this case, two-dimensional tensors): #
      # `
      # "instances": [
      # [
      # [0, 1, 2],
      # [3, 4, 5]
      # ],
      # ...
      # ]
      # `
      # 
# Images can be represented different ways. In this encoding scheme the first # two dimensions represent the rows and columns of the image, and the third # contains lists (vectors) of the R, G, and B values for each pixel. #
      # `
      # "instances": [
      # [
      # [
      # [138, 30, 66],
      # [130, 20, 56],
      # ...
      # ],
      # [
      # [126, 38, 61],
      # [122, 24, 57],
      # ...
      # ],
      # ...
      # ],
      # ...
      # ]
      # `
      # 
# JSON strings must be encoded as UTF-8. To send binary data, you must # base64-encode the data and mark it as binary. To mark a JSON string # as binary, replace it with a JSON object with a single attribute named `b64`: #
`"b64": "..."` 
# For example: # Two Serialized tf.Examples (fake data, for illustrative purposes only): #
      # `"instances": [`"b64": "X5ad6u"`, `"b64": "IA9j4nx"`]`
      # 
# Two JPEG image byte strings (fake data, for illustrative purposes only): #
      # `"instances": [`"b64": "ASa8asdf"`, `"b64": "JLK7ljk3"`]`
      # 
# If your data includes named references, format each instance as a JSON object # with the named references as the keys: # JSON input data to be preprocessed: #
      # `
      # "instances": [
      # `
      # "a": 1.0,
      # "b": true,
      # "c": "x"
      # `,
      # `
      # "a": -2.0,
      # "b": false,
      # "c": "y"
      # `
      # ]
      # `
      # 
# Some models have an underlying TensorFlow graph that accepts multiple input # tensors. In this case, you should use the names of JSON name/value pairs to # identify the input tensors, as shown in the following exmaples: # For a graph with input tensor aliases "tag" (string) and "image" # (base64-encoded string): #
      # `
      # "instances": [
      # `
      # "tag": "beach",
      # "image": `"b64": "ASa8asdf"`
      # `,
      # `
      # "tag": "car",
      # "image": `"b64": "JLK7ljk3"`
      # `
      # ]
      # `
      # 
# For a graph with input tensor aliases "tag" (string) and "image" # (3-dimensional array of 8-bit ints): #
      # `
      # "instances": [
      # `
      # "tag": "beach",
      # "image": [
      # [
      # [138, 30, 66],
      # [130, 20, 56],
      # ...
      # ],
      # [
      # [126, 38, 61],
      # [122, 24, 57],
      # ...
      # ],
      # ...
      # ]
      # `,
      # `
      # "tag": "car",
      # "image": [
      # [
      # [255, 0, 102],
      # [255, 0, 97],
      # ...
      # ],
      # [
      # [254, 1, 101],
      # [254, 2, 93],
      # ...
      # ],
      # ...
      # ]
      # `,
      # ...
      # ]
      # `
      # 
# If the call is successful, the response body will contain one prediction # entry per instance in the request body. If prediction fails for any # instance, the response body will contain no predictions and will contian # a single error entry instead. class GoogleCloudMlV1PredictRequest include Google::Apis::Core::Hashable # Message that represents an arbitrary HTTP body. It should only be used for # payload formats that can't be represented as JSON, such as raw binary or # an HTML page. # This message can be used both in streaming and non-streaming API methods in # the request as well as the response. # It can be used as a top-level request field, which is convenient if one # wants to extract parameters from either the URL or HTTP template into the # request fields and also want access to the raw HTTP body. # Example: # message GetResourceRequest ` # // A unique request id. # string request_id = 1; # // The raw HTTP body is bound to this field. # google.api.HttpBody http_body = 2; # ` # service ResourceService ` # rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); # rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); # ` # Example with streaming methods: # service CaldavService ` # rpc GetCalendar(stream google.api.HttpBody) # returns (stream google.api.HttpBody); # rpc UpdateCalendar(stream google.api.HttpBody) # returns (stream google.api.HttpBody); # ` # Use of this type only changes how the request and response bodies are # handled, all other features will continue to work unchanged. # Corresponds to the JSON property `httpBody` # @return [Google::Apis::MlV1::GoogleApiHttpBody] attr_accessor :http_body def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @http_body = args[:http_body] if args.key?(:http_body) end end # An observed value of a metric. class GoogleCloudMlV1HyperparameterOutputHyperparameterMetric include Google::Apis::Core::Hashable # The objective value at this training step. # Corresponds to the JSON property `objectiveValue` # @return [Float] attr_accessor :objective_value # The global training step for this metric. # Corresponds to the JSON property `trainingStep` # @return [Fixnum] attr_accessor :training_step def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @objective_value = args[:objective_value] if args.key?(:objective_value) @training_step = args[:training_step] if args.key?(:training_step) end end # Represents a version of the model. # Each version is a trained model deployed in the cloud, ready to handle # prediction requests. A model can have multiple versions. You can get # information about all of the versions of a given model by calling # [projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models. # versions/list). class GoogleCloudMlV1Version include Google::Apis::Core::Hashable # Output only. The time the version was last used for prediction. # Corresponds to the JSON property `lastUseTime` # @return [String] attr_accessor :last_use_time # Optional. The Google Cloud ML runtime version to use for this deployment. # If not set, Google Cloud ML will choose a version. # Corresponds to the JSON property `runtimeVersion` # @return [String] attr_accessor :runtime_version # Optional. The description specified for the version when it was created. # Corresponds to the JSON property `description` # @return [String] attr_accessor :description # Required. The Google Cloud Storage location of the trained model used to # create the version. See the # [overview of model # deployment](/ml-engine/docs/concepts/deployment-overview) for more # informaiton. # When passing Version to # [projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models. # versions/create) # the model service uses the specified location as the source of the model. # Once deployed, the model version is hosted by the prediction service, so # this location is useful only as a historical record. # The total number of model files can't exceed 1000. # Corresponds to the JSON property `deploymentUri` # @return [String] attr_accessor :deployment_uri # Output only. If true, this version will be used to handle prediction # requests that do not specify a version. # You can change the default version by calling # [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1/projects. # models.versions/setDefault). # Corresponds to the JSON property `isDefault` # @return [Boolean] attr_accessor :is_default alias_method :is_default?, :is_default # Output only. The time the version was created. # Corresponds to the JSON property `createTime` # @return [String] attr_accessor :create_time # Options for manually scaling a model. # Corresponds to the JSON property `manualScaling` # @return [Google::Apis::MlV1::GoogleCloudMlV1ManualScaling] attr_accessor :manual_scaling # Required.The name specified for the version when it was created. # The version name must be unique within the model it is created in. # Corresponds to the JSON property `name` # @return [String] attr_accessor :name def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @last_use_time = args[:last_use_time] if args.key?(:last_use_time) @runtime_version = args[:runtime_version] if args.key?(:runtime_version) @description = args[:description] if args.key?(:description) @deployment_uri = args[:deployment_uri] if args.key?(:deployment_uri) @is_default = args[:is_default] if args.key?(:is_default) @create_time = args[:create_time] if args.key?(:create_time) @manual_scaling = args[:manual_scaling] if args.key?(:manual_scaling) @name = args[:name] if args.key?(:name) end end # Represents a single hyperparameter to optimize. class GoogleCloudMlV1ParameterSpec include Google::Apis::Core::Hashable # Required if type is `CATEGORICAL`. The list of possible categories. # Corresponds to the JSON property `categoricalValues` # @return [Array] attr_accessor :categorical_values # Required. The parameter name must be unique amongst all ParameterConfigs in # a HyperparameterSpec message. E.g., "learning_rate". # Corresponds to the JSON property `parameterName` # @return [String] attr_accessor :parameter_name # Required if type is `DOUBLE` or `INTEGER`. This field # should be unset if type is `CATEGORICAL`. This value should be integers if # type is INTEGER. # Corresponds to the JSON property `minValue` # @return [Float] attr_accessor :min_value # Required if type is `DISCRETE`. # A list of feasible points. # The list should be in strictly increasing order. For instance, this # parameter might have possible settings of 1.5, 2.5, and 4.0. This list # should not contain more than 1,000 values. # Corresponds to the JSON property `discreteValues` # @return [Array] attr_accessor :discrete_values # Optional. How the parameter should be scaled to the hypercube. # Leave unset for categorical parameters. # Some kind of scaling is strongly recommended for real or integral # parameters (e.g., `UNIT_LINEAR_SCALE`). # Corresponds to the JSON property `scaleType` # @return [String] attr_accessor :scale_type # Required if typeis `DOUBLE` or `INTEGER`. This field # should be unset if type is `CATEGORICAL`. This value should be integers if # type is `INTEGER`. # Corresponds to the JSON property `maxValue` # @return [Float] attr_accessor :max_value # Required. The type of the parameter. # Corresponds to the JSON property `type` # @return [String] attr_accessor :type def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @categorical_values = args[:categorical_values] if args.key?(:categorical_values) @parameter_name = args[:parameter_name] if args.key?(:parameter_name) @min_value = args[:min_value] if args.key?(:min_value) @discrete_values = args[:discrete_values] if args.key?(:discrete_values) @scale_type = args[:scale_type] if args.key?(:scale_type) @max_value = args[:max_value] if args.key?(:max_value) @type = args[:type] if args.key?(:type) end end # Represents input parameters for a prediction job. class GoogleCloudMlV1PredictionInput include Google::Apis::Core::Hashable # Use this field if you want to specify a version of the model to use. The # string is formatted the same way as `model_version`, with the addition # of the version information: # `"projects/[YOUR_PROJECT]/models/YOUR_MODEL/versions/[ # YOUR_VERSION]"` # Corresponds to the JSON property `versionName` # @return [String] attr_accessor :version_name # Use this field if you want to use the default version for the specified # model. The string must use the following format: # `"projects/[YOUR_PROJECT]/models/[YOUR_MODEL]"` # Corresponds to the JSON property `modelName` # @return [String] attr_accessor :model_name # Required. The output Google Cloud Storage location. # Corresponds to the JSON property `outputPath` # @return [String] attr_accessor :output_path # Optional. The maximum number of workers to be used for parallel processing. # Defaults to 10 if not specified. # Corresponds to the JSON property `maxWorkerCount` # @return [Fixnum] attr_accessor :max_worker_count # Use this field if you want to specify a Google Cloud Storage path for # the model to use. # Corresponds to the JSON property `uri` # @return [String] attr_accessor :uri # Required. The format of the input data files. # Corresponds to the JSON property `dataFormat` # @return [String] attr_accessor :data_format # Optional. The Google Cloud ML runtime version to use for this batch # prediction. If not set, Google Cloud ML will pick the runtime version used # during the CreateVersion request for this model version, or choose the # latest stable version when model version information is not available # such as when the model is specified by uri. # Corresponds to the JSON property `runtimeVersion` # @return [String] attr_accessor :runtime_version # Required. The Google Cloud Storage location of the input data files. # May contain wildcards. # Corresponds to the JSON property `inputPaths` # @return [Array] attr_accessor :input_paths # Required. The Google Compute Engine region to run the prediction job in. # Corresponds to the JSON property `region` # @return [String] attr_accessor :region def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @version_name = args[:version_name] if args.key?(:version_name) @model_name = args[:model_name] if args.key?(:model_name) @output_path = args[:output_path] if args.key?(:output_path) @max_worker_count = args[:max_worker_count] if args.key?(:max_worker_count) @uri = args[:uri] if args.key?(:uri) @data_format = args[:data_format] if args.key?(:data_format) @runtime_version = args[:runtime_version] if args.key?(:runtime_version) @input_paths = args[:input_paths] if args.key?(:input_paths) @region = args[:region] if args.key?(:region) end end # Represents the metadata of the long-running operation. class GoogleCloudMlV1beta1OperationMetadata include Google::Apis::Core::Hashable # Indicates whether a request to cancel this operation has been made. # Corresponds to the JSON property `isCancellationRequested` # @return [Boolean] attr_accessor :is_cancellation_requested alias_method :is_cancellation_requested?, :is_cancellation_requested # The time the operation was submitted. # Corresponds to the JSON property `createTime` # @return [String] attr_accessor :create_time # Contains the name of the model associated with the operation. # Corresponds to the JSON property `modelName` # @return [String] attr_accessor :model_name # Represents a version of the model. # Each version is a trained model deployed in the cloud, ready to handle # prediction requests. A model can have multiple versions. You can get # information about all of the versions of a given model by calling # [projects.models.versions.list](/ml-engine/reference/rest/v1beta1/projects. # models.versions/list). # Corresponds to the JSON property `version` # @return [Google::Apis::MlV1::GoogleCloudMlV1beta1Version] attr_accessor :version # The time operation processing completed. # Corresponds to the JSON property `endTime` # @return [String] attr_accessor :end_time # The operation type. # Corresponds to the JSON property `operationType` # @return [String] attr_accessor :operation_type # The time operation processing started. # Corresponds to the JSON property `startTime` # @return [String] attr_accessor :start_time def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @is_cancellation_requested = args[:is_cancellation_requested] if args.key?(:is_cancellation_requested) @create_time = args[:create_time] if args.key?(:create_time) @model_name = args[:model_name] if args.key?(:model_name) @version = args[:version] if args.key?(:version) @end_time = args[:end_time] if args.key?(:end_time) @operation_type = args[:operation_type] if args.key?(:operation_type) @start_time = args[:start_time] if args.key?(:start_time) end end # Represents the metadata of the long-running operation. class GoogleCloudMlV1OperationMetadata include Google::Apis::Core::Hashable # The time operation processing started. # Corresponds to the JSON property `startTime` # @return [String] attr_accessor :start_time # Indicates whether a request to cancel this operation has been made. # Corresponds to the JSON property `isCancellationRequested` # @return [Boolean] attr_accessor :is_cancellation_requested alias_method :is_cancellation_requested?, :is_cancellation_requested # The time the operation was submitted. # Corresponds to the JSON property `createTime` # @return [String] attr_accessor :create_time # Contains the name of the model associated with the operation. # Corresponds to the JSON property `modelName` # @return [String] attr_accessor :model_name # Represents a version of the model. # Each version is a trained model deployed in the cloud, ready to handle # prediction requests. A model can have multiple versions. You can get # information about all of the versions of a given model by calling # [projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models. # versions/list). # Corresponds to the JSON property `version` # @return [Google::Apis::MlV1::GoogleCloudMlV1Version] attr_accessor :version # The time operation processing completed. # Corresponds to the JSON property `endTime` # @return [String] attr_accessor :end_time # The operation type. # Corresponds to the JSON property `operationType` # @return [String] attr_accessor :operation_type def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @start_time = args[:start_time] if args.key?(:start_time) @is_cancellation_requested = args[:is_cancellation_requested] if args.key?(:is_cancellation_requested) @create_time = args[:create_time] if args.key?(:create_time) @model_name = args[:model_name] if args.key?(:model_name) @version = args[:version] if args.key?(:version) @end_time = args[:end_time] if args.key?(:end_time) @operation_type = args[:operation_type] if args.key?(:operation_type) end end # Represents a set of hyperparameters to optimize. class GoogleCloudMlV1HyperparameterSpec include Google::Apis::Core::Hashable # Required. The set of parameters to tune. # Corresponds to the JSON property `params` # @return [Array] attr_accessor :params # Optional. How many training trials should be attempted to optimize # the specified hyperparameters. # Defaults to one. # Corresponds to the JSON property `maxTrials` # @return [Fixnum] attr_accessor :max_trials # Optional. The number of training trials to run concurrently. # You can reduce the time it takes to perform hyperparameter tuning by adding # trials in parallel. However, each trail only benefits from the information # gained in completed trials. That means that a trial does not get access to # the results of trials running at the same time, which could reduce the # quality of the overall optimization. # Each trial will use the same scale tier and machine types. # Defaults to one. # Corresponds to the JSON property `maxParallelTrials` # @return [Fixnum] attr_accessor :max_parallel_trials # Required. The type of goal to use for tuning. Available types are # `MAXIMIZE` and `MINIMIZE`. # Defaults to `MAXIMIZE`. # Corresponds to the JSON property `goal` # @return [String] attr_accessor :goal # Optional. The Tensorflow summary tag name to use for optimizing trials. For # current versions of Tensorflow, this tag name should exactly match what is # shown in Tensorboard, including all scopes. For versions of Tensorflow # prior to 0.12, this should be only the tag passed to tf.Summary. # By default, "training/hptuning/metric" will be used. # Corresponds to the JSON property `hyperparameterMetricTag` # @return [String] attr_accessor :hyperparameter_metric_tag def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @params = args[:params] if args.key?(:params) @max_trials = args[:max_trials] if args.key?(:max_trials) @max_parallel_trials = args[:max_parallel_trials] if args.key?(:max_parallel_trials) @goal = args[:goal] if args.key?(:goal) @hyperparameter_metric_tag = args[:hyperparameter_metric_tag] if args.key?(:hyperparameter_metric_tag) end end # Response message for the ListJobs method. class GoogleCloudMlV1ListJobsResponse include Google::Apis::Core::Hashable # The list of jobs. # Corresponds to the JSON property `jobs` # @return [Array] attr_accessor :jobs # Optional. Pass this token as the `page_token` field of the request for a # subsequent call. # Corresponds to the JSON property `nextPageToken` # @return [String] attr_accessor :next_page_token def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @jobs = args[:jobs] if args.key?(:jobs) @next_page_token = args[:next_page_token] if args.key?(:next_page_token) end end # Request message for the SetDefaultVersion request. class GoogleCloudMlV1SetDefaultVersionRequest include Google::Apis::Core::Hashable def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) end end # This resource represents a long-running operation that is the result of a # network API call. class GoogleLongrunningOperation include Google::Apis::Core::Hashable # The normal response of the operation in case of success. If the original # method returns no data on success, such as `Delete`, the response is # `google.protobuf.Empty`. If the original method is standard # `Get`/`Create`/`Update`, the response should be the resource. For other # methods, the response should have the type `XxxResponse`, where `Xxx` # is the original method name. For example, if the original method name # is `TakeSnapshot()`, the inferred response type is # `TakeSnapshotResponse`. # Corresponds to the JSON property `response` # @return [Hash] attr_accessor :response # The server-assigned name, which is only unique within the same service that # originally returns it. If you use the default HTTP mapping, the # `name` should have the format of `operations/some/unique/name`. # Corresponds to the JSON property `name` # @return [String] attr_accessor :name # The `Status` type defines a logical error model that is suitable for different # programming environments, including REST APIs and RPC APIs. It is used by # [gRPC](https://github.com/grpc). The error model is designed to be: # - Simple to use and understand for most users # - Flexible enough to meet unexpected needs # # Overview # The `Status` message contains three pieces of data: error code, error message, # and error details. The error code should be an enum value of # google.rpc.Code, but it may accept additional error codes if needed. The # error message should be a developer-facing English message that helps # developers *understand* and *resolve* the error. If a localized user-facing # error message is needed, put the localized message in the error details or # localize it in the client. The optional error details may contain arbitrary # information about the error. There is a predefined set of error detail types # in the package `google.rpc` which can be used for common error conditions. # # Language mapping # The `Status` message is the logical representation of the error model, but it # is not necessarily the actual wire format. When the `Status` message is # exposed in different client libraries and different wire protocols, it can be # mapped differently. For example, it will likely be mapped to some exceptions # in Java, but more likely mapped to some error codes in C. # # Other uses # The error model and the `Status` message can be used in a variety of # environments, either with or without APIs, to provide a # consistent developer experience across different environments. # Example uses of this error model include: # - Partial errors. If a service needs to return partial errors to the client, # it may embed the `Status` in the normal response to indicate the partial # errors. # - Workflow errors. A typical workflow has multiple steps. Each step may # have a `Status` message for error reporting purpose. # - Batch operations. If a client uses batch request and batch response, the # `Status` message should be used directly inside batch response, one for # each error sub-response. # - Asynchronous operations. If an API call embeds asynchronous operation # results in its response, the status of those operations should be # represented directly using the `Status` message. # - Logging. If some API errors are stored in logs, the message `Status` could # be used directly after any stripping needed for security/privacy reasons. # Corresponds to the JSON property `error` # @return [Google::Apis::MlV1::GoogleRpcStatus] attr_accessor :error # Service-specific metadata associated with the operation. It typically # contains progress information and common metadata such as create time. # Some services might not provide such metadata. Any method that returns a # long-running operation should document the metadata type, if any. # Corresponds to the JSON property `metadata` # @return [Hash] attr_accessor :metadata # If the value is `false`, it means the operation is still in progress. # If true, the operation is completed, and either `error` or `response` is # available. # Corresponds to the JSON property `done` # @return [Boolean] attr_accessor :done alias_method :done?, :done def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @response = args[:response] if args.key?(:response) @name = args[:name] if args.key?(:name) @error = args[:error] if args.key?(:error) @metadata = args[:metadata] if args.key?(:metadata) @done = args[:done] if args.key?(:done) end end # Represents a machine learning solution. # A model can have multiple versions, each of which is a deployed, trained # model ready to receive prediction requests. The model itself is just a # container. class GoogleCloudMlV1Model include Google::Apis::Core::Hashable # Optional. The list of regions where the model is going to be deployed. # Currently only one region per model is supported. # Defaults to 'us-central1' if nothing is set. # Note: # * No matter where a model is deployed, it can always be accessed by # users from anywhere, both for online and batch prediction. # * The region for a batch prediction job is set by the region field when # submitting the batch prediction job and does not take its value from # this field. # Corresponds to the JSON property `regions` # @return [Array] attr_accessor :regions # Required. The name specified for the model when it was created. # The model name must be unique within the project it is created in. # Corresponds to the JSON property `name` # @return [String] attr_accessor :name # Optional. The description specified for the model when it was created. # Corresponds to the JSON property `description` # @return [String] attr_accessor :description # Optional. If true, enables StackDriver Logging for online prediction. # Default is false. # Corresponds to the JSON property `onlinePredictionLogging` # @return [Boolean] attr_accessor :online_prediction_logging alias_method :online_prediction_logging?, :online_prediction_logging # Represents a version of the model. # Each version is a trained model deployed in the cloud, ready to handle # prediction requests. A model can have multiple versions. You can get # information about all of the versions of a given model by calling # [projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models. # versions/list). # Corresponds to the JSON property `defaultVersion` # @return [Google::Apis::MlV1::GoogleCloudMlV1Version] attr_accessor :default_version def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @regions = args[:regions] if args.key?(:regions) @name = args[:name] if args.key?(:name) @description = args[:description] if args.key?(:description) @online_prediction_logging = args[:online_prediction_logging] if args.key?(:online_prediction_logging) @default_version = args[:default_version] if args.key?(:default_version) end end # A generic empty message that you can re-use to avoid defining duplicated # empty messages in your APIs. A typical example is to use it as the request # or the response type of an API method. For instance: # service Foo ` # rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); # ` # The JSON representation for `Empty` is empty JSON object ````. class GoogleProtobufEmpty include Google::Apis::Core::Hashable def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) end end # Request message for the CancelJob method. class GoogleCloudMlV1CancelJobRequest include Google::Apis::Core::Hashable def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) end end # Response message for the ListVersions method. class GoogleCloudMlV1ListVersionsResponse include Google::Apis::Core::Hashable # Optional. Pass this token as the `page_token` field of the request for a # subsequent call. # Corresponds to the JSON property `nextPageToken` # @return [String] attr_accessor :next_page_token # The list of versions. # Corresponds to the JSON property `versions` # @return [Array] attr_accessor :versions def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @next_page_token = args[:next_page_token] if args.key?(:next_page_token) @versions = args[:versions] if args.key?(:versions) end end # Options for manually scaling a model. class GoogleCloudMlV1beta1ManualScaling include Google::Apis::Core::Hashable # The number of nodes to allocate for this model. These nodes are always up, # starting from the time the model is deployed, so the cost of operating # this model will be proportional to nodes * number of hours since # deployment. # Corresponds to the JSON property `nodes` # @return [Fixnum] attr_accessor :nodes def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @nodes = args[:nodes] if args.key?(:nodes) end end # The `Status` type defines a logical error model that is suitable for different # programming environments, including REST APIs and RPC APIs. It is used by # [gRPC](https://github.com/grpc). The error model is designed to be: # - Simple to use and understand for most users # - Flexible enough to meet unexpected needs # # Overview # The `Status` message contains three pieces of data: error code, error message, # and error details. The error code should be an enum value of # google.rpc.Code, but it may accept additional error codes if needed. The # error message should be a developer-facing English message that helps # developers *understand* and *resolve* the error. If a localized user-facing # error message is needed, put the localized message in the error details or # localize it in the client. The optional error details may contain arbitrary # information about the error. There is a predefined set of error detail types # in the package `google.rpc` which can be used for common error conditions. # # Language mapping # The `Status` message is the logical representation of the error model, but it # is not necessarily the actual wire format. When the `Status` message is # exposed in different client libraries and different wire protocols, it can be # mapped differently. For example, it will likely be mapped to some exceptions # in Java, but more likely mapped to some error codes in C. # # Other uses # The error model and the `Status` message can be used in a variety of # environments, either with or without APIs, to provide a # consistent developer experience across different environments. # Example uses of this error model include: # - Partial errors. If a service needs to return partial errors to the client, # it may embed the `Status` in the normal response to indicate the partial # errors. # - Workflow errors. A typical workflow has multiple steps. Each step may # have a `Status` message for error reporting purpose. # - Batch operations. If a client uses batch request and batch response, the # `Status` message should be used directly inside batch response, one for # each error sub-response. # - Asynchronous operations. If an API call embeds asynchronous operation # results in its response, the status of those operations should be # represented directly using the `Status` message. # - Logging. If some API errors are stored in logs, the message `Status` could # be used directly after any stripping needed for security/privacy reasons. class GoogleRpcStatus include Google::Apis::Core::Hashable # The status code, which should be an enum value of google.rpc.Code. # Corresponds to the JSON property `code` # @return [Fixnum] attr_accessor :code # A developer-facing error message, which should be in English. Any # user-facing error message should be localized and sent in the # google.rpc.Status.details field, or localized by the client. # Corresponds to the JSON property `message` # @return [String] attr_accessor :message # A list of messages that carry the error details. There will be a # common set of message types for APIs to use. # Corresponds to the JSON property `details` # @return [Array>] attr_accessor :details def initialize(**args) update!(**args) end # Update properties of this object def update!(**args) @code = args[:code] if args.key?(:code) @message = args[:message] if args.key?(:message) @details = args[:details] if args.key?(:details) end end end end end