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google-api-ruby-client/google-api-client/generated/google/apis/ml_v1/classes.rb

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# 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
# 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
# The HTTP Content-Type header value specifying the content type of the body.
# Corresponds to the JSON property `contentType`
# @return [String]
attr_accessor :content_type
# The HTTP request/response body as raw binary.
# Corresponds to the JSON property `data`
# NOTE: Values are automatically base64 encoded/decoded in the client library.
# @return [String]
attr_accessor :data
# Application specific response metadata. Must be set in the first response for
# streaming APIs.
# Corresponds to the JSON property `extensions`
# @return [Array<Hash<String,Object>>]
attr_accessor :extensions
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@content_type = args[:content_type] if args.key?(:content_type)
@data = args[:data] if args.key?(:data)
@extensions = args[:extensions] if args.key?(:extensions)
end
end
#
class GoogleCloudMlV1AutomatedStoppingConfigDecayCurveAutomatedStoppingConfig
include Google::Apis::Core::Hashable
# If true, measurement.elapsed_time is used as the x-axis of each Trials Decay
# Curve. Otherwise, Measurement.steps will be used as the x-axis.
# Corresponds to the JSON property `useElapsedTime`
# @return [Boolean]
attr_accessor :use_elapsed_time
alias_method :use_elapsed_time?, :use_elapsed_time
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@use_elapsed_time = args[:use_elapsed_time] if args.key?(:use_elapsed_time)
end
end
# The median automated stopping rule stops a pending trial if the trial's best
# objective_value is strictly below the median 'performance' of all completed
# trials reported up to the trial's last measurement. Currently, 'performance'
# refers to the running average of the objective values reported by the trial in
# each measurement.
class GoogleCloudMlV1AutomatedStoppingConfigMedianAutomatedStoppingConfig
include Google::Apis::Core::Hashable
# If true, the median automated stopping rule applies to measurement.
# use_elapsed_time, which means the elapsed_time field of the current trial's
# latest measurement is used to compute the median objective value for each
# completed trial.
# Corresponds to the JSON property `useElapsedTime`
# @return [Boolean]
attr_accessor :use_elapsed_time
alias_method :use_elapsed_time?, :use_elapsed_time
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@use_elapsed_time = args[:use_elapsed_time] if args.key?(:use_elapsed_time)
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
# A message representing a metric in the measurement.
class GoogleCloudMlV1MeasurementMetric
include Google::Apis::Core::Hashable
# Required. Metric name.
# Corresponds to the JSON property `metric`
# @return [String]
attr_accessor :metric
# Required. The value for this metric.
# Corresponds to the JSON property `value`
# @return [Float]
attr_accessor :value
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@metric = args[:metric] if args.key?(:metric)
@value = args[:value] if args.key?(:value)
end
end
#
class GoogleCloudMlV1StudyConfigParameterSpecCategoricalValueSpec
include Google::Apis::Core::Hashable
# Must be specified if type is `CATEGORICAL`. The list of possible categories.
# Corresponds to the JSON property `values`
# @return [Array<String>]
attr_accessor :values
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@values = args[:values] if args.key?(:values)
end
end
#
class GoogleCloudMlV1StudyConfigParameterSpecDiscreteValueSpec
include Google::Apis::Core::Hashable
# Must be specified 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 `values`
# @return [Array<Float>]
attr_accessor :values
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@values = args[:values] if args.key?(:values)
end
end
#
class GoogleCloudMlV1StudyConfigParameterSpecDoubleValueSpec
include Google::Apis::Core::Hashable
# Must be specified if type is `DOUBLE`. Maximum value of the parameter.
# Corresponds to the JSON property `maxValue`
# @return [Float]
attr_accessor :max_value
# Must be specified if type is `DOUBLE`. Minimum value of the parameter.
# Corresponds to the JSON property `minValue`
# @return [Float]
attr_accessor :min_value
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@max_value = args[:max_value] if args.key?(:max_value)
@min_value = args[:min_value] if args.key?(:min_value)
end
end
#
class GoogleCloudMlV1StudyConfigParameterSpecIntegerValueSpec
include Google::Apis::Core::Hashable
# Must be specified if type is `INTEGER`. Maximum value of the parameter.
# Corresponds to the JSON property `maxValue`
# @return [Fixnum]
attr_accessor :max_value
# Must be specified if type is `INTEGER`. Minimum value of the parameter.
# Corresponds to the JSON property `minValue`
# @return [Fixnum]
attr_accessor :min_value
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@max_value = args[:max_value] if args.key?(:max_value)
@min_value = args[:min_value] if args.key?(:min_value)
end
end
# Represents the spec to match categorical values from parent parameter.
class GoogleCloudMlV1StudyConfigParameterSpecMatchingParentCategoricalValueSpec
include Google::Apis::Core::Hashable
# Matches values of the parent parameter with type 'CATEGORICAL'. All values
# must exist in `categorical_value_spec` of parent parameter.
# Corresponds to the JSON property `values`
# @return [Array<String>]
attr_accessor :values
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@values = args[:values] if args.key?(:values)
end
end
# Represents the spec to match discrete values from parent parameter.
class GoogleCloudMlV1StudyConfigParameterSpecMatchingParentDiscreteValueSpec
include Google::Apis::Core::Hashable
# Matches values of the parent parameter with type 'DISCRETE'. All values must
# exist in `discrete_value_spec` of parent parameter.
# Corresponds to the JSON property `values`
# @return [Array<Float>]
attr_accessor :values
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@values = args[:values] if args.key?(:values)
end
end
# Represents the spec to match integer values from parent parameter.
class GoogleCloudMlV1StudyConfigParameterSpecMatchingParentIntValueSpec
include Google::Apis::Core::Hashable
# Matches values of the parent parameter with type 'INTEGER'. All values must
# lie in `integer_value_spec` of parent parameter.
# Corresponds to the JSON property `values`
# @return [Array<Fixnum>]
attr_accessor :values
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@values = args[:values] if args.key?(:values)
end
end
# Represents a metric to optimize.
class GoogleCloudMlV1StudyConfigMetricSpec
include Google::Apis::Core::Hashable
# Required. The optimization goal of the metric.
# Corresponds to the JSON property `goal`
# @return [String]
attr_accessor :goal
# Required. The name of the metric.
# Corresponds to the JSON property `metric`
# @return [String]
attr_accessor :metric
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@goal = args[:goal] if args.key?(:goal)
@metric = args[:metric] if args.key?(:metric)
end
end
# Represents a single parameter to optimize.
class GoogleCloudMlV1StudyConfigParameterSpec
include Google::Apis::Core::Hashable
# The value spec for a 'CATEGORICAL' parameter.
# Corresponds to the JSON property `categoricalValueSpec`
# @return [Google::Apis::MlV1::GoogleCloudMlV1StudyConfigParameterSpecCategoricalValueSpec]
attr_accessor :categorical_value_spec
# A child node is active if the parameter's value matches the child node's
# matching_parent_values. If two items in child_parameter_specs have the same
# name, they must have disjoint matching_parent_values.
# Corresponds to the JSON property `childParameterSpecs`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1StudyConfigParameterSpec>]
attr_accessor :child_parameter_specs
# The value spec for a 'DISCRETE' parameter.
# Corresponds to the JSON property `discreteValueSpec`
# @return [Google::Apis::MlV1::GoogleCloudMlV1StudyConfigParameterSpecDiscreteValueSpec]
attr_accessor :discrete_value_spec
# The value spec for a 'DOUBLE' parameter.
# Corresponds to the JSON property `doubleValueSpec`
# @return [Google::Apis::MlV1::GoogleCloudMlV1StudyConfigParameterSpecDoubleValueSpec]
attr_accessor :double_value_spec
# The value spec for an 'INTEGER' parameter.
# Corresponds to the JSON property `integerValueSpec`
# @return [Google::Apis::MlV1::GoogleCloudMlV1StudyConfigParameterSpecIntegerValueSpec]
attr_accessor :integer_value_spec
# Required. The parameter name must be unique amongst all ParameterSpecs.
# Corresponds to the JSON property `parameter`
# @return [String]
attr_accessor :parameter
# Represents the spec to match categorical values from parent parameter.
# Corresponds to the JSON property `parentCategoricalValues`
# @return [Google::Apis::MlV1::GoogleCloudMlV1StudyConfigParameterSpecMatchingParentCategoricalValueSpec]
attr_accessor :parent_categorical_values
# Represents the spec to match discrete values from parent parameter.
# Corresponds to the JSON property `parentDiscreteValues`
# @return [Google::Apis::MlV1::GoogleCloudMlV1StudyConfigParameterSpecMatchingParentDiscreteValueSpec]
attr_accessor :parent_discrete_values
# Represents the spec to match integer values from parent parameter.
# Corresponds to the JSON property `parentIntValues`
# @return [Google::Apis::MlV1::GoogleCloudMlV1StudyConfigParameterSpecMatchingParentIntValueSpec]
attr_accessor :parent_int_values
# How the parameter should be scaled. Leave unset for categorical parameters.
# Corresponds to the JSON property `scaleType`
# @return [String]
attr_accessor :scale_type
# 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_value_spec = args[:categorical_value_spec] if args.key?(:categorical_value_spec)
@child_parameter_specs = args[:child_parameter_specs] if args.key?(:child_parameter_specs)
@discrete_value_spec = args[:discrete_value_spec] if args.key?(:discrete_value_spec)
@double_value_spec = args[:double_value_spec] if args.key?(:double_value_spec)
@integer_value_spec = args[:integer_value_spec] if args.key?(:integer_value_spec)
@parameter = args[:parameter] if args.key?(:parameter)
@parent_categorical_values = args[:parent_categorical_values] if args.key?(:parent_categorical_values)
@parent_discrete_values = args[:parent_discrete_values] if args.key?(:parent_discrete_values)
@parent_int_values = args[:parent_int_values] if args.key?(:parent_int_values)
@scale_type = args[:scale_type] if args.key?(:scale_type)
@type = args[:type] if args.key?(:type)
end
end
# A message representing a parameter to be tuned. Contains the name of the
# parameter and the suggested value to use for this trial.
class GoogleCloudMlV1TrialParameter
include Google::Apis::Core::Hashable
# Must be set if ParameterType is DOUBLE or DISCRETE.
# Corresponds to the JSON property `floatValue`
# @return [Float]
attr_accessor :float_value
# Must be set if ParameterType is INTEGER
# Corresponds to the JSON property `intValue`
# @return [Fixnum]
attr_accessor :int_value
# The name of the parameter.
# Corresponds to the JSON property `parameter`
# @return [String]
attr_accessor :parameter
# Must be set if ParameterTypeis CATEGORICAL
# Corresponds to the JSON property `stringValue`
# @return [String]
attr_accessor :string_value
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@float_value = args[:float_value] if args.key?(:float_value)
@int_value = args[:int_value] if args.key?(:int_value)
@parameter = args[:parameter] if args.key?(:parameter)
@string_value = args[:string_value] if args.key?(:string_value)
end
end
# Represents a hardware accelerator request config. Note that the
# AcceleratorConfig can be used in both Jobs and Versions. Learn more about [
# accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for
# online prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
class GoogleCloudMlV1AcceleratorConfig
include Google::Apis::Core::Hashable
# The number of accelerators to attach to each machine running the job.
# Corresponds to the JSON property `count`
# @return [Fixnum]
attr_accessor :count
# The type of accelerator to use.
# 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)
@count = args[:count] if args.key?(:count)
@type = args[:type] if args.key?(:type)
end
end
# The request message for the AddTrialMeasurement service method.
class GoogleCloudMlV1AddTrialMeasurementRequest
include Google::Apis::Core::Hashable
# A message representing a measurement.
# Corresponds to the JSON property `measurement`
# @return [Google::Apis::MlV1::GoogleCloudMlV1Measurement]
attr_accessor :measurement
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@measurement = args[:measurement] if args.key?(:measurement)
end
end
# Options for automatically scaling a model.
class GoogleCloudMlV1AutoScaling
include Google::Apis::Core::Hashable
# The maximum number of nodes to scale this model under load. The actual value
# will depend on resource quota and availability.
# Corresponds to the JSON property `maxNodes`
# @return [Fixnum]
attr_accessor :max_nodes
# MetricSpec contains the specifications to use to calculate the desired nodes
# count.
# Corresponds to the JSON property `metrics`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1MetricSpec>]
attr_accessor :metrics
# Optional. The minimum number of nodes to allocate for this model. These nodes
# are always up, starting from the time the model is deployed. Therefore, the
# cost of operating this model will be at least `rate` * `min_nodes` * number of
# hours since last billing cycle, where `rate` is the cost per node-hour as
# documented in the [pricing guide](/ml-engine/docs/pricing), even if no
# predictions are performed. There is additional cost for each prediction
# performed. Unlike manual scaling, if the load gets too heavy for the nodes
# that are up, the service will automatically add nodes to handle the increased
# load as well as scale back as traffic drops, always maintaining at least `
# min_nodes`. You will be charged for the time in which additional nodes are
# used. If `min_nodes` is not specified and AutoScaling is used with a [legacy (
# MLS1) machine type](/ml-engine/docs/machine-types-online-prediction), `
# min_nodes` defaults to 0, in which case, when traffic to a model stops (and
# after a cool-down period), nodes will be shut down and no charges will be
# incurred until traffic to the model resumes. If `min_nodes` is not specified
# and AutoScaling is used with a [Compute Engine (N1) machine type](/ml-engine/
# docs/machine-types-online-prediction), `min_nodes` defaults to 1. `min_nodes`
# must be at least 1 for use with a Compute Engine machine type. Note that you
# cannot use AutoScaling if your version uses [GPUs](#Version.FIELDS.
# accelerator_config). Instead, you must use ManualScaling. You can set `
# min_nodes` when creating the model version, and you can also update `min_nodes`
# for an existing version: update_body.json: ` 'autoScaling': ` 'minNodes': 5 `
# ` HTTP request: PATCH https://ml.googleapis.com/v1/`name=projects/*/models/*/
# versions/*`?update_mask=autoScaling.minNodes -d @./update_body.json
# Corresponds to the JSON property `minNodes`
# @return [Fixnum]
attr_accessor :min_nodes
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@max_nodes = args[:max_nodes] if args.key?(:max_nodes)
@metrics = args[:metrics] if args.key?(:metrics)
@min_nodes = args[:min_nodes] if args.key?(:min_nodes)
end
end
# Configuration for Automated Early Stopping of Trials. If no
# implementation_config is set, automated early stopping will not be run.
class GoogleCloudMlV1AutomatedStoppingConfig
include Google::Apis::Core::Hashable
#
# Corresponds to the JSON property `decayCurveStoppingConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1AutomatedStoppingConfigDecayCurveAutomatedStoppingConfig]
attr_accessor :decay_curve_stopping_config
# The median automated stopping rule stops a pending trial if the trial's best
# objective_value is strictly below the median 'performance' of all completed
# trials reported up to the trial's last measurement. Currently, 'performance'
# refers to the running average of the objective values reported by the trial in
# each measurement.
# Corresponds to the JSON property `medianAutomatedStoppingConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1AutomatedStoppingConfigMedianAutomatedStoppingConfig]
attr_accessor :median_automated_stopping_config
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@decay_curve_stopping_config = args[:decay_curve_stopping_config] if args.key?(:decay_curve_stopping_config)
@median_automated_stopping_config = args[:median_automated_stopping_config] if args.key?(:median_automated_stopping_config)
end
end
# Represents output related to a built-in algorithm Job.
class GoogleCloudMlV1BuiltInAlgorithmOutput
include Google::Apis::Core::Hashable
# Framework on which the built-in algorithm was trained.
# Corresponds to the JSON property `framework`
# @return [String]
attr_accessor :framework
# The Cloud Storage path to the `model/` directory where the training job saves
# the trained model. Only set for successful jobs that don't use hyperparameter
# tuning.
# Corresponds to the JSON property `modelPath`
# @return [String]
attr_accessor :model_path
# Python version on which the built-in algorithm was trained.
# Corresponds to the JSON property `pythonVersion`
# @return [String]
attr_accessor :python_version
# AI Platform runtime version on which the built-in algorithm was trained.
# Corresponds to the JSON property `runtimeVersion`
# @return [String]
attr_accessor :runtime_version
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@framework = args[:framework] if args.key?(:framework)
@model_path = args[:model_path] if args.key?(:model_path)
@python_version = args[:python_version] if args.key?(:python_version)
@runtime_version = args[:runtime_version] if args.key?(:runtime_version)
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
#
class GoogleCloudMlV1Capability
include Google::Apis::Core::Hashable
# Available accelerators for the capability.
# Corresponds to the JSON property `availableAccelerators`
# @return [Array<String>]
attr_accessor :available_accelerators
#
# 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)
@available_accelerators = args[:available_accelerators] if args.key?(:available_accelerators)
@type = args[:type] if args.key?(:type)
end
end
# This message will be placed in the metadata field of a google.longrunning.
# Operation associated with a CheckTrialEarlyStoppingState request.
class GoogleCloudMlV1CheckTrialEarlyStoppingStateMetatdata
include Google::Apis::Core::Hashable
# The time at which the operation was submitted.
# Corresponds to the JSON property `createTime`
# @return [String]
attr_accessor :create_time
# The name of the study that the trial belongs to.
# Corresponds to the JSON property `study`
# @return [String]
attr_accessor :study
# The trial name.
# Corresponds to the JSON property `trial`
# @return [String]
attr_accessor :trial
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@create_time = args[:create_time] if args.key?(:create_time)
@study = args[:study] if args.key?(:study)
@trial = args[:trial] if args.key?(:trial)
end
end
# The request message for the CheckTrialEarlyStoppingState service method.
class GoogleCloudMlV1CheckTrialEarlyStoppingStateRequest
include Google::Apis::Core::Hashable
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
end
end
# The message will be placed in the response field of a completed google.
# longrunning.Operation associated with a CheckTrialEarlyStoppingState request.
class GoogleCloudMlV1CheckTrialEarlyStoppingStateResponse
include Google::Apis::Core::Hashable
# The time at which operation processing completed.
# Corresponds to the JSON property `endTime`
# @return [String]
attr_accessor :end_time
# True if the Trial should stop.
# Corresponds to the JSON property `shouldStop`
# @return [Boolean]
attr_accessor :should_stop
alias_method :should_stop?, :should_stop
# The time at which the operation was 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)
@end_time = args[:end_time] if args.key?(:end_time)
@should_stop = args[:should_stop] if args.key?(:should_stop)
@start_time = args[:start_time] if args.key?(:start_time)
end
end
# The request message for the CompleteTrial service method.
class GoogleCloudMlV1CompleteTrialRequest
include Google::Apis::Core::Hashable
# A message representing a measurement.
# Corresponds to the JSON property `finalMeasurement`
# @return [Google::Apis::MlV1::GoogleCloudMlV1Measurement]
attr_accessor :final_measurement
# Optional. A human readable reason why the trial was infeasible. This should
# only be provided if `trial_infeasible` is true.
# Corresponds to the JSON property `infeasibleReason`
# @return [String]
attr_accessor :infeasible_reason
# Optional. True if the trial cannot be run with the given Parameter, and
# final_measurement will be ignored.
# Corresponds to the JSON property `trialInfeasible`
# @return [Boolean]
attr_accessor :trial_infeasible
alias_method :trial_infeasible?, :trial_infeasible
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@final_measurement = args[:final_measurement] if args.key?(:final_measurement)
@infeasible_reason = args[:infeasible_reason] if args.key?(:infeasible_reason)
@trial_infeasible = args[:trial_infeasible] if args.key?(:trial_infeasible)
end
end
#
class GoogleCloudMlV1Config
include Google::Apis::Core::Hashable
# The service account Cloud ML uses to run on TPU node.
# Corresponds to the JSON property `tpuServiceAccount`
# @return [String]
attr_accessor :tpu_service_account
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@tpu_service_account = args[:tpu_service_account] if args.key?(:tpu_service_account)
end
end
# Represents a network port in a single container. This message is a subset of
# the [Kubernetes ContainerPort v1 core specification](https://kubernetes.io/
# docs/reference/generated/kubernetes-api/v1.18/#containerport-v1-core).
class GoogleCloudMlV1ContainerPort
include Google::Apis::Core::Hashable
# Number of the port to expose on the container. This must be a valid port
# number: 0 < PORT_NUMBER < 65536.
# Corresponds to the JSON property `containerPort`
# @return [Fixnum]
attr_accessor :container_port
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@container_port = args[:container_port] if args.key?(:container_port)
end
end
# Specification of a custom container for serving predictions. This message is a
# subset of the [Kubernetes Container v1 core specification](https://kubernetes.
# io/docs/reference/generated/kubernetes-api/v1.18/#container-v1-core).
class GoogleCloudMlV1ContainerSpec
include Google::Apis::Core::Hashable
# Immutable. Specifies arguments for the command that runs when the container
# starts. This overrides the container's [`CMD`](https://docs.docker.com/engine/
# reference/builder/#cmd). Specify this field as an array of executable and
# arguments, similar to a Docker `CMD`'s "default parameters" form. If you don't
# specify this field but do specify the command field, then the command from the
# `command` field runs without any additional arguments. See the [Kubernetes
# documentation about how the `command` and `args` fields interact with a
# container's `ENTRYPOINT` and `CMD`](https://kubernetes.io/docs/tasks/inject-
# data-application/define-command-argument-container/#notes). If you don't
# specify this field and don't specify the `commmand` field, then the container'
# s [`ENTRYPOINT`](https://docs.docker.com/engine/reference/builder/#cmd) and `
# CMD` determine what runs based on their default behavior. See the [Docker
# documentation about how `CMD` and `ENTRYPOINT` interact](https://docs.docker.
# com/engine/reference/builder/#understand-how-cmd-and-entrypoint-interact). In
# this field, you can reference [environment variables set by AI Platform
# Prediction](/ai-platform/prediction/docs/custom-container-requirements#aip-
# variables) and environment variables set in the env field. You cannot
# reference environment variables set in the Docker image. In order for
# environment variables to be expanded, reference them by using the following
# syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion,
# which does not use parentheses. If a variable cannot be resolved, the
# reference in the input string is used unchanged. To avoid variable expansion,
# you can escape this syntax with `$$`; for example: $$(VARIABLE_NAME) This
# field corresponds to the `args` field of the [Kubernetes Containers v1 core
# API](https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.18/#
# container-v1-core).
# Corresponds to the JSON property `args`
# @return [Array<String>]
attr_accessor :args
# Immutable. Specifies the command that runs when the container starts. This
# overrides the container's [`ENTRYPOINT`](https://docs.docker.com/engine/
# reference/builder/#entrypoint). Specify this field as an array of executable
# and arguments, similar to a Docker `ENTRYPOINT`'s "exec" form, not its "shell"
# form. If you do not specify this field, then the container's `ENTRYPOINT` runs,
# in conjunction with the args field or the container's [`CMD`](https://docs.
# docker.com/engine/reference/builder/#cmd), if either exists. If this field is
# not specified and the container does not have an `ENTRYPOINT`, then refer to
# the [Docker documentation about how `CMD` and `ENTRYPOINT` interact](https://
# docs.docker.com/engine/reference/builder/#understand-how-cmd-and-entrypoint-
# interact). If you specify this field, then you can also specify the `args`
# field to provide additional arguments for this command. However, if you
# specify this field, then the container's `CMD` is ignored. See the [Kubernetes
# documentation about how the `command` and `args` fields interact with a
# container's `ENTRYPOINT` and `CMD`](https://kubernetes.io/docs/tasks/inject-
# data-application/define-command-argument-container/#notes). In this field, you
# can reference [environment variables set by AI Platform Prediction](/ai-
# platform/prediction/docs/custom-container-requirements#aip-variables) and
# environment variables set in the env field. You cannot reference environment
# variables set in the Docker image. In order for environment variables to be
# expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note
# that this differs from Bash variable expansion, which does not use parentheses.
# If a variable cannot be resolved, the reference in the input string is used
# unchanged. To avoid variable expansion, you can escape this syntax with `$$`;
# for example: $$(VARIABLE_NAME) This field corresponds to the `command` field
# of the [Kubernetes Containers v1 core API](https://kubernetes.io/docs/
# reference/generated/kubernetes-api/v1.18/#container-v1-core).
# Corresponds to the JSON property `command`
# @return [Array<String>]
attr_accessor :command
# Immutable. List of environment variables to set in the container. After the
# container starts running, code running in the container can read these
# environment variables. Additionally, the command and args fields can reference
# these variables. Later entries in this list can also reference earlier entries.
# For example, the following example sets the variable `VAR_2` to have the
# value `foo bar`: ```json [ ` "name": "VAR_1", "value": "foo" `, ` "name": "
# VAR_2", "value": "$(VAR_1) bar" ` ] ``` If you switch the order of the
# variables in the example, then the expansion does not occur. This field
# corresponds to the `env` field of the [Kubernetes Containers v1 core API](
# https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.18/#container-
# v1-core).
# Corresponds to the JSON property `env`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1EnvVar>]
attr_accessor :env
# URI of the Docker image to be used as the custom container for serving
# predictions. This URI must identify [an image in Artifact Registry](/artifact-
# registry/docs/overview) and begin with the hostname ``REGION`-docker.pkg.dev`,
# where ``REGION`` is replaced by the region that matches AI Platform Prediction
# [regional endpoint](/ai-platform/prediction/docs/regional-endpoints) that you
# are using. For example, if you are using the `us-central1-ml.googleapis.com`
# endpoint, then this URI must begin with `us-central1-docker.pkg.dev`. To use a
# custom container, the [AI Platform Google-managed service account](/ai-
# platform/prediction/docs/custom-service-account#default) must have permission
# to pull (read) the Docker image at this URI. The AI Platform Google-managed
# service account has the following format: `service-`PROJECT_NUMBER`@cloud-ml.
# google.com.iam.gserviceaccount.com` `PROJECT_NUMBER` is replaced by your
# Google Cloud project number. By default, this service account has necessary
# permissions to pull an Artifact Registry image in the same Google Cloud
# project where you are using AI Platform Prediction. In this case, no
# configuration is necessary. If you want to use an image from a different
# Google Cloud project, learn how to [grant the Artifact Registry Reader (roles/
# artifactregistry.reader) role for a repository](/artifact-registry/docs/access-
# control#grant-repo) to your projet's AI Platform Google-managed service
# account. To learn about the requirements for the Docker image itself, read [
# Custom container requirements](/ai-platform/prediction/docs/custom-container-
# requirements).
# Corresponds to the JSON property `image`
# @return [String]
attr_accessor :image
# Immutable. List of ports to expose from the container. AI Platform Prediction
# sends any prediction requests that it receives to the first port on this list.
# AI Platform Prediction also sends [liveness and health checks](/ai-platform/
# prediction/docs/custom-container-requirements#health) to this port. If you do
# not specify this field, it defaults to following value: ```json [ ` "
# containerPort": 8080 ` ] ``` AI Platform Prediction does not use ports other
# than the first one listed. This field corresponds to the `ports` field of the [
# Kubernetes Containers v1 core API](https://kubernetes.io/docs/reference/
# generated/kubernetes-api/v1.18/#container-v1-core).
# Corresponds to the JSON property `ports`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1ContainerPort>]
attr_accessor :ports
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@args = args[:args] if args.key?(:args)
@command = args[:command] if args.key?(:command)
@env = args[:env] if args.key?(:env)
@image = args[:image] if args.key?(:image)
@ports = args[:ports] if args.key?(:ports)
end
end
# Represents the config of disk options.
class GoogleCloudMlV1DiskConfig
include Google::Apis::Core::Hashable
# Size in GB of the boot disk (default is 100GB).
# Corresponds to the JSON property `bootDiskSizeGb`
# @return [Fixnum]
attr_accessor :boot_disk_size_gb
# Type of the boot disk (default is "pd-standard"). Valid values: "pd-ssd" (
# Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk
# Drive).
# Corresponds to the JSON property `bootDiskType`
# @return [String]
attr_accessor :boot_disk_type
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@boot_disk_size_gb = args[:boot_disk_size_gb] if args.key?(:boot_disk_size_gb)
@boot_disk_type = args[:boot_disk_type] if args.key?(:boot_disk_type)
end
end
# Represents a custom encryption key configuration that can be applied to a
# resource.
class GoogleCloudMlV1EncryptionConfig
include Google::Apis::Core::Hashable
# The Cloud KMS resource identifier of the customer-managed encryption key used
# to protect a resource, such as a training job. It has the following format: `
# projects/`PROJECT_ID`/locations/`REGION`/keyRings/`KEY_RING_NAME`/cryptoKeys/`
# KEY_NAME``
# Corresponds to the JSON property `kmsKeyName`
# @return [String]
attr_accessor :kms_key_name
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@kms_key_name = args[:kms_key_name] if args.key?(:kms_key_name)
end
end
# Represents an environment variable to be made available in a container. This
# message is a subset of the [Kubernetes EnvVar v1 core specification](https://
# kubernetes.io/docs/reference/generated/kubernetes-api/v1.18/#envvar-v1-core).
class GoogleCloudMlV1EnvVar
include Google::Apis::Core::Hashable
# Name of the environment variable. Must be a [valid C identifier](https://
# github.com/kubernetes/kubernetes/blob/v1.18.8/staging/src/k8s.io/apimachinery/
# pkg/util/validation/validation.go#L258) and must not begin with the prefix `
# AIP_`.
# Corresponds to the JSON property `name`
# @return [String]
attr_accessor :name
# Value of the environment variable. Defaults to an empty string. In this field,
# you can reference [environment variables set by AI Platform Prediction](/ai-
# platform/prediction/docs/custom-container-requirements#aip-variables) and
# environment variables set earlier in the same env field as where this message
# occurs. You cannot reference environment variables set in the Docker image. In
# order for environment variables to be expanded, reference them by using the
# following syntax: $(VARIABLE_NAME) Note that this differs from Bash variable
# expansion, which does not use parentheses. If a variable cannot be resolved,
# the reference in the input string is used unchanged. To avoid variable
# expansion, you can escape this syntax with `$$`; for example: $$(VARIABLE_NAME)
# Corresponds to the JSON property `value`
# @return [String]
attr_accessor :value
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@name = args[:name] if args.key?(:name)
@value = args[:value] if args.key?(:value)
end
end
# Request for explanations to be issued against a trained model.
class GoogleCloudMlV1ExplainRequest
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
# Message holding configuration options for explaining model predictions. There
# are three feature attribution methods supported for TensorFlow models:
# integrated gradients, sampled Shapley, and XRAI. [Learn more about feature
# attributions.](/ai-platform/prediction/docs/ai-explanations/overview)
class GoogleCloudMlV1ExplanationConfig
include Google::Apis::Core::Hashable
# Attributes credit by computing the Aumann-Shapley value taking advantage of
# the model's fully differentiable structure. Refer to this paper for more
# details: https://arxiv.org/abs/1703.01365
# Corresponds to the JSON property `integratedGradientsAttribution`
# @return [Google::Apis::MlV1::GoogleCloudMlV1IntegratedGradientsAttribution]
attr_accessor :integrated_gradients_attribution
# An attribution method that approximates Shapley values for features that
# contribute to the label being predicted. A sampling strategy is used to
# approximate the value rather than considering all subsets of features.
# Corresponds to the JSON property `sampledShapleyAttribution`
# @return [Google::Apis::MlV1::GoogleCloudMlV1SampledShapleyAttribution]
attr_accessor :sampled_shapley_attribution
# Attributes credit by computing the XRAI taking advantage of the model's fully
# differentiable structure. Refer to this paper for more details: https://arxiv.
# org/abs/1906.02825 Currently only implemented for models with natural image
# inputs.
# Corresponds to the JSON property `xraiAttribution`
# @return [Google::Apis::MlV1::GoogleCloudMlV1XraiAttribution]
attr_accessor :xrai_attribution
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@integrated_gradients_attribution = args[:integrated_gradients_attribution] if args.key?(:integrated_gradients_attribution)
@sampled_shapley_attribution = args[:sampled_shapley_attribution] if args.key?(:sampled_shapley_attribution)
@xrai_attribution = args[:xrai_attribution] if args.key?(:xrai_attribution)
end
end
# Returns service account information associated with a project.
class GoogleCloudMlV1GetConfigResponse
include Google::Apis::Core::Hashable
#
# Corresponds to the JSON property `config`
# @return [Google::Apis::MlV1::GoogleCloudMlV1Config]
attr_accessor :config
# The service account Cloud ML uses to access resources in the project.
# Corresponds to the JSON property `serviceAccount`
# @return [String]
attr_accessor :service_account
# The project number for `service_account`.
# Corresponds to the JSON property `serviceAccountProject`
# @return [Fixnum]
attr_accessor :service_account_project
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@config = args[:config] if args.key?(:config)
@service_account = args[:service_account] if args.key?(:service_account)
@service_account_project = args[:service_account_project] if args.key?(:service_account_project)
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
# All recorded object metrics for this trial. This field is not currently
# populated.
# Corresponds to the JSON property `allMetrics`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1HyperparameterOutputHyperparameterMetric>]
attr_accessor :all_metrics
# Represents output related to a built-in algorithm Job.
# Corresponds to the JSON property `builtInAlgorithmOutput`
# @return [Google::Apis::MlV1::GoogleCloudMlV1BuiltInAlgorithmOutput]
attr_accessor :built_in_algorithm_output
# Output only. End time for the trial.
# Corresponds to the JSON property `endTime`
# @return [String]
attr_accessor :end_time
# An observed value of a metric.
# Corresponds to the JSON property `finalMetric`
# @return [Google::Apis::MlV1::GoogleCloudMlV1HyperparameterOutputHyperparameterMetric]
attr_accessor :final_metric
# The hyperparameters given to this trial.
# Corresponds to the JSON property `hyperparameters`
# @return [Hash<String,String>]
attr_accessor :hyperparameters
# True if the trial is stopped early.
# Corresponds to the JSON property `isTrialStoppedEarly`
# @return [Boolean]
attr_accessor :is_trial_stopped_early
alias_method :is_trial_stopped_early?, :is_trial_stopped_early
# Output only. Start time for the trial.
# Corresponds to the JSON property `startTime`
# @return [String]
attr_accessor :start_time
# Output only. The detailed state of the trial.
# Corresponds to the JSON property `state`
# @return [String]
attr_accessor :state
# The trial id for these results.
# Corresponds to the JSON property `trialId`
# @return [String]
attr_accessor :trial_id
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@all_metrics = args[:all_metrics] if args.key?(:all_metrics)
@built_in_algorithm_output = args[:built_in_algorithm_output] if args.key?(:built_in_algorithm_output)
@end_time = args[:end_time] if args.key?(:end_time)
@final_metric = args[:final_metric] if args.key?(:final_metric)
@hyperparameters = args[:hyperparameters] if args.key?(:hyperparameters)
@is_trial_stopped_early = args[:is_trial_stopped_early] if args.key?(:is_trial_stopped_early)
@start_time = args[:start_time] if args.key?(:start_time)
@state = args[:state] if args.key?(:state)
@trial_id = args[:trial_id] if args.key?(:trial_id)
end
end
# Represents a set of hyperparameters to optimize.
class GoogleCloudMlV1HyperparameterSpec
include Google::Apis::Core::Hashable
# Optional. The search algorithm specified for the hyperparameter tuning job.
# Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
# Corresponds to the JSON property `algorithm`
# @return [String]
attr_accessor :algorithm
# Optional. Indicates if the hyperparameter tuning job enables auto trial early
# stopping.
# Corresponds to the JSON property `enableTrialEarlyStopping`
# @return [Boolean]
attr_accessor :enable_trial_early_stopping
alias_method :enable_trial_early_stopping?, :enable_trial_early_stopping
# 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
# Optional. The number of failed trials that need to be seen before failing the
# hyperparameter tuning job. You can specify this field to override the default
# failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero,
# which means the service decides when a hyperparameter job should fail.
# Corresponds to the JSON property `maxFailedTrials`
# @return [Fixnum]
attr_accessor :max_failed_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
# 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
# Required. The set of parameters to tune.
# Corresponds to the JSON property `params`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1ParameterSpec>]
attr_accessor :params
# Optional. The prior hyperparameter tuning job id that users hope to continue
# with. The job id will be used to find the corresponding vizier study guid and
# resume the study.
# Corresponds to the JSON property `resumePreviousJobId`
# @return [String]
attr_accessor :resume_previous_job_id
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@algorithm = args[:algorithm] if args.key?(:algorithm)
@enable_trial_early_stopping = args[:enable_trial_early_stopping] if args.key?(:enable_trial_early_stopping)
@goal = args[:goal] if args.key?(:goal)
@hyperparameter_metric_tag = args[:hyperparameter_metric_tag] if args.key?(:hyperparameter_metric_tag)
@max_failed_trials = args[:max_failed_trials] if args.key?(:max_failed_trials)
@max_parallel_trials = args[:max_parallel_trials] if args.key?(:max_parallel_trials)
@max_trials = args[:max_trials] if args.key?(:max_trials)
@params = args[:params] if args.key?(:params)
@resume_previous_job_id = args[:resume_previous_job_id] if args.key?(:resume_previous_job_id)
end
end
# Attributes credit by computing the Aumann-Shapley value taking advantage of
# the model's fully differentiable structure. Refer to this paper for more
# details: https://arxiv.org/abs/1703.01365
class GoogleCloudMlV1IntegratedGradientsAttribution
include Google::Apis::Core::Hashable
# Number of steps for approximating the path integral. A good value to start is
# 50 and gradually increase until the sum to diff property is met within the
# desired error range.
# Corresponds to the JSON property `numIntegralSteps`
# @return [Fixnum]
attr_accessor :num_integral_steps
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@num_integral_steps = args[:num_integral_steps] if args.key?(:num_integral_steps)
end
end
# Represents a training or prediction job.
class GoogleCloudMlV1Job
include Google::Apis::Core::Hashable
# Output only. When the job was created.
# Corresponds to the JSON property `createTime`
# @return [String]
attr_accessor :create_time
# Output only. When the job processing was completed.
# Corresponds to the JSON property `endTime`
# @return [String]
attr_accessor :end_time
# Output only. The details of a failure or a cancellation.
# Corresponds to the JSON property `errorMessage`
# @return [String]
attr_accessor :error_message
# `etag` is used for optimistic concurrency control as a way to help prevent
# simultaneous updates of a job from overwriting each other. It is strongly
# suggested that systems make use of the `etag` in the read-modify-write cycle
# to perform job updates in order to avoid race conditions: An `etag` is
# returned in the response to `GetJob`, and systems are expected to put that
# etag in the request to `UpdateJob` to ensure that their change will be applied
# to the same version of the job.
# Corresponds to the JSON property `etag`
# NOTE: Values are automatically base64 encoded/decoded in the client library.
# @return [String]
attr_accessor :etag
# Required. The user-specified id of the job.
# Corresponds to the JSON property `jobId`
# @return [String]
attr_accessor :job_id
# Optional. One or more labels that you can add, to organize your jobs. Each
# label is a key-value pair, where both the key and the value are arbitrary
# strings that you supply. For more information, see the documentation on using
# labels.
# Corresponds to the JSON property `labels`
# @return [Hash<String,String>]
attr_accessor :labels
# Represents input parameters for a prediction job.
# Corresponds to the JSON property `predictionInput`
# @return [Google::Apis::MlV1::GoogleCloudMlV1PredictionInput]
attr_accessor :prediction_input
# Represents results of a prediction job.
# Corresponds to the JSON property `predictionOutput`
# @return [Google::Apis::MlV1::GoogleCloudMlV1PredictionOutput]
attr_accessor :prediction_output
# Output only. When the job processing was started.
# Corresponds to the JSON property `startTime`
# @return [String]
attr_accessor :start_time
# Output only. The detailed state of a job.
# Corresponds to the JSON property `state`
# @return [String]
attr_accessor :state
# Represents input parameters for a training job. When using the gcloud command
# to submit your training job, you can specify the input parameters as command-
# line arguments and/or in a YAML configuration file referenced from the --
# config command-line argument. For details, see the guide to [submitting a
# training job](/ai-platform/training/docs/training-jobs).
# Corresponds to the JSON property `trainingInput`
# @return [Google::Apis::MlV1::GoogleCloudMlV1TrainingInput]
attr_accessor :training_input
# Represents results of a training job. Output only.
# Corresponds to the JSON property `trainingOutput`
# @return [Google::Apis::MlV1::GoogleCloudMlV1TrainingOutput]
attr_accessor :training_output
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@create_time = args[:create_time] if args.key?(:create_time)
@end_time = args[:end_time] if args.key?(:end_time)
@error_message = args[:error_message] if args.key?(:error_message)
@etag = args[:etag] if args.key?(:etag)
@job_id = args[:job_id] if args.key?(:job_id)
@labels = args[:labels] if args.key?(:labels)
@prediction_input = args[:prediction_input] if args.key?(:prediction_input)
@prediction_output = args[:prediction_output] if args.key?(:prediction_output)
@start_time = args[:start_time] if args.key?(:start_time)
@state = args[:state] if args.key?(:state)
@training_input = args[:training_input] if args.key?(:training_input)
@training_output = args[:training_output] if args.key?(:training_output)
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<Google::Apis::MlV1::GoogleCloudMlV1Job>]
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
#
class GoogleCloudMlV1ListLocationsResponse
include Google::Apis::Core::Hashable
# Locations where at least one type of CMLE capability is available.
# Corresponds to the JSON property `locations`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1Location>]
attr_accessor :locations
# 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)
@locations = args[:locations] if args.key?(:locations)
@next_page_token = args[:next_page_token] if args.key?(:next_page_token)
end
end
# 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<Google::Apis::MlV1::GoogleCloudMlV1Model>]
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
# The request message for the ListTrials service method.
class GoogleCloudMlV1ListOptimalTrialsRequest
include Google::Apis::Core::Hashable
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
end
end
# The response message for the ListOptimalTrials method.
class GoogleCloudMlV1ListOptimalTrialsResponse
include Google::Apis::Core::Hashable
# The pareto-optimal trials for multiple objective study or the optimal trial
# for single objective study. The definition of pareto-optimal can be checked in
# wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency
# Corresponds to the JSON property `trials`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1Trial>]
attr_accessor :trials
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@trials = args[:trials] if args.key?(:trials)
end
end
#
class GoogleCloudMlV1ListStudiesResponse
include Google::Apis::Core::Hashable
# The studies associated with the project.
# Corresponds to the JSON property `studies`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1Study>]
attr_accessor :studies
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@studies = args[:studies] if args.key?(:studies)
end
end
# The response message for the ListTrials method.
class GoogleCloudMlV1ListTrialsResponse
include Google::Apis::Core::Hashable
# The trials associated with the study.
# Corresponds to the JSON property `trials`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1Trial>]
attr_accessor :trials
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@trials = args[:trials] if args.key?(:trials)
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<Google::Apis::MlV1::GoogleCloudMlV1Version>]
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
#
class GoogleCloudMlV1Location
include Google::Apis::Core::Hashable
# Capabilities available in the location.
# Corresponds to the JSON property `capabilities`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1Capability>]
attr_accessor :capabilities
#
# 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)
@capabilities = args[:capabilities] if args.key?(:capabilities)
@name = args[:name] if args.key?(:name)
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 last billing
# cycle plus the cost for each prediction performed.
# 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
# A message representing a measurement.
class GoogleCloudMlV1Measurement
include Google::Apis::Core::Hashable
# Output only. Time that the trial has been running at the point of this
# measurement.
# Corresponds to the JSON property `elapsedTime`
# @return [String]
attr_accessor :elapsed_time
# Provides a list of metrics that act as inputs into the objective function.
# Corresponds to the JSON property `metrics`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1MeasurementMetric>]
attr_accessor :metrics
# The number of steps a machine learning model has been trained for. Must be non-
# negative.
# Corresponds to the JSON property `stepCount`
# @return [Fixnum]
attr_accessor :step_count
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@elapsed_time = args[:elapsed_time] if args.key?(:elapsed_time)
@metrics = args[:metrics] if args.key?(:metrics)
@step_count = args[:step_count] if args.key?(:step_count)
end
end
# MetricSpec contains the specifications to use to calculate the desired nodes
# count when autoscaling is enabled.
class GoogleCloudMlV1MetricSpec
include Google::Apis::Core::Hashable
# metric name.
# Corresponds to the JSON property `name`
# @return [String]
attr_accessor :name
# Target specifies the target value for the given metric; once real metric
# deviates from the threshold by a certain percentage, the node count changes.
# Corresponds to the JSON property `target`
# @return [Fixnum]
attr_accessor :target
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@name = args[:name] if args.key?(:name)
@target = args[:target] if args.key?(:target)
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
# 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.
# Corresponds to the JSON property `defaultVersion`
# @return [Google::Apis::MlV1::GoogleCloudMlV1Version]
attr_accessor :default_version
# Optional. The description specified for the model when it was created.
# Corresponds to the JSON property `description`
# @return [String]
attr_accessor :description
# `etag` is used for optimistic concurrency control as a way to help prevent
# simultaneous updates of a model from overwriting each other. It is strongly
# suggested that systems make use of the `etag` in the read-modify-write cycle
# to perform model updates in order to avoid race conditions: An `etag` is
# returned in the response to `GetModel`, and systems are expected to put that
# etag in the request to `UpdateModel` to ensure that their change will be
# applied to the model as intended.
# Corresponds to the JSON property `etag`
# NOTE: Values are automatically base64 encoded/decoded in the client library.
# @return [String]
attr_accessor :etag
# Optional. One or more labels that you can add, to organize your models. Each
# label is a key-value pair, where both the key and the value are arbitrary
# strings that you supply. For more information, see the documentation on using
# labels.
# Corresponds to the JSON property `labels`
# @return [Hash<String,String>]
attr_accessor :labels
# 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. If true, online prediction nodes send `stderr` and `stdout` streams
# to Cloud Logging. These can be more verbose than the standard access logs (see
# `onlinePredictionLogging`) and can incur higher cost. However, they are
# helpful for debugging. Note that [logs may incur a cost](/stackdriver/pricing),
# especially if your project receives prediction requests at a high QPS.
# Estimate your costs before enabling this option. Default is false.
# Corresponds to the JSON property `onlinePredictionConsoleLogging`
# @return [Boolean]
attr_accessor :online_prediction_console_logging
alias_method :online_prediction_console_logging?, :online_prediction_console_logging
# Optional. If true, online prediction access logs are sent to Cloud Logging.
# These logs are like standard server access logs, containing information like
# timestamp and latency for each request. Note that [logs may incur a cost](/
# stackdriver/pricing), especially if your project receives prediction requests
# at a high queries per second rate (QPS). Estimate your costs before enabling
# this option. Default is false.
# Corresponds to the JSON property `onlinePredictionLogging`
# @return [Boolean]
attr_accessor :online_prediction_logging
alias_method :online_prediction_logging?, :online_prediction_logging
# Optional. The list of regions where the model is going to be deployed. Only
# one region per model is supported. Defaults to 'us-central1' if nothing is set.
# See the available regions for AI Platform services. 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<String>]
attr_accessor :regions
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@default_version = args[:default_version] if args.key?(:default_version)
@description = args[:description] if args.key?(:description)
@etag = args[:etag] if args.key?(:etag)
@labels = args[:labels] if args.key?(:labels)
@name = args[:name] if args.key?(:name)
@online_prediction_console_logging = args[:online_prediction_console_logging] if args.key?(:online_prediction_console_logging)
@online_prediction_logging = args[:online_prediction_logging] if args.key?(:online_prediction_logging)
@regions = args[:regions] if args.key?(:regions)
end
end
# Represents the metadata of the long-running operation.
class GoogleCloudMlV1OperationMetadata
include Google::Apis::Core::Hashable
# The time the operation was submitted.
# Corresponds to the JSON property `createTime`
# @return [String]
attr_accessor :create_time
# The time operation processing completed.
# Corresponds to the JSON property `endTime`
# @return [String]
attr_accessor :end_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 user labels, inherited from the model or the model version being operated
# on.
# Corresponds to the JSON property `labels`
# @return [Hash<String,String>]
attr_accessor :labels
# Contains the name of the model associated with the operation.
# Corresponds to the JSON property `modelName`
# @return [String]
attr_accessor :model_name
# The operation type.
# Corresponds to the JSON property `operationType`
# @return [String]
attr_accessor :operation_type
# Contains the project number associated with the operation.
# Corresponds to the JSON property `projectNumber`
# @return [Fixnum]
attr_accessor :project_number
# The time operation processing started.
# Corresponds to the JSON property `startTime`
# @return [String]
attr_accessor :start_time
# 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.
# Corresponds to the JSON property `version`
# @return [Google::Apis::MlV1::GoogleCloudMlV1Version]
attr_accessor :version
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@create_time = args[:create_time] if args.key?(:create_time)
@end_time = args[:end_time] if args.key?(:end_time)
@is_cancellation_requested = args[:is_cancellation_requested] if args.key?(:is_cancellation_requested)
@labels = args[:labels] if args.key?(:labels)
@model_name = args[:model_name] if args.key?(:model_name)
@operation_type = args[:operation_type] if args.key?(:operation_type)
@project_number = args[:project_number] if args.key?(:project_number)
@start_time = args[:start_time] if args.key?(:start_time)
@version = args[:version] if args.key?(:version)
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<String>]
attr_accessor :categorical_values
# 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<Float>]
attr_accessor :discrete_values
# 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 `maxValue`
# @return [Float]
attr_accessor :max_value
# 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. 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
# 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. 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)
@discrete_values = args[:discrete_values] if args.key?(:discrete_values)
@max_value = args[:max_value] if args.key?(:max_value)
@min_value = args[:min_value] if args.key?(:min_value)
@parameter_name = args[:parameter_name] if args.key?(:parameter_name)
@scale_type = args[:scale_type] if args.key?(:scale_type)
@type = args[:type] if args.key?(:type)
end
end
# Request for predictions to be issued against a trained model.
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
# Represents input parameters for a prediction job.
class GoogleCloudMlV1PredictionInput
include Google::Apis::Core::Hashable
# Optional. Number of records per batch, defaults to 64. The service will buffer
# batch_size number of records in memory before invoking one Tensorflow
# prediction call internally. So take the record size and memory available into
# consideration when setting this parameter.
# Corresponds to the JSON property `batchSize`
# @return [Fixnum]
attr_accessor :batch_size
# Required. The format of the input data files.
# Corresponds to the JSON property `dataFormat`
# @return [String]
attr_accessor :data_format
# Required. The Cloud Storage location of the input data files. May contain
# wildcards.
# Corresponds to the JSON property `inputPaths`
# @return [Array<String>]
attr_accessor :input_paths
# 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 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
# Optional. Format of the output data files, defaults to JSON.
# Corresponds to the JSON property `outputDataFormat`
# @return [String]
attr_accessor :output_data_format
# Required. The output Google Cloud Storage location.
# Corresponds to the JSON property `outputPath`
# @return [String]
attr_accessor :output_path
# Required. The Google Compute Engine region to run the prediction job in. See
# the available regions for AI Platform services.
# Corresponds to the JSON property `region`
# @return [String]
attr_accessor :region
# Optional. The AI Platform runtime version to use for this batch prediction. If
# not set, AI Platform 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
# Optional. The name of the signature defined in the SavedModel to use for this
# job. Please refer to [SavedModel](https://tensorflow.github.io/serving/
# serving_basic.html) for information about how to use signatures. Defaults to [
# DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/
# tf/saved_model/signature_constants) , which is "serving_default".
# Corresponds to the JSON property `signatureName`
# @return [String]
attr_accessor :signature_name
# 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
# 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
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@batch_size = args[:batch_size] if args.key?(:batch_size)
@data_format = args[:data_format] if args.key?(:data_format)
@input_paths = args[:input_paths] if args.key?(:input_paths)
@max_worker_count = args[:max_worker_count] if args.key?(:max_worker_count)
@model_name = args[:model_name] if args.key?(:model_name)
@output_data_format = args[:output_data_format] if args.key?(:output_data_format)
@output_path = args[:output_path] if args.key?(:output_path)
@region = args[:region] if args.key?(:region)
@runtime_version = args[:runtime_version] if args.key?(:runtime_version)
@signature_name = args[:signature_name] if args.key?(:signature_name)
@uri = args[:uri] if args.key?(:uri)
@version_name = args[:version_name] if args.key?(:version_name)
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
# Node hours used by the batch prediction job.
# Corresponds to the JSON property `nodeHours`
# @return [Float]
attr_accessor :node_hours
# The output Google Cloud Storage location provided at the job creation time.
# Corresponds to the JSON property `outputPath`
# @return [String]
attr_accessor :output_path
# 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)
@node_hours = args[:node_hours] if args.key?(:node_hours)
@output_path = args[:output_path] if args.key?(:output_path)
@prediction_count = args[:prediction_count] if args.key?(:prediction_count)
end
end
# Represents the configuration for a replica in a cluster.
class GoogleCloudMlV1ReplicaConfig
include Google::Apis::Core::Hashable
# Represents a hardware accelerator request config. Note that the
# AcceleratorConfig can be used in both Jobs and Versions. Learn more about [
# accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for
# online prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
# Corresponds to the JSON property `acceleratorConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1AcceleratorConfig]
attr_accessor :accelerator_config
# Arguments to the entrypoint command. The following rules apply for
# container_command and container_args: - If you do not supply command or args:
# The defaults defined in the Docker image are used. - If you supply a command
# but no args: The default EntryPoint and the default Cmd defined in the Docker
# image are ignored. Your command is run without any arguments. - If you supply
# only args: The default Entrypoint defined in the Docker image is run with the
# args that you supplied. - If you supply a command and args: The default
# Entrypoint and the default Cmd defined in the Docker image are ignored. Your
# command is run with your args. It cannot be set if custom container image is
# not provided. Note that this field and [TrainingInput.args] are mutually
# exclusive, i.e., both cannot be set at the same time.
# Corresponds to the JSON property `containerArgs`
# @return [Array<String>]
attr_accessor :container_args
# The command with which the replica's custom container is run. If provided, it
# will override default ENTRYPOINT of the docker image. If not provided, the
# docker image's ENTRYPOINT is used. It cannot be set if custom container image
# is not provided. Note that this field and [TrainingInput.args] are mutually
# exclusive, i.e., both cannot be set at the same time.
# Corresponds to the JSON property `containerCommand`
# @return [Array<String>]
attr_accessor :container_command
# Represents the config of disk options.
# Corresponds to the JSON property `diskConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1DiskConfig]
attr_accessor :disk_config
# The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom containers](/ai-platform/
# training/docs/distributed-training-containers).
# Corresponds to the JSON property `imageUri`
# @return [String]
attr_accessor :image_uri
# The AI Platform runtime version that includes a TensorFlow version matching
# the one used in the custom container. This field is required if the replica is
# a TPU worker that uses a custom container. Otherwise, do not specify this
# field. This must be a [runtime version that currently supports training with
# TPUs](/ml-engine/docs/tensorflow/runtime-version-list#tpu-support). Note that
# the version of TensorFlow included in a runtime version may differ from the
# numbering of the runtime version itself, because it may have a different [
# patch version](https://www.tensorflow.org/guide/version_compat#
# semantic_versioning_20). In this field, you must specify the runtime version (
# TensorFlow minor version). For example, if your custom container runs
# TensorFlow `1.x.y`, specify `1.x`.
# Corresponds to the JSON property `tpuTfVersion`
# @return [String]
attr_accessor :tpu_tf_version
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@accelerator_config = args[:accelerator_config] if args.key?(:accelerator_config)
@container_args = args[:container_args] if args.key?(:container_args)
@container_command = args[:container_command] if args.key?(:container_command)
@disk_config = args[:disk_config] if args.key?(:disk_config)
@image_uri = args[:image_uri] if args.key?(:image_uri)
@tpu_tf_version = args[:tpu_tf_version] if args.key?(:tpu_tf_version)
end
end
# Configuration for logging request-response pairs to a BigQuery table. Online
# prediction requests to a model version and the responses to these requests are
# converted to raw strings and saved to the specified BigQuery table. Logging is
# constrained by [BigQuery quotas and limits](/bigquery/quotas). If your project
# exceeds BigQuery quotas or limits, AI Platform Prediction does not log request-
# response pairs, but it continues to serve predictions. If you are using [
# continuous evaluation](/ml-engine/docs/continuous-evaluation/), you do not
# need to specify this configuration manually. Setting up continuous evaluation
# automatically enables logging of request-response pairs.
class GoogleCloudMlV1RequestLoggingConfig
include Google::Apis::Core::Hashable
# Required. Fully qualified BigQuery table name in the following format: "
# project_id.dataset_name.table_name" The specified table must already exist,
# and the "Cloud ML Service Agent" for your project must have permission to
# write to it. The table must have the following [schema](/bigquery/docs/schemas)
# : Field nameType Mode model STRING REQUIRED model_version STRING REQUIRED time
# TIMESTAMP REQUIRED raw_data STRING REQUIRED raw_prediction STRING NULLABLE
# groundtruth STRING NULLABLE
# Corresponds to the JSON property `bigqueryTableName`
# @return [String]
attr_accessor :bigquery_table_name
# Percentage of requests to be logged, expressed as a fraction from 0 to 1. For
# example, if you want to log 10% of requests, enter `0.1`. The sampling window
# is the lifetime of the model version. Defaults to 0.
# Corresponds to the JSON property `samplingPercentage`
# @return [Float]
attr_accessor :sampling_percentage
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@bigquery_table_name = args[:bigquery_table_name] if args.key?(:bigquery_table_name)
@sampling_percentage = args[:sampling_percentage] if args.key?(:sampling_percentage)
end
end
# Specifies HTTP paths served by a custom container. AI Platform Prediction
# sends requests to these paths on the container; the custom container must run
# an HTTP server that responds to these requests with appropriate responses.
# Read [Custom container requirements](/ai-platform/prediction/docs/custom-
# container-requirements) for details on how to create your container image to
# meet these requirements.
class GoogleCloudMlV1RouteMap
include Google::Apis::Core::Hashable
# HTTP path on the container to send health checkss to. AI Platform Prediction
# intermittently sends GET requests to this path on the container's IP address
# and port to check that the container is healthy. Read more about [health
# checks](/ai-platform/prediction/docs/custom-container-requirements#checks).
# For example, if you set this field to `/bar`, then AI Platform Prediction
# intermittently sends a GET request to the `/bar` path on the port of your
# container specified by the first value of Version.container.ports. If you don'
# t specify this field, it defaults to the following value: /v1/models/ MODEL/
# versions/VERSION The placeholders in this value are replaced as follows: *
# MODEL: The name of the parent Model. This does not include the "projects/
# PROJECT_ID/models/" prefix that the API returns in output; it is the bare
# model name, as provided to projects.models.create. * VERSION: The name of the
# model version. This does not include the "projects/PROJECT_ID /models/MODEL/
# versions/" prefix that the API returns in output; it is the bare version name,
# as provided to projects.models.versions.create.
# Corresponds to the JSON property `health`
# @return [String]
attr_accessor :health
# HTTP path on the container to send prediction requests to. AI Platform
# Prediction forwards requests sent using projects.predict to this path on the
# container's IP address and port. AI Platform Prediction then returns the
# container's response in the API response. For example, if you set this field
# to `/foo`, then when AI Platform Prediction receives a prediction request, it
# forwards the request body in a POST request to the `/foo` path on the port of
# your container specified by the first value of Version.container.ports. If you
# don't specify this field, it defaults to the following value: /v1/models/MODEL/
# versions/VERSION:predict The placeholders in this value are replaced as
# follows: * MODEL: The name of the parent Model. This does not include the "
# projects/PROJECT_ID/models/" prefix that the API returns in output; it is the
# bare model name, as provided to projects.models.create. * VERSION: The name of
# the model version. This does not include the "projects/PROJECT_ID/models/MODEL/
# versions/" prefix that the API returns in output; it is the bare version name,
# as provided to projects.models.versions.create.
# Corresponds to the JSON property `predict`
# @return [String]
attr_accessor :predict
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@health = args[:health] if args.key?(:health)
@predict = args[:predict] if args.key?(:predict)
end
end
# An attribution method that approximates Shapley values for features that
# contribute to the label being predicted. A sampling strategy is used to
# approximate the value rather than considering all subsets of features.
class GoogleCloudMlV1SampledShapleyAttribution
include Google::Apis::Core::Hashable
# The number of feature permutations to consider when approximating the Shapley
# values.
# Corresponds to the JSON property `numPaths`
# @return [Fixnum]
attr_accessor :num_paths
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@num_paths = args[:num_paths] if args.key?(:num_paths)
end
end
# All parameters related to scheduling of training jobs.
class GoogleCloudMlV1Scheduling
include Google::Apis::Core::Hashable
# Optional. The maximum job running time, expressed in seconds. The field can
# contain up to nine fractional digits, terminated by `s`. If not specified,
# this field defaults to `604800s` (seven days). If the training job is still
# running after this duration, AI Platform Training cancels it. The duration is
# measured from when the job enters the `RUNNING` state; therefore it does not
# overlap with the duration limited by Scheduling.max_wait_time. For example, if
# you want to ensure your job runs for no more than 2 hours, set this field to `
# 7200s` (2 hours * 60 minutes / hour * 60 seconds / minute). If you submit your
# training job using the `gcloud` tool, you can [specify this field in a `config.
# yaml` file](/ai-platform/training/docs/training-jobs#
# formatting_your_configuration_parameters). For example: ```yaml trainingInput:
# scheduling: maxRunningTime: 7200s ```
# Corresponds to the JSON property `maxRunningTime`
# @return [String]
attr_accessor :max_running_time
# Optional. The maximum job wait time, expressed in seconds. The field can
# contain up to nine fractional digits, terminated by `s`. If not specified,
# there is no limit to the wait time. The minimum for this field is `1800s` (30
# minutes). If the training job has not entered the `RUNNING` state after this
# duration, AI Platform Training cancels it. After the job begins running, it
# can no longer be cancelled due to the maximum wait time. Therefore the
# duration limited by this field does not overlap with the duration limited by
# Scheduling.max_running_time. For example, if the job temporarily stops running
# and retries due to a [VM restart](/ai-platform/training/docs/overview#restarts)
# , this cannot lead to a maximum wait time cancellation. However, independently
# of this constraint, AI Platform Training might stop a job if there are too
# many retries due to exhausted resources in a region. The following example
# describes how you might use this field: To cancel your job if it doesn't start
# running within 1 hour, set this field to `3600s` (1 hour * 60 minutes / hour *
# 60 seconds / minute). If the job is still in the `QUEUED` or `PREPARING` state
# after an hour of waiting, AI Platform Training cancels the job. If you submit
# your training job using the `gcloud` tool, you can [specify this field in a `
# config.yaml` file](/ai-platform/training/docs/training-jobs#
# formatting_your_configuration_parameters). For example: ```yaml trainingInput:
# scheduling: maxWaitTime: 3600s ```
# Corresponds to the JSON property `maxWaitTime`
# @return [String]
attr_accessor :max_wait_time
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@max_running_time = args[:max_running_time] if args.key?(:max_running_time)
@max_wait_time = args[:max_wait_time] if args.key?(:max_wait_time)
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
#
class GoogleCloudMlV1StopTrialRequest
include Google::Apis::Core::Hashable
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
end
end
# A message representing a Study.
class GoogleCloudMlV1Study
include Google::Apis::Core::Hashable
# Output only. Time at which the study was created.
# Corresponds to the JSON property `createTime`
# @return [String]
attr_accessor :create_time
# Output only. A human readable reason why the Study is inactive. This should be
# empty if a study is ACTIVE or COMPLETED.
# Corresponds to the JSON property `inactiveReason`
# @return [String]
attr_accessor :inactive_reason
# Output only. The name of a study.
# Corresponds to the JSON property `name`
# @return [String]
attr_accessor :name
# Output only. The detailed state of a study.
# Corresponds to the JSON property `state`
# @return [String]
attr_accessor :state
# Represents configuration of a study.
# Corresponds to the JSON property `studyConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1StudyConfig]
attr_accessor :study_config
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@create_time = args[:create_time] if args.key?(:create_time)
@inactive_reason = args[:inactive_reason] if args.key?(:inactive_reason)
@name = args[:name] if args.key?(:name)
@state = args[:state] if args.key?(:state)
@study_config = args[:study_config] if args.key?(:study_config)
end
end
# Represents configuration of a study.
class GoogleCloudMlV1StudyConfig
include Google::Apis::Core::Hashable
# The search algorithm specified for the study.
# Corresponds to the JSON property `algorithm`
# @return [String]
attr_accessor :algorithm
# Configuration for Automated Early Stopping of Trials. If no
# implementation_config is set, automated early stopping will not be run.
# Corresponds to the JSON property `automatedStoppingConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1AutomatedStoppingConfig]
attr_accessor :automated_stopping_config
# Metric specs for the study.
# Corresponds to the JSON property `metrics`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1StudyConfigMetricSpec>]
attr_accessor :metrics
# Required. The set of parameters to tune.
# Corresponds to the JSON property `parameters`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1StudyConfigParameterSpec>]
attr_accessor :parameters
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@algorithm = args[:algorithm] if args.key?(:algorithm)
@automated_stopping_config = args[:automated_stopping_config] if args.key?(:automated_stopping_config)
@metrics = args[:metrics] if args.key?(:metrics)
@parameters = args[:parameters] if args.key?(:parameters)
end
end
# Metadata field of a google.longrunning.Operation associated with a
# SuggestTrialsRequest.
class GoogleCloudMlV1SuggestTrialsMetadata
include Google::Apis::Core::Hashable
# The identifier of the client that is requesting the suggestion.
# Corresponds to the JSON property `clientId`
# @return [String]
attr_accessor :client_id
# The time operation was submitted.
# Corresponds to the JSON property `createTime`
# @return [String]
attr_accessor :create_time
# The name of the study that the trial belongs to.
# Corresponds to the JSON property `study`
# @return [String]
attr_accessor :study
# The number of suggestions requested.
# Corresponds to the JSON property `suggestionCount`
# @return [Fixnum]
attr_accessor :suggestion_count
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@client_id = args[:client_id] if args.key?(:client_id)
@create_time = args[:create_time] if args.key?(:create_time)
@study = args[:study] if args.key?(:study)
@suggestion_count = args[:suggestion_count] if args.key?(:suggestion_count)
end
end
# The request message for the SuggestTrial service method.
class GoogleCloudMlV1SuggestTrialsRequest
include Google::Apis::Core::Hashable
# Required. The identifier of the client that is requesting the suggestion. If
# multiple SuggestTrialsRequests have the same `client_id`, the service will
# return the identical suggested trial if the trial is pending, and provide a
# new trial if the last suggested trial was completed.
# Corresponds to the JSON property `clientId`
# @return [String]
attr_accessor :client_id
# Required. The number of suggestions requested.
# Corresponds to the JSON property `suggestionCount`
# @return [Fixnum]
attr_accessor :suggestion_count
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@client_id = args[:client_id] if args.key?(:client_id)
@suggestion_count = args[:suggestion_count] if args.key?(:suggestion_count)
end
end
# This message will be placed in the response field of a completed google.
# longrunning.Operation associated with a SuggestTrials request.
class GoogleCloudMlV1SuggestTrialsResponse
include Google::Apis::Core::Hashable
# The time at which operation processing completed.
# Corresponds to the JSON property `endTime`
# @return [String]
attr_accessor :end_time
# The time at which the operation was started.
# Corresponds to the JSON property `startTime`
# @return [String]
attr_accessor :start_time
# The state of the study.
# Corresponds to the JSON property `studyState`
# @return [String]
attr_accessor :study_state
# A list of trials.
# Corresponds to the JSON property `trials`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1Trial>]
attr_accessor :trials
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@end_time = args[:end_time] if args.key?(:end_time)
@start_time = args[:start_time] if args.key?(:start_time)
@study_state = args[:study_state] if args.key?(:study_state)
@trials = args[:trials] if args.key?(:trials)
end
end
# Represents input parameters for a training job. When using the gcloud command
# to submit your training job, you can specify the input parameters as command-
# line arguments and/or in a YAML configuration file referenced from the --
# config command-line argument. For details, see the guide to [submitting a
# training job](/ai-platform/training/docs/training-jobs).
class GoogleCloudMlV1TrainingInput
include Google::Apis::Core::Hashable
# Optional. Command-line arguments passed to the training application when it
# starts. If your job uses a custom container, then the arguments are passed to
# the container's `ENTRYPOINT` command.
# Corresponds to the JSON property `args`
# @return [Array<String>]
attr_accessor :args
# Represents a custom encryption key configuration that can be applied to a
# resource.
# Corresponds to the JSON property `encryptionConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1EncryptionConfig]
attr_accessor :encryption_config
# Represents the configuration for a replica in a cluster.
# Corresponds to the JSON property `evaluatorConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1ReplicaConfig]
attr_accessor :evaluator_config
# Optional. The number of evaluator replicas to use for the training job. Each
# replica in the cluster will be of the type specified in `evaluator_type`. This
# value can only be used when `scale_tier` is set to `CUSTOM`. If you set this
# value, you must also set `evaluator_type`. The default value is zero.
# Corresponds to the JSON property `evaluatorCount`
# @return [Fixnum]
attr_accessor :evaluator_count
# Optional. Specifies the type of virtual machine to use for your training job's
# evaluator nodes. The supported values are the same as those described in the
# entry for `masterType`. This value must be consistent with the category of
# machine type that `masterType` uses. In other words, both must be Compute
# Engine machine types or both must be legacy machine types. This value must be
# present when `scaleTier` is set to `CUSTOM` and `evaluatorCount` is greater
# than zero.
# Corresponds to the JSON property `evaluatorType`
# @return [String]
attr_accessor :evaluator_type
# Represents a set of hyperparameters to optimize.
# Corresponds to the JSON property `hyperparameters`
# @return [Google::Apis::MlV1::GoogleCloudMlV1HyperparameterSpec]
attr_accessor :hyperparameters
# 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 the configuration for a replica in a cluster.
# Corresponds to the JSON property `masterConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1ReplicaConfig]
attr_accessor :master_config
# Optional. Specifies the type of virtual machine to use for your training job's
# master worker. You must specify this field when `scaleTier` is set to `CUSTOM`.
# You can use certain Compute Engine machine types directly in this field. The
# following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-
# standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-
# highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-
# 32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `
# n1-highcpu-64` - `n1-highcpu-96` Learn more about [using Compute Engine
# machine types](/ml-engine/docs/machine-types#compute-engine-machine-types).
# Alternatively, you can use the following legacy machine types: - `standard` - `
# large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `
# standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100`
# - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `
# complex_model_m_v100` - `complex_model_l_v100` Learn more about [using legacy
# machine types](/ml-engine/docs/machine-types#legacy-machine-types). Finally,
# if you want to use a TPU for training, specify `cloud_tpu` in this field.
# Learn more about the [special configuration options for training with TPUs](/
# ml-engine/docs/tensorflow/using-tpus#configuring_a_custom_tpu_machine).
# Corresponds to the JSON property `masterType`
# @return [String]
attr_accessor :master_type
# Optional. The full name of the [Compute Engine network](/vpc/docs/vpc) to
# which the Job is peered. For example, `projects/12345/global/networks/myVPC`.
# The format of this field is `projects/`project`/global/networks/`network``,
# where `project` is a project number (like `12345`) and `network` is network
# name. Private services access must already be configured for the network. If
# left unspecified, the Job is not peered with any network. [Learn about using
# VPC Network Peering.](/ai-platform/training/docs/vpc-peering).
# Corresponds to the JSON property `network`
# @return [String]
attr_accessor :network
# 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<String>]
attr_accessor :package_uris
# Represents the configuration for a replica in a cluster.
# Corresponds to the JSON property `parameterServerConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1ReplicaConfig]
attr_accessor :parameter_server_config
# 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`.
# The default value is zero.
# Corresponds to the JSON property `parameterServerCount`
# @return [Fixnum]
attr_accessor :parameter_server_count
# 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 consistent with the category of
# machine type that `masterType` uses. In other words, both must be Compute
# Engine machine types or both must be legacy machine types. 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. The Python module name to run after installing the packages.
# Corresponds to the JSON property `pythonModule`
# @return [String]
attr_accessor :python_module
# Optional. The version of Python used in training. You must either specify this
# field or specify `masterConfig.imageUri`. The following Python versions are
# available: * Python '3.7' is available when `runtime_version` is set to '1.15'
# or later. * Python '3.5' is available when `runtime_version` is set to a
# version from '1.4' to '1.14'. * Python '2.7' is available when `
# runtime_version` is set to '1.15' or earlier. Read more about the Python
# versions available for [each runtime version](/ml-engine/docs/runtime-version-
# list).
# Corresponds to the JSON property `pythonVersion`
# @return [String]
attr_accessor :python_version
# Required. The region to run the training job in. See the [available regions](/
# ai-platform/training/docs/regions) for AI Platform Training.
# Corresponds to the JSON property `region`
# @return [String]
attr_accessor :region
# Optional. The AI Platform runtime version to use for training. You must either
# specify this field or specify `masterConfig.imageUri`. For more information,
# see the [runtime version list](/ai-platform/training/docs/runtime-version-list)
# and learn [how to manage runtime versions](/ai-platform/training/docs/
# versioning).
# Corresponds to the JSON property `runtimeVersion`
# @return [String]
attr_accessor :runtime_version
# 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
# All parameters related to scheduling of training jobs.
# Corresponds to the JSON property `scheduling`
# @return [Google::Apis::MlV1::GoogleCloudMlV1Scheduling]
attr_accessor :scheduling
# Optional. The email address of a service account to use when running the
# training appplication. You must have the `iam.serviceAccounts.actAs`
# permission for the specified service account. In addition, the AI Platform
# Training Google-managed service account must have the `roles/iam.
# serviceAccountAdmin` role for the specified service account. [Learn more about
# configuring a service account.](/ai-platform/training/docs/custom-service-
# account) If not specified, the AI Platform Training Google-managed service
# account is used by default.
# Corresponds to the JSON property `serviceAccount`
# @return [String]
attr_accessor :service_account
# Optional. Use `chief` instead of `master` in the `TF_CONFIG` environment
# variable when training with a custom container. Defaults to `false`. [Learn
# more about this field.](/ai-platform/training/docs/distributed-training-
# details#chief-versus-master) This field has no effect for training jobs that
# don't use a custom container.
# Corresponds to the JSON property `useChiefInTfConfig`
# @return [Boolean]
attr_accessor :use_chief_in_tf_config
alias_method :use_chief_in_tf_config?, :use_chief_in_tf_config
# Represents the configuration for a replica in a cluster.
# Corresponds to the JSON property `workerConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1ReplicaConfig]
attr_accessor :worker_config
# 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`. The default value is zero.
# 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
# worker nodes. The supported values are the same as those described in the
# entry for `masterType`. This value must be consistent with the category of
# machine type that `masterType` uses. In other words, both must be Compute
# Engine machine types or both must be legacy machine types. If you use `
# cloud_tpu` for this value, see special instructions for [configuring a custom
# TPU machine](/ml-engine/docs/tensorflow/using-tpus#
# configuring_a_custom_tpu_machine). 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
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@args = args[:args] if args.key?(:args)
@encryption_config = args[:encryption_config] if args.key?(:encryption_config)
@evaluator_config = args[:evaluator_config] if args.key?(:evaluator_config)
@evaluator_count = args[:evaluator_count] if args.key?(:evaluator_count)
@evaluator_type = args[:evaluator_type] if args.key?(:evaluator_type)
@hyperparameters = args[:hyperparameters] if args.key?(:hyperparameters)
@job_dir = args[:job_dir] if args.key?(:job_dir)
@master_config = args[:master_config] if args.key?(:master_config)
@master_type = args[:master_type] if args.key?(:master_type)
@network = args[:network] if args.key?(:network)
@package_uris = args[:package_uris] if args.key?(:package_uris)
@parameter_server_config = args[:parameter_server_config] if args.key?(:parameter_server_config)
@parameter_server_count = args[:parameter_server_count] if args.key?(:parameter_server_count)
@parameter_server_type = args[:parameter_server_type] if args.key?(:parameter_server_type)
@python_module = args[:python_module] if args.key?(:python_module)
@python_version = args[:python_version] if args.key?(:python_version)
@region = args[:region] if args.key?(:region)
@runtime_version = args[:runtime_version] if args.key?(:runtime_version)
@scale_tier = args[:scale_tier] if args.key?(:scale_tier)
@scheduling = args[:scheduling] if args.key?(:scheduling)
@service_account = args[:service_account] if args.key?(:service_account)
@use_chief_in_tf_config = args[:use_chief_in_tf_config] if args.key?(:use_chief_in_tf_config)
@worker_config = args[:worker_config] if args.key?(:worker_config)
@worker_count = args[:worker_count] if args.key?(:worker_count)
@worker_type = args[:worker_type] if args.key?(:worker_type)
end
end
# Represents results of a training job. Output only.
class GoogleCloudMlV1TrainingOutput
include Google::Apis::Core::Hashable
# Represents output related to a built-in algorithm Job.
# Corresponds to the JSON property `builtInAlgorithmOutput`
# @return [Google::Apis::MlV1::GoogleCloudMlV1BuiltInAlgorithmOutput]
attr_accessor :built_in_algorithm_output
# 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
# The amount of ML units consumed by the job.
# Corresponds to the JSON property `consumedMLUnits`
# @return [Float]
attr_accessor :consumed_ml_units
# The TensorFlow summary tag name used for optimizing hyperparameter tuning
# trials. See [`HyperparameterSpec.hyperparameterMetricTag`](#HyperparameterSpec.
# FIELDS.hyperparameter_metric_tag) for more information. Only set for
# hyperparameter tuning jobs.
# Corresponds to the JSON property `hyperparameterMetricTag`
# @return [String]
attr_accessor :hyperparameter_metric_tag
# Whether this job is a built-in Algorithm job.
# Corresponds to the JSON property `isBuiltInAlgorithmJob`
# @return [Boolean]
attr_accessor :is_built_in_algorithm_job
alias_method :is_built_in_algorithm_job?, :is_built_in_algorithm_job
# 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
# Results for individual Hyperparameter trials. Only set for hyperparameter
# tuning jobs.
# Corresponds to the JSON property `trials`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1HyperparameterOutput>]
attr_accessor :trials
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@built_in_algorithm_output = args[:built_in_algorithm_output] if args.key?(:built_in_algorithm_output)
@completed_trial_count = args[:completed_trial_count] if args.key?(:completed_trial_count)
@consumed_ml_units = args[:consumed_ml_units] if args.key?(:consumed_ml_units)
@hyperparameter_metric_tag = args[:hyperparameter_metric_tag] if args.key?(:hyperparameter_metric_tag)
@is_built_in_algorithm_job = args[:is_built_in_algorithm_job] if args.key?(:is_built_in_algorithm_job)
@is_hyperparameter_tuning_job = args[:is_hyperparameter_tuning_job] if args.key?(:is_hyperparameter_tuning_job)
@trials = args[:trials] if args.key?(:trials)
end
end
# A message representing a trial.
class GoogleCloudMlV1Trial
include Google::Apis::Core::Hashable
# Output only. The identifier of the client that originally requested this trial.
# Corresponds to the JSON property `clientId`
# @return [String]
attr_accessor :client_id
# Output only. Time at which the trial's status changed to COMPLETED.
# Corresponds to the JSON property `endTime`
# @return [String]
attr_accessor :end_time
# A message representing a measurement.
# Corresponds to the JSON property `finalMeasurement`
# @return [Google::Apis::MlV1::GoogleCloudMlV1Measurement]
attr_accessor :final_measurement
# Output only. A human readable string describing why the trial is infeasible.
# This should only be set if trial_infeasible is true.
# Corresponds to the JSON property `infeasibleReason`
# @return [String]
attr_accessor :infeasible_reason
# A list of measurements that are strictly lexicographically ordered by their
# induced tuples (steps, elapsed_time). These are used for early stopping
# computations.
# Corresponds to the JSON property `measurements`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1Measurement>]
attr_accessor :measurements
# Output only. Name of the trial assigned by the service.
# Corresponds to the JSON property `name`
# @return [String]
attr_accessor :name
# The parameters of the trial.
# Corresponds to the JSON property `parameters`
# @return [Array<Google::Apis::MlV1::GoogleCloudMlV1TrialParameter>]
attr_accessor :parameters
# Output only. Time at which the trial was started.
# Corresponds to the JSON property `startTime`
# @return [String]
attr_accessor :start_time
# The detailed state of a trial.
# Corresponds to the JSON property `state`
# @return [String]
attr_accessor :state
# Output only. If true, the parameters in this trial are not attempted again.
# Corresponds to the JSON property `trialInfeasible`
# @return [Boolean]
attr_accessor :trial_infeasible
alias_method :trial_infeasible?, :trial_infeasible
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@client_id = args[:client_id] if args.key?(:client_id)
@end_time = args[:end_time] if args.key?(:end_time)
@final_measurement = args[:final_measurement] if args.key?(:final_measurement)
@infeasible_reason = args[:infeasible_reason] if args.key?(:infeasible_reason)
@measurements = args[:measurements] if args.key?(:measurements)
@name = args[:name] if args.key?(:name)
@parameters = args[:parameters] if args.key?(:parameters)
@start_time = args[:start_time] if args.key?(:start_time)
@state = args[:state] if args.key?(:state)
@trial_infeasible = args[:trial_infeasible] if args.key?(:trial_infeasible)
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.
class GoogleCloudMlV1Version
include Google::Apis::Core::Hashable
# Represents a hardware accelerator request config. Note that the
# AcceleratorConfig can be used in both Jobs and Versions. Learn more about [
# accelerators for training](/ml-engine/docs/using-gpus) and [accelerators for
# online prediction](/ml-engine/docs/machine-types-online-prediction#gpus).
# Corresponds to the JSON property `acceleratorConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1AcceleratorConfig]
attr_accessor :accelerator_config
# Options for automatically scaling a model.
# Corresponds to the JSON property `autoScaling`
# @return [Google::Apis::MlV1::GoogleCloudMlV1AutoScaling]
attr_accessor :auto_scaling
# Specification of a custom container for serving predictions. This message is a
# subset of the [Kubernetes Container v1 core specification](https://kubernetes.
# io/docs/reference/generated/kubernetes-api/v1.18/#container-v1-core).
# Corresponds to the JSON property `container`
# @return [Google::Apis::MlV1::GoogleCloudMlV1ContainerSpec]
attr_accessor :container
# Output only. The time the version was created.
# Corresponds to the JSON property `createTime`
# @return [String]
attr_accessor :create_time
# The Cloud Storage URI of a directory containing trained model artifacts to be
# used to create the model version. See the [guide to deploying models](/ai-
# platform/prediction/docs/deploying-models) for more information. The total
# number of files under this directory must not exceed 1000. During projects.
# models.versions.create, AI Platform Prediction copies all files from the
# specified directory to a location managed by the service. From then on, AI
# Platform Prediction uses these copies of the model artifacts to serve
# predictions, not the original files in Cloud Storage, so this location is
# useful only as a historical record. If you specify container, then this field
# is optional. Otherwise, it is required. Learn [how to use this field with a
# custom container](/ai-platform/prediction/docs/custom-container-requirements#
# artifacts).
# Corresponds to the JSON property `deploymentUri`
# @return [String]
attr_accessor :deployment_uri
# Optional. The description specified for the version when it was created.
# Corresponds to the JSON property `description`
# @return [String]
attr_accessor :description
# Output only. The details of a failure or a cancellation.
# Corresponds to the JSON property `errorMessage`
# @return [String]
attr_accessor :error_message
# `etag` is used for optimistic concurrency control as a way to help prevent
# simultaneous updates of a model from overwriting each other. It is strongly
# suggested that systems make use of the `etag` in the read-modify-write cycle
# to perform model updates in order to avoid race conditions: An `etag` is
# returned in the response to `GetVersion`, and systems are expected to put that
# etag in the request to `UpdateVersion` to ensure that their change will be
# applied to the model as intended.
# Corresponds to the JSON property `etag`
# NOTE: Values are automatically base64 encoded/decoded in the client library.
# @return [String]
attr_accessor :etag
# Message holding configuration options for explaining model predictions. There
# are three feature attribution methods supported for TensorFlow models:
# integrated gradients, sampled Shapley, and XRAI. [Learn more about feature
# attributions.](/ai-platform/prediction/docs/ai-explanations/overview)
# Corresponds to the JSON property `explanationConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1ExplanationConfig]
attr_accessor :explanation_config
# Optional. The machine learning framework AI Platform uses to train this
# version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, `XGBOOST`.
# If you do not specify a framework, AI Platform will analyze files in the
# deployment_uri to determine a framework. If you choose `SCIKIT_LEARN` or `
# XGBOOST`, you must also set the runtime version of the model to 1.4 or greater.
# Do **not** specify a framework if you're deploying a [custom prediction
# routine](/ai-platform/prediction/docs/custom-prediction-routines) or if you're
# using a [custom container](/ai-platform/prediction/docs/use-custom-container).
# Corresponds to the JSON property `framework`
# @return [String]
attr_accessor :framework
# 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.
# Corresponds to the JSON property `isDefault`
# @return [Boolean]
attr_accessor :is_default
alias_method :is_default?, :is_default
# Optional. One or more labels that you can add, to organize your model versions.
# Each label is a key-value pair, where both the key and the value are
# arbitrary strings that you supply. For more information, see the documentation
# on using labels.
# Corresponds to the JSON property `labels`
# @return [Hash<String,String>]
attr_accessor :labels
# 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 type of machine on which to serve the model. Currently only
# applies to online prediction service. If this field is not specified, it
# defaults to `mls1-c1-m2`. Online prediction supports the following machine
# types: * `mls1-c1-m2` * `mls1-c4-m2` * `n1-standard-2` * `n1-standard-4` * `n1-
# standard-8` * `n1-standard-16` * `n1-standard-32` * `n1-highmem-2` * `n1-
# highmem-4` * `n1-highmem-8` * `n1-highmem-16` * `n1-highmem-32` * `n1-highcpu-
# 2` * `n1-highcpu-4` * `n1-highcpu-8` * `n1-highcpu-16` * `n1-highcpu-32` `mls1-
# c4-m2` is in beta. All other machine types are generally available. Learn more
# about the [differences between machine types](/ml-engine/docs/machine-types-
# online-prediction).
# Corresponds to the JSON property `machineType`
# @return [String]
attr_accessor :machine_type
# 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
# Optional. Cloud Storage paths (`gs://…`) of packages for [custom prediction
# routines](/ml-engine/docs/tensorflow/custom-prediction-routines) or [scikit-
# learn pipelines with custom code](/ml-engine/docs/scikit/exporting-for-
# prediction#custom-pipeline-code). For a custom prediction routine, one of
# these packages must contain your Predictor class (see [`predictionClass`](#
# Version.FIELDS.prediction_class)). Additionally, include any dependencies used
# by your Predictor or scikit-learn pipeline uses that are not already included
# in your selected [runtime version](/ml-engine/docs/tensorflow/runtime-version-
# list). If you specify this field, you must also set [`runtimeVersion`](#
# Version.FIELDS.runtime_version) to 1.4 or greater.
# Corresponds to the JSON property `packageUris`
# @return [Array<String>]
attr_accessor :package_uris
# Optional. The fully qualified name (module_name.class_name) of a class that
# implements the Predictor interface described in this reference field. The
# module containing this class should be included in a package provided to the [`
# packageUris` field](#Version.FIELDS.package_uris). Specify this field if and
# only if you are deploying a [custom prediction routine (beta)](/ml-engine/docs/
# tensorflow/custom-prediction-routines). If you specify this field, you must
# set [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and
# you must set `machineType` to a [legacy (MLS1) machine type](/ml-engine/docs/
# machine-types-online-prediction). The following code sample provides the
# Predictor interface: class Predictor(object): """Interface for constructing
# custom predictors.""" def predict(self, instances, **kwargs): """Performs
# custom prediction. Instances are the decoded values from the request. They
# have already been deserialized from JSON. Args: instances: A list of
# prediction input instances. **kwargs: A dictionary of keyword args provided as
# additional fields on the predict request body. Returns: A list of outputs
# containing the prediction results. This list must be JSON serializable. """
# raise NotImplementedError() @classmethod def from_path(cls, model_dir): """
# Creates an instance of Predictor using the given path. Loading of the
# predictor should be done in this method. Args: model_dir: The local directory
# that contains the exported model file along with any additional files uploaded
# when creating the version resource. Returns: An instance implementing this
# Predictor class. """ raise NotImplementedError() Learn more about [the
# Predictor interface and custom prediction routines](/ml-engine/docs/tensorflow/
# custom-prediction-routines).
# Corresponds to the JSON property `predictionClass`
# @return [String]
attr_accessor :prediction_class
# Required. The version of Python used in prediction. The following Python
# versions are available: * Python '3.7' is available when `runtime_version` is
# set to '1.15' or later. * Python '3.5' is available when `runtime_version` is
# set to a version from '1.4' to '1.14'. * Python '2.7' is available when `
# runtime_version` is set to '1.15' or earlier. Read more about the Python
# versions available for [each runtime version](/ml-engine/docs/runtime-version-
# list).
# Corresponds to the JSON property `pythonVersion`
# @return [String]
attr_accessor :python_version
# Configuration for logging request-response pairs to a BigQuery table. Online
# prediction requests to a model version and the responses to these requests are
# converted to raw strings and saved to the specified BigQuery table. Logging is
# constrained by [BigQuery quotas and limits](/bigquery/quotas). If your project
# exceeds BigQuery quotas or limits, AI Platform Prediction does not log request-
# response pairs, but it continues to serve predictions. If you are using [
# continuous evaluation](/ml-engine/docs/continuous-evaluation/), you do not
# need to specify this configuration manually. Setting up continuous evaluation
# automatically enables logging of request-response pairs.
# Corresponds to the JSON property `requestLoggingConfig`
# @return [Google::Apis::MlV1::GoogleCloudMlV1RequestLoggingConfig]
attr_accessor :request_logging_config
# Specifies HTTP paths served by a custom container. AI Platform Prediction
# sends requests to these paths on the container; the custom container must run
# an HTTP server that responds to these requests with appropriate responses.
# Read [Custom container requirements](/ai-platform/prediction/docs/custom-
# container-requirements) for details on how to create your container image to
# meet these requirements.
# Corresponds to the JSON property `routes`
# @return [Google::Apis::MlV1::GoogleCloudMlV1RouteMap]
attr_accessor :routes
# Required. The AI Platform runtime version to use for this deployment. For more
# information, see the [runtime version list](/ml-engine/docs/runtime-version-
# list) and [how to manage runtime versions](/ml-engine/docs/versioning).
# Corresponds to the JSON property `runtimeVersion`
# @return [String]
attr_accessor :runtime_version
# Optional. Specifies the service account for resource access control. If you
# specify this field, then you must also specify either the `containerSpec` or
# the `predictionClass` field. Learn more about [using a custom service account](
# /ai-platform/prediction/docs/custom-service-account).
# Corresponds to the JSON property `serviceAccount`
# @return [String]
attr_accessor :service_account
# Output only. The state of a version.
# Corresponds to the JSON property `state`
# @return [String]
attr_accessor :state
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@accelerator_config = args[:accelerator_config] if args.key?(:accelerator_config)
@auto_scaling = args[:auto_scaling] if args.key?(:auto_scaling)
@container = args[:container] if args.key?(:container)
@create_time = args[:create_time] if args.key?(:create_time)
@deployment_uri = args[:deployment_uri] if args.key?(:deployment_uri)
@description = args[:description] if args.key?(:description)
@error_message = args[:error_message] if args.key?(:error_message)
@etag = args[:etag] if args.key?(:etag)
@explanation_config = args[:explanation_config] if args.key?(:explanation_config)
@framework = args[:framework] if args.key?(:framework)
@is_default = args[:is_default] if args.key?(:is_default)
@labels = args[:labels] if args.key?(:labels)
@last_use_time = args[:last_use_time] if args.key?(:last_use_time)
@machine_type = args[:machine_type] if args.key?(:machine_type)
@manual_scaling = args[:manual_scaling] if args.key?(:manual_scaling)
@name = args[:name] if args.key?(:name)
@package_uris = args[:package_uris] if args.key?(:package_uris)
@prediction_class = args[:prediction_class] if args.key?(:prediction_class)
@python_version = args[:python_version] if args.key?(:python_version)
@request_logging_config = args[:request_logging_config] if args.key?(:request_logging_config)
@routes = args[:routes] if args.key?(:routes)
@runtime_version = args[:runtime_version] if args.key?(:runtime_version)
@service_account = args[:service_account] if args.key?(:service_account)
@state = args[:state] if args.key?(:state)
end
end
# Attributes credit by computing the XRAI taking advantage of the model's fully
# differentiable structure. Refer to this paper for more details: https://arxiv.
# org/abs/1906.02825 Currently only implemented for models with natural image
# inputs.
class GoogleCloudMlV1XraiAttribution
include Google::Apis::Core::Hashable
# Number of steps for approximating the path integral. A good value to start is
# 50 and gradually increase until the sum to diff property is met within the
# desired error range.
# Corresponds to the JSON property `numIntegralSteps`
# @return [Fixnum]
attr_accessor :num_integral_steps
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@num_integral_steps = args[:num_integral_steps] if args.key?(:num_integral_steps)
end
end
# Specifies the audit configuration for a service. The configuration determines
# which permission types are logged, and what identities, if any, are exempted
# from logging. An AuditConfig must have one or more AuditLogConfigs. If there
# are AuditConfigs for both `allServices` and a specific service, the union of
# the two AuditConfigs is used for that service: the log_types specified in each
# AuditConfig are enabled, and the exempted_members in each AuditLogConfig are
# exempted. Example Policy with multiple AuditConfigs: ` "audit_configs": [ ` "
# service": "allServices", "audit_log_configs": [ ` "log_type": "DATA_READ", "
# exempted_members": [ "user:jose@example.com" ] `, ` "log_type": "DATA_WRITE" `,
# ` "log_type": "ADMIN_READ" ` ] `, ` "service": "sampleservice.googleapis.com",
# "audit_log_configs": [ ` "log_type": "DATA_READ" `, ` "log_type": "DATA_WRITE"
# , "exempted_members": [ "user:aliya@example.com" ] ` ] ` ] ` For sampleservice,
# this policy enables DATA_READ, DATA_WRITE and ADMIN_READ logging. It also
# exempts jose@example.com from DATA_READ logging, and aliya@example.com from
# DATA_WRITE logging.
class GoogleIamV1AuditConfig
include Google::Apis::Core::Hashable
# The configuration for logging of each type of permission.
# Corresponds to the JSON property `auditLogConfigs`
# @return [Array<Google::Apis::MlV1::GoogleIamV1AuditLogConfig>]
attr_accessor :audit_log_configs
# Specifies a service that will be enabled for audit logging. For example, `
# storage.googleapis.com`, `cloudsql.googleapis.com`. `allServices` is a special
# value that covers all services.
# Corresponds to the JSON property `service`
# @return [String]
attr_accessor :service
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@audit_log_configs = args[:audit_log_configs] if args.key?(:audit_log_configs)
@service = args[:service] if args.key?(:service)
end
end
# Provides the configuration for logging a type of permissions. Example: ` "
# audit_log_configs": [ ` "log_type": "DATA_READ", "exempted_members": [ "user:
# jose@example.com" ] `, ` "log_type": "DATA_WRITE" ` ] ` This enables '
# DATA_READ' and 'DATA_WRITE' logging, while exempting jose@example.com from
# DATA_READ logging.
class GoogleIamV1AuditLogConfig
include Google::Apis::Core::Hashable
# Specifies the identities that do not cause logging for this type of permission.
# Follows the same format of Binding.members.
# Corresponds to the JSON property `exemptedMembers`
# @return [Array<String>]
attr_accessor :exempted_members
# The log type that this config enables.
# Corresponds to the JSON property `logType`
# @return [String]
attr_accessor :log_type
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@exempted_members = args[:exempted_members] if args.key?(:exempted_members)
@log_type = args[:log_type] if args.key?(:log_type)
end
end
# Associates `members` with a `role`.
class GoogleIamV1Binding
include Google::Apis::Core::Hashable
# Represents a textual expression in the Common Expression Language (CEL) syntax.
# CEL is a C-like expression language. The syntax and semantics of CEL are
# documented at https://github.com/google/cel-spec. Example (Comparison): title:
# "Summary size limit" description: "Determines if a summary is less than 100
# chars" expression: "document.summary.size() < 100" Example (Equality): title: "
# Requestor is owner" description: "Determines if requestor is the document
# owner" expression: "document.owner == request.auth.claims.email" Example (
# Logic): title: "Public documents" description: "Determine whether the document
# should be publicly visible" expression: "document.type != 'private' &&
# document.type != 'internal'" Example (Data Manipulation): title: "Notification
# string" description: "Create a notification string with a timestamp."
# expression: "'New message received at ' + string(document.create_time)" The
# exact variables and functions that may be referenced within an expression are
# determined by the service that evaluates it. See the service documentation for
# additional information.
# Corresponds to the JSON property `condition`
# @return [Google::Apis::MlV1::GoogleTypeExpr]
attr_accessor :condition
# Specifies the identities requesting access for a Cloud Platform resource. `
# members` can have the following values: * `allUsers`: A special identifier
# that represents anyone who is on the internet; with or without a Google
# account. * `allAuthenticatedUsers`: A special identifier that represents
# anyone who is authenticated with a Google account or a service account. * `
# user:`emailid``: An email address that represents a specific Google account.
# For example, `alice@example.com` . * `serviceAccount:`emailid``: An email
# address that represents a service account. For example, `my-other-app@appspot.
# gserviceaccount.com`. * `group:`emailid``: An email address that represents a
# Google group. For example, `admins@example.com`. * `deleted:user:`emailid`?uid=
# `uniqueid``: An email address (plus unique identifier) representing a user
# that has been recently deleted. For example, `alice@example.com?uid=
# 123456789012345678901`. If the user is recovered, this value reverts to `user:`
# emailid`` and the recovered user retains the role in the binding. * `deleted:
# serviceAccount:`emailid`?uid=`uniqueid``: An email address (plus unique
# identifier) representing a service account that has been recently deleted. For
# example, `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`.
# If the service account is undeleted, this value reverts to `serviceAccount:`
# emailid`` and the undeleted service account retains the role in the binding. *
# `deleted:group:`emailid`?uid=`uniqueid``: An email address (plus unique
# identifier) representing a Google group that has been recently deleted. For
# example, `admins@example.com?uid=123456789012345678901`. If the group is
# recovered, this value reverts to `group:`emailid`` and the recovered group
# retains the role in the binding. * `domain:`domain``: The G Suite domain (
# primary) that represents all the users of that domain. For example, `google.
# com` or `example.com`.
# Corresponds to the JSON property `members`
# @return [Array<String>]
attr_accessor :members
# Role that is assigned to `members`. For example, `roles/viewer`, `roles/editor`
# , or `roles/owner`.
# Corresponds to the JSON property `role`
# @return [String]
attr_accessor :role
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@condition = args[:condition] if args.key?(:condition)
@members = args[:members] if args.key?(:members)
@role = args[:role] if args.key?(:role)
end
end
# An Identity and Access Management (IAM) policy, which specifies access
# controls for Google Cloud resources. A `Policy` is a collection of `bindings`.
# A `binding` binds one or more `members` to a single `role`. Members can be
# user accounts, service accounts, Google groups, and domains (such as G Suite).
# A `role` is a named list of permissions; each `role` can be an IAM predefined
# role or a user-created custom role. For some types of Google Cloud resources,
# a `binding` can also specify a `condition`, which is a logical expression that
# allows access to a resource only if the expression evaluates to `true`. A
# condition can add constraints based on attributes of the request, the resource,
# or both. To learn which resources support conditions in their IAM policies,
# see the [IAM documentation](https://cloud.google.com/iam/help/conditions/
# resource-policies). **JSON example:** ` "bindings": [ ` "role": "roles/
# resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "
# group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@
# appspot.gserviceaccount.com" ] `, ` "role": "roles/resourcemanager.
# organizationViewer", "members": [ "user:eve@example.com" ], "condition": ` "
# title": "expirable access", "description": "Does not grant access after Sep
# 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", `
# ` ], "etag": "BwWWja0YfJA=", "version": 3 ` **YAML example:** bindings: -
# members: - user:mike@example.com - group:admins@example.com - domain:google.
# com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/
# resourcemanager.organizationAdmin - members: - user:eve@example.com role:
# roles/resourcemanager.organizationViewer condition: title: expirable access
# description: Does not grant access after Sep 2020 expression: request.time <
# timestamp('2020-10-01T00:00:00.000Z') - etag: BwWWja0YfJA= - version: 3 For a
# description of IAM and its features, see the [IAM documentation](https://cloud.
# google.com/iam/docs/).
class GoogleIamV1Policy
include Google::Apis::Core::Hashable
# Specifies cloud audit logging configuration for this policy.
# Corresponds to the JSON property `auditConfigs`
# @return [Array<Google::Apis::MlV1::GoogleIamV1AuditConfig>]
attr_accessor :audit_configs
# Associates a list of `members` to a `role`. Optionally, may specify a `
# condition` that determines how and when the `bindings` are applied. Each of
# the `bindings` must contain at least one member.
# Corresponds to the JSON property `bindings`
# @return [Array<Google::Apis::MlV1::GoogleIamV1Binding>]
attr_accessor :bindings
# `etag` is used for optimistic concurrency control as a way to help prevent
# simultaneous updates of a policy from overwriting each other. It is strongly
# suggested that systems make use of the `etag` in the read-modify-write cycle
# to perform policy updates in order to avoid race conditions: An `etag` is
# returned in the response to `getIamPolicy`, and systems are expected to put
# that etag in the request to `setIamPolicy` to ensure that their change will be
# applied to the same version of the policy. **Important:** If you use IAM
# Conditions, you must include the `etag` field whenever you call `setIamPolicy`.
# If you omit this field, then IAM allows you to overwrite a version `3` policy
# with a version `1` policy, and all of the conditions in the version `3` policy
# are lost.
# Corresponds to the JSON property `etag`
# NOTE: Values are automatically base64 encoded/decoded in the client library.
# @return [String]
attr_accessor :etag
# Specifies the format of the policy. Valid values are `0`, `1`, and `3`.
# Requests that specify an invalid value are rejected. Any operation that
# affects conditional role bindings must specify version `3`. This requirement
# applies to the following operations: * Getting a policy that includes a
# conditional role binding * Adding a conditional role binding to a policy *
# Changing a conditional role binding in a policy * Removing any role binding,
# with or without a condition, from a policy that includes conditions **
# Important:** If you use IAM Conditions, you must include the `etag` field
# whenever you call `setIamPolicy`. If you omit this field, then IAM allows you
# to overwrite a version `3` policy with a version `1` policy, and all of the
# conditions in the version `3` policy are lost. If a policy does not include
# any conditions, operations on that policy may specify any valid version or
# leave the field unset. To learn which resources support conditions in their
# IAM policies, see the [IAM documentation](https://cloud.google.com/iam/help/
# conditions/resource-policies).
# Corresponds to the JSON property `version`
# @return [Fixnum]
attr_accessor :version
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@audit_configs = args[:audit_configs] if args.key?(:audit_configs)
@bindings = args[:bindings] if args.key?(:bindings)
@etag = args[:etag] if args.key?(:etag)
@version = args[:version] if args.key?(:version)
end
end
# Request message for `SetIamPolicy` method.
class GoogleIamV1SetIamPolicyRequest
include Google::Apis::Core::Hashable
# An Identity and Access Management (IAM) policy, which specifies access
# controls for Google Cloud resources. A `Policy` is a collection of `bindings`.
# A `binding` binds one or more `members` to a single `role`. Members can be
# user accounts, service accounts, Google groups, and domains (such as G Suite).
# A `role` is a named list of permissions; each `role` can be an IAM predefined
# role or a user-created custom role. For some types of Google Cloud resources,
# a `binding` can also specify a `condition`, which is a logical expression that
# allows access to a resource only if the expression evaluates to `true`. A
# condition can add constraints based on attributes of the request, the resource,
# or both. To learn which resources support conditions in their IAM policies,
# see the [IAM documentation](https://cloud.google.com/iam/help/conditions/
# resource-policies). **JSON example:** ` "bindings": [ ` "role": "roles/
# resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "
# group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@
# appspot.gserviceaccount.com" ] `, ` "role": "roles/resourcemanager.
# organizationViewer", "members": [ "user:eve@example.com" ], "condition": ` "
# title": "expirable access", "description": "Does not grant access after Sep
# 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", `
# ` ], "etag": "BwWWja0YfJA=", "version": 3 ` **YAML example:** bindings: -
# members: - user:mike@example.com - group:admins@example.com - domain:google.
# com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/
# resourcemanager.organizationAdmin - members: - user:eve@example.com role:
# roles/resourcemanager.organizationViewer condition: title: expirable access
# description: Does not grant access after Sep 2020 expression: request.time <
# timestamp('2020-10-01T00:00:00.000Z') - etag: BwWWja0YfJA= - version: 3 For a
# description of IAM and its features, see the [IAM documentation](https://cloud.
# google.com/iam/docs/).
# Corresponds to the JSON property `policy`
# @return [Google::Apis::MlV1::GoogleIamV1Policy]
attr_accessor :policy
# OPTIONAL: A FieldMask specifying which fields of the policy to modify. Only
# the fields in the mask will be modified. If no mask is provided, the following
# default mask is used: `paths: "bindings, etag"`
# Corresponds to the JSON property `updateMask`
# @return [String]
attr_accessor :update_mask
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@policy = args[:policy] if args.key?(:policy)
@update_mask = args[:update_mask] if args.key?(:update_mask)
end
end
# Request message for `TestIamPermissions` method.
class GoogleIamV1TestIamPermissionsRequest
include Google::Apis::Core::Hashable
# The set of permissions to check for the `resource`. Permissions with wildcards
# (such as '*' or 'storage.*') are not allowed. For more information see [IAM
# Overview](https://cloud.google.com/iam/docs/overview#permissions).
# Corresponds to the JSON property `permissions`
# @return [Array<String>]
attr_accessor :permissions
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@permissions = args[:permissions] if args.key?(:permissions)
end
end
# Response message for `TestIamPermissions` method.
class GoogleIamV1TestIamPermissionsResponse
include Google::Apis::Core::Hashable
# A subset of `TestPermissionsRequest.permissions` that the caller is allowed.
# Corresponds to the JSON property `permissions`
# @return [Array<String>]
attr_accessor :permissions
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@permissions = args[:permissions] if args.key?(:permissions)
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<Google::Apis::MlV1::GoogleLongrunningOperation>]
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
# This resource represents a long-running operation that is the result of a
# network API call.
class GoogleLongrunningOperation
include Google::Apis::Core::Hashable
# 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
# 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). Each `Status` message contains three pieces of
# data: error code, error message, and error details. You can find out more
# about this error model and how to work with it in the [API Design Guide](https:
# //cloud.google.com/apis/design/errors).
# 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<String,Object>]
attr_accessor :metadata
# 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
# be a resource name ending with `operations/`unique_id``.
# Corresponds to the JSON property `name`
# @return [String]
attr_accessor :name
# 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<String,Object>]
attr_accessor :response
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@done = args[:done] if args.key?(:done)
@error = args[:error] if args.key?(:error)
@metadata = args[:metadata] if args.key?(:metadata)
@name = args[:name] if args.key?(:name)
@response = args[:response] if args.key?(:response)
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
# 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). Each `Status` message contains three pieces of
# data: error code, error message, and error details. You can find out more
# about this error model and how to work with it in the [API Design Guide](https:
# //cloud.google.com/apis/design/errors).
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 list of messages that carry the error details. There is a common set of
# message types for APIs to use.
# Corresponds to the JSON property `details`
# @return [Array<Hash<String,Object>>]
attr_accessor :details
# 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
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@code = args[:code] if args.key?(:code)
@details = args[:details] if args.key?(:details)
@message = args[:message] if args.key?(:message)
end
end
# Represents a textual expression in the Common Expression Language (CEL) syntax.
# CEL is a C-like expression language. The syntax and semantics of CEL are
# documented at https://github.com/google/cel-spec. Example (Comparison): title:
# "Summary size limit" description: "Determines if a summary is less than 100
# chars" expression: "document.summary.size() < 100" Example (Equality): title: "
# Requestor is owner" description: "Determines if requestor is the document
# owner" expression: "document.owner == request.auth.claims.email" Example (
# Logic): title: "Public documents" description: "Determine whether the document
# should be publicly visible" expression: "document.type != 'private' &&
# document.type != 'internal'" Example (Data Manipulation): title: "Notification
# string" description: "Create a notification string with a timestamp."
# expression: "'New message received at ' + string(document.create_time)" The
# exact variables and functions that may be referenced within an expression are
# determined by the service that evaluates it. See the service documentation for
# additional information.
class GoogleTypeExpr
include Google::Apis::Core::Hashable
# Optional. Description of the expression. This is a longer text which describes
# the expression, e.g. when hovered over it in a UI.
# Corresponds to the JSON property `description`
# @return [String]
attr_accessor :description
# Textual representation of an expression in Common Expression Language syntax.
# Corresponds to the JSON property `expression`
# @return [String]
attr_accessor :expression
# Optional. String indicating the location of the expression for error reporting,
# e.g. a file name and a position in the file.
# Corresponds to the JSON property `location`
# @return [String]
attr_accessor :location
# Optional. Title for the expression, i.e. a short string describing its purpose.
# This can be used e.g. in UIs which allow to enter the expression.
# Corresponds to the JSON property `title`
# @return [String]
attr_accessor :title
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@description = args[:description] if args.key?(:description)
@expression = args[:expression] if args.key?(:expression)
@location = args[:location] if args.key?(:location)
@title = args[:title] if args.key?(:title)
end
end
end
end
end
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