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google-api-ruby-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
# An observed value of a metric.
class GoogleCloudMlV1HyperparameterOutputHyperparameterMetric
include Google::Apis::Core::Hashable
# The objective value at this training step.
# Corresponds to the JSON property `objectiveValue`
# @return [Float]
attr_accessor :objective_value
# The global training step for this metric.
# Corresponds to the JSON property `trainingStep`
# @return [Fixnum]
attr_accessor :training_step
def initialize(**args)
update!(**args)
end
# Update properties of this object
def update!(**args)
@objective_value = args[:objective_value] if args.key?(:objective_value)
@training_step = args[:training_step] if args.key?(:training_step)
end
end
# Represents a 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
# Options for automatically scaling a model.
class GoogleCloudMlV1AutoScaling
include Google::Apis::Core::Hashable
# 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:
# <pre>
# update_body.json:
# `
# 'autoScaling': `
# 'minNodes': 5
# `
# `
# </pre>
# HTTP request:
# <pre style="max-width: 626px;">
# PATCH
# https://ml.googleapis.com/v1/`name=projects/*/models/*/versions/*`?update_mask=
# autoScaling.minNodes
# -d @./update_body.json
# </pre>
# 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)
@min_nodes = args[:min_nodes] if args.key?(:min_nodes)
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
#
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
# 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.
# Currently, the only supported mechanism to explain a model's prediction is
# through attributing its output back to its inputs which is essentially a
# credit assignment task. We support multiple attribution methods, some
# specific to particular frameworks like Tensorflow and XGBoost.
# Next idx: 7.
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: http://proceedings.mlr.press/v70/sundararajan17a.html
# 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
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)
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: http://proceedings.mlr.press/v70/sundararajan17a.html
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
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
# 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
# <a href="/ml-engine/docs/tensorflow/training-jobs">submitting a training
# job</a>.
# 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
# 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
# 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](/ml-engine/reference/rest/v1/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
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
# 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 Stackdriver 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
# [Stackdriver 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 StackDriver
# Logging. These logs are like standard server access logs, containing
# information like timestamp and latency for each request. Note that
# [Stackdriver 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.
# Currently only one region per model is supported.
# Defaults to 'us-central1' if nothing is set.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# 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](/ml-engine/reference/rest/v1/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
# <a href="/storage/docs/gsutil/addlhelp/WildcardNames">wildcards</a>.
# 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 <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# 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
# The Docker image to run on the replica. This image must be in Container
# Registry. Learn more about [configuring custom
# containers](/ml-engine/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)
@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:
# "<var>project_id</var>.<var>dataset_name</var>.<var>table_name</var>"
# The specifcied 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):
# <table>
# <tr><th>Field name</th><th style="display: table-cell">Type</th>
# <th style="display: table-cell">Mode</th></tr>
# <tr><td>model</td><td>STRING</td><td>REQUIRED</td></tr>
# <tr><td>model_version</td><td>STRING</td><td>REQUIRED</td></tr>
# <tr><td>time</td><td>TIMESTAMP</td><td>REQUIRED</td></tr>
# <tr><td>raw_data</td><td>STRING</td><td>REQUIRED</td></tr>
# <tr><td>raw_prediction</td><td>STRING</td><td>NULLABLE</td></tr>
# <tr><td>groundtruth</td><td>STRING</td><td>NULLABLE</td></tr>
# </table>
# 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
# 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
# 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
# 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
# <a href="/ml-engine/docs/tensorflow/training-jobs">submitting a training
# job</a>.
class GoogleCloudMlV1TrainingInput
include Google::Apis::Core::Hashable
# Optional. Command line arguments to pass to the program.
# Corresponds to the JSON property `args`
# @return [Array<String>]
attr_accessor :args
# 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
# 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. If not set, the default
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
# to '1.4' and above. Python '2.7' works with all supported
# <a href="/ml-engine/docs/runtime-version-list">runtime versions</a>.
# Corresponds to the JSON property `pythonVersion`
# @return [String]
attr_accessor :python_version
# Required. The Google Compute Engine region to run the training job in.
# See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
# for AI Platform services.
# Corresponds to the JSON property `region`
# @return [String]
attr_accessor :region
# Optional. The AI Platform runtime version to use for training. If not
# set, AI Platform uses the default stable version, 1.0. For more
# information, see the
# <a href="/ml-engine/docs/runtime-version-list">runtime version list</a>
# and
# <a href="/ml-engine/docs/versioning">how to manage runtime versions</a>.
# 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
# Optional. Use 'chief' instead of 'master' in TF_CONFIG when Custom
# Container is used and evaluator is not specified.
# Defaults to false.
# 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)
@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)
@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)
@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
# Represents a version of the model.
# Each version is a trained model deployed in the cloud, ready to handle
# prediction requests. A model can have multiple versions. You can get
# information about all of the versions of a given model by calling
# [projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.
# versions/list).
class GoogleCloudMlV1Version
include Google::Apis::Core::Hashable
# 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
# Output only. The time the version was created.
# Corresponds to the JSON property `createTime`
# @return [String]
attr_accessor :create_time
# Required. The Cloud Storage location of the trained model used to
# create the version. See the
# [guide to model
# deployment](/ml-engine/docs/tensorflow/deploying-models) for more
# information.
# When passing Version to
# [projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.
# versions/create)
# the model service uses the specified location as the source of the model.
# Once deployed, the model version is hosted by the prediction service, so
# this location is useful only as a historical record.
# The total number of model files can't exceed 1000.
# Corresponds to the JSON property `deploymentUri`
# @return [String]
attr_accessor :deployment_uri
# 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.
# Currently, the only supported mechanism to explain a model's prediction is
# through attributing its output back to its inputs which is essentially a
# credit assignment task. We support multiple attribution methods, some
# specific to particular frameworks like Tensorflow and XGBoost.
# Next idx: 7.
# 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](/ml-engine/docs/tensorflow/custom-prediction-routines).
# If you specify a [Compute Engine (N1) machine
# type](/ml-engine/docs/machine-types-online-prediction) in the
# `machineType` field, you must specify `TENSORFLOW`
# for the framework.
# 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](/ml-engine/reference/rest/v1/projects.
# models.versions/setDefault).
# Corresponds to the JSON property `isDefault`
# @return [Boolean]
attr_accessor :is_default
alias_method :is_default?, :is_default
# 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
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
# 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-c1-m2` is generally available. All other machine types are available
# in beta. 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
# (<var>module_name</var>.<var>class_name</var>) 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:
# <pre style="max-width: 626px;">
# 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()
# </pre>
# 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
# Optional. The version of Python used in prediction. If not set, the default
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
# to '1.4' and above. Python '2.7' works with all supported runtime versions.
# 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
# Optional. The AI Platform runtime version to use for this deployment.
# If not set, AI Platform uses the default stable version, 1.0. 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.
# 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)
@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)
@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
# 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 an expression text. Example:
# title: "User account presence"
# description: "Determines whether the request has a user account"
# expression: "size(request.user) > 0"
# 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.
# Optionally, a `binding` can 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.
# **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.
# 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.
# Optionally, a `binding` can 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.
# **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"
# This field is only used by Cloud IAM.
# 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 an expression text. Example:
# title: "User account presence"
# description: "Determines whether the request has a user account"
# expression: "size(request.user) > 0"
class GoogleTypeExpr
include Google::Apis::Core::Hashable
# An 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.
# The application context of the containing message determines which
# well-known feature set of CEL is supported.
# Corresponds to the JSON property `expression`
# @return [String]
attr_accessor :expression
# An 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
# An 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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