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Autogenerated update (2019-05-18)

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Update:
- cloudtasks_v2
- cloudtasks_v2beta3
- dataproc_v1
- dataproc_v1beta2
- ml_v1
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Google APIs committed 2019-05-18 00:37:53 +00:00
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generated/google/apis/ml_v1/classes.rb
+73 -55
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@@ -205,7 +205,7 @@ module Google
# @return [String]
attr_accessor :python_version
# Cloud ML Engine runtime version on which the built-in algorithm was
# AI Platform runtime version on which the built-in algorithm was
# trained.
# Corresponds to the JSON property `runtimeVersion`
# @return [String]
@@ -372,7 +372,7 @@ module Google
# Optional. The search algorithm specified for the hyperparameter
# tuning job.
# Uses the default CloudML Engine hyperparameter tuning
# Uses the default AI Platform hyperparameter tuning
# algorithm if unspecified.
# Corresponds to the JSON property `algorithm`
# @return [String]
@@ -403,7 +403,7 @@ module Google
# 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 Cloud ML Engine hyperparameter tuning jobs.
# 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`
@@ -725,7 +725,6 @@ module Google
# 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).
# Next ID: 30
# Corresponds to the JSON property `defaultVersion`
# @return [Google::Apis::MlV1::GoogleCloudMlV1Version]
attr_accessor :default_version
@@ -762,13 +761,13 @@ module Google
# @return [String]
attr_accessor :name
# Optional. If true, enables logging of stderr and stdout streams
# for online prediction in Stackdriver Logging. These can be more verbose
# than the standard access logs (see `online_prediction_logging`) and thus
# can incur higher cost. However, they are helpful for debugging. Note that
# since Stackdriver logs may incur a cost, particularly if the total QPS
# in your project is high, be sure to estimate your costs before enabling
# this flag.
# 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]
@@ -778,8 +777,9 @@ module Google
# 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, particular if the total QPS in your
# project is high.
# [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]
@@ -790,7 +790,7 @@ module Google
# 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 ML Engine services.
# 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.
@@ -870,7 +870,6 @@ module Google
# 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).
# Next ID: 30
# Corresponds to the JSON property `version`
# @return [Google::Apis::MlV1::GoogleCloudMlV1Version]
attr_accessor :version
@@ -1055,13 +1054,13 @@ module Google
# 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 ML Engine services.
# for AI Platform services.
# Corresponds to the JSON property `region`
# @return [String]
attr_accessor :region
# Optional. The Cloud ML Engine runtime version to use for this batch
# prediction. If not set, Cloud ML Engine will pick the runtime version used
# 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.
@@ -1364,7 +1363,7 @@ module Google
# 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 Cloud ML Engine machine
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
# This value must be present when `scaleTier` is set to `CUSTOM` and
# `parameter_server_count` is greater than zero.
@@ -1387,13 +1386,13 @@ module Google
# 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 ML Engine services.
# for AI Platform services.
# Corresponds to the JSON property `region`
# @return [String]
attr_accessor :region
# Optional. The Cloud ML Engine runtime version to use for training. If not
# set, Cloud ML Engine uses the default stable version, 1.0. For more
# 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
@@ -1427,7 +1426,7 @@ module Google
# 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 Cloud ML Engine machine
# `masterType` uses. In other words, both must be AI Platform machine
# types or both must be Compute Engine machine types.
# If you use `cloud_tpu` for this value, see special instructions for
# [configuring a custom TPU
@@ -1524,7 +1523,6 @@ module Google
# 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).
# Next ID: 30
class GoogleCloudMlV1Version
include Google::Apis::Core::Hashable
@@ -1538,7 +1536,7 @@ module Google
# @return [String]
attr_accessor :create_time
# Required. The Google Cloud Storage location of the trained model used to
# 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
@@ -1576,12 +1574,14 @@ module Google
# @return [String]
attr_accessor :etag
# Optional. The machine learning framework Cloud ML Engine uses to train
# 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, Cloud ML Engine
# `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).
# Corresponds to the JSON property `framework`
# @return [String]
attr_accessor :framework
@@ -1639,45 +1639,63 @@ module Google
# @return [String]
attr_accessor :name
# Optional. The Google Cloud Storage location of the packages for custom
# prediction and any additional dependencies.
# 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
# class PredictionClass(object):
# """A Model performs predictions on a given list of instances.
# The input instances are the raw values sent by the user. It is the
# responsibility of a Model to translate these instances into
# actual predictions.
# The input instances and the output use python data types. The input
# instances have been decoded prior to being passed to the predict
# method. The output, which should use python data types is
# encoded after being returned from the predict method.
# """
# 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.
# The following code sample provides the Predictor interface:
# ```py
# class Predictor(object):
# """Interface for constructing custom predictors."""
# def predict(self, instances, **kwargs):
# """Returns predictions for the provided instances.
# Instances are the decoded values from the request. Clients need not
# worry about decoding json nor base64 decoding.
# """Performs custom prediction.
# Instances are the decoded values from the request. They have already
# been deserialized from JSON.
# Args:
# instances: A list of instances, as described in the API.
# **kwargs: Additional keyword arguments, will be passed into the
# client's predict method.
# 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.
# A list of outputs containing the prediction results. This list must
# be JSON serializable.
# """
# raise NotImplementedError()
# @classmethod
# def from_path(cls, model_path):
# """Creates a model using the given model path.
# Path is useful, e.g., to load files from the exported directory
# containing the model.
# 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_path: The local directory that contains the exported model
# 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 Model class.
# An instance implementing this Predictor class.
# """
# raise NotImplementedError()
# ```
# Learn more about [the Predictor interface and custom prediction
# routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
# Corresponds to the JSON property `predictionClass`
# @return [String]
attr_accessor :prediction_class
@@ -1689,8 +1707,8 @@ module Google
# @return [String]
attr_accessor :python_version
# Optional. The Cloud ML Engine runtime version to use for this deployment.
# If not set, Cloud ML Engine uses the default stable version, 1.0. For more
# 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).
@@ -2175,7 +2193,7 @@ module Google
# The server-assigned name, which is only unique within the same service that
# originally returns it. If you use the default HTTP mapping, the
# `name` should have the format of `operations/some/unique/name`.
# `name` should be a resource name ending with `operations/`unique_id``.
# Corresponds to the JSON property `name`
# @return [String]
attr_accessor :name
generated/google/apis/ml_v1/service.rb
+1 -1
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@@ -114,7 +114,7 @@ module Google
end
# Performs prediction on the data in the request.
# Cloud ML Engine implements a custom `predict` verb on top of an HTTP POST
# AI Platform implements a custom `predict` verb on top of an HTTP POST
# method. <p>For details of the request and response format, see the **guide
# to the [predict request format](/ml-engine/docs/v1/predict-request)**.
# @param [String] name