generated/google/apis/ml_v1/classes.rb in google-api-client-0.33.2 vs generated/google/apis/ml_v1/classes.rb in google-api-client-0.34.0
- old
+ new
@@ -107,11 +107,14 @@
@training_step = args[:training_step] if args.key?(:training_step)
end
end
# Represents a hardware accelerator request config.
- # Note that the AcceleratorConfig could be used in both Jobs and Versions.
+ # 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`
@@ -149,13 +152,22 @@
# 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 not specified, `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 [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:
# `
@@ -163,11 +175,11 @@
# 'minNodes': 5
# `
# `
# </pre>
# HTTP request:
- # <pre>
+ # <pre style="max-width: 626px;">
# PATCH
# https://ml.googleapis.com/v1/`name=projects/*/models/*/versions/*`?update_mask=
# autoScaling.minNodes
# -d @./update_body.json
# </pre>
@@ -1174,11 +1186,14 @@
# 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 could be used in both Jobs and Versions.
+ # 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
@@ -1634,11 +1649,14 @@
# versions/list).
class GoogleCloudMlV1Version
include Google::Apis::Core::Hashable
# Represents a hardware accelerator request config.
- # Note that the AcceleratorConfig could be used in both Jobs and Versions.
+ # 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.
@@ -1695,10 +1713,14 @@
# 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
@@ -1724,23 +1746,33 @@
# 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.
- # <dl>
- # <dt>mls1-c1-m2</dt>
- # <dd>
- # The <b>default</b> machine type, with 1 core and 2 GB RAM. The deprecated
- # name for this machine type is "mls1-highmem-1".
- # </dd>
- # <dt>mls1-c4-m2</dt>
- # <dd>
- # In <b>Beta</b>. This machine type has 4 cores and 2 GB RAM. The
- # deprecated name for this machine type is "mls1-highcpu-4".
- # </dd>
- # </dl>
+ # 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.
@@ -1776,13 +1808,15 @@
# 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.
+ # [`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:
- # ```py
+ # <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
@@ -1806,10 +1840,10 @@
# 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