google.cloud.gcp_mlengine_version module – Creates a GCP Version

Note

This module is part of the google.cloud collection (version 1.4.1).

You might already have this collection installed if you are using the ansible package. It is not included in ansible-core. To check whether it is installed, run ansible-galaxy collection list.

To install it, use: ansible-galaxy collection install google.cloud. You need further requirements to be able to use this module, see Requirements for details.

To use it in a playbook, specify: google.cloud.gcp_mlengine_version.

Note

The google.cloud collection will be removed from Ansible 12 due to violations of the Ansible inclusion requirements. The collection has unresolved sanity test failures. See the discussion thread for more information.

Synopsis

  • Each version is a trained model deployed in the cloud, ready to handle prediction requests. A model can have multiple versions .

Requirements

The below requirements are needed on the host that executes this module.

  • python >= 2.6

  • requests >= 2.18.4

  • google-auth >= 1.3.0

Parameters

Parameter

Comments

access_token

string

An OAuth2 access token if credential type is accesstoken.

auth_kind

string / required

The type of credential used.

Choices:

  • "application"

  • "machineaccount"

  • "serviceaccount"

  • "accesstoken"

auto_scaling

dictionary

Automatically scale the number of nodes used to serve the model in response to increases and decreases in traffic. Care should be taken to ramp up traffic according to the model’s ability to scale or you will start seeing increases in latency and 429 response codes.

min_nodes

integer

The minimum number of nodes to allocate for this mode.

deployment_uri

string / required

The Cloud Storage location of the trained model used to create the version.

description

string

The description specified for the version when it was created.

env_type

string

Specifies which Ansible environment you’re running this module within.

This should not be set unless you know what you’re doing.

This only alters the User Agent string for any API requests.

framework

string

The machine learning framework AI Platform uses to train this version of the model.

Some valid choices include: “FRAMEWORK_UNSPECIFIED”, “TENSORFLOW”, “SCIKIT_LEARN”, “XGBOOST”

is_default

aliases: default

boolean

If true, this version will be used to handle prediction requests that do not specify a version.

Choices:

  • false

  • true

labels

dictionary

One or more labels that you can add, to organize your model versions.

machine_type

string

The type of machine on which to serve the model. Currently only applies to online prediction service.

Some valid choices include: “mls1-c1-m2”, “mls1-c4-m2”

manual_scaling

dictionary

Manually select the number of nodes to use for serving the model. You should generally use autoScaling with an appropriate minNodes instead, but this option is available if you want more predictable billing. Beware that latency and error rates will increase if the traffic exceeds that capability of the system to serve it based on the selected number of nodes.

nodes

integer

The number of nodes to allocate for this model. These nodes are always up, starting from the time the model is deployed.

model

dictionary / required

The model that this version belongs to.

This field represents a link to a Model resource in GCP. It can be specified in two ways. First, you can place a dictionary with key ‘name’ and value of your resource’s name Alternatively, you can add `register: name-of-resource` to a gcp_mlengine_model task and then set this model field to “{{ name-of-resource }}”

name

string / required

The name specified for the version when it was created.

The version name must be unique within the model it is created in.

prediction_class

string

The fully qualified name (module_name.class_name) of a class that implements the Predictor interface described in this reference field. The module containing this class should be included in a package provided to the packageUris field.

project

string

The Google Cloud Platform project to use.

python_version

string

The version of Python used in prediction. If not set, the default version is ‘2.7’. Python ‘3.5’ is available when runtimeVersion is set to ‘1.4’ and above. Python ‘2.7’ works with all supported runtime versions.

Some valid choices include: “2.7”, “3.5”

runtime_version

string

The AI Platform runtime version to use for this deployment.

scopes

list / elements=string

Array of scopes to be used

service_account

string

Specifies the service account for resource access control.

service_account_contents

jsonarg

The contents of a Service Account JSON file, either in a dictionary or as a JSON string that represents it.

service_account_email

string

An optional service account email address if machineaccount is selected and the user does not wish to use the default email.

service_account_file

path

The path of a Service Account JSON file if serviceaccount is selected as type.

state

string

Whether the given object should exist in GCP

Choices:

  • "present" ← (default)

  • "absent"

Examples

- name: create a model
  google.cloud.gcp_mlengine_model:
    name: model_version
    description: My model
    regions:
    - us-central1
    online_prediction_logging: 'true'
    online_prediction_console_logging: 'true'
    project: "{{ gcp_project }}"
    auth_kind: "{{ gcp_cred_kind }}"
    service_account_file: "{{ gcp_cred_file }}"
    state: present
  register: model

- name: create a version
  google.cloud.gcp_mlengine_version:
    name: "{{ resource_name | replace('-', '_') }}"
    model: "{{ model }}"
    runtime_version: 1.13
    python_version: 3.5
    is_default: 'true'
    deployment_uri: gs://ansible-cloudml-bucket/
    project: test_project
    auth_kind: serviceaccount
    service_account_file: "/tmp/auth.pem"
    state: present

Return Values

Common return values are documented here, the following are the fields unique to this module:

Key

Description

autoScaling

complex

Automatically scale the number of nodes used to serve the model in response to increases and decreases in traffic. Care should be taken to ramp up traffic according to the model’s ability to scale or you will start seeing increases in latency and 429 response codes.

Returned: success

minNodes

integer

The minimum number of nodes to allocate for this mode.

Returned: success

createTime

string

The time the version was created.

Returned: success

deploymentUri

string

The Cloud Storage location of the trained model used to create the version.

Returned: success

description

string

The description specified for the version when it was created.

Returned: success

errorMessage

string

The details of a failure or cancellation.

Returned: success

framework

string

The machine learning framework AI Platform uses to train this version of the model.

Returned: success

isDefault

boolean

If true, this version will be used to handle prediction requests that do not specify a version.

Returned: success

labels

dictionary

One or more labels that you can add, to organize your model versions.

Returned: success

lastUseTime

string

The time the version was last used for prediction.

Returned: success

machineType

string

The type of machine on which to serve the model. Currently only applies to online prediction service.

Returned: success

manualScaling

complex

Manually select the number of nodes to use for serving the model. You should generally use autoScaling with an appropriate minNodes instead, but this option is available if you want more predictable billing. Beware that latency and error rates will increase if the traffic exceeds that capability of the system to serve it based on the selected number of nodes.

Returned: success

nodes

integer

The number of nodes to allocate for this model. These nodes are always up, starting from the time the model is deployed.

Returned: success

model

dictionary

The model that this version belongs to.

Returned: success

name

string

The name specified for the version when it was created.

The version name must be unique within the model it is created in.

Returned: success

packageUris

list / elements=string

Cloud Storage paths (gs://…) of packages for custom prediction routines or scikit-learn pipelines with custom code.

Returned: success

predictionClass

string

The fully qualified name (module_name.class_name) of a class that implements the Predictor interface described in this reference field. The module containing this class should be included in a package provided to the packageUris field.

Returned: success

pythonVersion

string

The version of Python used in prediction. If not set, the default version is ‘2.7’. Python ‘3.5’ is available when runtimeVersion is set to ‘1.4’ and above. Python ‘2.7’ works with all supported runtime versions.

Returned: success

runtimeVersion

string

The AI Platform runtime version to use for this deployment.

Returned: success

serviceAccount

string

Specifies the service account for resource access control.

Returned: success

state

string

The state of a version.

Returned: success

Authors

  • Google Inc. (@googlecloudplatform)