google.cloud.gcp_mlengine_model module – Creates a GCP Model

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_model.

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

  • 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.

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"

default_version

dictionary

The default version of the model. This version will be used to handle prediction requests that do not specify a version.

name

string / required

The name specified for the version when it was created.

description

string

The description specified for the model 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.

labels

dictionary

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

name

string / required

The name specified for the model.

online_prediction_console_logging

boolean

If true, online prediction nodes send stderr and stdout streams to Stackdriver Logging.

Choices:

  • false

  • true

online_prediction_logging

boolean

If true, online prediction access logs are sent to StackDriver Logging.

Choices:

  • false

  • true

project

string

The Google Cloud Platform project to use.

regions

list / elements=string

The list of regions where the model is going to be deployed.

Currently only one region per model is supported .

scopes

list / elements=string

Array of scopes to be used

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"

Notes

Note

  • API Reference: https://cloud.google.com/ai-platform/prediction/docs/reference/rest/v1/projects.models

  • Official Documentation: https://cloud.google.com/ai-platform/prediction/docs/deploying-models

  • for authentication, you can set service_account_file using the GCP_SERVICE_ACCOUNT_FILE env variable.

  • for authentication, you can set service_account_contents using the GCP_SERVICE_ACCOUNT_CONTENTS env variable.

  • For authentication, you can set service_account_email using the GCP_SERVICE_ACCOUNT_EMAIL env variable.

  • For authentication, you can set access_token using the GCP_ACCESS_TOKEN env variable.

  • For authentication, you can set auth_kind using the GCP_AUTH_KIND env variable.

  • For authentication, you can set scopes using the GCP_SCOPES env variable.

  • Environment variables values will only be used if the playbook values are not set.

  • The service_account_email and service_account_file options are mutually exclusive.

Examples

- name: create a model
  google.cloud.gcp_mlengine_model:
    name: "{{ resource_name | replace('-', '_') }}"
    description: My model
    regions:
    - us-central1
    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

defaultVersion

complex

The default version of the model. This version will be used to handle prediction requests that do not specify a version.

Returned: success

name

string

The name specified for the version when it was created.

Returned: success

description

string

The description specified for the model when it was created.

Returned: success

labels

dictionary

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

Returned: success

name

string

The name specified for the model.

Returned: success

onlinePredictionConsoleLogging

boolean

If true, online prediction nodes send stderr and stdout streams to Stackdriver Logging.

Returned: success

onlinePredictionLogging

boolean

If true, online prediction access logs are sent to StackDriver Logging.

Returned: success

regions

list / elements=string

The list of regions where the model is going to be deployed.

Currently only one region per model is supported .

Returned: success

Authors

  • Google Inc. (@googlecloudplatform)