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google.cloud.gcp_mlengine_model module – Creates a GCP Model

Note

This module is part of the google.cloud collection (version 1.3.0).

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.

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)

google.cloud.gcp_mlengine_model module – Creates a GCP Model

Synopsis

Requirements

Parameters

Notes

Examples

Return Values