google.cloud.gcp_mlengine_version_info – Gather info for GCP Version

Note

This plugin is part of the google.cloud collection (version 1.0.2).

To install it use: ansible-galaxy collection install google.cloud.

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

Synopsis

• Gather info for GCP Version

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	Choices/Defaults	Comments
auth_kind string / required	Choices: application machineaccount serviceaccount	The type of credential used.
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.
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 }}"
project string		The Google Cloud Platform project to use.
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.

Notes

Note

• 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 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: get info on a version
  gcp_mlengine_version_info:
    model: "{{ model }}"
    project: test_project
    auth_kind: serviceaccount
    service_account_file: "/tmp/auth.pem"

Return Values

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

Key			Returned	Description
resources complex			always	List of resources
	autoScaling complex		success	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.
		minNodes integer	success	The minimum number of nodes to allocate for this mode.
	createTime string		success	The time the version was created.
	deploymentUri string		success	The Cloud Storage location of the trained model used to create the version.
	description string		success	The description specified for the version when it was created.
	errorMessage string		success	The details of a failure or cancellation.
	framework string		success	The machine learning framework AI Platform uses to train this version of the model.
	isDefault boolean		success	If true, this version will be used to handle prediction requests that do not specify a version.
	labels dictionary		success	One or more labels that you can add, to organize your model versions.
	lastUseTime string		success	The time the version was last used for prediction.
	machineType string		success	The type of machine on which to serve the model. Currently only applies to online prediction service.
	manualScaling complex		success	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	success	The number of nodes to allocate for this model. These nodes are always up, starting from the time the model is deployed.
	model dictionary		success	The model that this version belongs to.
	name string		success	The name specified for the version when it was created. The version name must be unique within the model it is created in.
	packageUris list / elements=string		success	Cloud Storage paths (gs://…) of packages for custom prediction routines or scikit-learn pipelines with custom code.
	predictionClass string		success	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.
	pythonVersion string		success	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.
	runtimeVersion string		success	The AI Platform runtime version to use for this deployment.
	serviceAccount string		success	Specifies the service account for resource access control.
	state string		success	The state of a version.

Authors

• Google Inc. (@googlecloudplatform)