#!/usr/bin/python # -*- coding: utf-8 -*- # # Copyright (C) 2017 Google # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # ---------------------------------------------------------------------------- # # *** AUTO GENERATED CODE *** AUTO GENERATED CODE *** # # ---------------------------------------------------------------------------- # # This file is automatically generated by Magic Modules and manual # changes will be clobbered when the file is regenerated. # # Please read more about how to change this file at # https://www.github.com/GoogleCloudPlatform/magic-modules # # ---------------------------------------------------------------------------- from __future__ import absolute_import, division, print_function __metaclass__ = type ################################################################################ # Documentation ################################################################################ ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ["preview"], 'supported_by': 'community'} DOCUMENTATION = ''' --- module: gcp_mlengine_version_info description: - Gather info for GCP Version short_description: Gather info for GCP Version version_added: '2.9' author: Google Inc. (@googlecloudplatform) requirements: - python >= 2.6 - requests >= 2.18.4 - google-auth >= 1.3.0 options: model: description: - 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 }}"' required: true type: dict project: description: - The Google Cloud Platform project to use. type: str auth_kind: description: - The type of credential used. type: str required: true choices: - application - machineaccount - serviceaccount service_account_contents: description: - The contents of a Service Account JSON file, either in a dictionary or as a JSON string that represents it. type: jsonarg service_account_file: description: - The path of a Service Account JSON file if serviceaccount is selected as type. type: path service_account_email: description: - An optional service account email address if machineaccount is selected and the user does not wish to use the default email. type: str scopes: description: - Array of scopes to be used type: list env_type: description: - 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. type: str notes: - for authentication, you can set service_account_file using the C(gcp_service_account_file) env variable. - for authentication, you can set service_account_contents using the C(GCP_SERVICE_ACCOUNT_CONTENTS) env variable. - For authentication, you can set service_account_email using the C(GCP_SERVICE_ACCOUNT_EMAIL) env variable. - For authentication, you can set auth_kind using the C(GCP_AUTH_KIND) env variable. - For authentication, you can set scopes using the C(GCP_SCOPES) env variable. - Environment variables values will only be used if the playbook values are not set. - The I(service_account_email) and I(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 = ''' resources: description: List of resources returned: always type: complex contains: name: description: - 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 type: str description: description: - The description specified for the version when it was created. returned: success type: str deploymentUri: description: - The Cloud Storage location of the trained model used to create the version. returned: success type: str createTime: description: - The time the version was created. returned: success type: str lastUseTime: description: - The time the version was last used for prediction. returned: success type: str runtimeVersion: description: - The AI Platform runtime version to use for this deployment. returned: success type: str machineType: description: - The type of machine on which to serve the model. Currently only applies to online prediction service. returned: success type: str state: description: - The state of a version. returned: success type: str errorMessage: description: - The details of a failure or cancellation. returned: success type: str packageUris: description: - Cloud Storage paths (gs://…) of packages for custom prediction routines or scikit-learn pipelines with custom code. returned: success type: list labels: description: - One or more labels that you can add, to organize your model versions. returned: success type: dict framework: description: - The machine learning framework AI Platform uses to train this version of the model. returned: success type: str pythonVersion: description: - 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 type: str serviceAccount: description: - Specifies the service account for resource access control. returned: success type: str autoScaling: description: - 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 type: complex contains: minNodes: description: - The minimum number of nodes to allocate for this mode. returned: success type: int manualScaling: description: - 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 type: complex contains: nodes: description: - The number of nodes to allocate for this model. These nodes are always up, starting from the time the model is deployed. returned: success type: int predictionClass: description: - 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 type: str model: description: - The model that this version belongs to. returned: success type: dict isDefault: description: - If true, this version will be used to handle prediction requests that do not specify a version. returned: success type: bool ''' ################################################################################ # Imports ################################################################################ from ansible.module_utils.gcp_utils import navigate_hash, GcpSession, GcpModule, GcpRequest, replace_resource_dict import json ################################################################################ # Main ################################################################################ def main(): module = GcpModule(argument_spec=dict(model=dict(required=True, type='dict'))) if not module.params['scopes']: module.params['scopes'] = ['https://www.googleapis.com/auth/cloud-platform'] return_value = {'resources': fetch_list(module, collection(module))} module.exit_json(**return_value) def collection(module): res = {'project': module.params['project'], 'model': replace_resource_dict(module.params['model'], 'name')} return "https://ml.googleapis.com/v1/projects/{project}/models/{model}/versions".format(**res) def fetch_list(module, link): auth = GcpSession(module, 'mlengine') return auth.list(link, return_if_object, array_name='versions') def return_if_object(module, response): # If not found, return nothing. if response.status_code == 404: return None # If no content, return nothing. if response.status_code == 204: return None try: module.raise_for_status(response) result = response.json() except getattr(json.decoder, 'JSONDecodeError', ValueError) as inst: module.fail_json(msg="Invalid JSON response with error: %s" % inst) if navigate_hash(result, ['error', 'errors']): module.fail_json(msg=navigate_hash(result, ['error', 'errors'])) return result if __name__ == "__main__": main()