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Diffstat (limited to 'lib/ansible/modules/cloud/google/gcp_mlengine_version.py')
| -rw-r--r-- | lib/ansible/modules/cloud/google/gcp_mlengine_version.py | 627 |
1 files changed, 0 insertions, 627 deletions
diff --git a/lib/ansible/modules/cloud/google/gcp_mlengine_version.py b/lib/ansible/modules/cloud/google/gcp_mlengine_version.py deleted file mode 100644 index f977ec212a..0000000000 --- a/lib/ansible/modules/cloud/google/gcp_mlengine_version.py +++ /dev/null @@ -1,627 +0,0 @@ -#!/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 -description: -- Each version is a trained model deployed in the cloud, ready to handle prediction - requests. A model can have multiple versions . -short_description: Creates a GCP Version -version_added: '2.9' -author: Google Inc. (@googlecloudplatform) -requirements: -- python >= 2.6 -- requests >= 2.18.4 -- google-auth >= 1.3.0 -options: - state: - description: - - Whether the given object should exist in GCP - choices: - - present - - absent - default: present - type: str - 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. - required: true - type: str - description: - description: - - The description specified for the version when it was created. - required: false - type: str - deployment_uri: - description: - - The Cloud Storage location of the trained model used to create the version. - required: true - type: str - runtime_version: - description: - - The AI Platform runtime version to use for this deployment. - required: false - type: str - machine_type: - description: - - 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"' - required: false - type: str - labels: - description: - - One or more labels that you can add, to organize your model versions. - required: false - type: dict - framework: - description: - - The machine learning framework AI Platform uses to train this version of the - model. - - 'Some valid choices include: "FRAMEWORK_UNSPECIFIED", "TENSORFLOW", "SCIKIT_LEARN", - "XGBOOST"' - required: false - type: str - python_version: - 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. - - 'Some valid choices include: "2.7", "3.5"' - required: false - type: str - service_account: - description: - - Specifies the service account for resource access control. - required: false - type: str - auto_scaling: - 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. - required: false - type: dict - suboptions: - min_nodes: - description: - - The minimum number of nodes to allocate for this mode. - required: false - type: int - manual_scaling: - 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. - required: false - type: dict - suboptions: - nodes: - description: - - The number of nodes to allocate for this model. These nodes are always up, - starting from the time the model is deployed. - required: false - type: int - prediction_class: - 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. - required: false - type: str - 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 - is_default: - description: - - If true, this version will be used to handle prediction requests that do not - specify a version. - required: false - type: bool - aliases: - - default - 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 -''' - -EXAMPLES = ''' -- name: create a model - 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 - 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 = ''' -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, remove_nones_from_dict, replace_resource_dict -import json -import time - -################################################################################ -# Main -################################################################################ - - -def main(): - """Main function""" - - module = GcpModule( - argument_spec=dict( - state=dict(default='present', choices=['present', 'absent'], type='str'), - name=dict(required=True, type='str'), - description=dict(type='str'), - deployment_uri=dict(required=True, type='str'), - runtime_version=dict(type='str'), - machine_type=dict(type='str'), - labels=dict(type='dict'), - framework=dict(type='str'), - python_version=dict(type='str'), - service_account=dict(type='str'), - auto_scaling=dict(type='dict', options=dict(min_nodes=dict(type='int'))), - manual_scaling=dict(type='dict', options=dict(nodes=dict(type='int'))), - prediction_class=dict(type='str'), - model=dict(required=True, type='dict'), - is_default=dict(type='bool', aliases=['default']), - ), - mutually_exclusive=[['auto_scaling', 'manual_scaling']], - ) - - if not module.params['scopes']: - module.params['scopes'] = ['https://www.googleapis.com/auth/cloud-platform'] - - state = module.params['state'] - - fetch = fetch_resource(module, self_link(module)) - changed = False - - if fetch: - if state == 'present': - if is_different(module, fetch): - update(module, self_link(module)) - fetch = fetch_resource(module, self_link(module)) - changed = True - else: - delete(module, self_link(module)) - fetch = {} - changed = True - else: - if state == 'present': - fetch = create(module, collection(module)) - if module.params.get('is_default') is True: - set_default(module) - changed = True - else: - fetch = {} - - fetch.update({'changed': changed}) - - module.exit_json(**fetch) - - -def create(module, link): - auth = GcpSession(module, 'mlengine') - return wait_for_operation(module, auth.post(link, resource_to_request(module))) - - -def update(module, link): - if module.params.get('is_default') is True: - set_default(module) - - -def delete(module, link): - auth = GcpSession(module, 'mlengine') - return wait_for_operation(module, auth.delete(link)) - - -def resource_to_request(module): - request = { - u'name': module.params.get('name'), - u'description': module.params.get('description'), - u'deploymentUri': module.params.get('deployment_uri'), - u'runtimeVersion': module.params.get('runtime_version'), - u'machineType': module.params.get('machine_type'), - u'labels': module.params.get('labels'), - u'framework': module.params.get('framework'), - u'pythonVersion': module.params.get('python_version'), - u'serviceAccount': module.params.get('service_account'), - u'autoScaling': VersionAutoscaling(module.params.get('auto_scaling', {}), module).to_request(), - u'manualScaling': VersionManualscaling(module.params.get('manual_scaling', {}), module).to_request(), - u'predictionClass': module.params.get('prediction_class'), - } - return_vals = {} - for k, v in request.items(): - if v or v is False: - return_vals[k] = v - - return return_vals - - -def fetch_resource(module, link, allow_not_found=True): - auth = GcpSession(module, 'mlengine') - return return_if_object(module, auth.get(link), allow_not_found) - - -def self_link(module): - res = {'project': module.params['project'], 'model': replace_resource_dict(module.params['model'], 'name'), 'name': module.params['name']} - return "https://ml.googleapis.com/v1/projects/{project}/models/{model}/versions/{name}".format(**res) - - -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 return_if_object(module, response, allow_not_found=False): - # If not found, return nothing. - if allow_not_found and 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): - module.fail_json(msg="Invalid JSON response with error: %s" % response.text) - - result = decode_response(result, module) - - if navigate_hash(result, ['error', 'errors']): - module.fail_json(msg=navigate_hash(result, ['error', 'errors'])) - - return result - - -def is_different(module, response): - request = resource_to_request(module) - response = response_to_hash(module, response) - request = decode_response(request, module) - - # Remove all output-only from response. - response_vals = {} - for k, v in response.items(): - if k in request: - response_vals[k] = v - - request_vals = {} - for k, v in request.items(): - if k in response: - request_vals[k] = v - - return GcpRequest(request_vals) != GcpRequest(response_vals) - - -# Remove unnecessary properties from the response. -# This is for doing comparisons with Ansible's current parameters. -def response_to_hash(module, response): - return { - u'name': response.get(u'name'), - u'description': response.get(u'description'), - u'deploymentUri': response.get(u'deploymentUri'), - u'createTime': response.get(u'createTime'), - u'lastUseTime': response.get(u'lastUseTime'), - u'runtimeVersion': response.get(u'runtimeVersion'), - u'machineType': response.get(u'machineType'), - u'state': response.get(u'state'), - u'errorMessage': response.get(u'errorMessage'), - u'packageUris': response.get(u'packageUris'), - u'labels': response.get(u'labels'), - u'framework': response.get(u'framework'), - u'pythonVersion': response.get(u'pythonVersion'), - u'serviceAccount': response.get(u'serviceAccount'), - u'autoScaling': VersionAutoscaling(response.get(u'autoScaling', {}), module).from_response(), - u'manualScaling': VersionManualscaling(response.get(u'manualScaling', {}), module).from_response(), - u'predictionClass': response.get(u'predictionClass'), - } - - -def async_op_url(module, extra_data=None): - if extra_data is None: - extra_data = {} - url = "https://ml.googleapis.com/v1/{op_id}" - combined = extra_data.copy() - combined.update(module.params) - return url.format(**combined) - - -def wait_for_operation(module, response): - op_result = return_if_object(module, response) - if op_result is None: - return {} - status = navigate_hash(op_result, ['done']) - wait_done = wait_for_completion(status, op_result, module) - raise_if_errors(wait_done, ['error'], module) - return navigate_hash(wait_done, ['response']) - - -def wait_for_completion(status, op_result, module): - op_id = navigate_hash(op_result, ['name']) - op_uri = async_op_url(module, {'op_id': op_id}) - while not status: - raise_if_errors(op_result, ['error'], module) - time.sleep(1.0) - op_result = fetch_resource(module, op_uri, False) - status = navigate_hash(op_result, ['done']) - return op_result - - -def raise_if_errors(response, err_path, module): - errors = navigate_hash(response, err_path) - if errors is not None: - module.fail_json(msg=errors) - - -# Short names are given (and expected) by the API -# but are returned as full names. -def decode_response(response, module): - if 'name' in response and 'metadata' not in response: - response['name'] = response['name'].split('/')[-1] - return response - - -# Sets this version as default. -def set_default(module): - res = {'project': module.params['project'], 'model': replace_resource_dict(module.params['model'], 'name'), 'name': module.params['name']} - link = "https://ml.googleapis.com/v1/projects/{project}/models/{model}/versions/{name}:setDefault".format(**res) - - auth = GcpSession(module, 'mlengine') - return_if_object(module, auth.post(link)) - - -class VersionAutoscaling(object): - def __init__(self, request, module): - self.module = module - if request: - self.request = request - else: - self.request = {} - - def to_request(self): - return remove_nones_from_dict({u'minNodes': self.request.get('min_nodes')}) - - def from_response(self): - return remove_nones_from_dict({u'minNodes': self.request.get(u'minNodes')}) - - -class VersionManualscaling(object): - def __init__(self, request, module): - self.module = module - if request: - self.request = request - else: - self.request = {} - - def to_request(self): - return remove_nones_from_dict({u'nodes': self.request.get('nodes')}) - - def from_response(self): - return remove_nones_from_dict({u'nodes': self.request.get(u'nodes')}) - - -if __name__ == '__main__': - main() |