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google.cloud/plugins/modules/gcp_mlengine_version.py
Alex Stephen 516fada880 Adding namespace on integration tests + examples 
Signed-off-by: Modular Magician <magic-modules@google.com>
2019-09-30 22:08:25 +00:00

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#!/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
google.cloud.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
google.cloud.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_collections.google.cloud.plugins.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()
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