Try SpotterCreates a GCP Version
Authors: Google Inc. (@googlecloudplatform)
preview | supported by community
Install with ansible-galaxy collection install delowan.googlecloud:==1.0.2
collections:
- name: delowan.googlecloud
version: 1.0.2Each version is a trained model deployed in the cloud, ready to handle prediction requests. A model can have multiple versions .
- 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
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
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
state:
choices:
- present
- absent
default: present
description:
- Whether the given object should exist in GCP
type: str
labels:
description:
- One or more labels that you can add, to organize your model versions.
required: false
type: dict
scopes:
description:
- Array of scopes to be used
elements: str
type: list
project:
description:
- The Google Cloud Platform project to use.
type: str
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
auth_kind:
choices:
- application
- machineaccount
- serviceaccount
description:
- The type of credential used.
required: true
type: str
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
is_default:
aliases:
- default
description:
- If true, this version will be used to handle prediction requests that do not specify
a version.
required: false
type: bool
description:
description:
- The description specified for the version when it was created.
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
suboptions:
min_nodes:
description:
- The minimum number of nodes to allocate for this mode.
required: false
type: int
type: dict
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
deployment_uri:
description:
- The Cloud Storage location of the trained model used to create the version.
required: true
type: str
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
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
type: dict
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
runtime_version:
description:
- The AI Platform runtime version to use for this deployment.
required: false
type: str
service_account:
description:
- Specifies the service account for resource access control.
required: false
type: str
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
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
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
autoScaling:
contains:
minNodes:
description:
- The minimum number of nodes to allocate for this mode.
returned: success
type: int
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
createTime:
description:
- The time the version 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
description:
description:
- The description specified for the version when it was created.
returned: success
type: str
errorMessage:
description:
- The details of a failure or cancellation.
returned: success
type: str
framework:
description:
- The machine learning framework AI Platform uses to train this version of the model.
returned: success
type: str
isDefault:
description:
- If true, this version will be used to handle prediction requests that do not specify
a version.
returned: success
type: bool
labels:
description:
- One or more labels that you can add, to organize your model versions.
returned: success
type: dict
lastUseTime:
description:
- The time the version was last used for prediction.
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
manualScaling:
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
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
model:
description:
- The model that this version belongs to.
returned: success
type: dict
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
packageUris:
description:
- "Cloud Storage paths (gs://\u2026) of packages for custom prediction routines\
\ or scikit-learn pipelines with custom code."
returned: success
type: list
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
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
runtimeVersion:
description:
- The AI Platform runtime version to use for this deployment.
returned: success
type: str
serviceAccount:
description:
- Specifies the service account for resource access control.
returned: success
type: str
state:
description:
- The state of a version.
returned: success
type: str