google.cloud.gcp_mlengine_version – Creates a GCP Version
Note
This plugin is part of the google.cloud collection (version 1.0.2).
You might already have this collection installed if you are using the ansible package. It is not included in ansible-core. To check whether it is installed, run ansible-galaxy collection list.
To install it, use: ansible-galaxy collection install google.cloud.
To use it in a playbook, specify: google.cloud.gcp_mlengine_version.
Synopsis
- Each version is a trained model deployed in the cloud, ready to handle prediction requests. A model can have multiple versions .
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
|
|
The type of credential used.
|
|
| auto_scaling
dictionary
|
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.
|
||
| min_nodes
integer
|
The minimum number of nodes to allocate for this mode.
|
||
| deployment_uri
string / required
|
The Cloud Storage location of the trained model used to create the version.
|
||
| description
string
|
The description specified for the version when it was created.
|
||
| 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.
|
||
| framework
string
|
The machine learning framework AI Platform uses to train this version of the model.
Some valid choices include: "FRAMEWORK_UNSPECIFIED", "TENSORFLOW", "SCIKIT_LEARN", "XGBOOST"
|
||
| is_default
boolean
|
|
If true, this version will be used to handle prediction requests that do not specify a version.
aliases: default |
|
| labels
dictionary
|
One or more labels that you can add, to organize your model versions.
|
||
| machine_type
string
|
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"
|
||
| manual_scaling
dictionary
|
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
|
The number of nodes to allocate for this model. These nodes are always up, starting from the time the model is deployed.
|
||
| 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 }}"
|
||
| name
string / required
|
The name specified for the version when it was created.
The version name must be unique within the model it is created in.
|
||
| prediction_class
string
|
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.
|
||
| project
string
|
The Google Cloud Platform project to use.
|
||
| python_version
string
|
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"
|
||
| runtime_version
string
|
The AI Platform runtime version to use for this deployment.
|
||
| scopes
list / elements=string
|
Array of scopes to be used
|
||
| service_account
string
|
Specifies the service account for resource access control.
|
||
| 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.
|
||
| state
string
|
|
Whether the given object should exist in GCP
|
|
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 Values
Common return values are documented here, the following are the fields unique to this module:
| Key | Returned | Description | |
|---|---|---|---|
| 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)
© 2012–2018 Michael DeHaan
© 2018–2021 Red Hat, Inc.
Licensed under the GNU General Public License version 3.
https://docs.ansible.com/ansible/latest/collections/google/cloud/gcp_mlengine_version_module.html