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gcp_mlengine_version – Creates a GCP Version

New in version 2.9.

Synopsis
Requirements
Parameters
Notes
Examples
Return Values
Status

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		Choices: application machineaccount serviceaccount	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		Choices: no yes	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			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		Choices: present ← absent	Whether the given object should exist in GCP

Notes

Note

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 GCP_SERVICE_ACCOUNT_EMAIL env variable.
For authentication, you can set auth_kind using the GCP_AUTH_KIND env variable.
For authentication, you can set scopes using the GCP_SCOPES env variable.
Environment variables values will only be used if the playbook values are not set.
The service_account_email and service_account_file options are mutually exclusive.

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 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		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.

Status

This module is not guaranteed to have a backwards compatible interface. [preview]
This module is maintained by the Ansible Community. [community]

Authors

Google Inc. (@googlecloudplatform)

Hint

If you notice any issues in this documentation, you can edit this document to improve it.

© 2012–2018 Michael DeHaan
© 2018–2019 Red Hat, Inc.
Licensed under the GNU General Public License version 3.
https://docs.ansible.com/ansible/2.9/modules/gcp_mlengine_version_module.html