google.cloud.gcp_vertexai_deployment_resource_pool module – Creates a GCP VertexAI.DeploymentResourcePool resource

Note

This module is part of the google.cloud collection (version 1.12.0).

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. You need further requirements to be able to use this module, see Requirements for details.

To use it in a playbook, specify: google.cloud.gcp_vertexai_deployment_resource_pool.

Synopsis 

‘DeploymentResourcePool can be shared by multiple deployed models,
whose underlying specification consists of dedicated resources.’

Requirements 

The below requirements are needed on the host that executes this module.

python >= 3.8
requests >= 2.18.4
google-auth >= 2.25.1

Parameters 

Parameter	Comments
access_token string	The access token used to authenticate.
auth_kind string / required	The type of credential used. Choices: `"accesstoken"` `"application"` `"machineaccount"` `"serviceaccount"`
dedicated_resources dictionary	The underlying dedicated resources that the deployment resource pool uses.
autoscaling_metric_specs list / elements=dictionary	A list of the metric specifications that overrides a resource utilization metric.
metric_name string / required	The resource metric name. Supported metrics: For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization`.
target integer	The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
machine_spec dictionary / required	The specification of a single machine used by the prediction.
accelerator_count integer	The number of accelerators to attach to the machine.
accelerator_type string	The type of accelerator(s) that may be attached to the machine as per accelerator_count. See possible values [here](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType).
machine_type string	The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types).
max_replica_count integer	The maximum number of replicas this DeployedModel may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the DeployedModel increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Vertex CPU and GPU quotas. Specifically, you will be charged for max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
min_replica_count integer / required	The minimum number of machine replicas this DeployedModel will be always deployed on. This value must be greater than or equal to 1. If traffic against the DeployedModel increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
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.
name string / required	The resource name of deployment resource pool. The maximum length is 63 characters, and valid characters are `/^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/`.
project string	The Google Cloud Platform project to use.
region string	The region of deployment resource pool. eg us-central1.
scopes list / elements=string	Array of scopes to be used.
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 resource should exist in GCP. Choices: `"present"` ← (default) `"absent"`

Notes 

Note

API Reference: https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.deploymentResourcePools
For authentication, you can set auth_kind using the GCP_AUTH_KIND env variable.
For authentication, you can set service_account_file using the GCP_SERVICE_ACCOUNT_FILE env variable.
For authentication, you can set service_account_contents using the 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 access_token using the GCP_ACCESS_TOKEN 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, service_account_file, service_account_file and access_token options are mutually exclusive.

Examples 

- name: Create Deployment Resource Pool
  google.cloud.gcp_vertexai_deployment_resource_pool:
    state: present
    name: my_deployment_resource_pool
    dedicated_resources:
      min_replica_count: 1
      max_replica_count: 3
      machine_spec:
        machine_type: n1-standard-4
        accelerator_type: NVIDIA_TESLA_P4
        accelerator_count: 1
    region: us-central1
    project: "{{ gcp_project }}"
    auth_kind: "{{ gcp_cred_kind }}"
    service_account_file: "{{ gcp_cred_file }}"

Return Values 

Common return values are documented here, the following are the fields unique to this module:

Key	Description
changed boolean	Whether the resource was changed. Returned: always
createTime string	A timestamp in RFC3339 UTC “Zulu” format, with nanosecond resolution and up to nine fractional digits. Returned: success
state string	The current state of the resource. Returned: always

Authors

Google Inc. (@googlecloudplatform)

google.cloud.gcp_vertexai_deployment_resource_pool module – Creates a GCP VertexAI.DeploymentResourcePool resource

Synopsis

Requirements

Parameters

Notes

Examples

Return Values