google.cloud.gcp_vertexai_deployment_resource_pool_info module – List VertexAI.DeploymentResourcePool resources

Note

This module is part of the google.cloud collection (version 1.15.0).

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_info.

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"

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.

filters

list / elements=string

A list of filter expression strings used to filter the resources returned by the API.

Each string is a filter expression (e.g. some_field = "SOME_VALUE").

Multiple expressions are combined with a logical AND.

Refer to the filter topic documentation https://cloud.google.com/sdk/gcloud/reference/topic/filters.

Refer to the AIP-160 filter syntax documentation https://google.aip.dev/160.

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.

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: List all gcp_vertexai_deployment_resource_pool_info resources
  google.cloud.gcp_vertexai_deployment_resource_pool_info:
    project: "{{ gcp_project }}"
    auth_kind: "{{ gcp_cred_kind }}"
    service_account_file: "{{ gcp_cred_file }}"
  register: result

- name: List gcp_vertexai_deployment_resource_pool_info resources matching given filters
  google.cloud.gcp_vertexai_deployment_resource_pool_info:
    filters:
      - display_name = "my-resource"
    project: "{{ gcp_project }}"
    auth_kind: "{{ gcp_cred_kind }}"
    service_account_file: "{{ gcp_cred_file }}"
  register: result

Return Values

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

Key

Description

changed

boolean

Whether any changes were made (always false for info modules).

Returned: always

resources

list / elements=dictionary

List of VertexAI.DeploymentResourcePool resources matching the supplied filters. May be empty, contain a single resource, or multiple resources.

Returned: always

createTime

string

A timestamp in RFC3339 UTC “Zulu” format, with nanosecond resolution and up to nine fractional digits.

Returned: success

dedicatedResources

dictionary

The underlying dedicated resources that the deployment resource pool uses.

Returned: when set

autoscalingMetricSpecs

list / elements=dictionary

A list of the metric specifications that overrides a resource utilization metric.

Returned: when set

metricName

string

The resource metric name.

Supported metrics: For Online Prediction: * `aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle` * `aiplatform.googleapis.com/prediction/online/cpu/utilization`.

Returned: always

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.

Returned: when set

machineSpec

dictionary

The specification of a single machine used by the prediction.

Returned: always

acceleratorCount

integer

The number of accelerators to attach to the machine.

Returned: when set

acceleratorType

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).

Returned: when set

machineType

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).

Returned: when set

maxReplicaCount

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).

Returned: when set

minReplicaCount

integer

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.

Returned: always

name

string

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])?$/`.

Returned: always

Authors

  • Google Inc. (@googlecloudplatform)