google.cloud.gcp_vertexai_endpoint_info module – List VertexAI.Endpoint 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_endpoint_info.

Synopsis

  • Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

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.

location

string / required

The location for the resource.

project

string

The Google Cloud Platform project to use.

region

string

The region for the resource.

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

Examples

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

- name: List gcp_vertexai_endpoint_info resources matching given filters
  google.cloud.gcp_vertexai_endpoint_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.Endpoint resources matching the supplied filters. May be empty, contain a single resource, or multiple resources.

Returned: always

createTime

string

Output only.

Timestamp when this Endpoint was created.

Returned: success

dedicatedEndpointDns

string

Output only.

DNS of the dedicated endpoint.

Will only be populated if dedicatedEndpointEnabled is true.

Format: `https://{endpointId}.{region}-{projectNumber}.prediction.vertexai.goog`.

Returned: success

dedicatedEndpointEnabled

boolean

If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns].

Your request to the dedicated DNS will be isolated from other users’ traffic and will have better performance and reliability.

Note: Once you enabled dedicated endpoint, you won’t be able to send request to the shared DNS {region}-aiplatform.googleapis.com.

The limitation will be removed soon.

Returned: when set

deployedModels

list / elements=dictionary

Output only.

The models deployed in this Endpoint.

To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.

Models can also be deployed and undeployed using the [Cloud Console](https://console.cloud.google.com/vertex-ai/).

Returned: success

automaticResources

dictionary

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.

Returned: success

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, a no upper bound for scaling under heavy traffic will be assume, though Vertex AI may be unable to scale beyond certain replica number.

Returned: success

minReplicaCount

integer

The minimum number of replicas this DeployedModel will be always deployed on.

If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed.

If the requested value is too large, the deployment will error.

Returned: success

createTime

string

Output only.

Timestamp when the DeployedModel was created.

Returned: success

dedicatedResources

dictionary

A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.

Returned: success

autoscalingMetricSpecs

list / elements=dictionary

The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator’s duty cycle, and so on) target value (default to 60 if not set).

At most one entry is allowed per metric.

If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator’s duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value.

The default target value is 60 for both metrics.

If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set.

For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to `aiplatform.googleapis.com/prediction/online/cpu/utilization` and autoscaling_metric_specs.target to `80`.

Returned: success

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: success

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: success

machineSpec

dictionary

The specification of a single machine used by the prediction.

Returned: success

acceleratorCount

integer

The number of accelerators to attach to the machine.

Returned: success

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: success

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) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types).

For DeployedModel this field is optional, and the default value is `n1-standard-2`.

For BatchPredictionJob or as part of WorkerPoolSpec this field is required.

TODO: Try to better unify the required vs optional.

Returned: success

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: success

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: success

displayName

string

The display name of the DeployedModel.

If not provided upon creation, the Model’s display_name is used.

Returned: success

enableAccessLogging

boolean

These logs are like standard server access logs, containing information like timestamp and latency for each prediction request.

Note that Stackdriver logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS).

Estimate your costs before enabling this option.

Returned: success

enableContainerLogging

boolean

If true, the container of the DeployedModel instances will send `stderr` and `stdout` streams to Stackdriver Logging.

Only supported for custom-trained Models and AutoML Tabular Models.

Returned: success

id

string

The ID of the DeployedModel.

If not provided upon deployment, Vertex AI will generate a value for this ID.

This value should be 1-10 characters, and valid characters are /[0-9]/.

Returned: success

model

string

The name of the Model that this is the deployment of.

Note that the Model may be in a different location than the DeployedModel’s Endpoint.

Returned: success

modelVersionId

string

Output only.

The version ID of the model that is deployed.

Returned: success

privateEndpoints

dictionary

Output only.

Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access.

This field is populated if network is configured.

Returned: success

explainHttpUri

string

Output only.

Http(s) path to send explain requests.

Returned: success

healthHttpUri

string

Output only.

Http(s) path to send health check requests.

Returned: success

predictHttpUri

string

Output only.

Http(s) path to send prediction requests.

Returned: success

serviceAttachment

string

Output only.

The name of the service attachment resource.

Populated if private service connect is enabled.

Returned: success

serviceAccount

string

The service account that the DeployedModel’s container runs as.

Specify the email address of the service account.

If this service account is not specified, the container runs as a service account that doesn’t have access to the resource project.

Users deploying the Model must have the `iam.serviceAccounts.actAs` permission on this service account.

Returned: success

sharedResources

string

The resource name of the shared DeploymentResourcePool to deploy on.

Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}.

Returned: success

description

string

The description of the Endpoint.

Returned: when set

displayName

string

The display name of the Endpoint.

The name can be up to 128 characters long and can consist of any UTF-8 characters.

Returned: always

encryptionSpec

dictionary

Customer-managed encryption key spec for an Endpoint.

If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key.

Returned: when set

kmsKeyName

string

The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource.

Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`.

The key needs to be in the same region as where the compute resource is created.

Returned: always

labels

dictionary

The labels with user-defined metadata to organize your Endpoints.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes.

International characters are allowed.

See https://goo.gl/xmQnxf for more information and examples of labels.

**Note**: This field is non-authoritative, and will only manage the labels present in your configuration.

Returned: when set

modelDeploymentMonitoringJob

string

Output only.

Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by CreateModelDeploymentMonitoringJob.

Format: `projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}`.

Returned: success

network

string

The full name of the Google Compute Engine [network](https://cloud.google.com//compute/docs/networks-and-firewalls#networks) to which the Endpoint should be peered.

Private services access must already be configured for the network.

If left unspecified, the Endpoint is not peered with any network.

Only one of the fields, network or enable_private_service_connect, can be set.

[Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert): `projects/{project}/global/networks/{network}`.

Where `{project}` is a project number, as in `12345`, and `{network}` is network name.

Only one of the fields, `network` or `privateServiceConnectConfig`, can be set.

Returned: when set

predictRequestResponseLoggingConfig

dictionary

Configures the request-response logging for online prediction.

Returned: when set

bigqueryDestination

dictionary

BigQuery table for logging.

If only given a project, a new dataset will be created with name `logging_<endpoint-display-name>_<endpoint-id>` where will be made BigQuery-dataset-name compatible (e.g.

most special characters will become underscores).

If no table name is given, a new table will be created with name `request_response_logging`.

Returned: when set

outputUri

string

BigQuery URI to a project or table, up to 2000 characters long.

When only the project is specified, the Dataset and Table is created.

When the full table reference is specified, the Dataset must exist and table must not exist.

Accepted forms: - BigQuery path.

For example: `bq://projectId` or `bq://projectId.bqDatasetId` or `bq://projectId.bqDatasetId.bqTableId`.

Returned: when set

enabled

boolean

If logging is enabled or not.

Returned: when set

samplingRate

float

Percentage of requests to be logged, expressed as a fraction in range(0,1].

Returned: when set

privateServiceConnectConfig

dictionary

Configuration for private service connect.

`network` and `privateServiceConnectConfig` are mutually exclusive.

Returned: when set

enablePrivateServiceConnect

boolean

If true, expose the IndexEndpoint via private service connect.

Returned: always

enableSecurePrivateServiceConnect

boolean

If set to true, enable secure private service connect with IAM authorization.

Otherwise, private service connect will be done without authorization.

Note latency will be slightly increased if authorization is enabled.

Returned: when set

projectAllowlist

list / elements=string

A list of Projects from which the forwarding rule will target the service attachment.

Returned: when set

pscAutomationConfigs

list / elements=dictionary

List of projects and networks where the PSC endpoints will be created.

This field is used by Online Inference(Prediction) only.

Returned: when set

errorMessage

string

Error message if the PSC service automation failed.

Returned: success

forwardingRule

string

Forwarding rule created by the PSC service automation.

Returned: success

ipAddress

string

IP address rule created by the PSC service automation.

Returned: success

network

string

The full name of the Google Compute Engine [network](https://cloud.google.com/compute/docs/networks-and-firewalls#networks).

[Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/get): projects/{project}/global/networks/{network}.

Returned: always

projectId

string

Project id used to create forwarding rule.

Returned: always

state

string

The state of the PSC service automation.

Returned: success

trafficSplit

string

A map from a DeployedModel’s id to the percentage of this Endpoint’s traffic that should be forwarded to that DeployedModel.

If a DeployedModel’s id is not listed in this map, then it receives no traffic.

The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment.

See the `deployModel` [example](https://cloud.google.com/vertex-ai/docs/general/deployment#deploy_a_model_to_an_endpoint) and [documentation](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.endpoints/deployModel) for more information.

~> **Note:** To set the map to empty, set `”{}”`, apply, and then remove the field from your config.

Returned: when set

updateTime

string

Output only.

Timestamp when this Endpoint was last updated.

Returned: success

Authors

  • Google Inc. (@googlecloudplatform)