google.cloud.gcp_vertexai_featurestore_entitytype module – Creates a GCP VertexAI.FeaturestoreEntitytype resource

Note

This module is part of the google.cloud collection (version 1.13.0).

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_featurestore_entitytype.

Synopsis 

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

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"`
description string	Description of the EntityType.
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.
featurestore string / required	The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.
labels dictionary	A set of key/value label pairs to assign to this EntityType.
monitoring_config dictionary	The default monitoring configuration for all Features under this EntityType. If this is populated with [FeaturestoreMonitoringConfig.monitoring_interval] specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring is disabled.
categorical_threshold_config dictionary	Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).
value string / required	Specify a threshold value that can trigger the alert. For categorical feature, the distribution distance is calculated by L-inifinity norm. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature. The default value is 0.3.
import_features_analysis dictionary	The config for ImportFeatures Analysis Based Feature Monitoring.
anomaly_detection_baseline string	Defines the baseline to do anomaly detection for feature values imported by each [entityTypes.importFeatureValues][] operation. The value must be one of the values below: * LATEST_STATS: Choose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics. * MOST_RECENT_SNAPSHOT_STATS: Use the statistics generated by the most recent snapshot analysis if exists. * PREVIOUS_IMPORT_FEATURES_STATS: Use the statistics generated by the previous import features analysis if exists.
state string	Whether to enable / disable / inherite default hebavior for import features analysis. The value must be one of the values below: * DEFAULT: The default behavior of whether to enable the monitoring. EntityType-level config: disabled. * ENABLED: Explicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it. * DISABLED: Explicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it.
numerical_threshold_config dictionary	Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
value string / required	Specify a threshold value that can trigger the alert. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature. The default value is 0.3.
snapshot_analysis dictionary	The config for Snapshot Analysis Based Feature Monitoring.
disabled boolean	The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoringInterval for Features under it. Choices: `false` ← (default) `true`
monitoring_interval string	Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. A duration in seconds with up to nine fractional digits, terminated by ‘s’. Example: “3.5s”.
monitoring_interval_days integer	Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days. The default value is 1. If both FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days and [FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval][] are set when creating/updating EntityTypes/Features, FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days will be used. Default: `1`
staleness_days integer	Customized export features time window for snapshot analysis. Unit is one day. The default value is 21 days. Minimum value is 1 day. Maximum value is 4000 days. Default: `21`
name string / required	The name of the EntityType. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number.
offline_storage_ttl_days integer	Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL. Default: `4000`
project string	The Google Cloud Platform project to use.
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.featurestores.entityTypes
Official Documentation Guide: https://cloud.google.com/vertex-ai/docs
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 Featurestore Entitytype
  google.cloud.gcp_vertexai_featurestore_entitytype:
    state: present
    name: my_featurestore_entitytype
    featurestore: "projects/{{ gcp_project }}/locations/{{ gcp_region }}/featurestores/{{ featurestore }}"
    # featurestore: "{{ _myfs.name }}"  # use previously registered variable
    monitoring_config:
      snapshot_analysis:
        disabled: false
        monitoring_interval_days: 1
        staleness_days: 30
    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	The timestamp of when the featurestore was created in RFC3339 UTC “Zulu” format, with nanosecond resolution and up to nine fractional digits. Returned: success
etag string	Used to perform consistent read-modify-write updates. Returned: success
state string	The current state of the resource. Returned: always
updateTime string	The timestamp of when the featurestore was last updated in RFC3339 UTC “Zulu” format, with nanosecond resolution and up to nine fractional digits. Returned: success

Authors

Google Inc. (@googlecloudplatform)

google.cloud.gcp_vertexai_featurestore_entitytype module – Creates a GCP VertexAI.FeaturestoreEntitytype resource

Synopsis

Requirements

Parameters

Notes

Examples

Return Values