google.cloud.gcp_vertexai_tensorboard_info module – List VertexAI.Tensorboard 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_tensorboard_info.

Synopsis 

Tensorboard is a physical database that stores users’ training metrics. A default Tensorboard is provided in each region of a GCP project. If needed users can also create extra Tensorboards in their projects.

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 the tensorboard. 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.tensorboards
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: List all gcp_vertexai_tensorboard_info resources
  google.cloud.gcp_vertexai_tensorboard_info:
    project: "{{ gcp_project }}"
    auth_kind: "{{ gcp_cred_kind }}"
    service_account_file: "{{ gcp_cred_file }}"
  register: result

- name: List gcp_vertexai_tensorboard_info resources matching given filters
  google.cloud.gcp_vertexai_tensorboard_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.Tensorboard resources matching the supplied filters. May be empty, contain a single resource, or multiple resources. Returned: always
blobStoragePathPrefix string	Consumer project Cloud Storage path prefix used to store blob data, which can either be a bucket or directory. Does not end with a ‘/’. Returned: success
createTime string	The timestamp of when the Tensorboard was created in RFC3339 UTC “Zulu” format, with nanosecond resolution and up to nine fractional digits. Returned: success
description string	Description of this Tensorboard. Returned: when set
displayName string	User provided name of this Tensorboard. Returned: always
encryptionSpec dictionary	Customer-managed encryption key spec for a Tensorboard. If set, this Tensorboard and all sub-resources of this Tensorboard 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 resource is created. Returned: always
labels dictionary	The labels with user-defined metadata to organize your Tensorboards. Note: This field is non-authoritative, and will only manage the labels present in your configuration. Returned: when set
name string	Name of the Tensorboard. Returned: success
runCount string	The number of Runs stored in this Tensorboard. Returned: success
updateTime string	The timestamp of when the Tensorboard 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_tensorboard_info module – List VertexAI.Tensorboard resources

Synopsis

Requirements

Parameters

Notes

Examples

Return Values