Reboot a persistent resource

You can reboot any persistent resource that's in theRUNNING orERROR state.Rebooting a persistent resource lets you recover from errors that the persistentresource can't recover from on its own. You can also reboot a persistentresource to manually obtain more up-to-date clusters. This page shows you how toreboot a persistent resource by using the Google Cloud console and the REST API.

Required roles

To get the permission that you need to reboot a persistent resource, ask your administrator to grant you theVertex AI Administrator (roles/aiplatform.admin) IAM role on your project. For more information about granting roles, seeManage access to projects, folders, and organizations.

This predefined role contains the aiplatform.persistentResources.update permission, which is required to reboot a persistent resource.

You might also be able to get this permission withcustom roles or otherpredefined roles.

Reboot a persistent resource

Select one of the following tabs for instructions on how to reboot a persistentresource. Make sure there's no training jobs running on the persistent resource.

Console

To reboot a persistent resource in the Google Cloud console, do the following:

  1. In the Google Cloud console, go to thePersistent resources page.

    Go to Persistent resources

  2. Next to the name of the persistent resource that you want to reboot, click the vertical ellipses ().

  3. ClickReboot.

  4. ClickConfirm.

gcloud

Before using any of the command data below, make the following replacements:

  • PROJECT_ID: The Project ID of the persistent resource that you want to reboot.
  • LOCATION: The region of the persistent resource that you want to reboot.
  • PERSISTENT_RESOURCE_ID: The ID of the persistent resource that you want to reboot.

Execute the following command:

Linux, macOS, or Cloud Shell

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running eithergcloud init; orgcloud auth login andgcloud config set project.
gcloudaipersistent-resourcesrebootPERSISTENT_RESOURCE_ID\--project=PROJECT_ID\--region=LOCATION

Windows (PowerShell)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running eithergcloud init; orgcloud auth login andgcloud config set project.
gcloudaipersistent-resourcesrebootPERSISTENT_RESOURCE_ID`--project=PROJECT_ID`--region=LOCATION

Windows (cmd.exe)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running eithergcloud init; orgcloud auth login andgcloud config set project.
gcloudaipersistent-resourcesrebootPERSISTENT_RESOURCE_ID^--project=PROJECT_ID^--region=LOCATION

You should receive a response similar to the following:

Using endpoint [https://us-central1-aiplatform.googleapis.com/]Request to reboot the PersistentResource [projects/sample-project/locations/us-central1/persistentResources/test-persistent-resource] has been sent.You may view the status of your persistent resource with the command  $ gcloud ai persistent-resources describe projects/sample-project/locations/us-central1/persistentResources/test-persistent-resource

REST

Before using any of the request data, make the following replacements:

  • PROJECT_ID: The Project ID of the persistent resource that you want to reboot.
  • LOCATION: The region of the persistent resource that you want to reboot.
  • PERSISTENT_RESOURCE_ID: The ID of the persistent resource that you want to reboot.

HTTP method and URL:

POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/persistentResources/PERSISTENT_RESOURCE_ID:reboot

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

Execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d "" \
     "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/persistentResources/PERSISTENT_RESOURCE_ID:reboot"

PowerShell (Windows)

Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -Uri "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/persistentResources/PERSISTENT_RESOURCE_ID:reboot" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

response:  {    "name": "projects/123456789012/locations/us-central1/persistentResources/test-persistent-resource/operations/1234567890123456789",    "metadata": {      "@type": "type.googleapis.com/google.cloud.aiplatform.v1.RebootPersistentResourceOperationMetadata",      "genericMetadata": {        "createTime": "2024-03-18T17:31:54.955004Z",        "updateTime": "2024-03-18T17:31:55.204817Z",        "state": "RUNNING",        "worksOn": [          "projects/123456789012/locations/us-central1/persistentResources/test-persistent-resource"        ]      },      "progressMessage": "Waiting for persistent resource shut down."    }  }

Rebooting a persistent resource is along running operation,during which the persistent resource can't be deleted. The operation contains aprogressMessage field that populates with an error status if one occurs. Afterthe operation indicates"done: true",check the statusof the persistent resource. If the persistent resource is in theRUNNINGstate, the reboot is successful and it's ready to run training jobs.

Limitations

The following are limitations for rebooting a persistent resource:

  • In some cases, it's possible to lose capacity of scarce resources whenrebooting a persistent resource. Full resource retention is not guaranteed.
  • Reboot is not available on Ray on Vertex AI.
  • Persistent resources containing autoscaled worker pools reboot with theminimum replica count.

What's next

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Last updated 2026-02-18 UTC.