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É possível reinicializar qualquer recurso permanente que esteja no estado RUNNING ou ERROR.
A reinicialização de um recurso persistente permite que você se recupere de erros dos quais o recurso
permanente não consegue se recuperar por conta própria. Também é possível reinicializar um recurso permanente para receber manualmente clusters mais atualizados. Nesta página, mostramos como reinicializar um recurso permanente usando o console do Google Cloud e a API REST.
Funções exigidas
Para receber a permissão necessária a fim de reiniciar um recurso permanente,
peça ao administrador para conceder a você o
papel do IAM de Administrador da Vertex AI (roles/aiplatform.admin) no projeto.
Para mais informações sobre a concessão de papéis, consulte Gerenciar o acesso a projetos, pastas e organizações.
Esse papel predefinido contém a
permissão
aiplatform.persistentResources.update,
que é necessária para
excluir um recurso permanente.
Selecione uma das guias a seguir para ver instruções sobre como reiniciar um recurso
permanente. Confirme se não há jobs de treinamento em execução no recurso persistente.
Reiniciar um recurso permanente é uma
operação de longa duração
em que não é possível excluir o recurso permanente. A operação contém um campo progressMessage que é preenchido com um status de erro, se um deles ocorrer. Após
a operação indicar "done: true",
verifique o status
do recurso persistente. Se o recurso permanente estiver no estado RUNNING, a reinicialização será bem-sucedida e está pronto para executar jobs de treinamento.
Limitações
Confira a seguir as limitações para reiniciar um recurso persistente:
Em alguns casos, é possível perder a capacidade de recursos escassos ao
reinicializar um recurso permanente. A retenção total de recursos não é garantida.
A reinicialização não está disponível no Ray na Vertex AI.
Os recursos permanentes que contêm pools de workers com escalonamento automático são reinicializados com a contagem mínima de réplicas.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-08-28 UTC."],[],[],null,["# Reboot a persistent resource\n\nYou can reboot any persistent resource that's in the `RUNNING` or `ERROR` state. Rebooting a persistent resource lets you recover from errors that the persistent resource can't recover from on its own. You can also reboot a persistent resource to manually obtain more up-to-date clusters. This page shows you how to reboot a persistent resource by using the Google Cloud console and the REST API.\n\n\u003cbr /\u003e\n\nRequired roles\n--------------\n\n\nTo get the permission that\nyou need to reboot a persistent resource,\n\nask your administrator to grant you the\n\n\n[Vertex AI Administrator](/iam/docs/roles-permissions/aiplatform#aiplatform.admin) (`roles/aiplatform.admin`)\nIAM role on your project.\n\n\nFor more information about granting roles, see [Manage access to projects, folders, and organizations](/iam/docs/granting-changing-revoking-access).\n\n\nThis predefined role contains the\n` aiplatform.persistentResources.update`\npermission,\nwhich is required to\nreboot a persistent resource.\n\n\nYou might also be able to get\nthis permission\nwith [custom roles](/iam/docs/creating-custom-roles) or\nother [predefined roles](/iam/docs/roles-overview#predefined).\n\nReboot a persistent resource\n----------------------------\n\nSelect one of the following tabs for instructions on how to reboot a persistent\nresource. Make sure there's no training jobs running on the persistent resource. \n\n### Console\n\nTo reboot a persistent resource in the Google Cloud console, do the following:\n\n1. In the Google Cloud console, go to the **Persistent resources** page.\n\n [Go to Persistent resources](https://console.cloud.google.com/vertex-ai/training/persistent-resources)\n2. Next to the name of the persistent resource that you want to reboot, click\n the vertical ellipses (more_vert).\n\n3. Click **Reboot**.\n\n4. Click **Confirm**.\n\n\n### gcloud\n\n\nBefore using any of the command data below,\nmake the following replacements:\n\n- \u003cvar class=\"edit\" scope=\"PROJECT_ID\" translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: The Project ID of the persistent resource that you want to reboot.\n- \u003cvar class=\"edit\" scope=\"LOCATION\" translate=\"no\"\u003eLOCATION\u003c/var\u003e: The region of the persistent resource that you want to reboot.\n- \u003cvar class=\"edit\" scope=\"PERSISTENT_RESOURCE_ID\" translate=\"no\"\u003ePERSISTENT_RESOURCE_ID\u003c/var\u003e: The ID of the persistent resource that you want to reboot.\n\n\nExecute the\n\nfollowing\n\ncommand:\n\n#### Linux, macOS, or Cloud Shell\n\n**Note:** Ensure you have initialized the Google Cloud CLI with authentication and a project by running either [gcloud init](/sdk/gcloud/reference/init); or [gcloud auth login](/sdk/gcloud/reference/auth/login) and [gcloud config set project](/sdk/gcloud/reference/config/set). \n\n```bash\ngcloud ai persistent-resources reboot PERSISTENT_RESOURCE_ID \\\n --project=PROJECT_ID \\\n --region=LOCATION\n```\n\n#### Windows (PowerShell)\n\n**Note:** Ensure you have initialized the Google Cloud CLI with authentication and a project by running either [gcloud init](/sdk/gcloud/reference/init); or [gcloud auth login](/sdk/gcloud/reference/auth/login) and [gcloud config set project](/sdk/gcloud/reference/config/set). \n\n```bash\ngcloud ai persistent-resources reboot PERSISTENT_RESOURCE_ID `\n --project=PROJECT_ID `\n --region=LOCATION\n```\n\n#### Windows (cmd.exe)\n\n**Note:** Ensure you have initialized the Google Cloud CLI with authentication and a project by running either [gcloud init](/sdk/gcloud/reference/init); or [gcloud auth login](/sdk/gcloud/reference/auth/login) and [gcloud config set project](/sdk/gcloud/reference/config/set). \n\n```bash\ngcloud ai persistent-resources reboot PERSISTENT_RESOURCE_ID ^\n --project=PROJECT_ID ^\n --region=LOCATION\n```\n\nYou should receive a response similar to the following:\n\n```\nUsing endpoint [https://us-central1-aiplatform.googleapis.com/]\nRequest to reboot the PersistentResource [projects/sample-project/locations/us-central1/persistentResources/test-persistent-resource] has been sent.\n\nYou may view the status of your persistent resource with the command\n\n $ gcloud ai persistent-resources describe projects/sample-project/locations/us-central1/persistentResources/test-persistent-resource\n```\n\n### REST\n\n\nBefore using any of the request data,\nmake the following replacements:\n\n- \u003cvar class=\"edit\" scope=\"PROJECT_ID\" translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: The Project ID of the persistent resource that you want to reboot.\n- \u003cvar class=\"edit\" scope=\"LOCATION\" translate=\"no\"\u003eLOCATION\u003c/var\u003e: The region of the persistent resource that you want to reboot.\n- \u003cvar class=\"edit\" scope=\"PERSISTENT_RESOURCE_ID\" translate=\"no\"\u003ePERSISTENT_RESOURCE_ID\u003c/var\u003e: The ID of the persistent resource that you want to reboot.\n\n\nHTTP method and URL:\n\n```\nPOST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/persistentResources/PERSISTENT_RESOURCE_ID:reboot\n```\n\nTo send your request, expand one of these options:\n\n#### curl (Linux, macOS, or Cloud Shell)\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) , or by using [Cloud Shell](/shell/docs), which automatically logs you into the `gcloud` CLI . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nExecute the following command:\n\n```\ncurl -X POST \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d \"\" \\\n \"https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/persistentResources/PERSISTENT_RESOURCE_ID:reboot\"\n```\n\n#### PowerShell (Windows)\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nExecute the following command:\n\n```\n$cred = gcloud auth print-access-token\n$headers = @{ \"Authorization\" = \"Bearer $cred\" }\n\nInvoke-WebRequest `\n -Method POST `\n -Headers $headers `\n -Uri \"https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/persistentResources/PERSISTENT_RESOURCE_ID:reboot\" | Select-Object -Expand Content\n```\n\nYou should receive a JSON response similar to the following:\n\n```\nresponse: \n {\n \"name\": \"projects/123456789012/locations/us-central1/persistentResources/test-persistent-resource/operations/1234567890123456789\",\n \"metadata\": {\n \"@type\": \"type.googleapis.com/google.cloud.aiplatform.v1.RebootPersistentResourceOperationMetadata\",\n \"genericMetadata\": {\n \"createTime\": \"2024-03-18T17:31:54.955004Z\",\n \"updateTime\": \"2024-03-18T17:31:55.204817Z\",\n \"state\": \"RUNNING\",\n \"worksOn\": [\n \"projects/123456789012/locations/us-central1/persistentResources/test-persistent-resource\"\n ]\n },\n \"progressMessage\": \"Waiting for persistent resource shut down.\"\n }\n }\n```\n\n\u003cbr /\u003e\n\nRebooting a persistent resource is a\n[long running operation](/vertex-ai/docs/general/long-running-operations),\nduring which the persistent resource can't be deleted. The operation contains a\n`progressMessage` field that populates with an error status if one occurs. After\nthe operation indicates `\"done: true\"`,\n[check the status](/vertex-ai/docs/training/persistent-resource-get#get_information_about_a_persistent_resource)\nof the persistent resource. If the persistent resource is in the `RUNNING`\nstate, the reboot is successful and it's ready to run training jobs.\n\nLimitations\n-----------\n\nThe following are limitations for rebooting a persistent resource:\n\n- In some cases, it's possible to lose capacity of scarce resources when rebooting a persistent resource. Full resource retention is not guaranteed.\n- Reboot is not available on Ray on Vertex AI.\n- Persistent resources containing autoscaled worker pools reboot with the minimum replica count.\n\nWhat's next\n-----------\n\n- [Learn about persistent resource](/vertex-ai/docs/training/persistent-resource-overview).\n- [Create and use a persistent resource](/vertex-ai/docs/training/persistent-resource-create).\n- [Run training jobs on a persistent resource](/vertex-ai/docs/training/persistent-resource-train).\n- [Get information about a persistent resource](/vertex-ai/docs/training/persistent-resource-get).\n- [Delete a persistent resource](/vertex-ai/docs/training/persistent-resource-delete)."]]
