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Criar uma versão específica de uma instância de notebooks gerenciados pelo usuário
Nesta página, você verá como criar uma instância de notebooks gerenciados pelo usuário
com base em uma
versão específica de
imagens de VM de aprendizado profundo.
Por que criar uma versão específica
Para garantir que a instância de notebooks gerenciada pelo usuário
tenha um software compatível com seu código ou aplicativo, convém criar uma
versão específica.
As instâncias de notebooks gerenciados pelo usuário são criadas com imagens da VM de aprendizado profundo. As imagens da VM de aprendizado profundo são atualizadas com frequência, e as versões específicas de software
e pacotes pré-instalados variam de acordo com a versão.
Antes de criar uma instância de notebooks gerenciados pelo usuário,
é necessário ter um
projeto doGoogle Cloud e ativar a API Notebooks
para esse projeto.
Sign in to your Google Cloud account. If you're new to
Google Cloud,
create an account to evaluate how our products perform in
real-world scenarios. New customers also get $300 in free credits to
run, test, and deploy workloads.
In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
Caso tenha criado o projeto, você terá o
papel do IAM de Proprietário (roles/owner) no projeto,
que inclui todas as permissões necessárias. Pule esta seção e
comece a criar sua instância de notebooks gerenciados pelo usuário. Se você não
criou o projeto, continue nesta seção.
Para receber as permissões necessárias para criar uma instância de notebooks gerenciados pelo usuário do Vertex AI Workbench, peça ao administrador para conceder a você os seguintes papéis do IAM no projeto:
Para criar uma instância de notebooks gerenciados pelo usuário com base
em uma determinada versão da VM de aprendizado profundo, é preciso
saber o nome da imagem da versão específica da VM de aprendizado profundo que você quer usar.
Cada versão da VM de aprendizado profundo inclui atualizações para muitas imagens diferentes, e cada imagem na versão tem seu próprio nome de imagem.
Para encontrar o nome da imagem específica que você quer:
Encontre o número da versão da VM de aprendizado profundo para receber os nomes das imagens.
Os números de lançamento estão incluídos nas notas de lançamento da VM de aprendizado profundo.
Os números de versão estão na forma de um M seguido pelo número da versão, por exemplo, M79.
Para listar os nomes das imagens de uma versão específica da VM de aprendizado profundo, execute o seguinte comando.
[[["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 2025-08-18 UTC."],[],[],null,["# Create a specific version of a Vertex AI Workbench user-managed notebooks instance\n\nCreate a specific version of a user-managed notebooks instance\n==============================================================\n\n\n| Vertex AI Workbench user-managed notebooks is\n| [deprecated](/vertex-ai/docs/deprecations). On\n| April 14, 2025, support for\n| user-managed notebooks will end and the ability to create user-managed notebooks instances\n| will be removed. Existing instances will continue to function\n| but patches, updates, and upgrades won't be available. To continue using\n| Vertex AI Workbench, we recommend that you\n| [migrate\n| your user-managed notebooks instances to Vertex AI Workbench instances](/vertex-ai/docs/workbench/user-managed/migrate-to-instances).\n\n\u003cbr /\u003e\n\nThis page describes how you can create\na user-managed notebooks instance based on a specific\n[Deep Learning VM Images](/deep-learning-vm/docs)\nversion.\n\nWhy you might want to create a specific version\n-----------------------------------------------\n\nTo ensure that your user-managed notebooks instance has software\nthat is compatible with your code or application, you might want to create\na specific version.\n\nUser-managed notebooks instances are created by using Deep Learning VM images. Deep Learning VM\nimages are updated frequently, and specific versions of preinstalled software\nand packages vary from version to version.\n\nTo learn more about specific Deep Learning VM versions,\nsee the [Deep Learning VM\nrelease notes](/deep-learning-vm/docs/release-notes).\n\nAfter you create a specific version of a user-managed notebooks instance, you can upgrade it. Upgrading the instance updates the preinstalled software and packages. For more information, see [Upgrade a user-managed\nnotebooks instance's environment](/vertex-ai/docs/workbench/user-managed/upgrade).\n\nBefore you begin\n----------------\n\nBefore you can create a user-managed notebooks instance, you must have a Google Cloud project and enable the Notebooks API for that project.\n\n- Sign in to your Google Cloud account. If you're new to Google Cloud, [create an account](https://console.cloud.google.com/freetrial) to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Notebooks API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=notebooks.googleapis.com&redirect=https://console.cloud.google.com)\n\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Notebooks API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=notebooks.googleapis.com&redirect=https://console.cloud.google.com)\n\n1. If you plan to use GPUs with your user-managed notebooks instance, [check the quotas page in the\n Google Cloud console](https://console.cloud.google.com/quotas) to ensure that you have enough GPUs available in your project. If GPUs are not listed on the quotas page, or you require additional GPU quota, you can request a quota increase. See [Requesting an increase in\n quota](/compute/quotas#requesting_additional_quota) on the Compute Engine [Resource quotas](/compute/quotas) page.\n\n\u003cbr /\u003e\n\n### Required roles\n\nIf you created the project, you have the\nOwner (`roles/owner`) IAM role on the project,\nwhich includes all required permissions. Skip this section and\nstart creating your user-managed notebooks instance. If you didn't\ncreate the project yourself, continue in this section.\n\n\nTo get the permissions that\nyou need to create a Vertex AI Workbench user-managed notebooks instance,\n\nask your administrator to grant you the\nfollowing IAM roles on the project:\n\n- Notebooks Admin ([`roles/notebooks.admin`](/vertex-ai/docs/workbench/user-managed/iam#notebooks.admin))\n- Service Account User ([`roles/iam.serviceAccountUser`](/iam/docs/understanding-roles#iam.serviceAccountUser))\n\n\nFor more information about granting roles, see [Manage access to projects, folders, and organizations](/iam/docs/granting-changing-revoking-access).\n\n\nYou might also be able to get\nthe required permissions through [custom\nroles](/iam/docs/creating-custom-roles) or other [predefined\nroles](/iam/docs/roles-overview#predefined).\n\nFind the specific version that you want\n---------------------------------------\n\nTo create a user-managed notebooks instance based on a specific\nDeep Learning VM version, you must know\nthe image name of the specific Deep Learning VM\nversion that you want to use.\n\nEach release of Deep Learning VM includes updates to\nmany different images, and each image in the release has its own\nimage name.\n\nTo find the specific image name that you want:\n\n1. Find the Deep Learning VM release number\n that you want to get image names for.\n Release numbers are included in the [Deep Learning VM\n release notes](/deep-learning-vm/docs/release-notes).\n Release numbers are in the form of an `M` followed by\n the number of the release, for example, `M79`.\n\n2. To list the image names for a specific Deep Learning VM\n release, run the following command.\n\n ```bash\n gcloud compute images list --project=\"deeplearning-platform-release\" \\\n --format=\"value(name)\" \\\n --filter=\"labels.release=\u003cvar translate=\"no\"\u003eRELEASE_NUMBER\u003c/var\u003e\" \\\n --show-deprecated\n ```\n\n Replace \u003cvar translate=\"no\"\u003eRELEASE_NUMBER\u003c/var\u003e with\n a Deep Learning VM release number, such as `M79`.\n3. Find the image name that you want to use.\n\nCreate a specific version from the command line\n-----------------------------------------------\n\nTo create a specific version of\na user-managed notebooks instance from\nthe command line, complete the following steps:\n\n1. Run the following [`gcloud\n notebooks`](/sdk/gcloud/reference/notebooks/instances/create) command:\n\n ```bash\n gcloud notebooks instances create INSTANCE_NAME \\\n --vm-image-project=\"deeplearning-platform-release\" \\\n --vm-image-name=VM_IMAGE_NAME \\\n --machine-type=MACHINE_TYPE \\\n --location=LOCATION\n ```\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eINSTANCE_NAME\u003c/var\u003e: the name of your new instance\n - \u003cvar translate=\"no\"\u003eVM_IMAGE_NAME\u003c/var\u003e: the image name that you want to use to create your instance\n - \u003cvar translate=\"no\"\u003eMACHINE_TYPE\u003c/var\u003e: the [machine\n type](/compute/docs/machine-resource) of your instance's VM\n - \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e: the Google Cloud [location](/vertex-ai/docs/general/locations#user-managed-notebooks-locations) where you want your new instance to be\n2. Access your instance from the\n [Google Cloud console](https://console.cloud.google.com/vertex-ai/workbench/user-managed).\n\nWhat's next\n-----------\n\n- Learn more about [upgrading\n user-managed notebooks instances](/vertex-ai/docs/workbench/user-managed/upgrade)\n to ensure that your instance upgrades only when you are ready.\n\n- [Install dependencies](/vertex-ai/docs/workbench/user-managed/dependencies) on\n your new user-managed notebooks instance.\n\n- Learn more about Deep Learning VM instances in the\n [Deep Learning VM\n documentation](/deep-learning-vm/docs).\n\n- Learn about [monitoring the health status](/vertex-ai/docs/workbench/user-managed/monitor-health) of\n your user-managed notebooks instance."]]