Mantenha tudo organizado com as coleções
Salve e categorize o conteúdo com base nas suas preferências.
Acessar buckets e arquivos do Cloud Storage no JupyterLab
Nesta página, mostramos como montar um bucket do Cloud Storage na interface do JupyterLab da
instância do Vertex AI Workbench
para procurar arquivos armazenados
no Cloud Storage. Também é possível abrir e editar arquivos compatíveis com o JupyterLab, como arquivos de texto e de notebook (IPYNB).
Visão geral
As instâncias do Vertex AI Workbench incluem uma integração com o
Cloud Storage que permite montar um bucket do Cloud Storage. Isso significa que é possível navegar pelo conteúdo do bucket e trabalhar com arquivos compatíveis na interface JupyterLab.
É possível acessar qualquer bucket e arquivos do Cloud Storage aos quais
sua instância tem acesso no mesmo projeto que
a instância do Vertex AI Workbench.
Antes de começar
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.
Para garantir que sua conta de usuário tenha as permissões
necessárias para ativar um bucket do Cloud Storage em uma instância do Vertex AI Workbench,
peça ao administrador para conceder à sua conta de usuário os
seguintes papéis do IAM no projeto:
Permissão necessária para ativar a montagem do armazenamento compartilhado
Para ativar a montagem do armazenamento compartilhado na instância do Vertex AI Workbench,
peça ao administrador para conceder à conta de serviço da instância
do Vertex AI Workbench a permissão storage.buckets.list no projeto.
A permissão storage.buckets.list é necessária para que o botão
Montar armazenamento compartilhado apareça na interface do JupyterLab da sua
instância do Vertex AI Workbench.
Criar um bucket e uma instância do Vertex AI Workbench
Você precisa ter acesso a pelo menos um bucket do Cloud Storage no
mesmo projeto da instância do Vertex AI Workbench.
Se você precisar criar um bucket do Cloud Storage,
consulte Criar buckets.
Ao lado do nome da instância do Vertex AI Workbench,
clique em Abrir JupyterLab.
Sua instância do Vertex AI Workbench abre o JupyterLab.
Montar um bucket do Cloud Storage
Para ativar e acessar um bucket do Cloud Storage, faça o seguinte:
No JupyterLab, verifique se a
guia folderNavegador de arquivos
está selecionada.
Na barra lateral esquerda, clique no botão
Ativar armazenamento
compartilhado. Se o botão não aparecer, arraste o lado direito
da barra lateral para expandi-la até encontrar o botão.
No campo Nome do bucket, insira o nome do bucket do Cloud Storage
que você quer ativar.
Clique em Mount.
Seu bucket do Cloud Storage aparece como uma pasta na guia
Navegador de arquivos da barra lateral esquerda. Clique duas vezes na pasta para abri-la
e navegar pelo conteúdo.
Resolver problemas
Para encontrar métodos para diagnosticar e resolver problemas com a montagem de um
bucket do Cloud Storage na instância, consulte Solução de problemas
do Vertex AI Workbench.
[[["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,["# Access Cloud Storage buckets and files in JupyterLab in a Vertex AI Workbench instance.\n\nAccess Cloud Storage buckets and files in JupyterLab\n====================================================\n\nThis page shows you how to mount a Cloud Storage bucket to the\nJupyterLab interface of your Vertex AI Workbench instance so that you can\nbrowse files that are stored in Cloud Storage. You can also open\nand edit files that are compatible with JupyterLab, such as text files and\nnotebook (IPYNB) files.\n\nOverview\n--------\n\nVertex AI Workbench instances include a Cloud Storage integration\nthat lets you mount a Cloud Storage bucket. This means you can\nbrowse the contents of the bucket and work with compatible files from within\nthe JupyterLab interface.\n\nYou can access any of the Cloud Storage buckets and files that\nyour instance has access to within the same project as\nyour Vertex AI Workbench instance.\n| **Note:** Your Vertex AI Workbench instance's access to Cloud Storage is determined by the single user or service account that you used to grant access to your instance. For example, if you granted a specific service account access to your instance, you must also grant that service account access to the Cloud Storage buckets that you want to use in JupyterLab.\n\nBefore you begin\n----------------\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\n\u003cbr /\u003e\n\n### Required roles\n\n\nTo get the permissions that\nyou need to mount a Cloud Storage bucket to a Vertex AI Workbench instance,\n\nask your administrator to grant you the\nfollowing IAM roles on the project:\n\n- [Notebooks Runner](/iam/docs/roles-permissions/notebooks#notebooks.runner) (`roles/notebooks.runner`)\n- [Storage Object User](/iam/docs/roles-permissions/storage#storage.objectUser) (`roles/storage.objectUser`)\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\n### Required permission for enabling shared storage mounting\n\nTo enable shared storage mounting in your Vertex AI Workbench instance,\nask your administrator to grant your Vertex AI Workbench instance's\nservice account the `storage.buckets.list` permission on the project.\n\nThe `storage.buckets.list` permission is required for the\n**Mount shared storage** button to appear in the JupyterLab interface of your\nVertex AI Workbench instance.\n\nCreate a bucket and a Vertex AI Workbench instance\n--------------------------------------------------\n\nYou must have access to at least one Cloud Storage bucket in the same project as your Vertex AI Workbench instance.\n\n1. If you need to create a Cloud Storage bucket, see [Create a bucket](/storage/docs/creating-buckets).\n2. If you haven't already, [create a Vertex AI Workbench instance](/vertex-ai/docs/workbench/instances/create) in the same project as your Cloud Storage bucket.\n\nOpen JupyterLab\n---------------\n\n1. In the Google Cloud console, go to the **Instances** page.\n\n\n [Go to Instances](https://console.cloud.google.com/vertex-ai/workbench/instances)\n2. Next to your Vertex AI Workbench instance's name,\n click **Open JupyterLab**.\n\n Your Vertex AI Workbench instance opens JupyterLab.\n\nMount the Cloud Storage bucket\n------------------------------\n\nTo mount and then access a Cloud Storage bucket, do the following:\n\n1. In JupyterLab, make sure the\n folder **File Browser** tab\n is selected.\n\n2. In the left sidebar, click the\n **Mount\n shared storage** button. If you don't see the button, drag the right side\n of the sidebar to expand the sidebar until you see the button.\n\n\n3. In the **Bucket name** field, enter the Cloud Storage\n bucket name that you want to mount.\n\n4. Click **Mount**.\n\n5. Your Cloud Storage bucket appears as a folder in the\n **File browser** tab of the left sidebar. Double-click the folder\n to open it and browse the contents.\n\nTroubleshoot\n------------\n\nTo find methods for diagnosing and resolving issues with mounting a\nCloud Storage bucket to your instance, see [Troubleshooting\nVertex AI Workbench](/vertex-ai/docs/general/troubleshooting-workbench#instances).\n\nWhat's next\n-----------\n\n- Learn more about [Cloud Storage](/storage/docs/introduction).\n\n- Learn how to [query data in BigQuery\n from within JupyterLab](/vertex-ai/docs/workbench/instances/bigquery)."]]
Ativar armazenamento
compartilhado. Se o botão não aparecer, arraste o lado direito
da barra lateral para expandi-la até encontrar o botão.