[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-09-02。"],[],[],null,["# Access Cloud Storage buckets and files in JupyterLab\n====================================================\n\n\n| Vertex AI Workbench managed notebooks is\n| [deprecated](/vertex-ai/docs/deprecations). On\n| April 14, 2025, support for\n| managed notebooks will end and the ability to create 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 managed notebooks instances to Vertex AI Workbench instances](/vertex-ai/docs/workbench/managed/migrate-to-instances).\n\n\u003cbr /\u003e\n\nThis page shows you how to mount a Cloud Storage bucket to the\nJupyterLab interface of\nyour Vertex AI Workbench managed notebooks instance\nso that you can browse files that are stored\nin Cloud Storage. You can also open and edit files that are compatible\nwith JupyterLab, such as text files and notebook (IPYNB) files.\n\nOverview\n--------\n\nVertex AI Workbench managed notebooks instances\ninclude a Cloud Storage integration that lets you\nmount a Cloud Storage bucket. This means you can browse the contents\nof the bucket and work with compatible files from within\nthe JupyterLab interface.\n\nYou can access any of\nthe Cloud Storage buckets and files that your instance\nhas access to within the same project as\nyour managed notebooks instance.\n| **Note:** Your managed notebooks 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\nThis guide requires you to have access to\nat least one Cloud Storage bucket in the same project\nas your managed notebooks instance.\n\n1. If you need to create a Cloud Storage bucket,\n see [Create buckets](/storage/docs/creating-buckets).\n\n2. If you haven't already,\n [create\n a managed notebooks instance](/vertex-ai/docs/workbench/managed/create-instance#create) in the same project\n as your Cloud Storage bucket.\n\nOpen JupyterLab\n---------------\n\n1. In the Google Cloud console, go to the **Managed notebooks** page.\n\n [Go to Managed notebooks](https://console.cloud.google.com/vertex-ai/workbench/managed)\n2. Next to your managed notebooks instance's name,\n click **Open JupyterLab**.\n\n Your managed notebooks instance opens JupyterLab.\n\nMount the Cloud Storage buckets and files\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\n3. In the **Bucket name** field, enter the Cloud Storage bucket name\n 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 to open\n it and browse the contents.\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/managed/bigquery)."]]
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