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Mengakses bucket dan file Cloud Storage di JupyterLab
Halaman ini menunjukkan cara memasang bucket Cloud Storage ke antarmuka JupyterLab instance notebook terkelola Vertex AI Workbench sehingga Anda dapat menjelajahi file yang disimpan di Cloud Storage. Anda juga dapat membuka dan mengedit file yang kompatibel
dengan JupyterLab, seperti file teks dan file notebook (IPYNB).
Ringkasan
Instance notebook terkelola Vertex AI Workbench mencakup integrasi Cloud Storage yang memungkinkan Anda memasang bucket Cloud Storage. Artinya, Anda dapat menjelajahi konten
bucket dan menggunakan file yang kompatibel dari dalam
antarmuka JupyterLab.
Anda dapat mengakses salah satu
bucket dan file Cloud Storage yang dapat diakses instance Anda
dalam project yang sama dengan
instance notebook terkelola Anda.
Sebelum memulai
Panduan ini mengharuskan Anda memiliki akses ke setidaknya satu bucket Cloud Storage di project yang sama dengan instance notebook terkelola Anda.
Jika Anda perlu membuat bucket Cloud Storage, lihat Membuat bucket.
Di samping nama instance notebook terkelola, klik Open JupyterLab.
Instance notebook terkelola Anda akan membuka JupyterLab.
Memasang bucket dan file Cloud Storage
Untuk memasang, lalu mengakses bucket Cloud Storage, lakukan tindakan berikut:
Di JupyterLab, pastikan tab
folderFile Browser
dipilih.
Di sidebar kiri, klik
tombol Mount
shared storage. Jika Anda tidak melihat tombol, tarik sisi kanan
sidebar untuk meluaskan sidebar hingga Anda melihat tombol.
Di kolom Bucket name, masukkan nama bucket Cloud Storage yang ingin Anda pasang.
Klik Mount.
Bucket Cloud Storage Anda akan muncul sebagai folder di tab File browser di sidebar kiri. Klik dua kali folder untuk membukanya dan menjelajahi kontennya.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-18 UTC."],[],[],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)."]]
Mount
shared storage. Jika Anda tidak melihat tombol, tarik sisi kanan
sidebar untuk meluaskan sidebar hingga Anda melihat tombol.