Vertex AI Workbench는 전체 데이터 과학 워크플로를 위한 단일 개발 환경입니다. 엔드 투 엔드 노트북 기반 프로덕션 환경을 설정하려면 통합 기능이 내장된 JupyterLab 인스턴스를 만드세요. Vertex AI를 처음 사용하는 경우 Vertex AI Workbench에 대해 자세히 알아보세요.
이 페이지에서는 노트북을 만들고 공유하며 노트북을 사용하여 Vertex AI 서비스와 상호작용하는 등 Vertex AI Workbench에서 JupyterLab 노트북을 관리하는 프로세스를 설명합니다. 이 페이지에서는 노트북을 호스팅하는 JupyterLab 인스턴스를 삭제하고 업데이트하는 방법도 보여줍니다.
새 JupyterLab 인스턴스 구성
Vertex AI Workbench는 새 버전의 JupyterLab으로 JupyterLab 인스턴스를 만듭니다. 예를 들어 새 JupyterLab 인스턴스에는 Distributed Cloud의 클라이언트 라이브러리 업데이트가 포함됩니다.
오래된 JupyterLab 인스턴스의 스토리지 버킷에서 새 JupyterLab 인스턴스로 파일을 복사합니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-04(UTC)"],[[["\u003cp\u003eVertex AI Workbench offers a unified environment for data science, allowing users to create and manage JupyterLab instances and notebooks.\u003c/p\u003e\n"],["\u003cp\u003eUsers must have either the "Workbench Notebooks Admin" role for full access or the "Workbench Notebooks Viewer" role for read-only access to manage notebooks within a project.\u003c/p\u003e\n"],["\u003cp\u003eCreating a JupyterLab notebook involves selecting a Docker image, cluster, and resources, and setting the notebook's name and storage.\u003c/p\u003e\n"],["\u003cp\u003eJupyterLab notebooks can be opened via their URL or through the Vertex AI Workbench interface, and they allow for interaction with Vertex AI services using client libraries.\u003c/p\u003e\n"],["\u003cp\u003eUsers can update JupyterLab instances by saving files, creating a new instance with the latest updates, copying the old files into the new instance, and deleting the outdated instance.\u003c/p\u003e\n"]]],[],null,["# Manage notebooks\n\nVertex AI Workbench is a single development environment for the entire\ndata science workflow. To set up an end-to-end notebook-based production\nenvironment, create JupyterLab instances with built-in integrations. If you are new\nto Vertex AI,\n[learn more about Vertex AI Workbench](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-workbench-intro).\n\nThis page describes the process of managing JupyterLab notebooks in\nVertex AI Workbench, including creating and sharing notebooks and using\nnotebooks to interact with Vertex AI services. This page also\nshows how to delete and update the JupyterLab instances that host your notebooks.\n\nFor information about backing up and restoring data, see\n[Create a backup and restore notebook data](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/backup-restore-notebook-data).\n\nBefore you begin\n----------------\n\nBefore using Vertex AI Workbench to manage notebooks, you must have a\nproject ready to run Vertex AI services. For more information, see\n[Set up a project for Vertex AI](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-set-up-project).\n\nTo get the permissions you need to manage notebook resources within a project\nnamespace, ask your Project IAM Admin to grant you one of the following roles:\n\n- **Workbench Notebooks Admin** (`workbench-notebooks-admin`): Get read and write access to all notebook resources in a project. You need this role to [create JupyterLab notebooks](#create-notebook).\n- **Workbench Notebooks Viewer** (`workbench-notebooks-viewer`): Get read-only access to all notebook resources in a project. You need this role to [open JupyterLab notebooks](#open-notebook).\n\nFor more information about these roles, see [Prepare IAM permissions](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-ao-permissions).\n\nCreate a JupyterLab notebook\n----------------------------\n\nThis section describes configuring a JupyterLab instance in\nVertex AI Workbench and creating a JupyterLab notebook in the instance.\n| **Note:** All JupyterLab instances have JupyterLab 3 preinstalled.\n\nAfter meeting the [prerequisites](#before-you-begin), follow these steps to\nconfigure a JupyterLab instance and create a JupyterLab notebook:\n\n1. [Sign in to the GDC console and select your project](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/iam/sign-in).\n2. In the navigation menu, click **Vertex AI** \\\u003e **Workbench**.\n3. Click add_box**New notebook**.\n4. On the **Create notebook** page, enter values for the following fields:\n\n - **Notebook name**: enter the name you want to give to your JupyterLab notebook. Vertex AI Workbench uses the name you choose to create a URL for accessing your notebook.\n - **Environment**: select a Docker image for your JupyterLab instance. This image provides a baseline for deployment and typical machine learning (ML) packages.\n - **Cluster**: select a Kubernetes cluster for your JupyterLab instance that meets your usage requirements. If a Kubernetes cluster isn't available, work with your administrator to add one or more clusters.\n - **CPUs / Memory**: enter the amount of CPUs and RAM you need for your workloads. For CPU-intensive workloads, you can choose more than one CPU.\n - **GPUs**: select the number of GPUs you need for your JupyterLab instance. In Distributed Cloud, a GPU is one NVIDIA Multi-Instance GPU (MIG) slice of an A100 Tensor Core GPU.\n - **Workspace volume**: enter the storage size you need in GB.\n5. Click **Create**.\n\nVertex AI Workbench configures the JupyterLab instance and creates your\nJupyterLab notebook. Save the notebook's URL for future access.\n| **Tip:** Bookmark the URL of the JupyterLab notebook for fast access.\n\nAfter you create a JupyterLab notebook in Vertex AI Workbench, open the\nintegrated development environment (IDE) in the JupyterLab environment. For more\ninformation, see [Open a JupyterLab notebook](#open-notebook).\n\nOpen a JupyterLab notebook\n--------------------------\n\nEnter the URL of a JupyterLab notebook in a web browser to open it. If you don't\nknow the URL, follow these steps to open the notebook:\n\n1. [Sign in to the GDC console and select your project](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/iam/sign-in).\n2. In the navigation menu, click **Vertex AI** \\\u003e **Workbench**.\n3. Find the JupyterLab notebook you want to open and click **Open JupyterLab** to open the JupyterLab instance IDE.\n4. If prompted to authenticate, follow the authentication steps for your identity provider.\n5. In the JupyterLab instance, open the JupyterLab notebook.\n\nShare the URL of a JupyterLab notebook with other users so that they can open it\ntoo. The intended user must have the Workbench Notebooks Viewer role.\n\nUse Vertex AI services from a JupyterLab notebook\n-------------------------------------------------\n\nUse [client libraries](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-install-libraries)\nto interact with a Vertex AI service from a JupyterLab notebook.\nVertex AI client libraries let you programmatically make API\ncalls to any Vertex AI service on Distributed Cloud.\n\nFollow these steps to use a Vertex AI service from a\nJupyterLab notebook:\n\n1. [Enable the corresponding Vertex AI API](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-enable-pre-trained-apis).\n2. [Install the corresponding Vertex AI client library](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-install-libraries).\n3. [Create a JupyterLab notebook](#create-notebook).\n4. [Open the JupyterLab notebook](#open-notebook) and use it to write code with the Vertex AI client libraries. For example, you can [translate text](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/quickstart-translation) using the Vertex AI Translation client library.\n\nDelete a JupyterLab instance\n----------------------------\n\n| **Tip:** Before deleting JupyterLab instances, consider creating backups of files in a storage bucket. For more information, see [Upload and download storage objects in projects](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/upload-download-storage-objects).\n\nFollow these steps to delete a JupyterLab instance:\n\n1. [Sign in to the GDC console and select your project](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/iam/sign-in).\n2. In the navigation menu, click **Vertex AI** \\\u003e **Workbench**.\n3. Find the notebook associated with the JupyterLab instance you want to delete.\n4. Select the checkbox of the JupyterLab notebook.\n5. Click **Delete**.\n6. In the **Delete notebooks** dialog, click **Delete**.\n\nUpdate a JupyterLab instance\n----------------------------\n\nAfter your Infrastructure Operator (IO) updates Distributed Cloud, you can\nupdate your JupyterLab instances.\n\nFollow these steps for each JupyterLab instance you want to update:\n\n1. Save the files from the JupyterLab instance you want to retain to a storage bucket. For more information, see [Upload and download storage objects in projects](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/upload-download-storage-objects).\n2. After the update, [sign in to the GDC console and select your project](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/iam/sign-in).\n3. [Configure a new JupyterLab instance](#create-notebook). Vertex AI Workbench creates a JupyterLab instance with a new version of JupyterLab. For example, the new JupyterLab instance contains client library updates from Distributed Cloud.\n4. Copy the files from the storage bucket of the outdated JupyterLab instance to the new JupyterLab instance.\n\nYou can delete the previous version of your JupyterLab instance. For more\ninformation, see [Delete a JupyterLab instance](#delete-notebook)."]]
