새로운 기능을 사용하고 프레임워크 업데이트, 패키지 업데이트, 버그 수정의 이점을 활용하기 위해 환경을 업그레이드할 수 있습니다. 환경을 수동으로 또는 자동 업데이트 설정을 통해 업그레이드할 수 있습니다.
자세한 내용은 사용자 관리 노트북 인스턴스의 환경 업그레이드를 참조하세요.
사용자 관리형 노트북 및 Dataproc Hub
Dataproc Hub는 맞춤설정된 JupyterHub 서버입니다.
관리자는 단일 사용자 Dataproc 클러스터를 생성하여 사용자 관리형 노트북 환경을 호스팅할 수 있는 Dataproc 허브 인스턴스를 만들 수 있습니다. 자세한 내용은 Dataproc Hub 구성을 참조하세요.
사용자 관리형 노트북 인스턴스는 맞춤설정이 쉬우며 사용자 환경을 세부적으로 제어해야 하는 사용자에게 적합할 수 있습니다.
따라서 사용자 관리형 노트북 인스턴스는 관리형 노트북 인스턴스보다 설정 및 관리에 더 많은 시간이 필요할 수 있습니다.
관리형 노트북 인스턴스는 환경을 세부적으로 제어할 필요가 없는 사용자에게 더 적합할 수 있습니다.
자세한 내용은 사용자 관리형 노트북소개를 참조하세요.
서드 파티 JupyterLab 확장 프로그램은 지원되지 않습니다.
Dataproc JupyterLab 플러그인은 사용자 관리형 노트북에 지원되지 않지만 Vertex AI Workbench 인스턴스에서 플러그인을 사용할 수 있습니다. Dataproc이 사용 설정된 인스턴스 만들기를 참조하세요.
Dataproc 허브 사용자 관리형 노트북 인스턴스의 경우 JupyterLab 사용자 인터페이스에서 파일 다운로드를 중지할 수 없습니다. Dataproc Hub 프레임워크를 사용하는 사용자 관리 노트북 인스턴스는 인스턴스를 만들 때 JupyterLab UI에서 파일 다운로드 사용 설정을 선택하지 않은 경우에도 파일 다운로드를 허용합니다.
Access Context Manager 및 Chrome Enterprise Premium 사용하여 컨텍스트 인식 액세스 제어로 관리형 노트북 인스턴스를 보호하면 사용자가 인스턴스에 인증할 때마다 액세스가 평가됩니다. 예를 들어 사용자가 JupyterLab에 처음 액세스할 때 액세스가 평가되고 이후 웹브라우저의 쿠키가 만료된 경우 액세스할 때마다 액세스가 평가됩니다.
[[["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: 2025-08-05(UTC)"],[],[],null,["# Introduction to Vertex AI Workbench user-managed notebooks\n\nIntroduction to user-managed notebooks\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\nVertex AI Workbench user-managed notebooks instances\nlet you create and manage deep learning virtual machine\n(VM) instances that are prepackaged with\n[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html).\n\nUser-managed notebooks instances have\na preinstalled suite of deep learning packages,\nincluding support for the TensorFlow and PyTorch\nframeworks. You can configure either CPU-only or GPU-enabled instances.\n\nYour user-managed notebooks instances are protected\nby Google Cloud\nauthentication and authorization and are available by using a\nuser-managed notebooks instance URL.\nUser-managed notebooks instances also integrate with\n[GitHub](https://github.com/)\nand can sync with a GitHub repository.\n\nUser-managed notebooks instances save you\nthe difficulty of creating and\nconfiguring a [Deep Learning virtual machine](/deep-learning-vm/docs)\nby providing verified, optimized, and tested images\nfor your chosen framework.\n\nPreinstalled software\n---------------------\n\nYou can configure a user-managed notebooks instance\nto include the following:\n\n- JupyterLab ([see version details](#jupyterlab-version))\n\n- Python 3, with key packages:\n\n - numpy\n - sklearn\n - scipy\n - pandas\n - nltk\n - pillow\n - [fairness-indicators](https://www.tensorflow.org/responsible_ai/fairness_indicators/guide) for TensorFlow 2.3 and 2.4 user-managed notebooks instances\n - many others\n- R version 4.*x*, with key packages:\n\n - xgboost\n - ggplot2\n - caret\n - nnet\n - rpy2 (an R package for accessing R in Python notebooks)\n - randomForest\n - many others\n- Anaconda\n\n- Nvidia packages with the latest Nvidia driver for GPU-enabled instances:\n\n - CUDA 11.*x* and 12.*x*\n - CuDNN 7.*x*\n - NCCL 2.*x*\n\nJupyterLab version details\n--------------------------\n\nJupyterLab 3.*x* is preinstalled on\nnew user-managed notebooks instances\nby default. For instances created before\nthe [M80 Deep Learning VM\nrelease](/deep-learning-vm/docs/release-notes#September_24_2021),\nJupyterLab 1.*x* was preinstalled.\n\nTo create an earlier version of a user-managed notebooks instance,\nsee [Create a specific version of a user-managed notebooks\ninstance](/vertex-ai/docs/workbench/user-managed/create-specific-version).\n\nVPC Service Controls\n--------------------\n\nVPC Service Controls provides additional security for your\nuser-managed notebooks instances.\nFor more information, see the [Overview of\nVPC Service Controls](/vpc-service-controls/docs/overview). To use\nuser-managed notebooks within a service perimeter, see [Use\na user-managed notebooks instance within a service\nperimeter](/vertex-ai/docs/workbench/user-managed/service-perimeter).\n\nUpgrades\n--------\n\nYou can upgrade your environment to use new capabilities and to benefit from\nframework updates, package updates, and bug fixes. You can\nupgrade environments manually or through an automatic update setting.\nTo learn more, see [Upgrade the environment of\na user-managed notebooks instance](/vertex-ai/docs/workbench/user-managed/upgrade).\n\nUser-managed notebooks and Dataproc Hub\n---------------------------------------\n\nDataproc Hub is a customized\n[JupyterHub](https://jupyter.org/hub) server.\nAdministrators can create Dataproc Hub instances that can\nspawn single-user [Dataproc](/dataproc/docs) clusters to host\nuser-managed notebooks environments. For more information, see\n[Configure Dataproc Hub](/dataproc/docs/tutorials/dataproc-hub-admins).\n\nUser-managed notebooks and Dataflow\n-----------------------------------\n\nYou can use user-managed notebooks within a pipeline,\nand then run\nthe pipeline on [Dataflow](/dataflow/docs). For information about\nhow to create an\n[Apache Beam](https://beam.apache.org/documentation/)\nuser-managed notebooks instance that you can use with\nDataflow, see [Developing interactively with Apache Beam\nnotebooks](/dataflow/docs/guides/interactive-pipeline-development).\n\nLimitations\n-----------\n\nConsider the following limitations of\nuser-managed notebooks when planning your project:\n\n- User-managed notebooks instances are highly\n customizable and can be\n ideal for users who need a lot of control over their environment.\n Therefore, user-managed notebooks instances\n can require more time to set up and manage than\n managed notebooks instances.\n Managed notebooks instances can be\n more ideal for users who don't need a lot of control over their environment.\n For more information, see [Introduction to\n managed notebooks](/vertex-ai/docs/workbench/managed/introduction).\n\n- Third party JupyterLab extensions are not supported.\n\n- The Dataproc JupyterLab plugin isn't supported for\n user-managed notebooks, but you can use the plugin in\n Vertex AI Workbench instances. See [Create a\n Dataproc-enabled\n instance](/vertex-ai/docs/workbench/instances/create-dataproc-enabled).\n\n- For Dataproc Hub user-managed notebooks instances,\n disabling file downloading from the JupyterLab user interface\n is not supported. User-managed notebooks instances\n that use the Dataproc Hub framework permit file downloading even\n if you don't select **Enable file downloading from JupyterLab UI**\n when you create the instance.\n\n- When you use [Access Context Manager](/access-context-manager/docs/create-basic-access-level#corporate-network-example)\n and [Chrome Enterprise Premium](/chrome-enterprise-premium/docs/access-levels)\n to protect managed notebooks instances with\n context-aware access controls, access is evaluated each time\n the user authenticates to the instance. For example, access\n is evaluated the first time the user accesses JupyterLab and\n whenever they access it thereafter if their web browser's\n cookie has expired.\n\nPricing\n-------\n\n[Learn more about Vertex AI Workbench\npricing](/vertex-ai/pricing).\n\nWhat's next\n-----------\n\nTo get started with user-managed notebooks, [create\na user-managed notebooks\ninstance](/vertex-ai/docs/workbench/user-managed/create-new),\nopen JupyterLab, and try\none of the samples in the **tutorials** folder.\n\nThen [install\ndependencies](/vertex-ai/docs/workbench/user-managed/dependencies) that you'll need\nto do your work."]]
