Vertex AI Workbench는 전체 데이터 과학 워크플로에 사용할 수 있는 JupyterLab 노트북 기반 개발 환경입니다. Vertex AI Workbench에서 제공하는 JupyterLab 인스턴스의 노트북 내에서 Google Distributed Cloud (GDC) 에어 갭에 있는 Vertex AI 및 해당 서비스와 상호작용할 수 있습니다.
Vertex AI Workbench 통합 및 기능을 사용하면 머신러닝 데이터에 더 쉽게 액세스하고, 데이터를 더 빠르게 공유하고 처리하고, Python 프로그래밍 언어를 사용하여 Vertex AI 서비스와 상호작용할 수 있습니다.
Python 스크립트에서 Vertex AI 서비스와 상호작용하고, API 요청을 인증하고, Vertex AI 기능을 사용합니다.
JupyterLab 인스턴스 데이터를 백업하고 복원합니다.
통합 개발 환경 (IDE)을 사용하여 JupyterLab 노트북의 기본 제공 통합을 사용합니다.
엔드 투 엔드 노트북 기반 프로덕션 환경을 설정합니다.
JupyterLab 인스턴스
Vertex AI Workbench는 엔드 투 엔드 노트북 기반 프로덕션 환경을 설정하는 데 도움이 되는 통합 기능이 내장된 JupyterLab 인스턴스를 제공합니다. JupyterLab 인스턴스는 관리형 인스턴스의 워크플로 중심 통합과 환경에 필요한 맞춤설정 및 제어 기능을 결합합니다.
Vertex AI Workbench에는 JupyterLab과 TensorFlow 및 PyTorch 프레임워크 지원을 포함한 딥 러닝 패키지 모음이 사전 설치된 인스턴스 유형이 포함되어 있습니다. 필요에 따라 CPU 전용 인스턴스 또는 GPU 지원 인스턴스를 선택할 수 있습니다.
JupyterLab 인스턴스 환경의 Docker 이미지와 클러스터를 선택할 수 있습니다. Docker를 사용하면 맞춤 JupyterLab 환경을 만들고 이미지로 빌드할 수 있습니다. 이 이미지는 필요한 모든 패키지와 도구를 포함하여 다양한 배포에서 일관성과 재현성을 보장합니다. 이 맞춤설정된 환경을 다른 사용자와 공유하거나 향후 개발의 기반으로 사용할 수 있습니다.
[[["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 is a JupyterLab notebook-based development environment that allows users to interact with Vertex AI and its services on Google Distributed Cloud (GDC) in an air-gapped environment.\u003c/p\u003e\n"],["\u003cp\u003eVertex AI Workbench simplifies machine learning workflows by providing easy access to data, faster processing, and the ability to interact with Vertex AI services through Python.\u003c/p\u003e\n"],["\u003cp\u003eJupyterLab instances in Vertex AI Workbench offer a managed environment with built-in integrations, customization, and pre-installed deep learning packages like TensorFlow and PyTorch, with options for CPU-only or GPU-enabled instances.\u003c/p\u003e\n"],["\u003cp\u003eUsers can customize their JupyterLab environment using Docker images, ensuring consistency and reproducibility across deployments and allowing for the sharing of customized environments with other users.\u003c/p\u003e\n"],["\u003cp\u003eVertex AI Workbench instances are secured by authentication and authorization, and it offers features to manage notebooks and create backups.\u003c/p\u003e\n"]]],[],null,["# Learn about Vertex AI Workbench\n\nVertex AI Workbench is a JupyterLab notebook-based development\nenvironment available for your entire data science workflow. You can interact\nwith Vertex AI and its services on Google Distributed Cloud (GDC) air-gapped\nfrom within a notebook of a JupyterLab instance that Vertex AI Workbench\nprovides.\n\nVertex AI Workbench integrations and features make accessing your\nmachine learning data easier, sharing and processing data faster, interacting\nwith Vertex AI services using the Python programming language,\nand more.\n\nFor example, Vertex AI Workbench lets you do the following:\n\n- Access and explore your machine learning data from within a [JupyterLab notebook](https://jupyter.org/).\n- Share your JupyterLab notebook with other users of your project.\n- Import [Vertex AI client libraries](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-install-libraries) to simplify accessing APIs programmatically.\n- Interact with Vertex AI services, authenticate API requests, and use Vertex AI features from Python scripts.\n- Create a backup and restore your JupyterLab instance data.\n- Use an integrated development environment (IDE) to use built-in integrations of JupyterLab notebooks.\n- Set up an end-to-end notebook-based production environment.\n\nJupyterLab instances\n--------------------\n\nVertex AI Workbench offers JupyterLab instances with built-in\nintegrations that help you set up an end-to-end notebook-based production\nenvironment. JupyterLab instances combine workflow-oriented integrations of a\nmanaged instance with the customization and control you need over your\nenvironment.\n\nVertex AI Workbench includes instance types preinstalled with\n[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html)\nand a suite of deep learning packages, including support for the\nTensorFlow and PyTorch frameworks. Depending on your needs, you can\nchoose between CPU-only or GPU-enabled instances.\n\nYou can select a Docker image and a cluster for your JupyterLab instance\nenvironment. Docker lets you create a custom JupyterLab environment and build it\ninto an image. This image ensures consistency and reproducibility across\ndifferent deployments, including all the necessary packages and tools. You can\nshare this customized environment with others or use it as a foundation for\nfuture development.\n\nJupyterLab instances are protected by authentication and authorization.\n\nWhat's next\n-----------\n\n- [Control access to Vertex AI Workbench](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-workbench-access).\n\n- [Manage JupyterLab notebooks](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-workbench).\n\n- [Create a backup and restore notebook data](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/backup-restore-notebook-data)."]]
