Vertex AI Workbench 是一种基于 JupyterLab 笔记本的开发环境,适用于整个数据科学工作流。您可以在 Vertex AI Workbench 提供的 JupyterLab 实例的笔记本中,与 Google Distributed Cloud (GDC) 上的 Vertex AI 及其服务进行交互。
借助 Vertex AI Workbench 集成和功能,您可以更轻松地访问机器学习数据、更快地共享和处理数据、使用 Python 编程语言与 Vertex AI 服务互动等。
[[["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):2025-09-04。"],[[["\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)."]]