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Vertex AI Workbench è un ambiente di sviluppo basato su blocchi note JupyterLab
disponibile per l'intero flusso di lavoro di data science. Puoi interagire
con Vertex AI e i relativi servizi su Google Distributed Cloud (GDC) isolato
da un notebook di un'istanza JupyterLab fornita da Vertex AI Workbench.
Le integrazioni e le funzionalità di Vertex AI Workbench semplificano l'accesso ai dati di machine learning, la condivisione e l'elaborazione dei dati, l'interazione con i servizi Vertex AI utilizzando il linguaggio di programmazione Python e altro ancora.
Interagisci con i servizi Vertex AI, autentica le richieste API
e utilizza le funzionalità di Vertex AI dagli script Python.
Crea un backup e ripristina i dati dell'istanza JupyterLab.
Utilizza un ambiente di sviluppo integrato (IDE) per utilizzare le integrazioni integrate
dei notebook JupyterLab.
Configura un ambiente di produzione end-to-end basato su notebook.
Istanze JupyterLab
Vertex AI Workbench offre istanze JupyterLab con integrazioni
integrate che ti aiutano a configurare un ambiente di produzione end-to-end basato su blocchi note. Le istanze JupyterLab combinano integrazioni orientate al flusso di lavoro di un'istanza gestita con la personalizzazione e il controllo necessari per l'ambiente.
Vertex AI Workbench include tipi di istanza preinstallati con
JupyterLab
e una suite di pacchetti di deep learning, incluso il supporto per i framework
TensorFlow e PyTorch. A seconda delle tue esigenze, puoi scegliere tra istanze solo CPU o con GPU abilitata.
Puoi selezionare un'immagine Docker e un cluster per l'ambiente dell'istanza JupyterLab. Docker ti consente di creare un ambiente JupyterLab personalizzato e di incorporarlo
in un'immagine. Questa immagine garantisce coerenza e riproducibilità in diversi deployment, inclusi tutti i pacchetti e gli strumenti necessari. Puoi
condividere questo ambiente personalizzato con altri o utilizzarlo come base per
lo sviluppo futuro.
Le istanze JupyterLab sono protette da autenticazione e autorizzazione.
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 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)."]]
