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Vertex AI Workbench adalah lingkungan pengembangan berbasis notebook JupyterLab yang tersedia untuk seluruh alur kerja data science Anda. Anda dapat berinteraksi dengan Vertex AI dan layanannya di Google Distributed Cloud (GDC) yang terisolasi dari jaringan publik dari dalam notebook instance JupyterLab yang disediakan Vertex AI Workbench.
Integrasi dan fitur Vertex AI Workbench mempermudah akses ke data machine learning, berbagi dan memproses data lebih cepat, berinteraksi dengan layanan Vertex AI menggunakan bahasa pemrograman Python, dan banyak lagi.
Misalnya, Vertex AI Workbench memungkinkan Anda melakukan hal berikut:
Mengakses dan menjelajahi data machine learning Anda dari dalam
notebook JupyterLab.
Bagikan notebook JupyterLab Anda kepada pengguna lain dalam project Anda.
Berinteraksi dengan layanan Vertex AI, mengautentikasi permintaan API, dan menggunakan fitur Vertex AI dari skrip Python.
Buat cadangan dan pulihkan data instance JupyterLab Anda.
Gunakan lingkungan pengembangan terintegrasi (IDE) untuk menggunakan integrasi bawaan
notebook JupyterLab.
Siapkan lingkungan produksi berbasis notebook secara menyeluruh.
Instance JupyterLab
Vertex AI Workbench menawarkan instance JupyterLab dengan integrasi bawaan yang membantu Anda menyiapkan lingkungan produksi berbasis notebook secara menyeluruh. Instance JupyterLab menggabungkan integrasi berorientasi alur kerja dari
instance terkelola dengan penyesuaian dan kontrol yang Anda butuhkan atas
lingkungan Anda.
Vertex AI Workbench mencakup jenis instance yang telah diinstal sebelumnya dengan
JupyterLab
dan rangkaian paket deep learning, termasuk dukungan untuk framework
TensorFlow dan PyTorch. Bergantung pada kebutuhan Anda, Anda dapat memilih antara instance khusus CPU atau yang mendukung GPU.
Anda dapat memilih image Docker dan cluster untuk lingkungan instance JupyterLab. Docker memungkinkan Anda membuat lingkungan JupyterLab kustom dan membangunnya menjadi image. Image ini memastikan konsistensi dan kemampuan reproduksi di berbagai deployment, termasuk semua paket dan alat yang diperlukan. Anda dapat
membagikan lingkungan yang disesuaikan ini kepada orang lain atau menggunakannya sebagai dasar untuk
pengembangan di masa mendatang.
Instance JupyterLab dilindungi oleh autentikasi dan otorisasi.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 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)."]]
