Vertex AI Workbench provides a managed JupyterLab notebook development environment to interact with Vertex AI services in Google Distributed Cloud (GDC) air-gapped. From a JupyterLab instance, you can explore machine learning data, share notebooks across your project, and run end-to-end data science workflows.
Importing Vertex AI client libraries lets you authenticate API requests and call Vertex AI services from Python scripts. You can also back up and restore your JupyterLab instance data.
JupyterLab instances
Vertex AI Workbench offers JupyterLab instances with built-in integrations that help you set up an end-to-end notebook-based production environment. JupyterLab instances combine workflow-oriented integrations of a managed instance with the customization and control you need over your environment.
Vertex AI Workbench includes instance types preinstalled with JupyterLab and a suite of deep learning packages, including support for the TensorFlow and PyTorch frameworks. You can configure your JupyterLab instance environment by selecting a cluster, a provided Docker image, and choosing between GPU-enabled or CPU-only instances based on your requirements. Note that custom images are not supported.
JupyterLab instances are protected by authentication and authorization.