/tags/gcp/ Recent content in Gcp on Verily Workbench Hugo en Fri, 27 Mar 2026 00:00:00 +0000 /docs/guides/cloud_apps/gcp/compute_config_options/ Mon, 17 Nov 2025 00:00:00 +0000 /docs/guides/cloud_apps/gcp/compute_config_options/ Prior reading: Cloud apps overview Purpose: This document provides detailed instructions for customizing the compute resources allocated to a cloud app through the Workbench UI. Note: This article does not apply to Dataproc cluster apps. To learn more about configuring a Dataproc cluster app, please see Use Dataproc (Spark) and Hail on Verily Workbench. Introduction Understand compute options Virtual machines CPUs (central processing units) Key differences between N1 & N2 GPUs (graphical processing units) Memory Disk storage Autostop Set compute options for a new app Virtual machines & CPUs GPUs Disk storage Autostop Update compute options for an existing app Virtual machines & CPUs GPUs Disk storage Autostop Introduction When creating a GCP-backed cloud app, you have the ability to customize it with the following compute options: /docs/references/feature_support/ Fri, 27 Mar 2026 00:00:00 +0000 /docs/references/feature_support/ Purpose: This document provides an overview of the various features available for GCP and AWS-backed Workbench workspaces. Summary Feature availability Summary Verily Workbench provides a secure research environment for governing and analyzing global multimodal data. Within Workbench, researchers can create and share workspaces to create and access cloud resources. Workbench supports two cloud platforms: Google Cloud (GCP) and Amazon Web Services (AWS). By providing these two options, Workbench makes it easier for users to use data sources, code repositories, or other resources that may be available on their preferred cloud platform.