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VESSL for Researchers
Documentation

On-demand GPUs, Docker-based environments, and team collaboration — built for ML.
VESSL lets you access GPUs on demand without managing infrastructure. Launch a workspace, connect using Jupyter or SSH, and collaborate using shared volumes.

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Why VESSL

  • Instant: launch Jupyter/SSH environments with a few clicks
  • Consistent: Docker-based images ensure identical environments across teams/projects
  • Scalable: scale GPU specs/count as needed; Pause/Terminate when done
  • Collaborative: share data and models with team volumes
  • Cost visibility: clear billing states for running/paused/terminated

Quickstart

Create your first Workspace in minutes.

Organization

Admin scope, policies, billing, responsibilities.

Team

Collaboration model and shared resources.

Workspace (Run Jupyter notebooks)

GPU/CPU containers and cost controls.

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More in introduction

Roles & permissions

Admin vs Member responsibilities and scopes.

Cluster

Execution environment for resource scheduling.

Volume

Shared vs workspace volume.

Billing

Credit model and cost states.