User Interface Overview¶

Run:ai provides a single user interface that, depending on your role, serves both as a control-plane management tool and a researcher workbench.

The control-plane part of the tool allows the administrator to:

• Analyze cluster status using dashboards.
• Manage Run:ai metadata such as users, departments, and projects.
• View Job details to be able to help researchers solve Job-related issues.

The researcher workbench part of the tool allows Researchers to submit, delete and pause Jobs, view Job logs etc.

Setup¶

The cluster installation process requires configuring a new cluster and downloading a YAML file. On SaaS-based installations, the cluster creation wizard requires a URL to the cluster as explained here.

Architecture¶

• Run:ai saves metadata such as users, projects, departments, clusters, and tenant settings, in the control plane residing on the Run:ai cloud.
• Workload information resides on (sometimes multiple) GPU clusters.
• The Run:ai user interface needs to work with both sources of information.

As such, the chosen architecture of the user interface is:

• The user interface is served from the management backend.
• The user interface connects directly to multiple GPU clusters using cross-origin access. This works using CORS: Cross-origin resource sharing. This allows submitting workloads and getting extended logging information directly from the GPU clusters.
• Meta-data, such as Projects, Settings, and Job information is synced into the management backend via a cluster-sync service. Cluster-sync creates an outbound-only channel with no incoming HTTPS connections.