USE CASES

Use cases

Combine packages, app modules and applets to suit your team. These are the most common ways to put the platform to work.

AI / ML teams

Manage training and inference services centrally; Jupyter, TensorBoard and other WebUIs run on one desktop; the GPU pool lets several members share one card.

Common tools: PyTorch, TensorFlow, YOLO, Stable Diffusion

Upload and organise data
- File Manager
- Image Viewer
Label the data
Review the dataset
- Dataset Viewer
- LLM Dataset Editor
Train and monitor
- Training Monitor
- GPU Monitor
Analyse the results
- Embedding Projector
- Chart Studio

Education and research

Teachers design Runbooks as packages and students run them with one click; projects isolate each user's data and environment, and GPU and memory limits prevent overuse.

Highlights: isolation, quotas, easy onboarding

Teachers build packages and Runbooks
Students run them in their own projects
Administrators set GPU and resource limits
- GPU Monitor
Results stay in the project and can be shared
- File Manager

Enterprise IT and DevOps

Internal tools are deployed to the desktop as apps or applets; project and package permissions keep departmental data apart; host Docker, status monitoring and the terminal are managed inside the platform.

Highlights: internal tools, access control, host operations

Monitor host status
- Host Status
- GPU Monitor
Operate remotely
Tiered permissions and audit
Roll out internal tools
- App Store
- Code Studio

Documents and teamwork

Open and edit Word and Excel files from a project right in the browser, with a whiteboard, charts and file management alongside, so the team's material stays in one place.

Related apps: Office Documents, Whiteboard, Chart Studio, File Manager