The Vantage AI Workbench
Workbench is where you build, train, and serve machine-learning models on Vantage. It organizes everything into five sections — Workspace, Develop, Compute, Train, and Serve — all sharing the same compute, storage, and cost envelope.
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Workbench replaces the Kubeflow dashboard. Everything you'd reach through Kubeflow Notebooks, KServe, Trainer, Katib, and the Pipelines UI is here under native Vantage primitives — without the namespaces, CRDs, and Istio routes leaking through.
Workspace
- Observability — Cluster-wide rollups of utilization, spend, idle GPU hours, and live alerts.
Develop
- Sessions — Interactive notebook environments — Jupyter, VS Code, RStudio — pinned to GPU pools and your team's storage.
- Presets — Reusable templates that define which IDE, images, compute sizes, and storage a session gets.
- Cloud Shell — Browser-based terminal sessions with optional Slurm access.
- Remote Desktop — VNC-based remote desktop sessions for full GUI access to GPU nodes.
- PVC Viewer — Browser-based file browser for PersistentVolumeClaims.
Compute
- Pools — The reusable shape of your compute: GPU type, count, autoscaling bounds, instance class.
Train
- Training Jobs — Distributed training on PyTorch, DeepSpeed, or MLX runtimes. Retry, suspend, resume.
- Pipelines — Multi-step DAGs that orchestrate ingestion, training, evaluation, and deployment.
- Sweeps — Hyperparameter search with Bayesian, grid, or random algorithms — tracked end-to-end.