Compute Resources

Compute resources are how PaletteAI turns your physical machines into Kubernetes clusters that run AI/ML applications and models.

• Compute — An automatically maintained inventory of the machines PaletteAI discovers through Palette, including each machine's CPU architecture, memory, and GPUs.

• Compute Pools — Kubernetes clusters provisioned from machines in the Compute inventory. Applications and models are deployed to Compute Pools.

• Compute Configs — Reusable blueprints of cluster settings, such as control plane size, networking, and SSH keys. Apply a Compute Config when creating a Compute Pool instead of re-entering the same values each time.

• Scaling Policies — Autoscaling rules that you attach to a Compute Pool to automatically add or remove machines based on CPU and GPU utilization.

How the Pieces Fit Together

The Compute Pool is the central resource: it draws machines from the Compute inventory, takes its defaults from a Compute Config, scales according to a Scaling Policy, and hosts your App and Model Deployments.

A typical workflow looks like this:

1. Machines are registered in Palette and appear in the Compute inventory, which you can review at the tenant scope under Tenant Settings > Compute.
2. An administrator creates a Compute Config with the cluster defaults for the Tenant or Project.
3. A user creates a Compute Pool, optionally applying the Compute Config and a Scaling Policy. PaletteAI selects matching machines from the inventory and provisions the cluster through Palette.
4. Data scientists deploy applications and models to the running Compute Pool.

Next Steps

• Compute Pools — Understand pool types, provisioning, and autoscaling.
• Create and Manage Compute Pools — Create your first Compute Pool.
• Create and Manage Scaling Policies — Automate scaling for a Compute Pool.