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tao-run-on-kubernetes

Kubernetes execution platform — submits TAO container jobs as k8s Jobs with NVIDIA GPU scheduling; single-pod for one node, Indexed Jobs for multi-node distributed training. Use when running on EKS / GKE / AKS / on-prem clusters with the NVIDIA GPU Operator installed, or when integrating TAO into an existing k8s-native ML platform.

skills/tao-run-on-kubernetes/SKILL.md

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# Kubernetes

> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill first (host preflight, credentials, cross-skill discovery).

Submits TAO container jobs as Kubernetes Jobs. Works on any cluster reachable via kubeconfig (EKS / GKE / AKS / on-prem) or in-cluster service account (when running inside a pod).

Single-pod by default; opt into multi-node distributed training via `num_nodes > 1` (uses Indexed Job + headless Service, see [Multi-node training](#multi-node-training-distributed) below).

## Preflight

Three checks: GPU host runtime ready, cluster reachable via `kubectl`, GPU
Operator/device plugin present.

```bash