Getting a GPU cluster from racked to running distributed training is where weeks disappear. A specialised AI-cluster recipe can carry 268 config values across 16 components, and a single mismatch can cost one or two percentage points of training throughput. We treat that recipe as an artifact you capture, version-lock, and replay, then prove it live. On stage, on a bare-metal H100 cluster, we bootstrap baseline Kubernetes, snapshot it with NVIDIA AI Cluster Runtime (AICR) into a version-locked recipe, render it as Helm values for Spectro Cloud's PaletteAI (a declarative Cluster API platform), deploy Slurm via the Slinky slurm-operator, and submit a multinode sbatch job, racked to running, end to end.
You leave with a workflow you can replay on any conformant Kubernetes cluster, validated against the CNCF Kubernetes AI Conformance requirements, and AI/HPC defaults you'd otherwise learn the hard way. For AI-infrastructure maintainers bridging Slurm-native HPC and cloud-native AI.