GPU kind bootstrap

This directory contains a self‑contained helper script for spinning up a GPU-enabled kind cluster using NVIDIA's nvkind.

Contents

File	Purpose
bootstrap.sh	Main automation script (cluster create → device plugin → smoke test).
nvkind-ingress.yaml	kind cluster configuration template consumed by nvkind.

Prerequisites

Install the dependencies first (Docker, kind, kubectl, Helm, Go, Make, jq, nvkind). The root bootstrap/install-deps.sh in this repo can do this for you.

Your host must expose NVIDIA GPUs and have the driver + CUDA toolkit stack installed. If Docker is not already configured for the NVIDIA runtime, run the script with CONFIGURE_TOOLKIT=true.

Usage

# optional: tweak defaults
export CLUSTER_NAME=kind-gpu
export CONFIGURE_TOOLKIT=true      # only needed the first time on a new host

bootstrap/bootstrap.sh

The script will:

1. Clone/build nvkind (or reuse an existing checkout)
2. Recreate the named cluster via nvkind using the provided template
3. Install the NVIDIA device plugin with runtimeClassName=nvidia
4. Verify that GPU allocatable resources are visible
5. Run a simple nvidia-smi smoke test pod (skip via SKIP_SMOKE_TEST=true)

Key environment variables:

Variable	Default	Description
CLUSTER_NAME	kind-gpu	Name of the kind cluster
NVKIND_DIR	../nvkind	Location of the nvkind checkout/binary
TEMPLATE_PATH	nvkind-ingress.yaml	Cluster template passed to nvkind
DELETE_EXISTING	true	Whether to delete an existing cluster with the same name
CONFIGURE_TOOLKIT	false	Configure NVIDIA container toolkit + restart Docker
SKIP_SMOKE_TEST	false	Skip the CUDA smoke test pod

Once the script prints Cluster '<name>' is ready for GPU workloads, you can start installing your application stack.