Docker for NVIDIA GPU Workloads

Docker for NVIDIA GPU Workloads

This skill documents the generic Docker conventions that GPU container workloads rely on. Model and data skills specify what image and what command to run; this skill covers how to run docker in a way that satisfies GPU + NVIDIA container requirements.

Sources: official Docker CLI reference (https://docs.docker.com/reference/cli/docker/) and NVIDIA Container Toolkit docs.

Prerequisites

1. Host GPU runtime — NVIDIA driver branch 580, CUDA Toolkit 13.0, and NVIDIA Container Toolkit 1.19.0. Check with the tao-setup-nvidia-gpu-host skill before any GPU workflow starts.
2. Docker — docker --version must return ≥ 20.10. Install: https://docs.docker.com/engine/install/.
3. NGC API key for nvcr.io/* pulls. Get from https://ngc.nvidia.com/.

TAO_SKILL_BANK_ROOT="${TAO_SKILL_BANK_ROOT:-$PWD}"
SETUP_SCRIPT="${TAO_SKILL_BANK_ROOT}/platform/tao-setup-nvidia-gpu-host/scripts/setup-nvidia-gpu-host.sh"

bash "$SETUP_SCRIPT" --backend docker --check-only || {
  echo "MISSING: TAO GPU host runtime is not ready."
  echo "After user approval, run (append --yes for non-interactive agent runs):"
  echo "  bash \"$SETUP_SCRIPT\" --backend docker --install"
  exit 1
}

docker --version
docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
[ -n "$NGC_KEY" ] || echo "NGC_KEY unset — cannot pull nvcr.io images"

NGC authentication

echo "$NGC_KEY" | docker login nvcr.io -u '$oauthtoken' --password-stdin

Persists in ~/.docker/config.json across reboots. Re-run on unauthorized errors.

docker run — canonical flags

docker run \