Docker for NVIDIA GPU Workloads

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).

The Docker execution platform: a consumer that runs a model/data skill's spec-bundle by implementing four verbs (submit/status/logs/cancel) over the docker CLI, on a local daemon or a remote GPU box via DOCKER_HOST=ssh://. The verbs (§ Execution) sit on top of the docker conventions in the rest of this file — GPU flags, mounts, NGC auth, inspection, error modes — which are the how the model/data skill defers to. Single-node only; for multi-node use SLURM or Kubernetes.

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

Prerequisites

1. Host GPU runtime — by default, NVIDIA driver >=580, CUDA Toolkit >=13.0, and NVIDIA Container Toolkit >=1.19.0. If the selected model's references/skill_info.yaml declares runtime_requirements.gpu_host, pass those values to tao-setup-nvidia-gpu-host instead. Model requirements override the defaults for that workflow.
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/.