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tao-run-on-docker

The Docker execution platform for TAO jobs — a local daemon or a remote GPU box via DOCKER_HOST=ssh://user@host. Implements the four-verb consumer contract (submit/status/logs/cancel) over the docker CLI, wired to the job-record, tao-data-io staging, and the redact lint, on top of the underlying docker conventions (--gpus, mounts, NGC auth, inspection, data-root relocation, error modes). Use to run any single-node TAO container action on Docker without the SDK. Trigger keywords — docker, docker run, run on docker, DOCKER_HOST, remote docker, nvcr.io, --gpus, single-node GPU job.

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

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Nov 6, 2026
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# 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.