Agent skill · software engineering · nvidia
tao-train-single-step
Standard single-step train/eval/export workflow for any TAO model. Use when training a TAO model on a dataset
Why this skill is useful
Adds a complete executable workflow for training TAO models, including scripts for platform selection and image resolution that are not publicly documented.
What it needs
Requires docker, nvidia-container-toolkit installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
Normal Train 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). Standard supervised fine-tuning: train a model on a labeled dataset, optionally evaluate, then optionally export. The most common TAO workflow for adapting a pretrained model to a new dataset. Steps 1. train — executed through AutoML when the selected model has automlenabled: true and automlpolicy is on; set automlpolicy=off for a plain single training run 2. eval — executed if evaldataseturi is resolved 3. export — optional, on user request after training Prerequisites The selected model skill's resolved containerimage is the default training runtime. Do not replace it with a host venv, uv environment, generic training image, or hand-written trainer unless the user explicitly requests that execution mode. SDK/controller Python environments are control-plane-only; the model action remains container-backed. Required model: A compatible TAO model (e.g., clip, nvdinov2, groundingdino) traindataseturi: URI of the training dataset (e.g., s3://bucket/train/) platform: Discover the execution platforms from the installed platform skills (tao-run-on-docker / -slurm / -kubernetes / -brev, plus any external one); on a runtime that surfaces only the core router skills, read skills/platform/tao-run-on-/SKILL.md frontmatter. container image confirmation: resolve the default image from the selected model/action config, show it to the user, and require confirmation or image=<override> before creating runner files or submitting training. Optional evaldataseturi: Some model skills mark this as required — check the resolved model skill before treating it as optional. basecheckpoint: If not provided, defaults to the NGC pretrained checkpoint listed in the model skill, or trains from scratch if no NGC checkpoint exists. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-train-single-step