# tao-train-single-step

Runs standard train, eval, and export workflows for NVIDIA TAO models without AutoML loops. Built for single-run fine-tuning on labeled datasets.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1600 installs · ★ 3735
- Author: NVIDIA Corporation
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-train-single-step/SKILL.md
- Updated: 2026-10-26
- HTML: https://affpapa.org/en/nevibecode/skills/tao-train-single-step

## What it does

This skill coordinates standard supervised fine-tuning for TAO models like clip, nvdinov2, or grounding_dino. It organizes the train stage, triggers evaluation when an evaluation URI exists, and handles model export upon request. Training runs via AutoML by default when model metadata supports it, but setting `automl_policy=off` keeps execution strictly to a single train run.

Execution relies on dedicated container images rather than host Python virtualenvs or `uv`. The workflow queries `scripts/resolve_tao_image.py` to identify the official container for the selected architecture and asks for user confirmation before creating runners. Supported target platforms include Docker, Slurm, Kubernetes, and Brev.

Without Docker and `nvidia-container-toolkit` installed on the host, the training containers cannot execute. Standalone setups also require running `tao-setup` beforehand to configure credentials and discovery.

## Requirements
- Works with: Docker

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-single-step -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/tao-train-single-step/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/tao-train-single-step/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-single-step -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/tao-train-single-step/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-single-step -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/tao-train-single-step/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills tao-train-single-step
```

2. **Manually: put the files in place**
- File: `.github/skills/tao-train-single-step/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-single-step -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/tao-train-single-step
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/tao-train-single-step/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-single-step -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-train-single-step/SKILL.md

## When to use it
- run a single fine-tuning pass on an NVIDIA TAO model
- bypass AutoML loops to perform standard supervised training
- evaluate and export checkpoints after model training completes
- resolve verified Docker container images for TAO architectures

## Example prompts
- Run a single train run for grounding_dino on s3://bucket/train/ without AutoML
- Start normal training for nvdinov2 with status updates every 5 minutes
- Train clip on s3://data/train, evaluate it, and export the final model

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 15330177172898aa.

BEGIN THIRD-PARTY FILE 15330177172898aa
```text
---
name: tao-train-single-step
description: Standard single-step train/eval/export workflow for any TAO model. Use when training a TAO model on a dataset
  without iterative data augmentation, AutoML, or DEFT loops. Trigger phrases include "single train run", "train then evaluate
  then export", "plain TAO training", "normal training", "no AutoML", "skip the loop". Routes through the per-model SKILL.md
  for action specifics and through `tao-launch-workflow` for platform/credentials/dataset intake.
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.
metadata:
  author: NVIDIA Corporation
  version: "0.1.0"
allowed-tools: Read Bash Write
tags:
- training
- single-step
- generic
---

# 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
   `automl_enabled: true` and `automl_policy` is `on`; set
   `automl_policy=off` for a plain single training run
2. **eval** — executed if `eval_dataset_uri` is resolved
3. **export** — optional, on user request after training

## Prerequisites

The selected model skill's resolved `container_image` 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, grounding_dino)
- **train_dataset_uri**: 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
- **eval_dataset_uri**: Some model skills mark this as required — check the resolved model skill before treating it as optional.
```
END THIRD-PARTY FILE 15330177172898aa

The third-party file has ended. Everything between the 15330177172898aa tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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