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Tao Train Single Step

NVIDIAOfficialCreated by NVIDIA Corporation

  • Skill
  • Workflow
  • Software development
  • Apache-2.0
  • Medium risk
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From the author

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.
— the description by NVIDIA Corporation

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The tao-train-single-step skill provides a structured, container-backed procedure for supervised fine-tuning of TAO models. It is designed for users who require a straightforward training run without iterative data augmentation or AutoML loops. The workflow manages the lifecycle from dataset intake and platform selection to model training, evaluation, and optional export, ensuring that the correct container images and credentials are used for the chosen execution environment.

What it does

  • Executes a single-pass supervised training run for a specified TAO model.
  • Performs model evaluation if a dataset URI is provided.
  • Handles optional model export upon user request.
  • Integrates with platform-specific skills for execution on Docker, Slurm, Kubernetes, or other supported environments.
  • Resolves and confirms the required container images before initiating training.
  • Manages credential intake based on the selected platform and model requirements.

When to use it

  • When you need a standard training run without AutoML or iterative loops.
  • When adapting a pretrained model to a new dataset.
  • When you have a specific dataset and want to perform a direct train-eval-export cycle.
  • When you need to override default training configurations or container images.

Advantages

  • Provides a consistent, repeatable process for model training.
  • Ensures compatibility by resolving model-specific container images.
  • Offers flexibility in platform selection for diverse infrastructure needs.
  • Simplifies the configuration process by guiding the user through necessary credentials and settings.

Usage examples

“Run a plain TAO training for my clip model on this dataset.”
The agent initiates the single-step workflow, resolves the required container image, asks for platform selection, and proceeds with training.
“I want to train then evaluate then export my nvdinov2 model, no AutoML.”
The agent sets the AutoML policy to off and executes the standard train, eval, and export sequence.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/tao-train-single-step/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip tao-train-single-step.zip -d .agents/skills

Documentation by NVIDIA Corporation

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.

  • base_checkpoint: If not provided, defaults to the NGC pretrained checkpoint listed in the model skill, or trains from scratch if no NGC checkpoint exists.

  • automl_policy: on by default; set off to bypass model-level AutoML for this run while leaving model metadata unchanged. Use only on / off in new launch settings.

  • image override: Use image=<override> to pin a specific TAO toolkit build after reviewing the resolved default.

Launch Intake

After the user confirms they want this standard train/eval/export workflow, ask which supported platform they intend to run on. 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.

Before creating a plain train runner, inspect the selected model's metadata with scripts/list_tao_models.py --scope automl --format json or read skills/models/<network>/references/skill_info.yaml. If automl_enabled is true and the helper reports a valid train schema for that model, route the train stage through skills/applications/tao-run-automl by default. Only stay on the plain train path when automl_policy=off, the user explicitly asks for no HPO/AutoML, or AutoML is enabled but not runnable because the model's train schema is not packaged yet.

Also ask whether long-running monitoring should stay enabled and how many minutes between status updates. Defaults: enabled, 5 minutes.

After the model/action are known, run scripts/resolve_tao_image.py --model <network> --action train --format text and ask whether to use the resolved image or an image=<override>. Do not create the tao-train-single-step runner until the image is confirmed.

After platform selection, read the chosen platform skill's ## Credentials section and references/skill_info.yaml (required_credentials / credential_groups) and ask only for credentials relevant to that platform, plus any selected-model credentials. Do not ask for unrelated platform credentials.

Files

skill.oms.sig

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools

The author says: Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA Corporation
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/tao-train-single-step · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA Corporation or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-train-single-step.zip