tao-train-single-step

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.
• 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.