tao-train-single-step

The full skill.

Original instructions from the publisher’s SKILL.md

# 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.