Normal Train

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.