Agent skill · research science · nvidia
tao-run-automl
Run container-backed AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm
Why this skill is useful
Adds executable scripts for hyperparameter optimization and model training that are specific to NVIDIA TAO networks, which the AI cannot generate on its own.
What it needs
Requires docker, nvidia-container-toolkit installed locally. About 10k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
TAO AutoML Standalone install? If this session was not initialized by the TAO skill bank plugin, run tao-setup first (host preflight, credentials, cross-skill discovery). Run automated hyperparameter optimization for a TAO model by combining: 1. The selected model skill under skills/models/<modelskill>/. 2. The selected platform skill under skills/platform/<platform>/. 3. AutoMLRunner, which generates recommendations, launches selected action jobs, extracts metrics, and feeds results back to the optimizer. Do not launch until model metadata, platform preflight, data visibility, credentials, image choice, and compute shape are all proven. Execution Runtime — Hard Gate Every recommendation, baseline evaluation, per-recommendation evaluation, and final evaluation runs in the selected model action's resolved containerimage by default. Resolve it from the model skill before any training-environment setup. A local checkpoint or Hugging Face model ID does not change this rule. Use venv-based model execution only when explicitly requested. Never infer venv mode from local-docker, local GPUs, Python, or pyproject.toml. If absent, execution is container-backed. A host/controller venv for taoautoml, TAO SDK, or a platform adapter is control-plane-only; keep child model actions in the resolved container image. Reference Map references/skillinfo.yaml: this workflow's structured metadata. Split detailed references: automl-preflight-concepts.md for prerequisites and support checks; automl-intent-algorithms.md for search policy; automl-compression-literature.md for distill/prune/quantize algorithm sufficiency and future compression-search roadmap; automl-runner-configuration.md for runner/API/WandB details; automl-advanced-monitoring.md for hooks, resume, and pitfalls; and automl-examples.md for conversation examples; and automl-common-pitfalls.md for recurring safety checks. detailed-guide.md is only the map. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-run-automl