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Get Started Free →nvidia/tao-run-automl
Run AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm selection (bayesian, hyperband, asha, bohb, llm, hybrid, autoresearch), WandB experiment tracking, job execution on any TAO SDK platform, result interpretation, and per-rec custom evaluation hooks.
Passed 18 of 22 test cases with the skill, and 12 of 22 without it.
and 5 more in the full report
8 fixed12 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
Before launching my 15 recommendation trials for object detection tuning, what step must be taken regarding the base model on the validation dataset?
| Checked | Without skill | With skill |
|---|---|---|
| Runs the evaluate action once on validation data before submitting recommendation trial jobs. | Missed | Passed |
The instructions your model reads when the skill loads.
Run automated hyperparameter optimization for a TAO model by combining:
skills/models/<network>/.skills/platform/<platform>/.AutoMLRunner, which generates recommendations, launches train 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.
references/skill_info.yaml: this workflow's structured metadata.automl-preflight-concepts.md for prerequisites
and support checks; automl-intent-algorithms.md for search policy;
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. detailed-guide.md is only
the map.skills/models/<network>/SKILL.md: model-specific dataset requirements, metrics,
HPO notes, checkpoint handoff, and known failures.skills/models/<network>/references/skill_info.yaml: train action contract,
container image, inputs, outputs, upload exclusions, and mode.skills/platform/<platform>/SKILL.md: selected platform preflight, credentials,
resource shape, monitoring, and cancellation.skills/core/tao-launch-workflow/SKILL.md: shared intake pattern for platform,
credentials, dataset visibility, image confirmation, and user confirmation.nvidia-tao-automl imports:python -c "import tao_automl; from tao_automl.runner import AutoMLRunner; print('OK')"
test cases passed, vs 12 / 22 without
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