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tao-run-automl

Automate hyperparameter optimization for NVIDIA TAO networks with AutoMLRunner.

83|20|Updated Apr 16, 2026
Search Tags:#hyperparameter optimization#wandb#automl#nvidia tao#tao-sdk
One-click install
npx skills add https://github.com/NVIDIA-TAO/tao-skill-bank --skill tao-run-automl
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tao-run-automl
Source: https://github.com/NVIDIA-TAO/tao-skill-bank/tree/main/skills/applications/tao-run-automl
Command: npx skills add https://github.com/NVIDIA-TAO/tao-skill-bank --skill tao-run-automl

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nvidia-tao-sdk, nvidia-tao-automl, openai, wandb, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates hyperparameter optimization and automated machine learning (AutoML) workflows for NVIDIA TAO networks, saving developers and engineers significant time and reducing manual error.

Core Features & Use Cases

  • Automated Hyperparameter Optimization: Perform HPO with predefined or user-specified algorithms, supporting platforms like Brev, SLURM, Kubernetes, and Docker.
  • WandB Experiment Tracking: Integrates with Weights & Biases for comprehensive experiment tracking.
  • Model-Specific Support: Offers model-specific configuration, parameter ranges, and search spaces.
  • Use Case: When you have a TAO network model and want to optimize its hyperparameters for better performance, this Skill automates the process, reducing the time spent on manual tuning.

Quick Start

To run the tao-run-automl skill for an AutoML workflow, follow these steps:

  1. Select the model you want to optimize.
  2. Choose the TAO platform (Brev, SLURM, Kubernetes, or Docker).
  3. Specify the dataset and metrics for optimization.
  4. Launch the AutoML workflow with the command: tao-run-automl --model <model_name> --platform <platform> --dataset <dataset_uri> --metric <metric_name> --algorithm <algorithm_name>

Frequently Asked Questions about tao-run-automl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate hyperparameter optimization for NVIDIA TAO networks?▼

Automate hyperparameter optimization for NVIDIA TAO networks by launching an AutoML workflow via the command line, specifying your model, platform, dataset, metric, and optimization algorithm. The process handles algorithm selection, job execution, and result interpretation automatically.

What platforms are supported for running TAO AutoML workflows?▼

TAO AutoML workflows support execution on Brev, SLURM, Kubernetes, and Docker platforms. You must specify your chosen platform when launching the job to ensure the SDK routes the hyperparameter search and evaluation tasks correctly.

Which hyperparameter search algorithms can I use with NVIDIA TAO AutoML?▼

NVIDIA TAO AutoML supports Bayesian, Hyperband, ASHA, LLM, Hybrid, and AutoResearch algorithms for hyperparameter search. You can select predefined algorithms or specify user-defined search spaces for model-specific configuration.

Does TAO AutoML integrate with Weights and Biases for experiment tracking?▼

Yes, TAO AutoML integrates with Weights and Biases (WandB) for comprehensive experiment tracking. It automatically logs hyperparameter search metrics, job execution states, and evaluation results across multiple TAO SDK platforms.

Do I need Docker and nvidia-container-toolkit to run TAO AutoML workflows?▼

Yes, you need Docker and nvidia-container-toolkit installed to run TAO AutoML workflows. These dependencies are required for the AutoMLRunner to execute hyperparameter search and model training jobs locally or on supported platforms.

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