tao-run-automl
TAO AutoML Skill
Run automated hyperparameter optimization (HPO) for any TAO network. The agent uses AutoMLRunner — a single interface that manages the full loop: generate recommendations, launch training jobs, extract metrics, and feed results back to the optimizer.
The runner is platform-agnostic — it takes any object implementing the standard SDK shape (create_job, get_job_status, get_job_logs, get_failure_analysis) and calls those methods. Pick whichever SDK matches where you want jobs to run:
| SDK | Best for AutoML |
|---|---|
LeptonSDK |
Multi-node sweeps on DGX Cloud; managed scheduling |
BrevSDK |
Cost-tuned sweeps on Brev instances (single-instance per rec, multi-GPU OK). Multi-credential / multi-workspace accounts must pass cloud_cred_id= and workspace_group_id= to create_job — see skills/platform/tao-run-on-brev/SKILL.md. |
SlurmSDK |
Large sweeps on shared HPC clusters with queue/quota |
KubernetesSDK |
Sweeps on EKS / GKE / AKS / on-prem clusters with the NVIDIA GPU Operator |
DockerSDK |
Local debugging or single-host sweeps |
Multi-node per rec works on Lepton, SLURM, and K8s (each rec is an N-node distributed training job). Brev and local Docker are single-host per rec — multi-GPU within one host still works (gpu_count > 1), but one rec can't span multiple hosts.
Workflow: (1) parse user intent + preflight, (2) select algorithm, (3) configure and run, (4) monitor/resume/query status, (5) interpret results. Each step below links the reference holding its full detail. Failure modes: references/pitfalls.md. Example exchanges: references/examples.md. Setup detail: references/prerequisites.md.