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/physical-ai-video-data-augmentation

@0482ebc
by NVIDIA Corporationnvidia/skills3.5k stars
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Use when running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data enrichment, auto labeling, VDA demo, OSMO workflow, pseudo labeling.

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Use this Skill: https://skilld.dev/gh/nvidia/skills/physical-ai-video-data-augmentation

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referencesnimREADME.md

≈601 tokens on demand. Your agent reads this file only when SKILL.md points to it.

VDA VLM/LLM Endpoints

Table of Contents

VDA workers call OpenAI-compatible VLM/LLM endpoints via vlm_url and llm_url. Default behavior is in-cluster persistent NIM reuse.

Option A: Reuse Existing In-Cluster NIMs (default)

Default endpoint values in VDA workflow YAMLs:

vlm_url=http://qwen3-vl.osmo-nims.svc.cluster.local:8000/v1
llm_url=http://qwen25-14b.osmo-nims.svc.cluster.local:8000/v1

Use these unless the user explicitly requests external mode or provides explicit URLs.

Option B: Deploy/Repair In-Cluster NIMs

This is the default action when either endpoint is missing/unhealthy — deploy automatically as a prerequisite; do not pause for user confirmation:

export NIM_SERVICES="qwen3-vl qwen25-14b"
skills/physical-ai-infrastructure-setup-and-resilient-scaling/components/inference-nim-operator/scripts/install.sh

Rules:

  • Keep the allow-list fixed to VDA-required services only.
  • Do not deploy unrelated services.
  • Never scale down or delete existing NIM deployments to free GPUs.

Verify Endpoint Health Before Submitting

Check deployments and model APIs:

kubectl -n osmo-nims get deploy,po -l 'app.kubernetes.io/name in (qwen3-vl,qwen25-14b)'

kubectl run curl-vlm -n osmo-nims --rm -i --restart=Never \
  --image=curlimages/curl -- \
  curl -fsS http://qwen3-vl.osmo-nims.svc.cluster.local:8000/v1/models

kubectl run curl-llm -n osmo-nims --rm -i --restart=Never \
  --image=curlimages/curl -- \
  curl -fsS http://qwen25-14b.osmo-nims.svc.cluster.local:8000/v1/models

Proceed only when both endpoints return healthy model lists.

Option C: External Endpoint Override (opt-in only)

Use external endpoints only when user explicitly asks, or provides explicit URLs:

--set-string vlm_url=https://<provider>/v1 llm_url=https://<provider>/v1

Worker scripts normalize OpenAI-compatible paths and run bounded readiness checks; they support in-cluster NIM, NVCF-style invoke endpoints, and other OpenAI-compatible providers.

Source: SKILL.md on GitHub

Third-party checks

2 warnings3mo3 checks · Risk SAFE
  • Gen Agent Trust Hub3mo

    The skill is a workflow orchestrator for video data augmentation and auto-labeling on the NVIDIA OSMO platform. It manages the end-to-end pipeline, including credential verification, configuration generation, worker execution, and result retrieval. No security issues or malicious patterns were detected; all external dependencies and network operations are associated with trusted vendors and the skill's primary functionality.

  • Socket3mo

    3 alerts: gptAnomaly

  • Snyk3mo

    Risk: MEDIUM · 1 issue

Provenance

Signed by skilld at 0482ebc. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 4 months ago

Capability

Other metadata
metadata
{
  "owner": "NVIDIA",
  "service": "data",
  "version": "1.0.0",
  "reviewed": "2026-05-26",
  "author": "NVIDIA",
  "tags": [
    "physical-ai",
    "video-data-augmentation",
    "auto-labeling",
    "cosmos"
  ]
}

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