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/physical-ai-video-data-augmentation

@0482ebc
by NVIDIA Corporationnvidia/skills3.5k stars
424

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.

Ask your Agent

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

This session only. Nothing lands on disk.

referencescontainer-images.md

≈596 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Video Data Augmentation Container Images

Canonical image references for the active VDA skill. Keep workflow YAML defaults and this file in sync when updating tags.

Main Runtime Components

Component Workflow variable/location Image Used by Notes
Setup/config generation tasks.setup.image nvcr.io/nvidia/base/ubuntu:22.04_20240212 all flows Copies scripts/cookbooks and materializes configs/ + .env
Augmentation worker cosmos_worker_*.image nvcr.io/nvidia/paidf-augmentation:1.0.0 augmentation_and_al, e2e, e2e_super_resolution Runs cosmos transfer workflow; expects cosmos cache URL mount
Auto-labeling worker pl_*_worker_*.image nvcr.io/nvidia/paidf-auto-labeling:1.0.0 all flows Runs original/augmented pseudo-labeling workers; expects auto-labeling cache URL mount

Setup Model Cache Workflow Images

Purpose Workflow file Image Notes
Cosmos cache download assets/configs/osmo/setup_model_cache.yaml task download_cosmos_cache nvcr.io/nvidia/base/ubuntu:22.04_20240212 Pulls HF artifacts and resolves symlinks before upload
Auto-labeling cache download assets/configs/osmo/setup_model_cache.yaml task download_auto_labeling_cache nvcr.io/nvidia/base/ubuntu:22.04_20240212 Pulls SeedVR2/ReID/RFDeTR assets before upload

Endpoint Runtime Note

VLM/LLM inference for VDA defaults to persistent in-cluster NIM endpoints:

  • qwen3-vl at http://qwen3-vl.osmo-nims.svc.cluster.local:8000/v1
  • qwen25-14b at http://qwen25-14b.osmo-nims.svc.cluster.local:8000/v1

Those endpoint containers are managed outside VDA workflow YAMLs (see references/nim/README.md).

Current Workflow Defaults

Workflow Runtime images
assets/configs/osmo/auto_labeling.yaml setup + auto-labeling
assets/configs/osmo/augmentation_and_al.yaml setup + augmentation + auto-labeling
assets/configs/osmo/e2e.yaml setup + augmentation + auto-labeling
assets/configs/osmo/e2e_super_resolution.yaml setup + augmentation + auto-labeling
assets/configs/osmo/setup_model_cache.yaml setup-cache ubuntu tasks

Update Rule

When changing runtime image tags:

  1. Update every impacted OSMO YAML default.
  2. Update this file.
  3. Search for stale tags in the skill directory and clean them.

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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