physical-ai-video-data-augmentation
CommunityOrchestrate end-to-end VDA workflows safely.
Authorsayalinvidia
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill coordinates the end-to-end orchestration of NVIDIA's Video Data Augmentation (VDA) workflows on OSMO, enabling safe preflight checks, dataset and model cache setup, and deterministic submit-time interpolation across flows.
Core Features & Use Cases
- Orchestrates setup, augmentation, auto-labeling, and end-to-end pipelines with a single submission surface.
- Manages prerequisites: credential wiring, cache prep, and pre-submit guard checks to reduce run-time errors.
- Provides post-run artifact staging, monitoring, and evidence collection to assess augmentation impact.
Quick Start
Provide dataset, run_id, storage_url, and gpu_platform, then submit a VDA workflow using a single --set-string payload.
Dependency Matrix
Required Modules
pyyamlomegaconf
Components
scriptsreferencesassets
💻 Claude Code Installation
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Please help me install this Skill: Name: physical-ai-video-data-augmentation Download link: https://github.com/sayalinvidia/sayali-skills-test/archive/main.zip#physical-ai-video-data-augmentation Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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