---
description: "Essential concepts for video data curation including distributed processing, pipeline stages, and execution modes"
categories: ["concepts-architecture"]
tags: ["concepts", "video-curation", "distributed", "pipeline", "ray", "autoscaling"]
personas: ["data-scientist-focused", "mle-focused"]
difficulty: "beginner"
content_type: "concept"
modality: "video-only"
---

# Video Curation Concepts

This document covers the essential concepts for video data curation in NVIDIA NeMo Curator. These concepts assume basic familiarity with data science and machine learning principles.

## Core Concept Areas

Video curation in NVIDIA NeMo Curator focuses on these key areas:

<Cards>

<Card title="Architecture" href="/about/concepts/video/architecture">

Core concepts for distributed processing, Ray foundation, and auto-scaling
</Card>

<Card title="Key Abstractions" href="/about/concepts/video/abstractions">

Stages, pipelines, and execution modes in video curation workflows
</Card>

<Card title="Data Flow" href="/about/concepts/video/data-flow">

How data moves through the system from ingestion to output
</Card>

</Cards>

## Notes on Modalities and Backends

Video pipelines in Curator run on Ray with the `XennaExecutor` integration for streaming and batch execution. Other modalities, such as text and image, also use RAPIDS and Curator’s distributed backends in parts of their workflows. Refer to the modality-specific guides for details.

## Infrastructure Components

The video curation concepts build on NVIDIA NeMo Curator's core infrastructure components. All modalities (text, image, video, and audio) use these components. These components include:

<Cards>

<Card title="Memory Management" href="/reference/infra/memory-management">
Optimize memory usage for large datasets
partitioning
batching
monitoring
</Card>

<Card title="GPU Acceleration" href="/reference/infra/gpu-processing">
Leverage NVIDIA GPU acceleration for faster data processing
cuda
rmm
performance
</Card>

<Card title="Resumable Processing" href="/reference/infra/resumable-processing">
Continue interrupted operations on large datasets
checkpoints
recovery
batching
</Card>

</Cards>