> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/curator/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/curator/_mcp/server.

# Video Curation Concepts

> Essential concepts for video data curation including distributed processing, pipeline stages, and execution modes

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:

#### [Architecture](/about/concepts/video/architecture)

Core concepts for distributed processing, Ray foundation, and auto-scaling

#### [Key Abstractions](/about/concepts/video/abstractions)

Stages, pipelines, and execution modes in video curation workflows

#### [Data Flow](/about/concepts/video/data-flow)

How data moves through the system from ingestion to output

## 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:

#### [Memory Management](/reference/infra/memory-management)

Optimize memory usage for large datasets

#### [GPU Acceleration](/reference/infra/gpu-processing)

Leverage NVIDIA GPU acceleration for faster data processing

#### [Resumable Processing](/reference/infra/resumable-processing)

Continue interrupted operations on large datasets