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Cosmos-Curate

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HPC Fork — What's new

This fork extends upstream cosmos-curate with four features and a full Apptainer deployment guide for Slurm HPC clusters.

Running on a Slurm cluster with Apptainer? Start here: docs/hpc-apptainer.md

Setting up Gemma4? The container needs a pip_overrides directory: docs/gemma4-setup.md

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Everything below this line is from the original NVIDIA cosmos-curate repository: https://github.com/NVIDIA/cosmos-curator

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A powerful video curation system that processes, analyzes, and organizes video content using advanced AI models and distributed computing.

Important

Please run git submodule sync if you have cloned the repository before and just pulled the latest update. We updated the URL for cosmos-xenna submodule on 08/04/2025.

Overview

Cosmos-Curate is a comprehensive solution for video processing and curation using state-of-the-art AI models, which powers the training data generation for Cosmos at NVIDIA. It is built on top of a framework optimized for GPU-accelerated streaming pipeline, which is now open-sourced independently as Cosmos-Xenna.

Features

• Video Processing: Efficient video splitting, annotation, filtering, deduplication, and dataset generation
• AI-Powered Analysis: Advanced video analysis using multiple model families
• Distributed Computing: Scalable processing using Cosmos-Xenna built on top of Ray
• Cloud Integration: Support for various platforms
• Pipeline System: Modular and extensible pipeline architecture

Documentation

Comprehensive documentation is available under docs/ directory.

User Documentation

• End User Guide - instructions to setup environment and run data pipelines
• Reference Video Pipelines Guide - details for general video processing pipelines
• Reference AV Pipelines Guide - details for multi-camera video, and (upcoming) GPS & LiDAR processing pipelines for autonomous vehicle (AV)
• NVCF Guide - deployment instruction on Nvidia Cloud Functions

Developer Documentation

• Developer Guide - information for contributors
• Architecture Guide - diagrams and description to help understand core architecture
• Pipeline Design Guide - detailed walk-through of the hello-world pipeline and performance optimization points
• Observability Guide - instructions to setup and understand monitoring dashboard

AI Agent Context Files

• AGENTS.md - Context file for Codex
• CLAUDE.md - Context file for Claude Code
• GEMINI.md - Context file for Gemini

Directory Structure

cosmos-curate/
├── cosmos_curate/         # Curate implementation
│   ├── client              # CLI to run locally
│       ├── image_cli       # Docker image management
│       ├── local_cli       # Launch pipelines by running local container
│       ├── nvcf_cli        # Launch pipelines on NVIDIA cloud function
│       ├── slurm_cli       # Launch pipelines on Slurm cluster
│       ├── utils           # Common utilities for various CLI apps
│   ├── core/               # Core functionality
│       ├── cf              # Service entry point for a cloud function deployment
│       ├── interfaces      # Core base class to integrate model and define new pipelines
│       ├── managers        # CLIs to run inside the container to manage models, databases, etc.
│       ├── utils           # Common utilities for pipelines
│   ├── models/             # AI model inference implementations
│   ├── pipelines/          # Pipeline implementations
│       ├── examples/       # Minimal example pipelines to help understand the framework
│       ├── video/          # Reference pipelines for video curation
│   ├── scripts/            # Startup scripts in various deployment environments
├── cosmos-xenna            # Git submodule for https://github.com/nvidia-cosmos/cosmos-xenna
├── packages                # Dockerfiles and scripts related to packaging
│   ├── cosmos_curate       # Dockerfile template and conda environment recipes for building cosmos_curate image
├── tests                   # Tests for testing
│   ├── cosmos_curate             
│       ├── pipelines       # Tests for models and pipeline stages for cosmos_curate
│       ├── client          # Tests for client CLIs
├── examples                # Example configuration files and scripts

Note: To initialize and update the cosmos-xenna submodule, run:

git submodule update --init --recursive

This ensures all submodule content is checked out correctly.

Support

For support and questions:

• Check the documentation
• Open an issue on GitHub

Acknowledgments

• cosmos-xenna team for the core library
• All contributors and users of the project

Responsible Use of AI Models

Responsible Use

License and Contact

This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.

NVIDIA Cosmos source code is released under the Apache 2 License.

NVIDIA Cosmos models are released under the NVIDIA Open Model License. For a custom license, please contact cosmos-license@nvidia.com.