NVIDIA/cosmos-framework

NVIDIA/cosmos-framework

Description: Our inference and training framework to run on the Cosmos Models

Language: Python

License: NOASSERTION

Stars: 215

Forks: 27

Open issues: 14

Created: 2026-05-19T16:27:26Z

Pushed: 2026-06-11T02:31:03Z

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README:

NVIDIA Cosmos | 🤗 Cosmos 3

Part of the NVIDIA Cosmos project family — the training and serving framework repository.

Cosmos-Framework

Cosmos-Framework is an end-to-end framework for training and serving world models, including the Cosmos3 model family. Everything lives in a single top-level [cosmos_framework/](./cosmos_framework) Python package:

• Training — distributed FSDP / TP / CP / PP trainer, native DCP checkpoints with HuggingFace safetensors import/export, JSONL / WebDataset / LeRobot dataset adapters. Entry point: cosmos_framework.scripts.train. See [docs/training.md](./docs/training.md).
• Inference — Diffusers / Transformers / vLLM backends with offline batch generation and online serving (Ray + Gradio). Entry point: cosmos_framework.scripts.inference. Ecosystem-facing shim libraries (lightweight standalone wrappers for downstream projects) live under [packages/](./packages).

Cosmos 3

Cosmos 3 is our newest model family [[Report]](https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf) [[Website]](https://research.nvidia.com/labs/cosmos-lab/cosmos3/). It is a suite of omnimodal world models designed to jointly process and generate language, images, video, audio, and action sequences within a unified Mixture-of-Transformers architecture. By supporting highly flexible input-output configurations, it seamlessly unifies critical modalities for Physical AI — effectively subsuming vision-language models, video generators, world simulators, and world-action models into a single framework. For a guided experience to test out Cosmos3, please visit [[Cosmos]](https://github.com/nvidia/cosmos).

Framework Documentation

• [Quickstart](#setup)
• [Setup](./docs/setup.md)
• [Training (Supervised Fine-Tuning)](./docs/training.md)
• [JSONL Dataset](./docs/dataset_jsonl.md)
• [Inference](./docs/inference.md)
• Reference
• [Code Structure](./docs/code_structure.md)
• [Environment Variables](./docs/environment_variables.md)
• [FAQ](./docs/faq.md)
• [AGENTS.md](./AGENTS.md)

Setup

For more details and alternative installation methods, see [Setup](./docs/setup.md#installation). Before installing, make sure your machine meets the [System Requirements](./docs/setup.md#system-requirements). If you want a curated PyTorch + CUDA environment, start from the [recommended NVIDIA NGC base image](./docs/setup.md#recommended-base-image).

Install system dependencies:

sudo apt-get install -y --no-install-recommends curl ffmpeg git-lfs libx11-dev tree wget

Install the package with uv (pick the dependency group that matches your CUDA toolkit — see [CUDA Variants](./docs/setup.md#cuda-variants)):

# CUDA 13.0 (recommended)
uv sync --all-extras --group=cu130-train
# Or, for CUDA 12.8:
# uv sync --all-extras --group=cu128-train
source .venv/bin/activate && export LD_LIBRARY_PATH=

If you are starting from the recommended NGC image (nvcr.io/nvidia/pytorch:25.09-py3), see the [one-shot quickstart](./docs/setup.md#quickstart-from-the-recommended-base-image).

Training

For the full guide (data preparation, base-checkpoint conversion, parallelism strategies, mixed precision, resuming), see [Training](./docs/training.md). The number of GPUs required depends on the recipe; the shipped recipes under [examples/](./examples/README.md) are 8-GPU configurations (tested on 8× H100 80 GB) launched via their paired launch shells, e.g.:

bash examples/launch_sft_vision_nano.sh

Users may adjust the GPU count to match their model and underlying hardware architecture — tune NPROC_PER_NODE and the parallelism degrees (DP/CP/FSDP shard) in the recipe accordingly.

Inference

See [Inference](./docs/inference.md) for the full guide — launch commands, supported modes, parallelism presets, and troubleshooting.

Quick single-GPU launch:

python -m cosmos_framework.scripts.inference \
--parallelism-preset=latency \
-i "inputs/omni/t2v.json" \
-o outputs/omni_nano \
--checkpoint-path Cosmos3-Nano \
--seed=0

Reference

| Topic | What it covers | | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------ | | [Setup](./docs/setup.md) | Hardware/software prerequisites, uv install paths, CUDA variants, Docker base image, and base-checkpoint downloading. | | [Code Structure](./docs/code_structure.md) | Repository layout and a per-subpackage tour of cosmos_framework/ — where each concern lives and where to add new code. | | [Training](./docs/training.md) | Launching multi-GPU and multi-node runs; parallelism strategies; mixed precision; resuming. | | [Inference (from a trained checkpoint)](./docs/inference.md) | Loading a trained checkpoint into one of the inference backends. | | [FAQ](./docs/faq.md) | Troubleshooting (OOM, NCCL hangs, slow training), environment variables, and common pitfalls. |