cosmos-xenna 0.1.2

A framework for building and running distributed, AI-powered data pipelines using Ray

Cosmos-xenna

Introduction

Cosmos-xenna is a Python library for building and running distributed data pipelines using Ray. It has a heavy focus on pipelines which are a series of inference steps using AI models. For example, a pipeline which downloads an image, runs a VLM on it to produce a caption, and then runs an embedding model to produce a text embedding and uploads the resulting data.

Cosmos-xenna simplifies the development of distributed AI pipelines by providing:

• A simple interface
• Autoscaling/autobalancing of stages
• Stateful actors which allow the user to load/download weights before running processing
• Independent allocation of NVDEC/NVENC hardware and "main" GPU compute

Installing

pip install cosmos-xenna[gpu]

Quick Start

For detailed examples, check out the examples/ directory.

Ray cluster requirements

Cosmos-xenna needs a few environment variables to be set before starting Ray clusters. These are set by Xenna when we start clusters locally, but if using an already existing cluster, they will need to be set in the processes initializing the cluster.

# Needed to give Xenna control over setting CUDA environment variables. Without this, Ray will overwrite the
# environment variables we set.
RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES="0"
# Needed to get debug info from as many actors as possible. By default, Ray only allows 10k
# actors to be listed. However, on large clusters, we may have more than 10k actors.
RAY_MAX_LIMIT_FROM_API_SERVER=40000
RAY_MAX_LIMIT_FROM_DATA_SOURCE=40000

Development

Setup development environment

We use UV for development. To get started, install UV, and run uv sync in this directory.

This will create a virtual environment at .venv based on the current lock file and will include all of the dependencies from core, dev, GPU, and examples.

Running commands

Use UV to run all commands. For example, to run the example pipeline, use:

uv run examples/simple_vlm_inference.py 

This will auto-sync dependencies if needed and execute the command in the UV-managed virtualenv.

VSCode integration

We provide recommended extensions and default settings for yotta via the .vscode/ folder. With these settings, VSCode should automatically format your code and raise linting/typing issues. VSCode will try to fix some minor linting issues on save.

Linting

We use Ruff and PyRight for static analysis. Using the default VSCode settings and recommended extensions, these should auto-run in VSCode. They can be run manually with:

uv run run_presubmit.py default

Adding dependencies

To add packages to the core dependencies, use uv add some-package-name

To add packages to dev use uv add --dev some-package-name

To add packages to other groups use uv add --group some-group some-package-name

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.