project-setup-python

name	project-setup-python
description	Setting up a new Python project that uses ovrtx. Use when user asks to create a new Python project, set up ovrtx in Python, create a pyproject.toml, or scaffold a Python app.
license	LicenseRef-NvidiaProprietary
version	0.3.0
author	NVIDIA ovrtx
tags	["ovrtx","python","setup"]
tools	["Read","Grep","Shell","Write"]

Project Setup (Python)

When to Use

Use this skill when the user asks to create a new Python project, set up ovrtx in Python, create a pyproject.toml, or scaffold a Python app.

Inputs

Resolve inputs in this order: existing repository files and referenced snippets, explicit user request, then broader agent context.

• Project language, package manager, build system, and target platform from the user request.
• Whether the project should use packaged ovrtx or the local dev-mode build.
• Repository source snippets and example projects referenced below. Treat these snippets as the API source of truth.

Prerequisites

• Use an ovrtx checkout that contains the referenced examples and docs tests.
• Confirm Python, CMake, compiler, and package-index requirements before giving setup commands.
• Avoid modifying tracked dependency files unless the user explicitly asks for project scaffolding changes.

Instructions

1. Identify the requested project type, platform, package source, and expected run command.
2. Read the minimal example and setup snippets before writing scaffolding.
3. Use the repository's existing Python or CMake conventions instead of inventing a new layout.
4. Include renderer creation and first-frame validation only after the project dependency setup is complete.
5. When changing code, run the narrow example setup or docs test whenever practical.

Output Format

• For explanations, cite the relevant API names, source snippets, and caveats.
• For code changes, summarize the files changed, snippets affected, and validation run.

Scripts

This skill has no scripts.

Limitations

• The referenced snippets remain the source of truth; update or add tested snippets before documenting new API usage.

Overview

ovrtx is distributed as a Python package on PyPI. The quickest way to get started is with uv (recommended) or pip. This skill shows how to scaffold a minimal project.

Project Structure

my-ovrtx-app/
  pyproject.toml
  main.py

Setup with uv (Recommended)

mkdir my-ovrtx-app && cd my-ovrtx-app
uv init
uv add ovrtx

This creates a pyproject.toml and uv.lock. Then add numpy (required for array/tensor operations):

uv add numpy

The resulting pyproject.toml will look like:

[project]
name = "my-ovrtx-app"
version = "0.1.0"
requires-python = ">=3.10,<3.14"
dependencies = [
    "ovrtx",
    "numpy",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

Adding optional dependencies

For image output:

uv add pillow

For GPU compute with Warp:

uv add warp-lang

For visualization with rerun:

uv add rerun-sdk

Setup with pip

pip install ovrtx

Minimal main.py

Source: examples/python/minimal/main.py snippet create-renderer

Followed by: examples/python/minimal/main.py snippet add-usd

Followed by: examples/python/minimal/main.py snippet step

Followed by: examples/python/minimal/main.py snippet read-render-output

Run

uv run main.py
# or with pip-installed ovrtx:
python main.py

Key Dependencies

Package	Purpose
ovrtx	Core renderer
pillow	Image I/O (PNG, JPEG)
numpy	Array manipulation, DLPack interop
warp-lang	GPU compute kernels
rerun-sdk	Real-time visualization

Troubleshooting

• ovrtx requires Python 3.10–3.13.
• ovrtx requires an NVIDIA GPU with up-to-date drivers.
• The first Renderer() call may take several seconds as the runtime initializes.
• USD files can be loaded from local paths, file:// URIs, or https:// URLs.

References

• Use the > **Source:** directives in this skill to locate tested snippets before reusing API patterns.
• Keep related skills, docs, and snippets synchronized when changing the workflow.