warp-lang 1.12.1

A Python framework for high-performance simulation and graphics programming

NVIDIA Warp

Documentation | Changelog

Warp is a Python framework for GPU-accelerated simulation, robotics, and machine learning. Warp takes regular Python functions and JIT compiles them to efficient kernel code that can run on the CPU or GPU.

Warp comes with a rich set of primitives for physics simulation, robotics, geometry processing, and more. Warp kernels are differentiable and can be used as part of machine-learning pipelines with frameworks such as PyTorch, JAX and Paddle.

A selection of physical simulations computed with Warp

Quick Start

Simulate one million particles under gravitational attraction, in 20 lines:

import warp as wp
import numpy as np

num_particles = 1_000_000
dt = 0.01

@wp.kernel
def gravity_step(pos: wp.array[wp.vec3], vel: wp.array[wp.vec3]):
    i = wp.tid()
    position = pos[i]
    dist_sq = wp.length_sq(position) + 0.01  # softened distance
    acc = -1000.0 / dist_sq * wp.normalize(position)  # gravitational pull toward origin
    vel[i] = vel[i] + acc * dt
    pos[i] = pos[i] + vel[i] * dt

rng = np.random.default_rng(42)
positions = wp.array(rng.normal(size=(num_particles, 3)), dtype=wp.vec3)
velocities = wp.array(rng.normal(size=(num_particles, 3)), dtype=wp.vec3)

for _ in range(100):
    wp.launch(gravity_step, dim=num_particles, inputs=[positions, velocities])

print(positions.numpy())

Installing

Python version 3.9 or newer is required. Warp can run on x86-64 and ARMv8 CPUs on Windows and Linux, and on Apple Silicon (ARMv8) on macOS. GPU support requires a CUDA-capable NVIDIA GPU and driver (minimum GeForce GTX 9xx).

The easiest way to install Warp is from PyPI:

pip install warp-lang

You can also use pip install warp-lang[examples] to install additional dependencies for running examples and USD-related features.

For nightly builds, conda, CUDA 13 builds, building from source, and CUDA driver requirements, see the Installation Guide.

Tutorial Notebooks

The NVIDIA Accelerated Computing Hub contains the current, actively maintained set of Warp tutorials:

Notebook	Colab Link
Introduction to NVIDIA Warp
GPU-Accelerated Ising Model Simulation in NVIDIA Warp

Additionally, several notebooks in the notebooks directory provide additional examples and cover key Warp features:

Notebook	Colab Link
Warp Core Tutorial: Basics
Warp Core Tutorial: Generics
Warp Core Tutorial: Points
Warp Core Tutorial: Meshes
Warp Core Tutorial: Volumes
Warp PyTorch Tutorial: Basics
Warp PyTorch Tutorial: Custom Operators

Running Examples

The warp/examples directory contains examples covering physics simulation, geometry processing, optimization, and tile-based GPU programming. Before running examples, install the optional example dependencies using:

pip install warp-lang[examples]

On Linux aarch64 systems (e.g., NVIDIA DGX Spark), the [examples] extra automatically installs usd-exchange instead of usd-core as a drop-in replacement, since usd-core wheels are not available for that platform.

Examples can be run from the command-line as follows:

python -m warp.examples.<example_subdir>.<example>

Most examples can be run on either the CPU or a CUDA-capable device, but a handful require a CUDA-capable device. These are marked at the top of the example script. Some examples generate USD files containing time-sampled animations in the current working directory. These can be viewed in Pixar's UsdView, Blender, or any USD-compatible viewer.

To browse the example source code, you can open the directory where the files are located like this:

python -m warp.examples.browse

warp/examples/core

dem	fluid	graph capture	marching cubes
mesh	nvdb	raycast	raymarch
sample mesh	sph	torch	wave
2-D incompressible turbulence in a periodic box

warp/examples/fem

diffusion 3d	mixed elasticity	apic fluid	streamlines
distortion energy	taylor green	kelvin helmholtz	magnetostatics
adaptive grid	nonconforming contact	darcy level-set optimization	elastic shape optimization

warp/examples/optim

diffray	fluid checkpoint	particle repulsion	navier-stokes perturbation

warp/examples/tile

mlp	nbody	mcgp

Learn More

Please see the following resources for additional background on Warp:

• Product Page
• SIGGRAPH 2024 Course Slides
• GTC 2024 Presentation
• GTC 2022 Presentation
• GTC 2021 Presentation
• SIGGRAPH Asia 2021 Differentiable Simulation Course

Support

See the FAQ for common questions.

Problems, questions, and feature requests can be opened on GitHub Issues.

For inquiries not suited for GitHub Issues, please email [e-mail].

Contributing

Contributions and pull requests from the community are welcome. Please see the Contribution Guide for more information on contributing to the development of Warp.

License

Warp is provided under the Apache License, Version 2.0. Please see LICENSE.md for full license text.

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

Publications & Citation

Research Using Warp

Our PUBLICATIONS.md file lists academic and research publications that leverage the capabilities of Warp. We encourage you to add your own published work using Warp to this list.

Citing Warp

If you use Warp in your research, please use the "Cite this repository" button on the GitHub repository page or refer to the CITATION.cff file for citation information.