NVIDIA Warp Documentation#

Warp is a Python framework for writing high-performance simulation and graphics code. Warp takes regular Python functions and JIT compiles them to efficient kernel code that can run on the CPU or GPU.

Warp is designed for spatial computing and comes with a rich set of primitives that make it easy to write programs for physics simulation, perception, robotics, and geometry processing. In addition, Warp kernels are differentiable and can be used as part of machine-learning pipelines with frameworks such as PyTorch and JAX.

Below are some examples of simulations implemented using Warp:

Quickstart#

Warp supports Python versions 3.7 onwards. It can run on x86-64 and ARMv8 CPUs on Windows, Linux, and 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

Pre-built binary packages are also available on the Releases page. To install in your local Python environment extract the archive and run the following command from the root directory:

$ pip install .

Basic Example#

An example first program that computes the lengths of random 3D vectors is given below:

import warp as wp
import numpy as np

wp.init()

num_points = 1024

@wp.kernel
def length(points: wp.array(dtype=wp.vec3),
           lengths: wp.array(dtype=float)):

    # thread index
    tid = wp.tid()

    # compute distance of each point from origin
    lengths[tid] = wp.length(points[tid])


# allocate an array of 3d points
points = wp.array(np.random.rand(num_points, 3), dtype=wp.vec3)
lengths = wp.zeros(num_points, dtype=float)

# launch kernel
wp.launch(kernel=length,
          dim=len(points),
          inputs=[points, lengths])

print(lengths)

Additional Examples#

The examples directory in the Github repository contains a number of scripts that show how to implement different simulation methods using the Warp API. Most examples will generate USD files containing time-sampled animations in the same directory as the example. Before running examples users should ensure that the usd-core package is installed using:

pip install usd-core

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.

USD files can be viewed or rendered inside NVIDIA Omniverse, Pixar’s UsdView, and Blender. Note that Preview in macOS is not recommended as it has limited support for time-sampled animations.

Built-in unit tests can be run from the command-line as follows:

python -m warp.tests

examples/core#


dem	fluid	graph capture	marching cubes

mesh	nvdb	raycast	raymarch

sph	torch	wave

examples/fem#


apic fluid	convection diffusion	diffusion 3d	diffusion

mixed elasticity	navier stokes	stokes transfer	stokes

examples/optim#


bounce	cloth throw	diffray	drone

inverse kinematics	spring cage	trajectory	walker

examples/sim#


cartpole	cloth	granular	granular collision sdf

jacobian ik	quadruped	rigid chain	rigid contact

rigid force	rigid gyroscopic	rigid soft contact	soft body

Omniverse#

A Warp Omniverse extension is available in the extension registry inside Omniverse Kit or USD Composer.

Enabling the extension will automatically install and initialize the Warp Python module inside the Kit Python environment. Please see the Omniverse Warp Documentation for more details on how to use Warp in Omniverse.

Learn More#

Please see the following resources for additional background on Warp:

The underlying technology in Warp has been used in a number of research projects at NVIDIA including the following publications:

Accelerated Policy Learning with Parallel Differentiable Simulation - Xu, J., Makoviychuk, V., Narang, Y., Ramos, F., Matusik, W., Garg, A., & Macklin, M. (2022)
DiSECt: Differentiable Simulator for Robotic Cutting - Heiden, E., Macklin, M., Narang, Y., Fox, D., Garg, A., & Ramos, F (2021)
gradSim: Differentiable Simulation for System Identification and Visuomotor Control - Murthy, J. Krishna, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine et al. (2021)

Citing#

If you use Warp in your research please use the following citation:

@misc{warp2022,
    title= {Warp: A High-performance Python Framework for GPU Simulation and Graphics},
    author = {Miles Macklin},
    month = {March},
    year = {2022},
    note= {NVIDIA GPU Technology Conference (GTC)},
    howpublished = {\url{https://github.com/nvidia/warp}}
}

License#

Warp is provided under the NVIDIA Software License, please see LICENSE.md for the full license text.

Full Table of Contents#

User's Guide

Full Index