NVIDIA Warp Documentation#

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.

Quickstart#

Install Warp from PyPI:

$ pip install warp-lang

PyPI and nightly wheels for Linux and Windows use CUDA Toolkit 13.4. They require an NVIDIA R580-series or newer driver and a Turing (sm_75) or newer GPU for CUDA acceleration. For CUDA 12 environments, download a +cu12 wheel from GitHub Releases or build Warp from source with CUDA 12. Warp can also run on the CPU. The Apple Silicon macOS wheel is CPU-only.

For conda, nightly builds, CUDA 12 compatibility wheels, building from source, and driver requirements, see Installation.

Example Gallery#

The warp/examples directory contains examples covering physics simulation, geometry processing, optimization, and tile-based GPU programming. Install the optional dependencies with pip install warp-lang[examples] and run them from the command line:

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

warp/examples/core#


dem	fluid	graph capture	mesh

nvdb	raycast	raymarch	sample_mesh

sph	torch	wave	2-D incompressible turbulence in a periodic box

3-D FDTD Luneburg lens

warp/examples/geometry#


isosurface	sparse marching cubes

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

cantilever topology optimization