Run a Warp example

Run a Warp example#

2026-11-05

3 min read time

Applies to Linux

These examples assume that Warp is installed as described in Install Warp. Run the examples in the same Docker container where Warp is installed.

Write and run a Warp kernel#

Warp kernels are Python functions decorated with @wp.kernel. They are compiled for your AMD GPU the first time they are launched. AMD GPUs are addressed through the cuda:N device names.

  1. Save the following code snippet to a Python script named warp_example.py.

    import numpy as np
import warp as wp


@wp.kernel
def saxpy(a: float, x: wp.array(dtype=float), y: wp.array(dtype=float)):
    i = wp.tid()
    y[i] = a * x[i] + y[i]


n = 1_000_000
device = wp.get_device("cuda:0")

x = wp.array(np.arange(n, dtype=np.float32), device=device)
y = wp.ones(n, dtype=float, device=device)

wp.launch(saxpy, dim=n, inputs=[2.0, x, y], device=device)

result = y.numpy()
print(f"Ran on {device.name} ({device.arch_str})")
print("y[:5] =", result[:5])
assert np.allclose(result, 2.0 * np.arange(n, dtype=np.float32) + 1.0)
print("SAXPY result is correct")

  2. Run the script.

    python warp_example.py

    After Warp prints its initialization banner, the output ends with lines similar to the following:

    Ran on AMD Instinct MI355X (gfx950:sramecc+:xnack-)
y[:5] = [1. 3. 5. 7. 9.]
SAXPY result is correct

    Compiled kernels are cached in ~/.cache/warp, so later runs start faster.

Run the built-in examples#

The warp/examples directory contains scripts, grouped into subdirectories, that show how to implement various simulation methods using the Warp API. Most examples write USD files with time-sampled animations to the current working directory.

  1. Install the packages used by the examples.

    pip install usd-core matplotlib

    If pip reports an externally-managed-environment error, add --break-system-packages to the pip install command.

  2. Run an example from the command line. Pass --headless so that examples that can open a plot window run without a display.

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

    For example, to run the discrete element method (DEM) simulation:

    python -m warp.examples.core.example_dem --headless

    This writes the animation to example_dem.usd, which you can open in any USD viewer.

The following examples run on AMD Instinct GPUs:

Module

Description

warp.examples.core.example_dem

Discrete element method (DEM) particle simulation with cohesion, using a hash grid for neighbor search

warp.examples.core.example_marching_cubes

Iso-surface extraction from a density field with marching cubes

warp.examples.core.example_mesh

Position-based dynamics particles colliding with a deforming triangle mesh

warp.examples.core.example_raycast

Basic ray tracer using the built-in wp.Mesh data structure

warp.examples.core.example_raymarch

Signed distance field (SDF) ray marching renderer

warp.examples.core.example_sample_mesh

Sampling points on a mesh surface using a cumulative distribution function

warp.examples.core.example_sph

Smoothed particle hydrodynamics (SPH) fluid simulation

warp.examples.core.example_torch

Optimizing the Rosenbrock function with the PyTorch Adam optimizer (requires PyTorch)

warp.examples.core.example_wave

2D wave equation solver with collision against a moving sphere

warp.examples.optim.example_diffray

Differentiable ray caster

warp.examples.tile.example_tile_mlp

Coordinate-based multilayer perceptron (MLP) trained on an image with tile primitives

Examples that use the OpenGL renderer, such as example_render_opengl, might not work because AMD Instinct GPUs run headless.

Learn more#

For more information about the Warp API, interoperability with PyTorch, and differentiable simulation, see the upstream Warp documentation.

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