warp-lang

What it is and what it does

Warp is a Python framework that takes regular Python functions decorated with @wp.kernel and JIT-compiles them to efficient GPU or CPU code. It bridges the gap between Python's ease of use and the performance demands of simulation, robotics, and machine learning by letting you write kernels in Python syntax while executing them at near-native speed on NVIDIA GPUs or CPUs. The framework includes built-in primitives for physics, geometry, and differentiable operations, and kernels can be integrated into PyTorch, JAX, and Paddle pipelines for end-to-end learning.

Warp handles the complexity of CUDA kernel generation, memory management, and differentiation automatically. You write Python functions, Warp compiles them, and you launch them with wp.launch() specifying the number of threads. It supports both CPU and GPU execution, making it suitable for prototyping on CPU and scaling to GPU without code changes. The framework is production-ready and actively maintained, with support for modern Python versions.

Use it for:

• Simulate large-scale particle systems by writing physics kernels in Python and running them on GPU.
• Build differentiable physics simulators for inverse problems and machine learning by composing Warp kernels with PyTorch or JAX.
• Accelerate geometry processing tasks without writing native CUDA code.
• Prototype CPU-based simulations and deploy them to GPU with minimal code changes.
• Implement robotics control loops and spatial computations that require both performance and ease of iteration.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Warp is a Python framework that JIT-compiles regular Python functions to GPU or CPU kernel code for physics simulation, robotics, geometry processing, and differentiable machine learning workloads.

Yes, if you need GPU-accelerated simulation or geometry processing in Python and want to avoid writing native CUDA. The framework is production-stable, actively maintained, has no known vulnerabilities, and integrates well with ML frameworks. Install friction is moderate due to platform-specific wheels and CUDA requirements, but pre-built wheels eliminate compilation overhead. Not suitable if you lack GPU hardware or need CPU-only performance without GPU fallback.

Install

warp-lang on PyPI

pip install warp-lang

uv add warp-lang

poetry add warp-lang

Installing warp-lang

Before you install

Medium install friction due to platform-specific wheels (macOS ARM64, Linux x86-64 and aarch64, Windows x64) and CUDA driver requirements for GPU support. Actively maintained with recent releases; no compilation needed for pre-built wheels.

License in practice

Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.

Quickstart

pip install warp-lang
import warp as wp
import numpy as np

@wp.kernel
def example(arr: wp.array):
    i = wp.tid()
    arr[i] = arr[i] * 2.0

data = wp.array(np.array([1.0, 2.0, 3.0]))
wp.launch(example, dim=3, inputs=[data])
print(data.numpy())

Python 3.10 or newer required; GPU support requires CUDA-capable NVIDIA GPU (minimum GeForce GTX 9xx) and compatible driver.

Verify before relying

• Performance characteristics and speedup factors compared to pure NumPy or native CUDA for typical workloads.
• Memory overhead of JIT compilation and kernel caching during runtime.
• Compatibility with specific PyTorch, JAX, and Paddle versions beyond basic integration claims.

Package facts

Evidence: warp_lang-1.16.0-py3-none-macosx_11_0_arm64.whl; warp_lang-1.16.0-py3-none-manylinux_2_28_x86_64.whl; warp_lang-1.16.0-py3-none-manylinux_2_34_aarch64.whl; warp_lang-1.16.0-py3-none-win_amd64.whl

Tags

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