# NVIDIA/warp

A Python framework for GPU-accelerated simulation, robotics, and machine learning.

Repository: https://github.com/NVIDIA/warp
Canonical: https://ross.abutalabs.com/products/nvidia-warp
Homepage: https://nvidia.github.io/warp/stable/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: differentiable-programming, python, gpu-acceleration, nvidia, gpu, cuda, nvidia-warp
Last push: 2026-08-26T18:36:26+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 96, longevity 100
- inputs: {"age_days": 1629, "days_push": 7, "days_rel": 27, "gap_med": 29.0, "n_releases_24m": 27}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7033, forks 602 (observed 2026-08-28T04:09:55.121003+00:00)

## What it is
NVIDIA Warp is a Python framework that JIT-compiles regular Python functions into efficient CPU or CUDA GPU kernels. It provides differentiable primitives for physics simulation, robotics, geometry processing, and machine-learning pipelines with PyTorch, JAX, and Paddle.

## Use cases
- simulate millions of particles on the GPU from Python
- write differentiable physics simulations for robotics
- accelerate geometry processing kernels with CUDA
- integrate custom GPU kernels into PyTorch or JAX training pipelines
- prototype tile-based GPU programs in Python
- run differentiable optimization like shape optimization or inverse problems

## When to choose
- you need GPU-accelerated, differentiable simulation code written in Python
- you want custom CUDA-like kernels without writing C++/CUDA
- you're building robotics or physics ML pipelines needing gradients through simulation

## When to avoid
- you only need general tensor math without simulation primitives (use NumPy or PyTorch)
- you need GPU acceleration on macOS (Warp supports CPU only there)
- your project requires non-NVIDIA GPU acceleration

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, simulation, gpu-computing, compiler, math
- domain: machine-learning, simulation, robotics, gpu-computing, graphics
- platform: python, windows
- tags: differentiable-programming, jit-compilation, physics-simulation, kernels, pytorch-integration, jax-integration, nvidia, linux, macos, gpu, cuda

## Member repositories
- NVIDIA/warp (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:55.121003+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:40:34.603900+00:00, confidence not recorded.
  - readme: https://github.com/NVIDIA/warp (fetched 2026-08-28T04:09:55.121003+00:00, sha 701686645784)
  - homepage: https://nvidia.github.io/warp/stable/ (fetched 2026-08-29T08:36:21.627238+00:00, sha e7a50a07a58b)
- Data as of 2026-08-30T08:39:29.467469+00:00.