NVIDIA/warp
A Python framework for GPU-accelerated simulation, robotics, and machine learning.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
4 releases in the last 90 days, the latest 22 days ago, with 108 contributors on GitHub.
Use Cases & Benefits
- A Python framework that JIT compiles functions for high-performance simulation and graphics on CPU or CUDA-capable GPU.
- Enables differentiable kernels for seamless integration with machine learning frameworks like PyTorch and JAX.
- Use for accelerating physics simulations and geometry processing in robotics and spatial computing applications.
- Use for developing differentiable simulation pipelines that require GPU acceleration and integration with ML models.
- Use for creating custom GPU-accelerated operators and kernels to optimize scientific computing workflows in Python.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
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, JAX and Paddle.
Please refer to the project Documentation for API and language reference and CHANGELOG.md for release history.
A selection of physical simulations computed with Warp
Python version 3.9 or newer is recommended. Warp 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:
You can also use
pip install warp-lang[examples]to install additional dependencies for running examples and USD-related features.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
673+99
Open issues
395+144
Compared with peers in Graphics
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| heygen-com/hyperframes | 79.6K | +4.1K | 45 | today |
| mrdoob/three.js | 117.7K | +463 | 65 | 6 days ago |
| NVIDIA/cuda-rust | 4.0K | +122 | 60 | 4 days ago |
| bilawalsidhu/gods-eye-view | 61.8K | — | 60 | 3 days ago |
| processing/p5.js | 24.2K | — | 65 | 7 days ago |
| NVIDIA/warp | 7.2K | — | 64 | 22 days ago |
| Category median | 61.8K | +463 | 60 | — |
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