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https://github.com/NVIDIA/warp
A Python framework for high performance GPU simulation and graphics
https://github.com/NVIDIA/warp
Last synced: 4 days ago
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A Python framework for high performance GPU simulation and graphics
- Host: GitHub
- URL: https://github.com/NVIDIA/warp
- Owner: NVIDIA
- License: other
- Created: 2022-03-18T21:56:23.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-31T18:04:19.000Z (3 months ago)
- Last Synced: 2024-07-31T21:48:50.829Z (3 months ago)
- Language: Python
- Homepage: https://nvidia.github.io/warp/
- Size: 38.9 MB
- Stars: 3,948
- Watchers: 55
- Forks: 214
- Open Issues: 53
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Codeowners: .gitlab/CODEOWNERS
- Security: SECURITY.md
Awesome Lists containing this project
- StarryDivineSky - NVIDIA/warp
README
[](https://badge.fury.io/py/warp-lang)
[](https://pepy.tech/project/warp-lang)
[](https://codecov.io/github/NVIDIA/warp)
[](https://discord.com/invite/nvidiaomniverse)
# NVIDIA Warp
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](https://en.wikipedia.org/wiki/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 and JAX.
Please refer to the project [Documentation](https://nvidia.github.io/warp/) for API and language reference and [CHANGELOG.md](./CHANGELOG.md) for release history.
A selection of physical simulations computed with Warp
## Installing
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](https://pypi.org/project/warp-lang/):
```text
pip install warp-lang
```
You can also use `pip install warp-lang[extras]` to install additional dependencies for running examples and USD-related features.
The binaries hosted on PyPI are currently built with the CUDA 12 runtime and therefore
require a minimum version of the CUDA driver of 525.60.13 (Linux x86-64) or 528.33 (Windows x86-64).
If you require GPU support on a system with an older CUDA driver, you can build Warp from source or
install wheels built with the CUDA 11.8 runtime from the [GitHub Releases](https://github.com/NVIDIA/warp/releases) page.
Copy the URL of the appropriate wheel file (`warp-lang-{ver}+cu12-py3-none-{platform}.whl`) and pass it to
the `pip install` command, e.g.
| Platform | Install Command |
| --------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| Linux aarch64 | `pip install https://github.com/NVIDIA/warp/releases/download/v1.3.1/warp_lang-1.3.1+cu11-py3-none-manylinux2014_aarch64.whl` |
| Linux x86-64 | `pip install https://github.com/NVIDIA/warp/releases/download/v1.3.1/warp_lang-1.3.1+cu11-py3-none-manylinux2014_x86_64.whl` |
| Windows x86-64 | `pip install https://github.com/NVIDIA/warp/releases/download/v1.3.1/warp_lang-1.3.1+cu11-py3-none-win_amd64.whl` |
The `--force-reinstall` option may need to be used to overwrite a previous installation.
### CUDA Requirements
* Warp packages built with CUDA Toolkit 11.x require NVIDIA driver 470 or newer.
* Warp packages built with CUDA Toolkit 12.x require NVIDIA driver 525 or newer.
This applies to pre-built packages distributed on PyPI and GitHub and also when building Warp from source.
Note that building Warp with the `--quick` flag changes the driver requirements. The quick build skips CUDA backward compatibility, so the minimum required driver is determined by the CUDA Toolkit version. Refer to the [latest CUDA Toolkit release notes](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html) to find the minimum required driver for different CUDA Toolkit versions (e.g., [this table from CUDA Toolkit 12.5](https://docs.nvidia.com/cuda/archive/12.5.0/cuda-toolkit-release-notes/index.html#id3)).
Warp checks the installed driver during initialization and will report a warning if the driver is not suitable, e.g.:
```text
Warp UserWarning:
Insufficient CUDA driver version.
The minimum required CUDA driver version is 12.0, but the installed CUDA driver version is 11.8.
Visit https://github.com/NVIDIA/warp/blob/main/README.md#installing for guidance.
```
This will make CUDA devices unavailable, but the CPU can still be used.
To remedy the situation there are a few options:
* Update the driver.
* Install a compatible pre-built Warp package.
* Build Warp from source using a CUDA Toolkit that's compatible with the installed driver.
## Getting Started
An example first program that computes the lengths of random 3D vectors is given below:
```python
import warp as wp
import numpy as np
num_points = 1024
@wp.kernel
def length(points: wp.array(dtype=wp.vec3),
lengths: wp.array(dtype=float)):
# thread index
tid = wp.tid()
# compute distance of each point from origin
lengths[tid] = wp.length(points[tid])
# allocate an array of 3d points
points = wp.array(np.random.rand(num_points, 3), dtype=wp.vec3)
lengths = wp.zeros(num_points, dtype=float)
# launch kernel
wp.launch(kernel=length,
dim=len(points),
inputs=[points, lengths])
print(lengths)
```
## Running Examples
The [warp/examples](./warp/examples/) directory contains a number of scripts categorized under subdirectories
that show how to implement various simulation methods using the Warp API.
Most examples will generate USD files containing time-sampled animations in the current working directory.
Before running examples, users should ensure that the ``usd-core``, ``matplotlib``, and ``pyglet`` packages are installed using:
```text
pip install warp-lang[extras]
```
These dependencies can also be manually installed using:
```text
pip install usd-core matplotlib pyglet
```
Examples can be run from the command-line as follows:
```text
python -m warp.examples..
```
To browse the example source code, you can open the directory where the files are located like this:
```text
python -m warp.examples.browse
```
Most examples can be run on either the CPU or a CUDA-capable device, but a handful require a CUDA-capable device. These are marked at the top of the example script.
USD files can be viewed or rendered inside [NVIDIA Omniverse](https://developer.nvidia.com/omniverse), Pixar's UsdView, and Blender. Note that Preview in macOS is not recommended as it has limited support for time-sampled animations.
Built-in unit tests can be run from the command-line as follows:
```text
python -m warp.tests
```
### warp/examples/core
dem
fluid
graph capture
marching cubes
mesh
nvdb
raycast
raymarch
sph
torch
wave
### warp/examples/fem
diffusion 3d
mixed elasticity
apic fluid
streamlines
convection diffusion
navier stokes
burgers
magnetostatics
### warp/examples/optim
bounce
cloth throw
diffray
drone
inverse kinematics
spring cage
trajectory
walker
### warp/examples/sim
cartpole
cloth
granular
granular collision sdf
jacobian ik
quadruped
rigid chain
rigid contact
rigid force
rigid gyroscopic
rigid soft contact
soft body
## Building
For developers who want to build the library themselves, the following tools are required:
* Microsoft Visual Studio 2019 upwards (Windows)
* GCC 9.4 upwards (Linux)
* CUDA Toolkit 11.5 or higher
* [Git LFS](https://git-lfs.github.com/) installed
After cloning the repository, users should run:
```text
python build_lib.py
```
Upon success, the script will output platform-specific binary files in `warp/bin/`.
The build script will look for the CUDA Toolkit in its default installation path.
This path can be overridden by setting the `CUDA_PATH` environment variable. Alternatively,
the path to the CUDA Toolkit can be passed to the build command as
`--cuda_path="..."`. After building, the Warp package should be installed using:
```text
pip install -e .
```
This ensures that subsequent modifications to the library will be reflected in the Python package.
## Learn More
Please see the following resources for additional background on Warp:
* [Product Page](https://developer.nvidia.com/warp-python)
* [GTC 2022 Presentation](https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41599)
* [GTC 2021 Presentation](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31838)
* [SIGGRAPH Asia 2021 Differentiable Simulation Course](https://dl.acm.org/doi/abs/10.1145/3476117.3483433)
* [GTC 2024 Presentation](https://www.nvidia.com/en-us/on-demand/session/gtc24-s63345/)
The underlying technology in Warp has been used in a number of research projects at NVIDIA including the following publications:
* Accelerated Policy Learning with Parallel Differentiable Simulation - Xu, J., Makoviychuk, V., Narang, Y., Ramos, F., Matusik, W., Garg, A., & Macklin, M. [(2022)](https://short-horizon-actor-critic.github.io)
* DiSECt: Differentiable Simulator for Robotic Cutting - Heiden, E., Macklin, M., Narang, Y., Fox, D., Garg, A., & Ramos, F [(2021)](https://github.com/NVlabs/DiSECt)
* gradSim: Differentiable Simulation for System Identification and Visuomotor Control - Murthy, J. Krishna, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine et al. [(2021)](https://gradsim.github.io)
## Frequently Asked Questions
See the [FAQ](https://nvidia.github.io/warp/faq.html) in the Warp documentation.
## Support
Problems, questions, and feature requests can be opened on [GitHub Issues](https://github.com/NVIDIA/warp/issues).
The Warp team also monitors the **#warp** channel on the public [Omniverse Discord](https://discord.com/invite/nvidiaomniverse) server, come chat with us!
## Versioning
Versions take the format X.Y.Z, similar to [Python itself](https://devguide.python.org/developer-workflow/development-cycle/#devcycle):
* Increments in X are reserved for major reworks of the project causing disruptive incompatibility (or reaching the 1.0 milestone).
* Increments in Y are for regular releases with a new set of features.
* Increments in Z are for bug fixes. In principle, there are no new features. Can be omitted if 0 or not relevant.
This is similar to [Semantic Versioning](https://semver.org/) but is less strict regarding backward compatibility.
Like with Python, some breaking changes can be present between minor versions if well-documented and gradually introduced.
Note that prior to 0.11.0, this schema was not strictly adhered to.
## License
Warp is provided under the NVIDIA Software License, please see [LICENSE.md](./LICENSE.md) for full license text.
## Contributing
Contributions and pull requests from the community are welcome and are taken under the
terms described in the **Feedback** section of [LICENSE.md](LICENSE.md#9-feedback).
[CONTRIBUTING.md](./CONTRIBUTING.md) provides additional information on how to open a pull request for Warp.
## Citing
If you use Warp in your research, please use the following citation:
```bibtex
@misc{warp2022,
title= {Warp: A High-performance Python Framework for GPU Simulation and Graphics},
author = {Miles Macklin},
month = {March},
year = {2022},
note= {NVIDIA GPU Technology Conference (GTC)},
howpublished = {\url{https://github.com/nvidia/warp}}
}
```