nanobind
warp
| nanobind | warp | |
|---|---|---|
| 11 | 9 | |
| 2,397 | 4,306 | |
| - | 2.3% | |
| 9.5 | 9.8 | |
| 9 days ago | 5 days ago | |
| C++ | Python | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nanobind
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Progress on No-GIL CPython
Take a look at https://github.com/wjakob/nanobind
> More concretely, benchmarks show up to ~4× faster compile time, ~5× smaller binaries, and ~10× lower runtime overheads compared to pybind11.
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Advanced Python Mastery – A Course by David Beazley
People should not take that an endorsement of Swig.
Please use ctypes, cffi or https://github.com/wjakob/nanobind
Beazley himself is amazed that it (Swig) is still in use.
- Swig – Connect C/C++ programs with high-level programming languages
- Nanobind: Tiny and efficient C++/Python bindings
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Create Python bindings for my C++ code with PyBind11
Nanobind made by the creator of PyBind11, it has a similar interface, but it takes leverage of C++17 and it aims to have more efficient bindings in space and speed.
- Nanobind – Seamless operability between C++17 and Python
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Cython Is 20
I would recommend using NanoBind, the follow up of PyBind11 by the same author (Wensel Jakob), and move as much performance critical code to C or C++. https://github.com/wjakob/nanobind
If you really care about performance called from Python, consider something like NVIDIA Warp (Preview). Warp jits and runs your code on CUDA or CPU. Although Warp targets physics simulation, geometry processing, and procedural animation, it can be used for other tasks as well. https://github.com/NVIDIA/warp
Jax is another option, by Google, jitting and vectorizing code for TPU, GPU or CPU. https://github.com/google/jax
- GitHub - wjakob/nanobind: nanobind — Seamless operability between C++17 and Python
warp
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This Week In Python
warp – A Python framework for high performance GPU simulation and graphics
- Nvidia Warp (a Python framework for writing high-performance code)
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Nvidia Warp: A Python framework for high performance GPU simulation and graphics
I would argue that this isn't "normal open source", though it is indeed not locked behind a login on their website. The license (1) is feels very much proprietary, even if the source code is available.
(1) https://github.com/NVIDIA/warp/blob/main/LICENSE.md
- Warp 0.5.0 is out! A Python framework for high performance GPU simulation and graphics
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Options for GPU accelerated python experiments?
About to embark on some physics simulation experiments and am hoping to get some input on available options for making use of my GPU through Python: Currently reading the docs for NVIDIA Warp, and CUDA python but would appreciate any other pointers on available packages or red flags on packages that are more hassle than they are worth to learn.
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Cython Is 20
I would recommend using NanoBind, the follow up of PyBind11 by the same author (Wensel Jakob), and move as much performance critical code to C or C++. https://github.com/wjakob/nanobind
If you really care about performance called from Python, consider something like NVIDIA Warp (Preview). Warp jits and runs your code on CUDA or CPU. Although Warp targets physics simulation, geometry processing, and procedural animation, it can be used for other tasks as well. https://github.com/NVIDIA/warp
Jax is another option, by Google, jitting and vectorizing code for TPU, GPU or CPU. https://github.com/google/jax
What are some alternatives?
pybind11 - Seamless operability between C++11 and Python
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
awesome-cython - A curated list of awesome Cython resources. Just a draft for now.
spacy-experimental - 🧪 Cutting-edge experimental spaCy components and features
avendish - declarative polyamorous cross-system intermedia objects
matplotlibcpp17 - Alternative to matplotlibcpp with better syntax, based on pybind
prysm - physical optics: integrated modeling, phase retrieval, segmented systems, polynomials and fitting, sequential raytracing...
epython - EPython is a typed-subset of the Python for extending the language new builtin types and methods
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4-3.13. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.