pybind11 on GitHub (pybind/pybind11)

pybind11 README

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pybind11 (v3) — Seamless interoperability between C++ and Python

Setuptools example <https://github.com/pybind/python_example>_ • Scikit-build example <https://github.com/pybind/scikit_build_example>_ • CMake example <https://github.com/pybind/cmake_example>_

.. start

pybind11 is a lightweight header-only library that exposes C++ types in Python and vice versa, mainly to create Python bindings of existing C++ code. Its goals and syntax are similar to the excellent Boost.Python <http://www.boost.org/doc/libs/1_58_0/libs/python/doc/>_ library by David Abrahams: to minimize boilerplate code in traditional extension modules by inferring type information using compile-time introspection.

The main issue with Boost.Python—and the reason for creating such a similar project—is Boost. Boost is an enormously large and complex suite of utility libraries that works with almost every C++ compiler in existence. This compatibility has its cost: arcane template tricks and workarounds are necessary to support the oldest and buggiest of compiler specimens. Now that C++11-compatible compilers are widely available, this heavy machinery has become an excessively large and unnecessary dependency.

Think of this library as a tiny self-contained version of Boost.Python with everything stripped away that isn't relevant for binding generation. Without comments, the core header files only require ~4K lines of code and depend on Python (CPython 3.9+, PyPy, or GraalPy) and the C++ standard library. This compact implementation was possible thanks to some C++11 language features (specifically: tuples, lambda functions and variadic templates). Since its creation, this library has grown beyond Boost.Python in many ways, leading to dramatically simpler binding code in many common situations.

Tutorial and reference documentation is provided at pybind11.readthedocs.io <https://pybind11.readthedocs.io/en/latest>. A PDF version of the manual is available here <https://pybind11.readthedocs.io/_/downloads/en/latest/pdf/>. And the source code is always available at github.com/pybind/pybind11 <https://github.com/pybind/pybind11>_.

Core features

pybind11 can map the following core C++ features to Python:

Functions accepting and returning custom data structures per value, reference, or pointer
Instance methods and static methods
Overloaded functions
Instance attributes and static attributes
Arbitrary exception types
Enumerations
Callbacks
Iterators and ranges
Custom operators
Single and multiple inheritance
STL data structures
Smart pointers with reference counting like std::shared_ptr
Internal references with correct reference counting
C++ classes with virtual (and pure virtual) methods can be extended in Python
Integrated NumPy support (NumPy 2 requires pybind11 2.12+)

Goodies

In addition to the core functionality, pybind11 provides some extra goodies:

CPython 3.9+, PyPy3 7.3.17+, and GraalPy 24.1+ are supported with an implementation-agnostic interface (see older versions for older CPython and PyPy versions).
It is possible to bind C++11 lambda functions with captured variables. The lambda capture data is stored inside the resulting Python function object.
pybind11 uses C++11 move constructors and move assignment operators whenever possible to efficiently transfer custom data types.
It's easy to expose the internal storage of custom data types through Pythons' buffer protocols. This is handy e.g. for fast conversion between C++ matrix classes like Eigen and NumPy without expensive copy operations.
pybind11 can automatically vectorize functions so that they are transparently applied to all entries of one or more NumPy array arguments.
Python's slice-based access and assignment operations can be supported with just a few lines of code.
Everything is contained in just a few header files; there is no need to link against any additional libraries.
Binaries are generally smaller by a factor of at least 2 compared to equivalent bindings generated by Boost.Python. A recent pybind11 conversion of PyRosetta, an enormous Boost.Python binding project, reported <https://graylab.jhu.edu/Sergey/2016.RosettaCon/PyRosetta-4.pdf>_ a binary size reduction of 5.4x and compile time reduction by 5.8x.
Function signatures are precomputed at compile time (using constexpr), leading to smaller binaries.
With little extra effort, C++ types can be pickled and unpickled similar to regular Python objects.

Supported platforms & compilers

pybind11 is exercised in continuous integration across a range of operating systems, Python versions, C++ standards, and toolchains. For an up-to-date view of the combinations we currently test, please see the pybind11 GitHub Actions <https://github.com/pybind/pybind11/actions?query=branch%3Amaster>_ and AppVeyor <https://ci.appveyor.com/project/wjakob/pybind11>_ logs.

The test matrix naturally evolves over time as older platforms and compilers fall out of use and new ones are added by the community. Closely related versions of a tested compiler or platform will often work as well in practice, but we cannot promise to validate every possible combination. If a configuration you rely on is missing from the matrix or regresses, issues and pull requests to extend coverage are very welcome. At the same time, we need to balance the size of the test matrix with the available CI resources, such as GitHub's limits on concurrent jobs under the free tier.

About

This project was created by Wenzel Jakob <http://rgl.epfl.ch/people/wjakob>_. Significant features and/or improvements to the code were contributed by Jonas Adler, Lori A. Burns, Sylvain Corlay, Eric Cousineau, Aaron Gokaslan, Ralf Grosse-Kunstleve, Trent Houliston, Axel Huebl, @hulucc, Yannick Jadoul, Sergey Lyskov, Johan Mabille, Tomasz Miąsko, Dean Moldovan, Ben Pritchard, Jason Rhinelander, Boris Schäling, Pim Schellart, Henry Schreiner, Ivan Smirnov, Dustin Spicuzza, Boris Staletic, Ethan Steinberg, Patrick Stewart, Ivor Wanders, and Xiaofei Wang.

We thank Google for a generous financial contribution to the continuous integration infrastructure used by this project.

Contributing


See the `contributing
guide <https://github.com/pybind/pybind11/blob/master/.github/CONTRIBUTING.md>`_
for information on building and contributing to pybind11.

License
~~~~~~~

pybind11 is provided under a BSD-style license that can be found in the
`LICENSE <https://github.com/pybind/pybind11/blob/master/LICENSE>`_
file. By using, distributing, or contributing to this project, you agree
to the terms and conditions of this license.

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Pybind11

What is this?

pybind11 is a lightweight header-only C++ library that exposes C++ types in Python and vice versa — mainly to create Python bindings for existing C++ code.

Is this a library I link against at runtime, or something else?

It's header-only: your build just includes a few C++ headers, and there is no additional library to link against.

How do I add it to a C++ project?

Include the headers and wire them into your build — the README links setuptools, scikit-build and CMake example setups, and the tutorial and reference docs live at pybind11.readthedocs.io.

What do I need installed before I can use it?

Just a C++11-compatible compiler and a Python interpreter: the core is ~4K lines of headers depending only on Python (CPython 3.9+, PyPy, or GraalPy) and the C++ standard library — no Boost.

Will it work with my Python interpreter and version?

CPython 3.9+, PyPy3 7.3.17+ and GraalPy 24.1+ are supported through one implementation-agnostic interface; older interpreters need older pybind11 releases.

Which platforms and compilers are actually tested?

Continuous integration exercises a range of operating systems, Python versions, C++ standards and toolchains; closely related combos usually work too but are not promised.

What can I use it under?

A permissive BSD-style license — the exact terms are in the LICENSE file in the repo.

How is it different from Boost.Python?

It was created as a tiny self-contained alternative to Boost.Python that drops the enormous Boost dependency. It has since grown beyond it in many ways: binaries are generally at least 2x smaller — a PyRosetta conversion reported 5.4x smaller binaries and 5.8x faster compiles — and common bindings need much less code.

What C++ features can I map to Python?

Functions and methods (instance, static, overloaded), custom types by value, reference or pointer, attributes, arbitrary exception types, enums, callbacks, iterators, custom operators, single and multiple inheritance, STL data structures, and smart pointers like std::shared_ptr.

How does it handle NumPy arrays?

NumPy support is integrated: functions can be automatically vectorized to run over every entry of an array argument, and buffer protocols allow fast conversion between C++ matrix classes like Eigen and NumPy without expensive copies.

Can Python code subclass my C++ classes?

Yes — C++ classes with virtual (and pure virtual) methods can be extended in Python.

What are similar projects?

spdlog, cpp-httplib, rapidjson, MyTinySTL, taskflow, scipy, cython, seaborn

Which topics is it in?

ffi, cpp11, header-only, cmake, numpy, python-library, bsd, cpp, c++, developers, library, python

Creator: pybind
Size: 13.4 MB
Stars: 18K
Updated: CommitOct 17 Releasev3.1.0Aug 06
Age: 11y
Commits · 90 Days

pybind11 README

Core features

Goodies

Supported platforms & compilers

About

pybind11 (pybind/pybind11)