intro.rst revision 12037:d28054ac6ec9
1.. image:: pybind11-logo.png 2 3About this project 4================== 5**pybind11** is a lightweight header-only library that exposes C++ types in Python 6and vice versa, mainly to create Python bindings of existing C++ code. Its 7goals and syntax are similar to the excellent `Boost.Python`_ library by David 8Abrahams: to minimize boilerplate code in traditional extension modules by 9inferring type information using compile-time introspection. 10 11.. _Boost.Python: http://www.boost.org/doc/libs/release/libs/python/doc/index.html 12 13The main issue with Boost.Python—and the reason for creating such a similar 14project—is Boost. Boost is an enormously large and complex suite of utility 15libraries that works with almost every C++ compiler in existence. This 16compatibility has its cost: arcane template tricks and workarounds are 17necessary to support the oldest and buggiest of compiler specimens. Now that 18C++11-compatible compilers are widely available, this heavy machinery has 19become an excessively large and unnecessary dependency. 20Think of this library as a tiny self-contained version of Boost.Python with 21everything stripped away that isn't relevant for binding generation. Without 22comments, the core header files only require ~4K lines of code and depend on 23Python (2.7 or 3.x, or PyPy2.7 >= 5.7) and the C++ standard library. This 24compact implementation was possible thanks to some of the new C++11 language 25features (specifically: tuples, lambda functions and variadic templates). Since 26its creation, this library has grown beyond Boost.Python in many ways, leading 27to dramatically simpler binding code in many common situations. 28 29Core features 30************* 31The following core C++ features can be mapped to Python 32 33- Functions accepting and returning custom data structures per value, reference, or pointer 34- Instance methods and static methods 35- Overloaded functions 36- Instance attributes and static attributes 37- Arbitrary exception types 38- Enumerations 39- Callbacks 40- Iterators and ranges 41- Custom operators 42- Single and multiple inheritance 43- STL data structures 44- Iterators and ranges 45- Smart pointers with reference counting like ``std::shared_ptr`` 46- Internal references with correct reference counting 47- C++ classes with virtual (and pure virtual) methods can be extended in Python 48 49Goodies 50******* 51In addition to the core functionality, pybind11 provides some extra goodies: 52 53- Python 2.7, 3.x, and PyPy (PyPy2.7 >= 5.7) are supported with an 54 implementation-agnostic interface. 55 56- It is possible to bind C++11 lambda functions with captured variables. The 57 lambda capture data is stored inside the resulting Python function object. 58 59- pybind11 uses C++11 move constructors and move assignment operators whenever 60 possible to efficiently transfer custom data types. 61 62- It's easy to expose the internal storage of custom data types through 63 Pythons' buffer protocols. This is handy e.g. for fast conversion between 64 C++ matrix classes like Eigen and NumPy without expensive copy operations. 65 66- pybind11 can automatically vectorize functions so that they are transparently 67 applied to all entries of one or more NumPy array arguments. 68 69- Python's slice-based access and assignment operations can be supported with 70 just a few lines of code. 71 72- Everything is contained in just a few header files; there is no need to link 73 against any additional libraries. 74 75- Binaries are generally smaller by a factor of at least 2 compared to 76 equivalent bindings generated by Boost.Python. A recent pybind11 conversion 77 of `PyRosetta`_, an enormous Boost.Python binding project, reported a binary 78 size reduction of **5.4x** and compile time reduction by **5.8x**. 79 80- When supported by the compiler, two new C++14 features (relaxed constexpr and 81 return value deduction) are used to precompute function signatures at compile 82 time, leading to smaller binaries. 83 84- With little extra effort, C++ types can be pickled and unpickled similar to 85 regular Python objects. 86 87.. _PyRosetta: http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf 88 89Supported compilers 90******************* 91 921. Clang/LLVM (any non-ancient version with C++11 support) 932. GCC 4.8 or newer 943. Microsoft Visual Studio 2015 or newer 954. Intel C++ compiler v15 or newer 96