Konstantin Bespalov
How to implement convenient Python bindings to C++
#1about 4 minutes
The business case for Python bindings to a C++ library
A C++ financial pricing library required a Python interface for quantitative analysts to perform research and development.
#2about 2 minutes
Evaluating an initial approach using .NET integration
The first attempt used a .NET assembly with pythonnet, but it suffered from poor performance and a lack of IDE support.
#3about 4 minutes
Choosing pybind11 for direct C++ to Python bindings
Pybind11 was selected over the Python/C API for its ability to minimize boilerplate code when creating direct C++ bindings.
#4about 2 minutes
Identifying usability gaps in the initial pybind11 bindings
The initial pybind11 implementation still lacked crucial features like type hints, Pythonic collections, and pickling support for multiprocessing.
#5about 2 minutes
Adding IDE support and type hints with stub files
PEP 484 and .pyi stub files provide a type interface for the C++ extension module, enabling autocompletion and navigation in IDEs.
#6about 2 minutes
Creating a Pythonic API with variants and magic methods
Using std::variant simplifies function arguments and implementing magic methods like __iter__ makes C++ collections behave like native Python ones.
#7about 1 minute
Enabling multiprocessing with custom pickling support
The copy_reg module is used to register custom serialization and deserialization functions, making C++ objects picklable for multiprocessing.
#8about 1 minute
Summarizing the solution and using code generation
The final solution combines pybind11 with several enhancements, and a code generator is recommended to maintain the bindings for large projects.
#9about 2 minutes
Q&A on exceptions, SWIG, and code generation tooling
The speaker answers audience questions about propagating C++ exceptions, comparing pybind11 to SWIG, and the specific code generator used.
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