Frequently asked questions

“ImportError: dynamic module does not define init function”

1. Make sure that the name specified in PYBIND11_MODULE is identical to the filename of the extension library (without suffixes such as .so).

2. If the above did not fix the issue, you are likely using an incompatible version of Python that does not match what you compiled with.

“Symbol not found: __Py_ZeroStruct / _PyInstanceMethod_Type”

See the first answer.

“SystemError: dynamic module not initialized properly”

See the first answer.

The Python interpreter immediately crashes when importing my module

See the first answer.

Limitations involving reference arguments

In C++, it’s fairly common to pass arguments using mutable references or mutable pointers, which allows both read and write access to the value supplied by the caller. This is sometimes done for efficiency reasons, or to realize functions that have multiple return values. Here are two very basic examples:

void increment(int &i) { i++; }
void increment_ptr(int *i) { (*i)++; }

In Python, all arguments are passed by reference, so there is no general issue in binding such code from Python.

However, certain basic Python types (like str, int, bool, float, etc.) are immutable. This means that the following attempt to port the function to Python doesn’t have the same effect on the value provided by the caller – in fact, it does nothing at all.

def increment(i):
    i += 1  # nope..

pybind11 is also affected by such language-level conventions, which means that binding increment or increment_ptr will also create Python functions that don’t modify their arguments.

Although inconvenient, one workaround is to encapsulate the immutable types in a custom type that does allow modifications.

An other alternative involves binding a small wrapper lambda function that returns a tuple with all output arguments (see the remainder of the documentation for examples on binding lambda functions). An example:

int foo(int &i) { i++; return 123; }

and the binding code

m.def("foo", [](int i) { int rv = foo(i); return std::make_tuple(rv, i); });

How can I reduce the build time?

It’s good practice to split binding code over multiple files, as in the following example:

example.cpp:

void init_ex1(py::module_ &);
void init_ex2(py::module_ &);
/* ... */

PYBIND11_MODULE(example, m) {
    init_ex1(m);
    init_ex2(m);
    /* ... */
}

ex1.cpp:

void init_ex1(py::module_ &m) {
    m.def("add", [](int a, int b) { return a + b; });
}

ex2.cpp:

void init_ex2(py::module_ &m) {
    m.def("sub", [](int a, int b) { return a - b; });
}

python:

>>> import example
>>> example.add(1, 2)
3
>>> example.sub(1, 1)
0

As shown above, the various init_ex functions should be contained in separate files that can be compiled independently from one another, and then linked together into the same final shared object. Following this approach will:

  1. reduce memory requirements per compilation unit.

  2. enable parallel builds (if desired).

  3. allow for faster incremental builds. For instance, when a single class definition is changed, only a subset of the binding code will generally need to be recompiled.

“recursive template instantiation exceeded maximum depth of 256”

If you receive an error about excessive recursive template evaluation, try specifying a larger value, e.g. -ftemplate-depth=1024 on GCC/Clang. The culprit is generally the generation of function signatures at compile time using C++14 template metaprogramming.

“‘SomeClass’ declared with greater visibility than the type of its field ‘SomeClass::member’ [-Wattributes]”

This error typically indicates that you are compiling without the required -fvisibility flag. pybind11 code internally forces hidden visibility on all internal code, but if non-hidden (and thus exported) code attempts to include a pybind type (for example, py::object or py::list) you can run into this warning.

To avoid it, make sure you are specifying -fvisibility=hidden when compiling pybind code.

As to why -fvisibility=hidden is necessary, because pybind modules could have been compiled under different versions of pybind itself, it is also important that the symbols defined in one module do not clash with the potentially-incompatible symbols defined in another. While Python extension modules are usually loaded with localized symbols (under POSIX systems typically using dlopen with the RTLD_LOCAL flag), this Python default can be changed, but even if it isn’t it is not always enough to guarantee complete independence of the symbols involved when not using -fvisibility=hidden.

Additionally, -fvisibility=hidden can deliver considerably binary size savings. (See the following section for more details.)

How can I create smaller binaries?

To do its job, pybind11 extensively relies on a programming technique known as template metaprogramming, which is a way of performing computation at compile time using type information. Template metaprogramming usually instantiates code involving significant numbers of deeply nested types that are either completely removed or reduced to just a few instructions during the compiler’s optimization phase. However, due to the nested nature of these types, the resulting symbol names in the compiled extension library can be extremely long. For instance, the included test suite contains the following symbol:

_​_​Z​N​8​p​y​b​i​n​d​1​1​1​2​c​p​p​_​f​u​n​c​t​i​o​n​C​1​I​v​8​E​x​a​m​p​l​e​2​J​R​N​S​t​3​_​_​1​6​v​e​c​t​o​r​I​N​S​3​_​1​2​b​a​s​i​c​_​s​t​r​i​n​g​I​w​N​S​3​_​1​1​c​h​a​r​_​t​r​a​i​t​s​I​w​E​E​N​S​3​_​9​a​l​l​o​c​a​t​o​r​I​w​E​E​E​E​N​S​8​_​I​S​A​_​E​E​E​E​E​J​N​S​_​4​n​a​m​e​E​N​S​_​7​s​i​b​l​i​n​g​E​N​S​_​9​i​s​_​m​e​t​h​o​d​E​A​2​8​_​c​E​E​E​M​T​0​_​F​T​_​D​p​T​1​_​E​D​p​R​K​T​2​_

which is the mangled form of the following function type:

pybind11::cpp_function::cpp_function<void, Example2, std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&, pybind11::name, pybind11::sibling, pybind11::is_method, char [28]>(void (Example2::*)(std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&), pybind11::name const&, pybind11::sibling const&, pybind11::is_method const&, char const (&) [28])

The memory needed to store just the mangled name of this function (196 bytes) is larger than the actual piece of code (111 bytes) it represents! On the other hand, it’s silly to even give this function a name – after all, it’s just a tiny cog in a bigger piece of machinery that is not exposed to the outside world. So we’ll generally only want to export symbols for those functions which are actually called from the outside.

This can be achieved by specifying the parameter -fvisibility=hidden to GCC and Clang, which sets the default symbol visibility to hidden, which has a tremendous impact on the final binary size of the resulting extension library. (On Visual Studio, symbols are already hidden by default, so nothing needs to be done there.)

In addition to decreasing binary size, -fvisibility=hidden also avoids potential serious issues when loading multiple modules and is required for proper pybind operation. See the previous FAQ entry for more details.

How can I properly handle Ctrl-C in long-running functions?

Ctrl-C is received by the Python interpreter, and holds it until the GIL is released, so a long-running function won’t be interrupted.

To interrupt from inside your function, you can use the PyErr_CheckSignals() function, that will tell if a signal has been raised on the Python side. This function merely checks a flag, so its impact is negligible. When a signal has been received, you must either explicitly interrupt execution by throwing py::error_already_set (which will propagate the existing KeyboardInterrupt), or clear the error (which you usually will not want):

PYBIND11_MODULE(example, m)
{
    m.def("long running_func", []()
    {
        for (;;) {
            if (PyErr_CheckSignals() != 0)
                throw py::error_already_set();
            // Long running iteration
        }
    });
}

CMake doesn’t detect the right Python version

The CMake-based build system will try to automatically detect the installed version of Python and link against that. When this fails, or when there are multiple versions of Python and it finds the wrong one, delete CMakeCache.txt and then add -DPYTHON_EXECUTABLE=$(which python) to your CMake configure line. (Replace $(which python) with a path to python if your prefer.)

You can alternatively try -DPYBIND11_FINDPYTHON=ON, which will activate the new CMake FindPython support instead of pybind11’s custom search. Requires CMake 3.12+, and 3.15+ or 3.18.2+ are even better. You can set this in your CMakeLists.txt before adding or finding pybind11, as well.

Inconsistent detection of Python version in CMake and pybind11

The functions find_package(PythonInterp) and find_package(PythonLibs) provided by CMake for Python version detection are modified by pybind11 due to unreliability and limitations that make them unsuitable for pybind11’s needs. Instead pybind11 provides its own, more reliable Python detection CMake code. Conflicts can arise, however, when using pybind11 in a project that also uses the CMake Python detection in a system with several Python versions installed.

This difference may cause inconsistencies and errors if both mechanisms are used in the same project.

There are three possible solutions:

  1. Avoid using find_package(PythonInterp) and find_package(PythonLibs) from CMake and rely on pybind11 in detecting Python version. If this is not possible, the CMake machinery should be called before including pybind11.

  2. Set PYBIND11_FINDPYTHON to True or use find_package(Python COMPONENTS Interpreter Development) on modern CMake (3.12+, 3.15+ better, 3.18.2+ best). Pybind11 in these cases uses the new CMake FindPython instead of the old, deprecated search tools, and these modules are much better at finding the correct Python.

  3. Set PYBIND11_NOPYTHON to TRUE. Pybind11 will not search for Python. However, you will have to use the target-based system, and do more setup yourself, because it does not know about or include things that depend on Python, like pybind11_add_module. This might be ideal for integrating into an existing system, like scikit-build’s Python helpers.

How to cite this project?

We suggest the following BibTeX template to cite pybind11 in scientific discourse:

@misc{pybind11,
   author = {Wenzel Jakob and Jason Rhinelander and Dean Moldovan},
   year = {2017},
   note = {https://github.com/pybind/pybind11},
   title = {pybind11 -- Seamless operability between C++11 and Python}
}