slsDetectorPackage/libs/pybind/docs/advanced/embedding.rst

Embedding the interpreter

While pybind11 is mainly focused on extending Python using C++, it's also possible to do the reverse: embed the Python interpreter into a C++ program. All of the other documentation pages still apply here, so refer to them for general pybind11 usage. This section will cover a few extra things required for embedding.

Getting started

A basic executable with an embedded interpreter can be created with just a few lines of CMake and the pybind11::embed target, as shown below. For more information, see /compiling.

cmake_minimum_required(VERSION 3.4)
project(example)

find_package(pybind11 REQUIRED)  # or `add_subdirectory(pybind11)`

add_executable(example main.cpp)
target_link_libraries(example PRIVATE pybind11::embed)

The essential structure of the main.cpp file looks like this:

#include <pybind11/embed.h> // everything needed for embedding
namespace py = pybind11;

int main() {
    py::scoped_interpreter guard{}; // start the interpreter and keep it alive

    py::print("Hello, World!"); // use the Python API
}

The interpreter must be initialized before using any Python API, which includes all the functions and classes in pybind11. The RAII guard class scoped_interpreter takes care of the interpreter lifetime. After the guard is destroyed, the interpreter shuts down and clears its memory. No Python functions can be called after this.

Executing Python code

There are a few different ways to run Python code. One option is to use eval, exec or eval_file, as explained in eval. Here is a quick example in the context of an executable with an embedded interpreter:

#include <pybind11/embed.h>
namespace py = pybind11;

int main() {
    py::scoped_interpreter guard{};

    py::exec(R"(
        kwargs = dict(name="World", number=42)
        message = "Hello, {name}! The answer is {number}".format(**kwargs)
        print(message)
    )");
}

Alternatively, similar results can be achieved using pybind11's API (see /advanced/pycpp/index for more details).

#include <pybind11/embed.h>
namespace py = pybind11;
using namespace py::literals;

int main() {
    py::scoped_interpreter guard{};

    auto kwargs = py::dict("name"_a="World", "number"_a=42);
    auto message = "Hello, {name}! The answer is {number}"_s.format(**kwargs);
    py::print(message);
}

The two approaches can also be combined:

#include <pybind11/embed.h>
#include <iostream>

namespace py = pybind11;
using namespace py::literals;

int main() {
    py::scoped_interpreter guard{};

    auto locals = py::dict("name"_a="World", "number"_a=42);
    py::exec(R"(
        message = "Hello, {name}! The answer is {number}".format(**locals())
    )", py::globals(), locals);

    auto message = locals["message"].cast<std::string>();
    std::cout << message;
}

Importing modules

Python modules can be imported using module_::import():

py::module_ sys = py::module_::import("sys");
py::print(sys.attr("path"));

For convenience, the current working directory is included in sys.path when embedding the interpreter. This makes it easy to import local Python files:

"""calc.py located in the working directory"""


def add(i, j):
    return i + j

py::module_ calc = py::module_::import("calc");
py::object result = calc.attr("add")(1, 2);
int n = result.cast<int>();
assert(n == 3);

Modules can be reloaded using module_::reload() if the source is modified e.g. by an external process. This can be useful in scenarios where the application imports a user defined data processing script which needs to be updated after changes by the user. Note that this function does not reload modules recursively.

Adding embedded modules

Embedded binary modules can be added using the PYBIND11_EMBEDDED_MODULE macro. Note that the definition must be placed at global scope. They can be imported like any other module.

#include <pybind11/embed.h>
namespace py = pybind11;

PYBIND11_EMBEDDED_MODULE(fast_calc, m) {
    // `m` is a `py::module_` which is used to bind functions and classes
    m.def("add", [](int i, int j) {
        return i + j;
    });
}

int main() {
    py::scoped_interpreter guard{};

    auto fast_calc = py::module_::import("fast_calc");
    auto result = fast_calc.attr("add")(1, 2).cast<int>();
    assert(result == 3);
}

Unlike extension modules where only a single binary module can be created, on the embedded side an unlimited number of modules can be added using multiple PYBIND11_EMBEDDED_MODULE definitions (as long as they have unique names).

These modules are added to Python's list of builtins, so they can also be imported in pure Python files loaded by the interpreter. Everything interacts naturally:

"""py_module.py located in the working directory"""
import cpp_module

a = cpp_module.a
b = a + 1

#include <pybind11/embed.h>
namespace py = pybind11;

PYBIND11_EMBEDDED_MODULE(cpp_module, m) {
    m.attr("a") = 1;
}

int main() {
    py::scoped_interpreter guard{};

    auto py_module = py::module_::import("py_module");

    auto locals = py::dict("fmt"_a="{} + {} = {}", **py_module.attr("__dict__"));
    assert(locals["a"].cast<int>() == 1);
    assert(locals["b"].cast<int>() == 2);

    py::exec(R"(
        c = a + b
        message = fmt.format(a, b, c)
    )", py::globals(), locals);

    assert(locals["c"].cast<int>() == 3);
    assert(locals["message"].cast<std::string>() == "1 + 2 = 3");
}

Interpreter lifetime

The Python interpreter shuts down when scoped_interpreter is destroyed. After this, creating a new instance will restart the interpreter. Alternatively, the initialize_interpreter / finalize_interpreter pair of functions can be used to directly set the state at any time.

Modules created with pybind11 can be safely re-initialized after the interpreter has been restarted. However, this may not apply to third-party extension modules. The issue is that Python itself cannot completely unload extension modules and there are several caveats with regard to interpreter restarting. In short, not all memory may be freed, either due to Python reference cycles or user-created global data. All the details can be found in the CPython documentation.

Warning

Creating two concurrent scoped_interpreter guards is a fatal error. So is calling initialize_interpreter for a second time after the interpreter has already been initialized.

Do not use the raw CPython API functions Py_Initialize and Py_Finalize as these do not properly handle the lifetime of pybind11's internal data.

Sub-interpreter support

Creating multiple copies of scoped_interpreter is not possible because it represents the main Python interpreter. Sub-interpreters are something different and they do permit the existence of multiple interpreters. This is an advanced feature of the CPython API and should be handled with care. pybind11 does not currently offer a C++ interface for sub-interpreters, so refer to the CPython documentation for all the details regarding this feature.

We'll just mention a couple of caveats the sub-interpreters support in pybind11:

1. Sub-interpreters will not receive independent copies of embedded modules. Instead, these are shared and modifications in one interpreter may be reflected in another.
2. Managing multiple threads, multiple interpreters and the GIL can be challenging and there are several caveats here, even within the pure CPython API (please refer to the Python docs for details). As for pybind11, keep in mind that gil_scoped_release and gil_scoped_acquire do not take sub-interpreters into account.