Python with C/C++ Libraries

Think Different - Dhiraj Patra
3 min readJan 2, 2024

Integrating C/C++ libraries into Python applications can be beneficial in various scenarios:

1. Performance Optimization:

- C/C++ code often executes faster than Python due to its lower-level nature.

- Critical sections of code that require high performance, such as numerical computations or data processing, can be implemented in C/C++ for improved speed.

2. Existing Libraries:

- Reuse existing C/C++ libraries that are well-established, optimized, and tested.

- Many powerful and specialized libraries in fields like scientific computing, machine learning, or image processing are originally written in C/C++. Integrating them into Python allows you to leverage their functionality without rewriting everything in Python.

3. Legacy Code Integration:

- If you have legacy C/C++ code that is still valuable, integrating it into a Python application allows you to modernize your software while preserving existing functionality.

4. System-Level Programming:

- For tasks requiring low-level system interactions, such as hardware access or interfacing with operating system APIs, C/C++ is often more suitable.

5. Embedding Performance-Critical Components:

- Embedding C/C++ code within a Python application can be useful when only certain components need optimization, while the rest of the application remains in Python.

6. Interface with Specific Technologies:

- Interfacing with technologies or libraries that are written in C/C++, such as graphics libraries or specialized hardware drivers.

7. Security and Stability:

- C/C++ code can offer more control over memory management and system resources, which can be crucial for applications requiring high stability and security.

While using C/C++ in Python applications can enhance performance, it also introduces challenges like increased complexity, potential for bugs, and a less straightforward development process. Therefore, the decision to use C/C++ in a Python application should be based on a careful consideration of performance requirements, existing codebase, and the specific needs of the project.

Let’s break down the process of using C/C++ libraries with Pybind11 in a Flask application step by step.

1. Set Up Your Development Environment:

- Make sure you have Python installed.

- Install Flask: `pip install Flask`.

- Install Pybind11: Follow the installation instructions on the [official Pybind11 repository](https://github.com/pybind/pybind11).

2. Write Your C++ Library Using Pybind11:

```cpp

// example.cpp

#include <pybind11/pybind11.h>

int add(int a, int b) {

return a + b;

}

PYBIND11_MODULE(example, m) {

m.def(“add”, &add, “Add two numbers”);

}

```

This is a simple example with a function `add` that adds two numbers.

3. Compile Your C++ Code:

Use a C++ compiler to compile the code into a shared library. For example, using g++:

```bash

g++ -O3 -Wall -shared -std=c++11 -fPIC `python3 -m pybind11 — includes` example.cpp -o example`python3-config — extension-suffix`

```

This will generate a shared library named `example.cpython-<version>-<platform>.so`.

4. Create Flask Application:

```python

# app.py

from flask import Flask, request, jsonify

import example # This is the compiled Pybind11 module

app = Flask(__name__)

@app.route(‘/add’, methods=[‘POST’])

def add_numbers():

data = request.get_json()

result = example.add(data[‘a’], data[‘b’])

return jsonify(result=result)

if __name__ == ‘__main__’:

app.run(debug=True)

```

5. Run the Flask Application:

```bash

python app.py

```

This will start your Flask application.

6. Test Your API:

Use a tool like `curl` or Postman to test your API.

```bash

curl -X POST -H “Content-Type: application/json” -d ‘{“a”: 5, “b”: 10}’ http://localhost:5000/add

```

You should get a response like:

```json

{“result”: 15}

```

This is a basic example, and you might need to adjust it based on your specific use case. The key is to have a solid understanding of how Pybind11 works, compile your C++ code into a shared library, and then integrate it into your Flask application.

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Think Different - Dhiraj Patra

I am a Software architect for AI, ML, IoT microservices cloud applications. Love to learn and share. https://dhirajpatra.github.io