Hybrid C++/Python Neural Network Inference Engine

prerequisites

• C++17 compatible compiler (GCC 7+, Clang 5+, MSVC 2017+)
• Python 3.6+ with NumPy
• CMake 3.12+
• pybind11 (automatically fetched during build)

installation

1. clone this repo

git clone <repository-url>
cd pycpp-eng

3. steps to build the project

# Make scripts executable
chmod +x build.sh run.sh setup.sh

# builds the engine
./build.sh

usage

run script

# Run the inference engine
./run.sh

configuration

The project uses CMake with the following default settings:

• C++ Standard: C++17
• Build Type: Release (with -O3 optimization)
• Python: Auto-detected Python 3.x with NumPy
• pybind11: Automatically fetched from GitHub

env values

# Set Python path for model loading
export PYTHONPATH="${PYTHONPATH}:$(pwd)/models:$(pwd)"

# Optional: Set specific Python interpreter
export PYTHON_EXECUTABLE=/path/to/python

testing

# Run the main test suite
./run.sh

# Run with debug information
cd build
cmake .. -DCMAKE_BUILD_TYPE=Debug
make
./hybrid_inference

test cases

1. MNIST-like Classification: Tests SimpleNN with 784-dimensional input
2. Binary Classification: Tests BinaryClassifier with 100-dimensional input
3. Performance Comparison: Compares C++ vs Python operations
4. Direct C++ Operations: Tests standalone C++ tensor operations

contributing

1. fork the repository
2. create a feature branch
3. commit your changes
4. push to the branch
5. open a PR

development Setup

# Clone and setup
git clone <repository-url>
cd pycpp-eng
./setup.sh

# Build in debug mode
cd build
cmake .. -DCMAKE_BUILD_TYPE=Debug
make

# Run tests
./hybrid_inference

output

License

This project is licensed under the MIT License - see the LICENSE file for details.