Install and Build

Use a compatible PyTorch or LibTorch installation. Native extensions must match the PyTorch major/minor, C++ ABI and platform toolchain. CUDA execution also requires a compatible CUDA build and NVIDIA driver.

Python Source Installation

These examples target TorchPairwise 1.0. Install PyTorch for your platform first, then build this checkout against that installation:

git clone https://github.com/inspiros/torchpairwise.git
cd torchpairwise
python -m pip install -e . --no-build-isolation

The build detects CUDA when a compatible toolkit is available. See the source build guide for CPU-only builds, toolchain options and matching architectures.

Standalone C++

Build and install the CMake package against your LibTorch prefix:

cmake -S . -B build/native \
  -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_PREFIX_PATH=/path/to/libtorch \
  -DCMAKE_INSTALL_PREFIX=/path/to/torchpairwise-install
cmake --build build/native --parallel 2
cmake --install build/native

For specialized CUDA kernels, enable TORCHPAIRWISE_WITH_CUDA=ON and choose compatible CUDA architectures. A build linked to CUDA LibTorch inherits its CUDA dependencies even if custom kernels are omitted. See the CMake guide.

Consume the exported target:

find_package(torchpairwise CONFIG REQUIRED)
add_executable(my_app main.cpp)
target_link_libraries(my_app PRIVATE torchpairwise::torchpairwise)

Include <torchpairwise.h>. The installed target carries native headers, C++20 and LibTorch linkage. Arrange runtime library search paths for both LibTorch and torchpairwise. No Python runtime is needed for built-in C++ execution.