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Installation

tensorrt_cpp_api is a Linux / CUDA 12 / TensorRT ≥ 10 / C++20 library. TensorRT and the CUDA toolkit are system/externally provided — they are not fetched by the build.

Prerequisites

  • CMake ≥ 3.22 and a C++20 compiler (GCC ≥ 11 or Clang ≥ 14).
  • CUDA Toolkit 12.x (nvcc is needed for the preprocessing kernel; the engine core itself only needs the CUDA runtime). CUDA 13 requires driver ≥ 580.
  • TensorRT 10.0 – 11.x, installed one of two ways:
    • apt (NVIDIA CUDA network repo): libnvinfer-dev + libnvonnxparsers-dev. The bundled FindTensorRT module searches the standard /usr/include/x86_64-linux-gnu + /usr/lib/x86_64-linux-gnu layout automatically.
    • tarball: download from NVIDIA and pass -DTensorRT_DIR=/path/to/TensorRT-10.x at configure.
  • Optional: spdlog (-DTRT_CPP_API_WITH_SPDLOG=ON), OpenCV core (-DTRT_CPP_API_WITH_OPENCV=ON), pybind11 + a Python 3.9–3.13 dev environment (for the bindings).

Build options

Option Default Effect
TRT_CPP_API_BUILD_PREPROC ON Build the fused preprocessing sublibrary (::preproc).
TRT_CPP_API_BUILD_TESTS OFF Build the GoogleTest suite (ctest; GPU tests are labeled gpu).
TRT_CPP_API_BUILD_EXAMPLES OFF Build the reference examples in-tree.
TRT_CPP_API_BUILD_PYTHON OFF Build the trtcpp pybind11 extension.
TRT_CPP_API_WITH_OPENCV OFF Build the optional OpenCV interop header/source.
TRT_CPP_API_WITH_SPDLOG OFF Build the optional spdlog logger adapter.
CMAKE_CUDA_ARCHITECTURES 75;80;86;89;90 Override for your target GPUs.

Build & install (C++)

cmake -S . -B build \
  -DCMAKE_BUILD_TYPE=Release \
  -DTRT_CPP_API_BUILD_PREPROC=ON
  # add -DTensorRT_DIR=/opt/TensorRT-10.x for a tarball TensorRT
  # add -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc if nvcc is not on PATH
cmake --build build -j$(nproc)
cmake --install build --prefix /opt/trtcpp

The install lays down include/tensorrt_cpp_api/…, the static libraries, and a CMake package (lib/cmake/tensorrt_cpp_api/) including the bundled FindTensorRT module.

Consume it downstream

find_package(tensorrt_cpp_api REQUIRED)   # set CMAKE_PREFIX_PATH=/opt/trtcpp (and TensorRT_DIR if tarball)
add_executable(myapp main.cpp)
target_link_libraries(myapp PRIVATE
    tensorrt_cpp_api::tensorrt_cpp_api    # core
    tensorrt_cpp_api::preproc)            # optional preprocessing

The package config re-resolves CUDA, TensorRT, Threads (and OpenCV/spdlog if the install was built with them), so the only thing a consumer must provide is the location of a system TensorRT.

Python bindings

pip install .                                    # builds the trtcpp wheel via scikit-build-core
# tarball TensorRT: pip install . --config-settings=cmake.define.TensorRT_DIR=/opt/TensorRT-10.x
python -c "import trtcpp; print(trtcpp.version_string())"

For zero-copy GPU interop install a matching CuPy (pip install cupy-cuda12x); see examples/python.

Verifying

cmake -S . -B build -DTRT_CPP_API_BUILD_TESTS=ON
cmake --build build -j$(nproc)
ctest --test-dir build -LE gpu          # CPU-only tests (no GPU needed)
ctest --test-dir build                  # full suite (needs an NVIDIA GPU)

Troubleshooting

  • Could NOT find CUDA: ... required is exact version "12.0" when building examples/consumers. This comes from a CUDA-enabled OpenCV whose CMake config pins an exact CUDA version that differs from your toolkit. The library core does not use OpenCV; build with the default -DTRT_CPP_API_WITH_OPENCV=OFF, and prefer the stb-based examples (no OpenCV).
  • Failed to detect a default CUDA architecture / nvcc not found. Pass -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc, or put nvcc on PATH.
  • Could NOT find TensorRT. Install libnvinfer-dev/libnvonnxparsers-dev, or pass -DTensorRT_DIR=<tarball-root>. TensorRT must be in the 10.0–11.x range.
  • Stale-engine rebuilds. Changing the ONNX, build options, driver/TensorRT version, or GPU invalidates a cached engine on purpose; delete engineCacheDir to force a clean rebuild.