CV-CUDA™ is an open-source, GPU accelerated library for cloud-scale image processing and computer vision.

CVCUDA CVCUDA Last update: May 24, 2023





Cuda GCC Python CMake

CV-CUDA is an open-source project that enables building efficient cloud-scale Artificial Intelligence (AI) imaging and computer vision (CV) applications. It uses graphics processing unit (GPU) acceleration to help developers build highly efficient pre- and post-processing pipelines. CV-CUDA originated as a collaborative effort between NVIDIA and ByteDance.

Refer to our Developer Guide for more information on the operators available as of release v0.3.0-beta.

Getting Started

To get a local copy up and running follow these steps.


  • Linux distro:
  • NVIDIA driver >= 515.43.04+
  • CUDA Toolkit >= 11.7
    • Toolkit >= 11.3 support for core library build and run
  • GCC >= 11.0
    • GCC >= 9.0 supported for core library build and run
  • Python == 3.7, 3.8, 3.10
  • cmake >= 3.22


The following steps describe how to install CV-CUDA from pre-built install packages. Choose the installation method that meets your environment needs.

Tar File Installation

tar -xvf nvcv-lib-0.3.0-cuda11-x86_64-linux.tar.xz
tar -xvf nvcv-dev-0.3.0-cuda11-x86_64-linux.tar.xz

DEB File Installation

sudo apt-get install -y ./nvcv-lib-0.3.0-cuda11-x86_64-linux.deb ./nvcv-dev-0.3.0-cuda11-x86_64-linux.deb

Python WHL File Installation

pip install nvcv_python-0.3.0-cp38-cp38-linux_x86_64.whl

Build from Source

Follow these instruction to build CV-CUDA from source:

  1. Set up your local CV-CUDA repository

    1. Install prerequisites needed to setup up the repository.

      On Ubuntu 22.04, install the following packages:

      • git-lfs: to retrieve binary files from remote repository
      sudo apt-get install -y git git-lfs
    2. After cloning the repository (assuming it was cloned in ~/cvcuda), it needs to be properly configured by running the script only once.

      cd ~/cvcuda
  2. Build CV-CUDA

    1. Install the dependencies required for building CV-CUDA

      On Ubuntu 22.04, install the following packages:

      • g++-11: compiler to be used
      • cmake, ninja-build (optional): manage build rules
      • python3-dev: for python bindings
      • libssl-dev: needed by the testsuite (MD5 hashing utilities)
      sudo apt-get install -y g++-11 cmake ninja-build python3-dev libssl-dev

      For CUDA Toolkit, any version of the 11.x series should work. CV-CUDA was tested with 11.7, thus it should be preferred.

      sudo apt-get install -y cuda-minimal-build-11-7
    2. Build the project


      This will compile a x86 release build of CV-CUDA inside build-rel directory. The library is in build-rel/lib, docs in build-rel/docs and executables (tests, etc...) are in build-rel/bin.

      The script accepts some parameters to control the creation of the build tree:

      ci/ [release|debug] [output build tree path]

      By default it builds for release.

      If output build tree path isn't specified, it'll be build-rel for release builds, and build-deb for debug.

  3. Build Documentation

    1. Install the dependencies required for building the documentation

      On Ubuntu 22.04, install the following packages:

      • doxygen: parse header files for reference documentation
      • python3, python3-pip: to install some python packages needed
      • sphinx, breathe, exhale, recommonmark, graphiviz: to render the documentation
      • sphinx-rtd-theme: documenation theme used
      sudo apt-get install -y doxygen graphviz python3 python3-pip
      sudo python3 -m pip install sphinx==4.5.0 breathe exhale recommonmark graphviz sphinx-rtd-theme
    2. Build the documentation

      ci/ [build folder]

      Example: ci/ build_docs

  4. Build and run Samples

    1. For instructions on how to build samples from source and run them, see the Samples documentation.
  5. Run Tests

    1. Install the dependencies required for running the tests

      On Ubuntu 22.04, install the following packages:

      • python3, python3-pip: to run python bindings tests
      • torch: dependencies needed by python bindings tests
      sudo apt-get install -y python3 python3-pip
      sudo python3 -m pip install pytest torch
    2. Run the tests

      The tests are in <buildtree>/bin. You can run the script below to run all tests at once. Here's an example when build tree is created in build-rel

  6. Package installers

    Installers can be generated using the following cpack command once you have successfully built the project

    cd build-rel
    cpack .

    This will generate in the build directory both Debian installers and tarballs (*.tar.xz), needed for integration in other distros.

    For a fine-grained choice of what installers to generate, the full syntax is:

    cpack . -G [DEB|TXZ]
    • DEB for Debian packages
    • TXZ for *.tar.xz tarballs.


  1. CV-CUDA make operator tool

    This tool will create an noop operator; python bindings, and tests.

    This tool is located in 'tools/mkop'. To run it, navigate to the directory and execute the command './ OperatorName', where 'OperatorName' is the desired name of the operator.


CV-CUDA is an open source project. As part of the Open Source Community, we are committed to the cycle of learning, improving, and updating that makes this community thrive. However, as of release v0.3.0-beta, CV-CUDA is not yet ready for external contributions.

To understand the process for contributing the CV-CUDA, see our Contributing page. To understand our committment to the Open Source Community, and providing an environment that both supports and respects the efforts of all contributors, please read our Code of Conduct.


CV-CUDA operates under the Apache-2.0 license.


CV-CUDA, as a NVIDIA program, is committed to secure development practices. Please read our Security page to learn more.


CV-CUDA is developed jointly by NVIDIA and ByteDance.


Subscribe to our newsletter