CV-CUDA

CV-CUDA is an open-source project that enables building efficient cloud-scale Artificial Intelligence (AI) imaging and computer vision (CV) applications. It uses 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 avaliable as of release v0.2.0-alpha.

Getting Started

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

Pre-requisites

• Linux distro:
  • Ubuntu x86_64 >= 18.04
  • WSL2 with Ubuntu >= 20.04 (tested with 20.04)
• CUDA Driver >= 11.7 (Not tested on 12.0)
• GCC >= 11.0
• Python >= 3.7
• cmake >= 3.22

Installation

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-0.2.0-cuda11-x86_64-linux.tar.xz

DEB File Installation

sudo dpkg -i nvcv-lib-0.2.0-cuda11-x86_64-linux.deb

Python WHL File Installation

pip install nvcv_python-0.2.0-cp38-cp38-linux_x86_64.whl

Build from Source

Follow these instruction to successfully build CV-CUDA from source:

1. Build CV-CUDA

   cd ~/cvcuda
   ci/build.sh
   

   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...) in build-rel/bin.

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

   ci/build.sh [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.

2. Build Documentation

   ci/build_docs.sh [build folder]
   

   Example: `ci/build_docs.sh build

3. Build Samples

   ./ci/build_samples.sh [build folder]
   

   (For instructions on how to compile samples outside of the CV-CUDA project, see the Samples documentation)

4. Run 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

   build-rel/bin/run_tests.sh
   

5. Run Samples

   The samples are installed in <buildtree>/bin. You can run the script below to download and serialize the model and run the sample with the test data provided.

   ./ci/run_samples.sh
   

6. Package installers

   From a succesfully built project, installers can be generated using cpack:

   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:

   cmake . -G [DEB|TXZ]
   

   • DEB for Debian packages
   • TXZ for *.tar.xz tarballs.

Contributing

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 v0.2.0-alpha, 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.

License

CV-CUDA operates under the Apache-2.0 license.

Security

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

Acknowledgements

CV-CUDA is developed jointly by NVIDIA and ByteDance.