v0.11.0-beta

Release Highlights

CV-CUDA v0.11.0 includes critical bug fixes alongside the following changes:​

  • New Features:​

    • Enable NVCV to be built as static library​

    • Improve Python doc generation and structure ​

  • Bug Fixes:​

    • Update pybind11 2.10.0 to 2.13.1. Fixes rare race conditions with Python garbage collector, adds compatibility with numpy2​

Compatibility and Known Limitations

  • Pre-existing limitations:

    • We note a bug in the YUV(420) color conversion API (NVCV_COLOR_RGB2YUV_I420) which incorrectly computes the U and V plane index​

      • This persists through this release and we intend to address this bug in CV-CUDA v0.12.0​

For the full list, see main README on CV-CUDA GitHub.

License

CV-CUDA is licensed under the Apache 2.0 license.

Resources

  1. CV-CUDA GitHub

  2. CV-CUDA Increasing Throughput and Reducing Costs for AI-Based Computer Vision with CV-CUDA

  3. NVIDIA Announces Microsoft, Tencent, Baidu Adopting CV-CUDA for Computer Vision AI

  4. CV-CUDA helps Tencent Cloud audio and video PaaS platform achieve full-process GPU acceleration for video enhancement AI

Acknowledgements

CV-CUDA is developed jointly by NVIDIA and the ByteDance Machine Learning team.

1

These fixes and features add micro-second-level overhead to Python operator calls. Based on the performance analysis of our Python samples, we expect the production- and pipeline-level impact to be negligible. CUDA kernel and C++ call performance is not affected. We aim to investigate and reduce this overhead further in a future release.​