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.. _v0.11.0-beta:

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 <https://github.com/CVCUDA/CV-CUDA>`_.

License
-------

CV-CUDA is licensed under the `Apache 2.0 <https://github.com/CVCUDA/CV-CUDA/blob/main/LICENSE.md>`_ license.

Resources
---------

1. `CV-CUDA GitHub <https://github.com/CVCUDA/CV-CUDA>`_
2. `CV-CUDA Increasing Throughput and Reducing Costs for AI-Based Computer Vision with CV-CUDA <https://developer.nvidia.com/blog/increasing-throughput-and-reducing-costs-for-computer-vision-with-cv-cuda/>`_
3. `NVIDIA Announces Microsoft, Tencent, Baidu Adopting CV-CUDA for Computer Vision AI <https://blogs.nvidia.com/blog/2023/03/21/cv-cuda-ai-computer-vision/>`_
4. `CV-CUDA helps Tencent Cloud audio and video PaaS platform achieve full-process GPU acceleration for video enhancement AI <https://developer.nvidia.com/zh-cn/blog/cv-cuda-high-performance-image-processing/>`_

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.​
