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.. _v0.13.0-beta:

v0.13.0-beta
============

Release Highlights
------------------

CV-CUDA v0.13.0 includes ManyLinux 2014 compliant wheels alongside the following changes:​

* **New Features**:​

  * Added Python wheel generation compliant with ManyLinux 2014 and PyPI standards.

    * The multiple python version wheels are now unified into a single wheel file per CUDA version​.

    * Included scripts to build two ManyLinux 2014 Docker images (CUDA 11, CUDA 12) for build, and four Ubuntu images (20.04 and 22.04 x CUDA 11, CUDA 12) for testing.

    * Python wheels must be built within the ManyLinux 2014 docker images to guarantee ManyLinux2014 compliance.

* **Bug Fixes**:​

  * Upgraded pybind11 to version 2.13.6 for improved compatibility and functionality.​

    * Resolved Python ABI compatibility issues present in previous versions by upgrading pybind11 reported in previous versions.​


Compatibility and Known Limitations
-----------------------------------

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.