..
  # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  # SPDX-License-Identifier: Apache-2.0
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.

.. _v0.14.0-beta:

v0.14.0-beta
============

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

Python x86_64 wheels are now available on PyPi, `cvcuda-cu11 <https://pypi.org/project/cvcuda-cu11/>`_ and `cvcuda-cu12 <https://pypi.org/project/cvcuda-cu12/>`_ for CUDA 11 and CUDA 12, respectively ::

   pip install cvcuda-cu<CUDA_VERSION>


CV-CUDA v0.14.0 includes the following changes:​

* **New Features**:​

  * Added support for SBSA ARM/Grace cuda 12, including ManyLinux2014-compliant Python wheel generation

    * We do not provide SBSA-compatible aarch64_cu11 packages yet, this will be addressed in an upcoming release

    * aarch64_cu12 packages distributed on Github and Pypi are the SBSA-compatible ones. Jetson builds can be found in explicitly named "Jetson" archives in Github release assets.

  * Added support for compiling NVCV on QNX

  * Added support for VYUY and YUV8p formats in NVCV

  * Improved test coverage for NVCV and operators

  * Minor corrections to documentation

* **Bug Fixes**:​

  * Made Python cache thread-local to avoid race conditions and potential crashes in Python gilless multithreaded setups


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

* Starting with v0.14, aarch64_cu12 packages (deb, tar.xz or wheels) distributed on Github (release "assets") and Pypi are the SBSA-compatible ones. Jetson builds (deb, tar.xz, whl) can be found in explicitly named "Jetson" archives in Github release assets.

* We do not provide SBSA-compatible aarch64_cu11 packages yet, this will be addressed in an upcoming release.

* Only x86_64 wheels are available on PyPi as of 02/28/25. SBSA/Grace CUDA 12 wheels will be added shortly.

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.