# v0.16.0-beta ## Release Highlights CV-CUDA v0.16.0 includes the following changes:​ - **New Features and Enhancements**:​ - Added support for Python 3.14​, CUDA 13, GCC-12 to GCC-14 and Blackwell GPU architecture, including Jetson Thor - Improved documentation, samples and framework interoperability examples - Added new multi-architecture (x86_64, aarch64) Docker images for building (ManyLinux-based) and developing CV-CUDA (Ubuntu-based) - Improved Python wheels generation and packaging​ - **Bug Fixes**: - Fixed Coverity security findings​ - **Deprecated Features**: - Dropped official support for CUDA 11 - Dropped official support for CUDA Compute Capability SM7 (Volta architecture) - Dropped official support for Ubuntu 20.04 - Dropped official support for Python 3.8 ## 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. [Optimizing Microsoft Bing Visual Search with NVIDIA Accelerated Libraries](https://developer.nvidia.com/blog/optimizing-microsoft-bing-visual-search-with-nvidia-accelerated-libraries/) 3. [Accelerating AI Pipelines: Boosting Visual Search Efficiency](https://www.nvidia.com/en-us/on-demand/session/gtc25-s71676/) 4. [Optimize Short-Form Video Processing Toward the Speed of Light](https://www.nvidia.com/en-us/on-demand/session/gtc25-s73178/) 5. [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/) 6. [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/) 7. [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 originated as a collaborative effort between NVIDIA and the ByteDance Machine Learning team.

v0.15.0-beta ============ **Full Changelog**: https://github.com/CVCUDA/CV-CUDA/compare/v0.14.0-beta...v0.15.0-beta Release Highlights ------------------ CV-CUDA v0.15.0 includes the following changes:​ - **New Features**:​ - Added support for Python 3.12 and 3.13​ - Added artifacts for CUDA 11 ARM/SBSA - Added Hello World sample - Added tests for multiple operators - **Bug Fixes**: - Fixed issue with Python wheels packaging the CUDA runtime and creating potential mismatch with driver version on host system - Fixed codeQL SAST findings 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. [Optimizing Microsoft Bing Visual Search with NVIDIA Accelerated Libraries](https://developer.nvidia.com/blog/optimizing-microsoft-bing-visual-search-with-nvidia-accelerated-libraries/) 3. [Accelerating AI Pipelines: Boosting Visual Search Efficiency](https://www.nvidia.com/en-us/on-demand/session/gtc25-s71676/) 4. [Optimize Short-Form Video Processing Toward the Speed of Light](https://www.nvidia.com/en-us/on-demand/session/gtc25-s73178/) 5. [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/) 6. [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/) 7. [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.

# v0.13.0 Beta CV-CUDA v0.13.0 includes ManyLinux 2014 compliant wheels alongside the following changes: **Full Changelog**: https://github.com/CVCUDA/CV-CUDA/compare/v0.12.0-beta...v0.13.0-beta ## 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 ManyLinux 2014 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. # Compatibility and Known Limitations For the full list, see the 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.

# v0.12.0-beta ## Release Highlights CV-CUDA v0.12.0 includes the following changes: ### New Features​ - Increased functional test coverage of color conversions. ​ - Reintroduced from 24.07: Improved performance of color conversion operators (e.g., 2x faster RGB2YUV). ### Bug Fixes - Fixed bug in YUV(420) conversions: The CvtColor operator incorrectly computed the data location of the second chromaticity channel for conversions.​ - Fixed bug in YUV(422) conversions: The CvtColor operator incorrectly interpreted the interleaved YUV(422) data layout as a three-channel tensor.​ - Prevent CV_16F alpha addition: some color conversions in the CvtColor operator allowed for the addition of an alpha channel to the destination tensor, which is undefined for the CV_16F data type. ## Compatibility and Known Limitations For the full list, see the 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.

# 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 a static library - Improve Python doc generation and structure ### Bug Fixes - Update pybind11 from 2.10.0 to 2.13.1. Fixes rare race conditions with Python garbage collector, adds compatibility with numpy29 **Full Changelog**: https://github.com/CVCUDA/CV-CUDA/compare/v0.10.1-beta...v0.11.0-beta ## Compatibility and Known Limitations ### Pre-existing limitations - The CvtColor operator incorrectly computes the data location of the second chromaticity channel for conversions that involve YUV(420) semi-planar formats. This issue persists through the current release and we intend to address this bug in CV-CUDA v0.12. We do not recommend using these formats. - Known affected formats: - NVCV_COLOR_YUV2RGB_I420 - NVCV_COLOR_RGB2YUV_I420 - NVCV_COLOR_YUV2BGR_I420 - NVCV_COLOR_BGR2YUV_I420 - NVCV_COLOR_YUV2RGBA_I420 - NVCV_COLOR_RGBA2YUV_I420 - NVCV_COLOR_YUV2BGRA_I420 - NVCV_COLOR_BGRA2YUV_I420 - NVCV_COLOR_RGB2YUV_I420 - NVCV_COLOR_YUV2RGB_YV12 - NVCV_COLOR_RGB2YUV_YV12 - NVCV_COLOR_YUV2BGR_YV12 - NVCV_COLOR_BGR2YUV_YV12 - NVCV_COLOR_YUV2RGBA_YV12 - NVCV_COLOR_RGBA2YUV_YV12 - NVCV_COLOR_YUV2BGRA_YV12 - NVCV_COLOR_BGRA2YUV_YV12 - NVCV_COLOR_RGB2YUV_YV12 - NVCV_COLOR_YUV2GRAY_420 For the full list, see the 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.

0.10.1-beta ============ Release Highlights ------------------ CV-CUDA v0.10.1 reverts the OpCvtColor performance improvements introduced in v0.10.0 due to [discovered bugs](https://github.com/CVCUDA/CV-CUDA/issues/182). These optimizations will be reintroduced, with consolidated testing, in a future release. 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.

v0.10.0-beta ============ Release Highlights ------------------ CV-CUDA v0.10.0 includes a critical bug fix (cache growth management) alongside the following changes: - **New Features**: - Added mechanism to limit and manage cache memory consumption (includes new \"Best Practices\" documentation). - Performance improvements of color conversion operators (e.g., 2x faster RGB2YUV). - Refactored codebase to allow independent build of NVCV library (data structures). - **Bug Fixes**: - Fixed unbounded cache memory consumption issue. - Improved management of Python-created object lifetimes, decoupled from cache management. - Fixed potential crash in Resize operator\'s linear and nearest neighbor interpolation from non-aligned vectorized writes. - Fixed Python CvtColor operator to correctly handle NV12 and NV21 outputs. - Fixed Resize and RandomResizedCrop linear interpolation weight for border rows and columns. - Fixed missing parameter in C API for fused ResizeCropConvertReformat. - Fixed several minor documentation and error output issues. - Fixed minor compiler warning while building Resize operator. Compatibility and Known Limitations ----------------------------------- - **New limitations**: - Cache/resource management introduced in v0.10 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.​ - Sporadic Pybind11-deallocation crashes have been reported in long-lasting multi-threaded Python pipelines with externally allocated memory (eg wrapped Pytorch buffers). We are evaluating an upgrade of Pybind11 (currently using 2.10) as a potential fix in an upcoming release. 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.

v0.9.0-beta =========== Release Highlights ------------------ CV-CUDA v0.9.0 includes the following changes: * **New Features**: * Improved Resize performance (up to 4x for u8 inputs, up to 3x for RGB8) * Improved performance of cubic interpolation, eg in Rotate, WarpAffine and WarpPerspective (up to 2x faster) * Added optional scaling to ResizeCropConvertReformat fused operator * Improved structure of Python documentation and optimized its generation (>5min to <30s) by removing the Exhale index * Added 64bit stride support to various operators * limited to 32bit strides to avoid performance regressions: AdaptiveThreshold, AdvCvtColor, AverageBlur, BilateralFilter, BrightnessContrast, ColorTwist, BoxBlur, CenterCrop, ConvertTo, CopyMakeBorder, CustomCrop, GaussianNoise, Gaussian, Flip, HistogramEq, JointBilateralFilter, Laplacian, Morphology, Normalize, RandomResizedCrop, Reformat, Remap, Resize, Rotate, SIFT, WarpAffine, WarpPerspective * **Bug Fixes**: * Added exception handling on CApi in Python: now forward C/C++exceptions to Python * Fixed coordinate rounding bug in Resize operator with nearest neighbor interpolation Compatibility and Known Limitations ----------------------------------- * Documentation built on Ubuntu 20.04 needs an up-to-date version of sphinx (`pip install --upgrade sphinx`) as well as explicitly parsing the system's default python version `./ci/build_docs path/to/build -DPYTHON_VERSIONS="<py_ver>"`. * Python bindings installed via Debian packages and Python tests fail with Numpy 2.0. We recommend using an older version of Numpy (e.g. 1.26) until we have implemented a fix. * The Resize and RandomResizedCrop operators incorrectly interpolate pixel values near the boundary of an image or tensor when using linear and cubic interpolation. This will be fixed in an upcoming release. 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.