Feat/dlesage/v0.15 (#252)

adding code for release v0.15

Author: dlesage-nvidia

CMakeLists.txt

@@ -23,7 +23,7 @@ endif()
 
 project(cvcuda
         LANGUAGES C CXX
-        VERSION 0.14.0
+        VERSION 0.15.0
         DESCRIPTION "CUDA-accelerated Computer Vision algorithms"
 )
 

LICENSE.md

@@ -1,5 +1,5 @@
 
-[//]: # "SPDX-FileCopyrightText: Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved."
+[//]: # "SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved."
 [//]: # "SPDX-License-Identifier: Apache-2.0"
 [//]: # ""
 [//]: # "Licensed under the Apache License, Version 2.0 (the 'License');"
@@ -102,3 +102,7 @@ To apply the Apache License to your work, attach the following boilerplate notic
     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.
+
+
+-----------------------------------------------
+See [THIRD_PARTY_LICENSES](THIRD_PARTY_LICENSES.md) for licenses of software redistributed as part of CV-CUDA's code or binary packages.

README.md

@@ -18,13 +18,13 @@
 
 [![License](https://img.shields.io/badge/License-Apache_2.0-yellogreen.svg)](https://opensource.org/licenses/Apache-2.0)
 
-![Version](https://img.shields.io/badge/Version-v0.14.0--beta-blue)
+![Version](https://img.shields.io/badge/Version-v0.15.0--beta-blue)
 
 ![Platform](https://img.shields.io/badge/Platform-linux--64_%7C_win--64_wsl2%7C_aarch64-gray)
 
 [![CUDA](https://img.shields.io/badge/CUDA-v11.7-%2376B900?logo=nvidia)](https://developer.nvidia.com/cuda-toolkit-archive)
 [![GCC](https://img.shields.io/badge/GCC-v11.0-yellow)](https://gcc.gnu.org/gcc-11/changes.html)
-[![Python](https://img.shields.io/badge/python-v3.8_%7c_v3.9_%7c_v3.10%7c_v3.11-blue?logo=python)](https://www.python.org/)
+[![Python](https://img.shields.io/badge/python-v3.8_%7c_v3.9_%7c_v3.10%7c_v3.11%7c_v3.12%7c_v3.13-blue?logo=python)](https://www.python.org/)
 [![CMake](https://img.shields.io/badge/CMake-v3.20-%23008FBA?logo=cmake)](https://cmake.org/)
 
 CV-CUDA is an open-source project that enables building efficient cloud-scale
@@ -44,21 +44,21 @@ To get a local copy up and running follow these steps.
 
 |CV-CUDA Build|Platform|CUDA Version|CUDA Compute Capability|Hardware Architectures|Nvidia Driver|Python Versions|Supported Compilers (build from source)|API compatibility with prebuilt binaries|OS/Linux distributions tested with prebuilt packages|
 |-|-|-|-|-|-|-|-|-|-|
-|x86_64_cu11|x86_64|11.7 or later|SM7 and later|Volta, Turing, Ampere, Hopper, Ada Lovelace|r525 or later*** |3.8, 3.9, 3.10, 3.11|gcc>=9* <br> gcc>=11**|gcc>=9|ManyLinux2014-compliant, Ubuntu>= 20.04<br>WSL2/Ubuntu>=20.04|
-|x86_64_cu12|x86_64|12.2 or later|SM7 and later|Volta, Turing, Ampere, Hopper, Ada Lovelace|r525 or later***|3.8, 3.9, 3.10, 3.11|gcc>=9* <br> gcc>=11**|gcc>=9|ManyLinux2014-compliant, Ubuntu>= 20.04<br>WSL2/Ubuntu>=20.04|
-|aarch64_cu12|aarch64 SBSA****|12.2 or later|SM7 and later|ARM SBSA: Volta, Turing, Ampere, Hopper, Ada Lovelace, Grace Hopper|r525 or later***|3.8, 3.9, 3.10, 3.11|gcc>=9* <br> gcc>=11**|gcc>=9|ManyLinux2014-compliant, Ubuntu>= 20.04|
+|x86_64_cu11|x86_64|11.7 or later|SM7 and later|Volta, Turing, Ampere, Ada Lovelace, Hopper|r525 or later*** |3.8 - 3.13|gcc>=9* <br> gcc>=11**|gcc>=9|ManyLinux2014-compliant, Ubuntu>= 20.04<br>WSL2/Ubuntu>=20.04|
+|x86_64_cu12|x86_64|12.2 or later|SM7 and later|Volta, Turing, Ampere, Ada Lovelace, Hopper|r525 or later***|3.8 - 3.13|gcc>=9* <br> gcc>=11**|gcc>=9|ManyLinux2014-compliant, Ubuntu>= 20.04<br>WSL2/Ubuntu>=20.04|
+|aarch64_cu11|aarch64 SBSA****|11.7 or later|SM7 and later|ARM SBSA (incl. Grace): Volta, Turing, Ampere, Ada Lovelace, Hopper|r525 or later***|3.8 - 3.13|gcc>=9* <br> gcc>=11**|gcc>=9|ManyLinux2014-compliant, Ubuntu>= 20.04|
+|aarch64_cu12|aarch64 SBSA****|12.2 or later|SM7 and later|ARM SBSA (incl. Grace): Volta, Turing, Ampere, Ada Lovelace, Hopper|r525 or later***|3.8 - 3.13|gcc>=9* <br> gcc>=11**|gcc>=9|ManyLinux2014-compliant, Ubuntu>= 20.04|
 |aarch64_cu11|aarch64 Jetson****|11.4|SM7 and later|Jetson AGX Orin|JetPack 5.1|3.8|gcc>=9* <br> gcc>=11**|gcc>=9|Jetson Linux 35.x|
 |aarch64_cu12|aarch64 Jetson****|12.2|SM7 and later|Jetson AGX Orin, IGX Orin + Ampere RTX6000, IGX Orin + ADA RTX6000|JetPack 6.0 DP, r535 (IGX OS v0.6)|3.10|gcc>=9* <br> gcc>=11**|gcc>=9|Jetson Linux 36.2<br> IGX OS v0.6|
 
 \* partial build, no test module (see Known Limitations) <br>
 \** full build, including test module <br>
 \*** [samples][CV-CUDA Samples] require driver r535 or later to run and are only officially supported with CUDA 12. <br>
-\**** starting with v0.14, aarch64_cu12 packages (deb, tar.xz or wheels) distributed on Github (release "assets") or Pypi are SBSA-compatible unless noted otherwise. Jetson builds (deb, tar.xz, whl) can be found in explicitly named "Jetson" archives in Github release assets.
+\**** starting with v0.14, aarch64 packages (deb, tar.xz or wheels) distributed on Github (release "assets") or Pypi are SBSA-compatible unless noted otherwise. Jetson builds (deb, tar.xz, whl) can be found in explicitly named "Jetson" archives in Github release assets.
 
 ### Known limitations and issues
 
-- 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.
+- Starting with v0.14, aarch64 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.
 - For GCC versions lower than 11.0, C++17 support needs to be enabled when compiling CV-CUDA.
 - The C++ test module cannot build with gcc<11 (requires specific C++-20 features).  With gcc-9 or gcc-10, please build with option `-DBUILD_TESTS=0`
 - [CV-CUDA Samples] require driver r535 or later to run and are only officially supported with CUDA 12.
@@ -83,10 +83,10 @@ Check pypi.org projects for support for your platform of choice, [cvcuda-cu11][c
 
 Use the following command to install the latest available version:
    ```shell
-   pip install cvcuda_<cu_ver>
+   pip install cvcuda-<cu_ver>
    ```
 
-where <cu_ver> is the desired CUDA version.
+where <cu_ver> is the desired CUDA version, 'cu11' or 'cu12'.
 
 Alternatively, download the appropriate .whl file for your computer architecture, Python and CUDA version from the release assets of current CV-CUDA release. Release information of all CV-CUDA releases can be found [here][CV-CUDA GitHub Releases]. Once downloaded, execute the `pip install` command to install the Python wheel. For example:
    ```shell
@@ -320,6 +320,10 @@ If the script is run from a different location, provide the path to the CV-CUDA
 
 CV-CUDA operates under the [Apache-2.0](LICENSE.md) license.
 
+## Third-party software redistribution
+
+See [THIRD_PARTY_LICENSES](THIRD_PARTY_LICENSES.md) for licenses of software redistributed as part of CV-CUDA's code or binary packages.
+
 ## Security
 
 CV-CUDA, as a NVIDIA program, is committed to secure development practices.
@@ -329,9 +333,25 @@ Please read our [Security](SECURITY.md) page to learn more.
 
 CV-CUDA is developed jointly by NVIDIA and ByteDance.
 
+References:
+
+- [Optimizing Microsoft Bing Visual Search with NVIDIA Accelerated Libraries][bing-blog]
+- [Accelerating AI Pipelines: Boosting Visual Search Efficiency, GTC 2025][bing-gtc25]
+- [Optimize Short-Form Video Processing Toward the Speed of Light, GTC 2025][cosmos-splitting-gtc25]
+- [CV-CUDA Increasing Throughput and Reducing Costs for AI-Based Computer Vision with CV-CUDA][increased-throughput-blog]
+- [NVIDIA Announces Microsoft, Tencent, Baidu Adopting CV-CUDA for Computer Vision AI][cv-cuda-announcement]
+- [CV-CUDA helps Tencent Cloud audio and video PaaS platform achieve full-process GPU acceleration for video enhancement AI][tencent-blog]
+
 [NVIDIA Develop]: https://developer.nvidia.com/
 [ByteDance]: https://www.bytedance.com/
 [CV-CUDA GitHub Releases]: https://github.com/CVCUDA/CV-CUDA/releases
 [CV-CUDA Samples]: https://github.com/CVCUDA/CV-CUDA/blob/main/samples/README.md
 [cvcuda-cu11]: https://pypi.org/project/cvcuda-cu11/
 [cvcuda-cu12]: https://pypi.org/project/cvcuda-cu12/
+
+[bing-blog]: https://developer.nvidia.com/blog/optimizing-microsoft-bing-visual-search-with-nvidia-accelerated-libraries/
+[bing-gtc25]: https://www.nvidia.com/en-us/on-demand/session/gtc25-s71676/
+[cosmos-splitting-gtc25]: https://www.nvidia.com/en-us/on-demand/session/gtc25-s73178/
+[increased-throughput-blog]: https://developer.nvidia.com/blog/increasing-throughput-and-reducing-costs-for-computer-vision-with-cv-cuda/
+[cv-cuda-announcement]: https://blogs.nvidia.com/blog/2023/03/21/cv-cuda-ai-computer-vision/
+[tencent-blog]: https://developer.nvidia.com/zh-cn/blog/cv-cuda-high-performance-image-processing/