{"id":15028420,"url":"https://github.com/cvcuda/cv-cuda","last_synced_at":"2026-10-20T20:29:32.404Z","repository":{"id":65010218,"uuid":"527819251","full_name":"CVCUDA/CV-CUDA","owner":"CVCUDA","description":"CV-CUDA™ is an open-source, GPU accelerated library for cloud-scale image processing and computer vision.","archived":false,"fork":false,"pushed_at":"2026-10-14T01:10:01.000Z","size":47040,"stargazers_count":2729,"open_issues_count":71,"forks_count":264,"subscribers_count":42,"default_branch":"main","last_synced_at":"2026-10-14T05:57:32.101Z","etag":null,"topics":["bytedance","cloud","computer-vision","cpp","cuda","cv-cuda","gpu","image-processing","machine-learning","nvidia","python"],"latest_commit_sha":null,"homepage":"https://cvcuda.github.io","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CVCUDA.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.md","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":"SECURITY.md","accessibility":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":"AGENTS.md","claude":"CLAUDE.md","gemini":null,"cursor":null,"copilot":null,"dco":null,"cla":null,"disclosure":null}},"created_at":"2022-08-23T03:30:37.000Z","updated_at":"2026-10-09T07:26:21.000Z","dependencies_parsed_at":"2026-10-14T03:23:37.392Z","dependency_job_id":null,"html_url":"https://github.com/CVCUDA/CV-CUDA","commit_stats":null,"previous_names":[],"tags_count":22,"template":false,"template_full_name":null,"purl":"pkg:github/CVCUDA/CV-CUDA","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CVCUDA%2FCV-CUDA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CVCUDA%2FCV-CUDA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CVCUDA%2FCV-CUDA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CVCUDA%2FCV-CUDA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CVCUDA","download_url":"https://codeload.github.com/CVCUDA/CV-CUDA/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CVCUDA%2FCV-CUDA/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":344836268,"owners_count":38332409,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-10-03T21:59:58.778Z","status":"online","status_checked_at":"2026-10-17T02:00:06.547Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bytedance","cloud","computer-vision","cpp","cuda","cv-cuda","gpu","image-processing","machine-learning","nvidia","python"],"created_at":"2024-09-24T20:08:17.623Z","updated_at":"2026-10-17T04:13:43.285Z","avatar_url":"https://github.com/CVCUDA.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n[//]: # \"SPDX-FileCopyrightText: Copyright (c) 2022-2026 NVIDIA CORPORATION \u0026 AFFILIATES. All rights reserved.\"\n[//]: # \"SPDX-License-Identifier: Apache-2.0\"\n[//]: # \"\"\n[//]: # \"Licensed under the Apache License, Version 2.0 (the 'License');\"\n[//]: # \"you may not use this file except in compliance with the License.\"\n[//]: # \"You may obtain a copy of the License at\"\n[//]: # \"http://www.apache.org/licenses/LICENSE-2.0\"\n[//]: # \"\"\n[//]: # \"Unless required by applicable law or agreed to in writing, software\"\n[//]: # \"distributed under the License is distributed on an 'AS IS' BASIS\"\n[//]: # \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\"\n[//]: # \"See the License for the specific language governing permissions and\"\n[//]: # \"limitations under the License.\"\n\n# CV-CUDA\n\n![Version](https://img.shields.io/badge/Version-v0.18.0-blue)\n[![PyPI](https://img.shields.io/badge/PyPI-available-blue?logo=pypi\u0026logoColor=white)](https://pypi.org/search/?q=cvcuda)\n[![License](https://img.shields.io/badge/License-Apache_2.0-yellogreen.svg)](https://opensource.org/licenses/Apache-2.0)\n\n![Platform](https://img.shields.io/badge/Platform-linux--64_%7C_win--64_wsl2%7C_aarch64-gray)\n\n[![CUDA](https://img.shields.io/badge/CUDA-v12.2+_%7c_v13.x-%2376B900?logo=nvidia)](https://developer.nvidia.com/cuda-toolkit-archive)\n[![GCC](https://img.shields.io/badge/GCC-v11.0-yellow)](https://gcc.gnu.org/gcc-11/changes.html)\n[![Python](https://img.shields.io/badge/python-3.10--3.14-blue?logo=python)](https://www.python.org/)\n[![CMake](https://img.shields.io/badge/CMake-v3.24.3+-%23008FBA?logo=cmake)](https://cmake.org/)\n\nCV-CUDA is an open-source library of GPU-accelerated computer vision algorithms designed for speed and scalability. It delivers high-throughput, low-latency image/video processing for AI pipelines across NVIDIA cloud, desktop, and edge platforms. CV-CUDA is built for performance and works seamlessly with C/C++ and Python Image and AI frameworks.\n\nFor more information on available operators, API documentation, and getting started guides, refer to our [online documentation][CV-CUDA Documentation].\n\n\u003cimg src=\"docs/sphinx/content/cvcuda_pipeline.svg\" width=\"500\" alt=\"CV-CUDA Pipeline\"\u003e\n\n\u003cimg src=\"docs/sphinx/content/cvcuda_arch.svg\" width=\"500\" alt=\"CV-CUDA Architecture\"\u003e\n\n\n## CV-CUDA in Action\n\nFully GPU-accelerated image resizing with [nvImageCodec](https://docs.nvidia.com/cuda/nvimagecodec/index.html) and CV-CUDA.\n\n```python\nimport cvcuda\nfrom nvidia import nvimgcodec\n\n# Decode image directly to GPU\ndecoder = nvimgcodec.Decoder()\nimage = decoder.read(\"input.jpg\")\n\n# Convert to CV-CUDA tensor and process\ncvcuda_tensor = cvcuda.as_tensor(image, \"HWC\")\nresized = cvcuda.resize(cvcuda_tensor, (224, 224, 3), cvcuda.Interp.LINEAR)\n```\n\n## Installation\n\nCV-CUDA can be installed from pre-built packages (Python wheels, Debian packages, or tar archives) or built from source.\nWe provide pre-built Python wheels on pypi.org for a variety of Python versions (3.10 to 3.14) and Linux-based platforms (x86_64 and aarch64). See [cvcuda-cu12] and [cvcuda-cu13] for CUDA 12 and CUDA 13, respectively.\n\n| CUDA Version | Installation Command |\n|--------------|---------------------|\n| CUDA 12 | `pip install cvcuda-cu12` |\n| CUDA 13 | `pip install cvcuda-cu13` |\n\nSee [Installation](https://cvcuda.github.io/CV-CUDA/installation.html) for complete installation instructions including building from source, installing Debian packages, and tar archives.\n\n\u003e **Building from source:** CV-CUDA no longer uses git submodules — running\n\u003e `git submodule update --init` is not required and will find nothing. Build dependencies\n\u003e are pre-installed in the Docker devel images (see [`docker/README.md`](docker/README.md))\n\u003e and resolved via CMake's `find_package`, with a documented `FetchContent` fallback for\n\u003e DLPack when it is not already installed. To build outside Docker, see the build\n\u003e dependencies in the [Installation guide](https://cvcuda.github.io/CV-CUDA/installation.html).\n\n### Compatibility\n\n|CV-CUDA Build|Platform|CUDA Version|CUDA Compute Capability|Hardware Architectures|Nvidia Driver|Python Versions|Supported Compilers (build from source and API compatiblity)|API compatibility with prebuilt binaries|OS/Linux distributions tested with prebuilt packages|\n|-|-|-|-|-|-|-|-|-|-|\n|x86_64_cu12|x86_64|≥12.2|≥SM7.5|Turing, Ampere, Ada Lovelace, Hopper, Blackwell|≥r525**|3.10 - 3.14|gcc≥10*|gcc≥10 \u003cbr\u003e clang≥11|ManyLinux2014-compliant, Ubuntu≥22.04\u003cbr\u003eWSL2/Ubuntu≥22.04|\n|x86_64_cu13|x86_64|≥13.0|≥SM7.5|Turing, Ampere, Ada Lovelace, Hopper, Blackwell|≥r580**|3.10 - 3.14|gcc≥10*|gcc≥10 \u003cbr\u003e clang≥11|ManyLinux2014-compliant, Ubuntu≥22.04\u003cbr\u003eWSL2/Ubuntu≥22.04|\n|aarch64_cu12|aarch64 SBSA***|≥12.2|≥SM7.5|ARM SBSA (incl. Grace): Turing, Ampere, Ada Lovelace, Hopper, Blackwell|≥r525**|3.10 - 3.14|gcc≥10*|gcc≥10 \u003cbr\u003e clang≥11|ManyLinux2014-compliant, Ubuntu≥22.04|\n|aarch64_cu12|aarch64 Jetson***|12.2|≥SM7.5|Jetson AGX Orin, IGX Orin + Ampere RTX6000, IGX Orin + ADA RTX6000|JetPack 6.0 DP, r535 (IGX OS v0.6)|3.10|gcc≥10*|gcc≥10 \u003cbr\u003e clang≥11|Jetson Linux 36.2\u003cbr\u003e IGX OS v0.6|\n|aarch64_cu13|aarch64 SBSA and Jetson Thor***|≥13.0|≥SM7.5|ARM SBSA (incl. Grace): Turing, Ampere, Ada Lovelace, Hopper, Blackwell, Jetson Thor|≥r580**|3.10 - 3.14|gcc≥10*|gcc≥10 \u003cbr\u003e clang≥11|ManyLinux2014-compliant, Ubuntu≥22.04|\n\n\n\n\\* test module with partial coverage, need gcc≥11 for full coverage (see Known Limitations) \u003cbr\u003e\n\\** CUDA 12 x86_64 and aarch64 SBSA packages require driver ≥r525. [Samples][CV-CUDA Samples] require driver ≥r535, Jetson Orin packages follow JetPack 6/r535, and CUDA 13 requires ≥r580. \u003cbr\u003e\n\\*** 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 6 builds (deb, tar.xz, whl) can be found in explicitly named \"Jetson\" archives in Github release assets. Packages marked 'aarch64_cu13' are built with the unified CUDA toolkit, compatible with both server-class and embedded platforms (Jetson Thor). \u003cbr\u003e\n\n### Known limitations\n\n- CV-CUDA does not currently support native Windows, only [WSL2](https://cvcuda.github.io/CV-CUDA/wsl2.html)\n- Starting with v0.16, CV-CUDA is dropping official support for CUDA 11, SM7 (Volta), Ubuntu 20.04, Python 3.8.\n- When building from source for Jetson Orin, use `-DCVCUDA_AARCH64_JETSON=ON` to target only Orin-relevant GPU architectures and reduce build time.\n- 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.\n- The C++ test module builds with gcc≥10 with partial coverage. Full coverage requires gcc≥11 with full C++20 support (NTTP).\n- [CV-CUDA Samples] are only officially supported with CUDA 12.\n- Only one CUDA version (CUDA 12.x or CUDA 13.x) of CV-CUDA packages (Debian packages, tarballs, Python Wheels) can be installed at a time. Please uninstall all packages from a given CUDA version before installing packages from a different version.\n- The OSD operator's text rendering functionality has known issues on Jetson/aarch64 platforms, to be fixed in an upcoming release.\n\n## Contributing\n\nCV-CUDA is an open source project. As part of the Open Source Community, we are\ncommitted to the cycle of learning, improving, and updating that makes this\ncommunity thrive. However, CV-CUDA is not yet ready\nfor external contributions.\n\nTo report a bug, request a new feature, or ask a general question, please file a [GitHub issue](https://github.com/CVCUDA/CV-CUDA/issues/new/choose).\n\nTo understand our commitment to the Open\nSource Community, and providing an environment that both supports and respects\nthe efforts of all contributors, please read our\n[Code of Conduct](CODE_OF_CONDUCT.md).\n\n## License\n\nCV-CUDA operates under the [Apache-2.0](LICENSE.md) license.\n\n## Security\n\nCV-CUDA, as a NVIDIA program, is committed to secure development practices.\nPlease read our [Security](SECURITY.md) page to learn more.\n\n## Acknowledgements\n\nCV-CUDA originated as a collaboration between [NVIDIA][NVIDIA Develop] and [ByteDance][ByteDance].\n\n## References:\n\n- [CV-CUDA Online Documentation][CV-CUDA Documentation]\n- [Optimizing Microsoft Bing Visual Search with NVIDIA Accelerated Libraries][bing-blog]\n- [Accelerating AI Pipelines: Boosting Visual Search Efficiency, GTC 2025][bing-gtc25]\n- [Optimize Short-Form Video Processing Toward the Speed of Light, GTC 2025][cosmos-splitting-gtc25]\n- [CV-CUDA Increasing Throughput and Reducing Costs for AI-Based Computer Vision with CV-CUDA][increased-throughput-blog]\n- [NVIDIA Announces Microsoft, Tencent, Baidu Adopting CV-CUDA for Computer Vision AI][cv-cuda-announcement]\n- [CV-CUDA helps Tencent Cloud audio and video PaaS platform achieve full-process GPU acceleration for video enhancement AI][tencent-blog]\n\n[CV-CUDA Documentation]: https://cvcuda.github.io/CV-CUDA/\n[NVIDIA Develop]: https://developer.nvidia.com/\n[ByteDance]: https://www.bytedance.com/\n[CV-CUDA GitHub Releases]: https://github.com/CVCUDA/CV-CUDA/releases\n[CV-CUDA Samples]: https://github.com/CVCUDA/CV-CUDA/blob/main/samples/README.md\n[cvcuda-cu12]: https://pypi.org/project/cvcuda-cu12/\n[cvcuda-cu13]: https://pypi.org/project/cvcuda-cu13/\n\n[bing-blog]: https://developer.nvidia.com/blog/optimizing-microsoft-bing-visual-search-with-nvidia-accelerated-libraries/\n[bing-gtc25]: https://www.nvidia.com/en-us/on-demand/session/gtc25-s71676/\n[cosmos-splitting-gtc25]: https://www.nvidia.com/en-us/on-demand/session/gtc25-s73178/\n[increased-throughput-blog]: https://developer.nvidia.com/blog/increasing-throughput-and-reducing-costs-for-computer-vision-with-cv-cuda/\n[cv-cuda-announcement]: https://blogs.nvidia.com/blog/2023/03/21/cv-cuda-ai-computer-vision/\n[tencent-blog]: https://developer.nvidia.com/zh-cn/blog/cv-cuda-high-performance-image-processing/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcvcuda%2Fcv-cuda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcvcuda%2Fcv-cuda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcvcuda%2Fcv-cuda/lists"}