[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9711":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":45,"readmeContent":46,"aiSummary":47,"trendingCount":16,"starSnapshotCount":16,"syncStatus":48,"lastSyncTime":49,"discoverSource":50},9711,"openvino","openvinotoolkit\u002Fopenvino","openvinotoolkit","OpenVINO™ is an open source toolkit for optimizing and deploying AI inference","https:\u002F\u002Fdocs.openvino.ai",null,"C++",10355,3240,188,307,0,4,26,140,19,101,"Apache License 2.0",false,"master",true,[27,28,29,30,31,32,33,34,35,36,37,5,38,39,40,41,42,43,44],"ai","computer-vision","deep-learning","deploy-ai","diffusion-models","generative-ai","good-first-issue","inference","llm-inference","natural-language-processing","nlp","optimize-ai","performance-boost","recommendation-system","speech-recognition","stable-diffusion","transformers","yolo","2026-06-12 04:00:46","\u003Cdiv align=\"center\">\n\u003Cimg src=\"docs\u002Fdev\u002Fassets\u002Fopenvino-logo-purple-black.svg\" width=\"400px\">\n\n\u003Ch3 align=\"center\">\nOpen-source software toolkit for optimizing and deploying deep learning models.\n\u003C\u002Fh3>\n\n\u003Cp align=\"center\">\n \u003Ca href=\"https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Findex.html\">\u003Cb>Documentation\u003C\u002Fb>\u003C\u002Fa> • \u003Ca href=\"https:\u002F\u002Fblog.openvino.ai\">\u003Cb>Blog\u003C\u002Fb>\u003C\u002Fa> • \u003Ca href=\"https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fabout-openvino\u002Fkey-features.html\">\u003Cb>Key Features\u003C\u002Fb>\u003C\u002Fa> • \u003Ca href=\"https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fget-started\u002Flearn-openvino.html\">\u003Cb>Tutorials\u003C\u002Fb>\u003C\u002Fa> • \u003Ca href=\"https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fdocumentation\u002Fopenvino-ecosystem.html\">\u003Cb>Integrations\u003C\u002Fb>\u003C\u002Fa> • \u003Ca href=\"https:\u002F\u002Fwww.intel.com\u002Fcontent\u002Fwww\u002Fus\u002Fen\u002Fdeveloper\u002Ftools\u002Fopenvino-toolkit\u002Fmodel-hub.html\">\u003Cb>Benchmarks\u003C\u002Fb>\u003C\u002Fa> • \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino.genai\">\u003Cb>Generative AI\u003C\u002Fb>\u003C\u002Fa>\n\u003C\u002Fp>\n\n[![PyPI Status](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fopenvino.svg)](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fopenvino)\n[![Anaconda Status](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fopenvino\u002Fbadges\u002Fversion.svg)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fopenvino)\n[![brew Status](https:\u002F\u002Fimg.shields.io\u002Fhomebrew\u002Fv\u002Fopenvino)](https:\u002F\u002Fformulae.brew.sh\u002Fformula\u002Fopenvino)\n\n[![PyPI Downloads](https:\u002F\u002Fstatic.pepy.tech\u002Fbadge\u002Fopenvino)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Fopenvino)\n[![Anaconda Downloads](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Flibopenvino\u002Fbadges\u002Fdownloads.svg)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fopenvino\u002Ffiles)\n[![brew Downloads](https:\u002F\u002Fimg.shields.io\u002Fhomebrew\u002Finstalls\u002Fdy\u002Fopenvino)](https:\u002F\u002Fformulae.brew.sh\u002Fformula\u002Fopenvino)\n \u003C\u002Fdiv>\n\n\n- **Inference Optimization**: Boost deep learning performance in computer vision, automatic speech recognition, generative AI, natural language processing with large and small language models, and many other common tasks.\n- **Flexible Model Support**: Use models trained with popular frameworks such as PyTorch, TensorFlow, ONNX, Keras, PaddlePaddle, and JAX\u002FFlax. Directly integrate models built with transformers and diffusers from the Hugging Face Hub using Optimum Intel. Convert and deploy models without original frameworks.\n- **Broad Platform Compatibility**: Reduce resource demands and efficiently deploy on a range of platforms from edge to cloud. OpenVINO™ supports inference on CPU (x86, ARM), GPU (Intel integrated & discrete GPU) and AI accelerators (Intel NPU).\n- **Community and Ecosystem**: Join an active community contributing to the enhancement of deep learning performance across various domains.\n\nCheck out the [OpenVINO Cheat Sheet](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002F_static\u002Fdownload\u002FOpenVINO_Quick_Start_Guide.pdf) and [Key Features](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fabout-openvino\u002Fkey-features.html) for a quick reference.\n\n\n## Installation\n\n[Get your preferred distribution of OpenVINO](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fget-started\u002Finstall-openvino.html) or use this command for quick installation:\n\n```sh\npip install -U openvino\n```\n\nCheck [system requirements](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fabout-openvino\u002Frelease-notes-openvino\u002Fsystem-requirements.html) and [supported devices](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fdocumentation\u002Fcompatibility-and-support\u002Fsupported-devices.html) for detailed information.\n\n## Tutorials and Examples\n\n[OpenVINO Quickstart example](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fget-started.html) will walk you through the basics of deploying your first model.\n\nLearn how to optimize and deploy popular models with the [OpenVINO Notebooks](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino_notebooks)📚:\n- [Create an LLM-powered Chatbot using OpenVINO](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino_notebooks\u002Fblob\u002Flatest\u002Fnotebooks\u002Fllm-chatbot\u002Fllm-chatbot-generate-api.ipynb)\n- [YOLOv11 Optimization](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino_notebooks\u002Fblob\u002Flatest\u002Fnotebooks\u002Fyolov11-optimization\u002Fyolov11-object-detection.ipynb)\n- [Text-to-Image Generation](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino_notebooks\u002Fblob\u002Flatest\u002Fnotebooks\u002Ftext-to-image-genai\u002Ftext-to-image-genai.ipynb)\n- [Multimodal assistant with LLaVa and OpenVINO](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino_notebooks\u002Fblob\u002Flatest\u002Fnotebooks\u002Fllava-multimodal-chatbot\u002Fllava-multimodal-chatbot-genai.ipynb)\n- [Automatic speech recognition using Whisper and OpenVINO](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino_notebooks\u002Fblob\u002Flatest\u002Fnotebooks\u002Fwhisper-asr-genai\u002Fwhisper-asr-genai.ipynb)\n\nDiscover more examples in the [OpenVINO Samples (Python & C++)](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fget-started\u002Flearn-openvino\u002Fopenvino-samples.html) and [Notebooks (Python)](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fget-started\u002Flearn-openvino\u002Finteractive-tutorials-python.html).\n\nHere are easy-to-follow code examples demonstrating how to run PyTorch and TensorFlow model inference using OpenVINO:\n\n**PyTorch Model**\n\n```python\nimport openvino as ov\nimport torch\nimport torchvision\n\n# load PyTorch model into memory\nmodel = torch.hub.load(\"pytorch\u002Fvision\", \"shufflenet_v2_x1_0\", weights=\"DEFAULT\")\n\n# convert the model into OpenVINO model\nexample = torch.randn(1, 3, 224, 224)\nov_model = ov.convert_model(model, example_input=(example,))\n\n# compile the model for CPU device\ncore = ov.Core()\ncompiled_model = core.compile_model(ov_model, 'CPU')\n\n# infer the model on random data\noutput = compiled_model({0: example.numpy()})\n```\n\n**TensorFlow Model**\n\n```python\nimport numpy as np\nimport openvino as ov\nimport tensorflow as tf\n\n# load TensorFlow model into memory\nmodel = tf.keras.applications.MobileNetV2(weights='imagenet')\n\n# convert the model into OpenVINO model\nov_model = ov.convert_model(model)\n\n# compile the model for CPU device\ncore = ov.Core()\ncompiled_model = core.compile_model(ov_model, 'CPU')\n\n# infer the model on random data\ndata = np.random.rand(1, 224, 224, 3)\noutput = compiled_model({0: data})\n```\n\nOpenVINO supports the CPU, GPU, and NPU [devices](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fopenvino-workflow\u002Frunning-inference\u002Finference-devices-and-modes.html) and works with models from PyTorch, TensorFlow, ONNX, TensorFlow Lite, PaddlePaddle, and JAX\u002FFlax [frameworks](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fopenvino-workflow\u002Fmodel-preparation.html). It includes [APIs](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fapi\u002Fapi_reference.html) in C++, Python, C, NodeJS, and offers the GenAI API for optimized model pipelines and performance.\n\n## Generative AI with OpenVINO\n\nGet started with the OpenVINO GenAI [installation](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fget-started\u002Finstall-openvino\u002Finstall-openvino-genai.html) and refer to the [detailed guide](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fopenvino-workflow-generative\u002Fgenerative-inference.html) to explore the capabilities of Generative AI using OpenVINO.\n\nLearn how to run LLMs and GenAI with [Samples](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino.genai\u002Ftree\u002Fmaster\u002Fsamples) in the [OpenVINO™ GenAI repo](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino.genai). See GenAI in action with Jupyter notebooks: [LLM-powered Chatbot](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino_notebooks\u002Ftree\u002Flatest\u002Fnotebooks\u002Fllm-chatbot) and [LLM Instruction-following pipeline](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino_notebooks\u002Ftree\u002Flatest\u002Fnotebooks\u002Fllm-question-answering).\n\n## Documentation\n\n[User documentation](https:\u002F\u002Fdocs.openvino.ai\u002F) contains detailed information about OpenVINO and guides you from installation through optimizing and deploying models for your AI applications.\n\n[Developer documentation](.\u002Fdocs\u002Fdev\u002Findex.md) focuses on the OpenVINO architecture and describes [building](.\u002Fdocs\u002Fdev\u002Fbuild.md)  and [contributing](.\u002FCONTRIBUTING.md) processes.\n\n## OpenVINO Ecosystem\n\n### OpenVINO Tools\n\n-   [Neural Network Compression Framework (NNCF)](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fnncf) - advanced model optimization techniques including quantization, and sparsity.\n-   [GenAI Repository](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino.genai) and [OpenVINO Tokenizers](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino_tokenizers) - resources and tools for developing and optimizing Generative AI applications.\n-   [OpenVINO™ Model Server (OVMS)](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fmodel_server) - a scalable, high-performance solution for serving models optimized for Intel architectures.\n-   [Intel® Geti™](https:\u002F\u002Fgeti.intel.com\u002F) - an interactive video and image annotation tool for computer vision use cases.\n\n### Integrations\n\n-   [🤗Optimum Intel](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Foptimum-intel) - grab and use models leveraging OpenVINO within the Hugging Face API.\n-   [Torch.compile](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fopenvino-workflow\u002Ftorch-compile.html) - use OpenVINO for Python-native applications by JIT-compiling code into optimized kernels.\n-   [ExecuTorch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fexecutorch\u002Fblob\u002Fmain\u002Fbackends\u002Fopenvino\u002FREADME.md) - use ExecuTorch with OpenVINO to optimize and run AI models efficiently.\n-   [OpenVINO LLMs inference and serving with vLLM​](https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm-openvino) - enhance vLLM's fast and easy model serving with the OpenVINO backend.\n-   [OpenVINO Execution Provider for ONNX Runtime](https:\u002F\u002Fonnxruntime.ai\u002Fdocs\u002Fexecution-providers\u002FOpenVINO-ExecutionProvider.html) - use OpenVINO as a backend with your existing ONNX Runtime code.\n-   [LlamaIndex](https:\u002F\u002Fdocs.llamaindex.ai\u002Fen\u002Fstable\u002Fexamples\u002Fllm\u002Fopenvino\u002F) - build context-augmented GenAI applications with the LlamaIndex framework and enhance runtime performance with OpenVINO.\n-   [LangChain](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fintegrations\u002Fllms\u002Fopenvino\u002F) - integrate OpenVINO with the LangChain framework to enhance runtime performance for GenAI applications.\n-   [Keras 3](https:\u002F\u002Fgithub.com\u002Fkeras-team\u002Fkeras) - Keras 3 is a multi-backend deep learning framework. Users can switch model inference to the OpenVINO backend using the Keras API.\n\nCheck out the [Awesome OpenVINO](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fawesome-openvino) repository to discover a collection of community-made AI projects based on OpenVINO!\n\n## Performance\n\nExplore [OpenVINO Performance Benchmarks](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fabout-openvino\u002Fperformance-benchmarks.html) to discover the optimal hardware configurations and plan your AI deployment based on verified data.\n\n## Contribution and Support\n\nCheck out [Contribution Guidelines](.\u002FCONTRIBUTING.md) for more details.\nRead the [Good First Issues section](.\u002FCONTRIBUTING.md#3-start-working-on-your-good-first-issue), if you're looking for a place to start contributing. We welcome contributions of all kinds!\n\nYou can ask questions and get support on:\n\n* [GitHub Issues](https:\u002F\u002Fgithub.com\u002Fopenvinotoolkit\u002Fopenvino\u002Fissues).\n* OpenVINO channels on the [Intel DevHub Discord server](https:\u002F\u002Fdiscord.gg\u002F7pVRxUwdWG).\n* The [`openvino`](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fopenvino) tag on Stack Overflow\\*.\n\n\n## Resources\n\n* [Release Notes](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fabout-openvino\u002Frelease-notes-openvino.html)\n* [OpenVINO Blog](https:\u002F\u002Fblog.openvino.ai\u002F)\n* [OpenVINO™ toolkit on Medium](https:\u002F\u002Fmedium.com\u002F@openvino)\n\n\n## Telemetry\n\nOpenVINO™ collects software performance and usage data for the purpose of improving OpenVINO™ tools.\nThis data is collected directly by OpenVINO™ or through the use of Google Analytics 4.\nYou can opt-out at any time by running the command:\n\n``` bash\nopt_in_out --opt_out\n```\n\nMore Information is available at [OpenVINO™ Telemetry](https:\u002F\u002Fdocs.openvino.ai\u002F2026\u002Fabout-openvino\u002Fadditional-resources\u002Ftelemetry.html).\n\n## License\n\nOpenVINO™ Toolkit is licensed under [Apache License Version 2.0](LICENSE).\nBy contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.\n\n---\n\\* Other names and brands may be claimed as the property of others.\n","OpenVINO™ 是一个开源工具包，用于优化和部署深度学习模型。它支持从计算机视觉、自动语音识别到自然语言处理等多种任务的推理性能提升，并且能够兼容包括PyTorch、TensorFlow在内的多种流行框架训练出的模型。此外，OpenVINO™ 支持在CPU（x86, ARM）、GPU（Intel集成与独立显卡）以及AI加速器上进行高效部署，使得资源需求降低的同时还能实现跨平台应用。该项目非常适合需要高性能AI推理的应用场景，如边缘计算设备或云端服务等。",2,"2026-06-11 03:24:21","top_topic"]