# OpenVINO **Repository Path**: openvinotoolkit-prc/openvino ## Basic Information - **Project Name**: OpenVINO - **Description**: OpenVINO allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic. - **Primary Language**: C++ - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 85 - **Forks**: 31 - **Created**: 2020-09-25 - **Last Updated**: 2025-08-02 ## Categories & Tags **Categories**: machine-learning **Tags**: None ## README

Open-source software toolkit for optimizing and deploying deep learning models.

Documentation • Blog • Key Features • Tutorials • Integrations • Benchmarks • Generative AI

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- **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. - **Flexible Model Support**: Use models trained with popular frameworks such as PyTorch, TensorFlow, ONNX, Keras, PaddlePaddle, and JAX/Flax. Directly integrate models built with transformers and diffusers from the Hugging Face Hub using Optimum Intel. Convert and deploy models without original frameworks. - **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). - **Community and Ecosystem**: Join an active community contributing to the enhancement of deep learning performance across various domains. Check out the [OpenVINO Cheat Sheet](https://docs.openvino.ai/2025/_static/download/OpenVINO_Quick_Start_Guide.pdf) and [Key Features](https://docs.openvino.ai/2025/about-openvino/key-features.html) for a quick reference. ## Installation [Get your preferred distribution of OpenVINO](https://docs.openvino.ai/2025/get-started/install-openvino.html) or use this command for quick installation: ```sh pip install -U openvino ``` Check [system requirements](https://docs.openvino.ai/2025/about-openvino/release-notes-openvino/system-requirements.html) and [supported devices](https://docs.openvino.ai/2025/documentation/compatibility-and-support/supported-devices.html) for detailed information. ## Tutorials and Examples [OpenVINO Quickstart example](https://docs.openvino.ai/2025/get-started.html) will walk you through the basics of deploying your first model. Learn how to optimize and deploy popular models with the [OpenVINO Notebooks](https://github.com/openvinotoolkit/openvino_notebooks)📚: - [Create an LLM-powered Chatbot using OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/llm-chatbot-generate-api.ipynb) - [YOLOv11 Optimization](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/yolov11-optimization/yolov11-object-detection.ipynb) - [Text-to-Image Generation](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/text-to-image-genai/text-to-image-genai.ipynb) - [Multimodal assistant with LLaVa and OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llava-multimodal-chatbot/llava-multimodal-chatbot-genai.ipynb) - [Automatic speech recognition using Whisper and OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/whisper-asr-genai/whisper-asr-genai.ipynb) Discover more examples in the [OpenVINO Samples (Python & C++)](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples.html) and [Notebooks (Python)](https://docs.openvino.ai/2025/get-started/learn-openvino/interactive-tutorials-python.html). Here are easy-to-follow code examples demonstrating how to run PyTorch and TensorFlow model inference using OpenVINO: **PyTorch Model** ```python import openvino as ov import torch import torchvision # load PyTorch model into memory model = torch.hub.load("pytorch/vision", "shufflenet_v2_x1_0", weights="DEFAULT") # convert the model into OpenVINO model example = torch.randn(1, 3, 224, 224) ov_model = ov.convert_model(model, example_input=(example,)) # compile the model for CPU device core = ov.Core() compiled_model = core.compile_model(ov_model, 'CPU') # infer the model on random data output = compiled_model({0: example.numpy()}) ``` **TensorFlow Model** ```python import numpy as np import openvino as ov import tensorflow as tf # load TensorFlow model into memory model = tf.keras.applications.MobileNetV2(weights='imagenet') # convert the model into OpenVINO model ov_model = ov.convert_model(model) # compile the model for CPU device core = ov.Core() compiled_model = core.compile_model(ov_model, 'CPU') # infer the model on random data data = np.random.rand(1, 224, 224, 3) output = compiled_model({0: data}) ``` OpenVINO supports the CPU, GPU, and NPU [devices](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html) and works with models from PyTorch, TensorFlow, ONNX, TensorFlow Lite, PaddlePaddle, and JAX/Flax [frameworks](https://docs.openvino.ai/2025/openvino-workflow/model-preparation.html). It includes [APIs](https://docs.openvino.ai/2025/api/api_reference.html) in C++, Python, C, NodeJS, and offers the GenAI API for optimized model pipelines and performance. ## Generative AI with OpenVINO Get started with the OpenVINO GenAI [installation](https://docs.openvino.ai/2025/get-started/install-openvino/install-openvino-genai.html) and refer to the [detailed guide](https://docs.openvino.ai/2025/openvino-workflow-generative/generative-inference.html) to explore the capabilities of Generative AI using OpenVINO. Learn how to run LLMs and GenAI with [Samples](https://github.com/openvinotoolkit/openvino.genai/tree/master/samples) in the [OpenVINO™ GenAI repo](https://github.com/openvinotoolkit/openvino.genai). See GenAI in action with Jupyter notebooks: [LLM-powered Chatbot](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-chatbot) and [LLM Instruction-following pipeline](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-question-answering). ## Documentation [User documentation](https://docs.openvino.ai/) contains detailed information about OpenVINO and guides you from installation through optimizing and deploying models for your AI applications. [Developer documentation](./docs/dev/index.md) focuses on the OpenVINO architecture and describes [building](./docs/dev/build.md) and [contributing](./CONTRIBUTING.md) processes. ## OpenVINO Ecosystem ### OpenVINO Tools - [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) - advanced model optimization techniques including quantization, filter pruning, binarization, and sparsity. - [GenAI Repository](https://github.com/openvinotoolkit/openvino.genai) and [OpenVINO Tokenizers](https://github.com/openvinotoolkit/openvino_tokenizers) - resources and tools for developing and optimizing Generative AI applications. - [OpenVINO™ Model Server (OVMS)](https://github.com/openvinotoolkit/model_server) - a scalable, high-performance solution for serving models optimized for Intel architectures. - [Intel® Geti™](https://geti.intel.com/) - an interactive video and image annotation tool for computer vision use cases. ### Integrations - [🤗Optimum Intel](https://github.com/huggingface/optimum-intel) - grab and use models leveraging OpenVINO within the Hugging Face API. - [Torch.compile](https://docs.openvino.ai/2025/openvino-workflow/torch-compile.html) - use OpenVINO for Python-native applications by JIT-compiling code into optimized kernels. - [OpenVINO LLMs inference and serving with vLLM​](https://github.com/vllm-project/vllm-openvino) - enhance vLLM's fast and easy model serving with the OpenVINO backend. - [OpenVINO Execution Provider for ONNX Runtime](https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html) - use OpenVINO as a backend with your existing ONNX Runtime code. - [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/openvino/) - build context-augmented GenAI applications with the LlamaIndex framework and enhance runtime performance with OpenVINO. - [LangChain](https://python.langchain.com/docs/integrations/llms/openvino/) - integrate OpenVINO with the LangChain framework to enhance runtime performance for GenAI applications. - [Keras 3](https://github.com/keras-team/keras) - Keras 3 is a multi-backend deep learning framework. Users can switch model inference to the OpenVINO backend using the Keras API. Check out the [Awesome OpenVINO](https://github.com/openvinotoolkit/awesome-openvino) repository to discover a collection of community-made AI projects based on OpenVINO! ## Performance Explore [OpenVINO Performance Benchmarks](https://docs.openvino.ai/2025/about-openvino/performance-benchmarks.html) to discover the optimal hardware configurations and plan your AI deployment based on verified data. ## Contribution and Support Check out [Contribution Guidelines](./CONTRIBUTING.md) for more details. Read the [Good First Issues section](./CONTRIBUTING.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! You can ask questions and get support on: * [GitHub Issues](https://github.com/openvinotoolkit/openvino/issues). * OpenVINO channels on the [Intel DevHub Discord server](https://discord.gg/7pVRxUwdWG). * The [`openvino`](https://stackoverflow.com/questions/tagged/openvino) tag on Stack Overflow\*. ## Resources * [Release Notes](https://docs.openvino.ai/2025/about-openvino/release-notes-openvino.html) * [OpenVINO Blog](https://blog.openvino.ai/) * [OpenVINO™ toolkit on Medium](https://medium.com/@openvino) ## Telemetry OpenVINO™ collects software performance and usage data for the purpose of improving OpenVINO™ tools. This data is collected directly by OpenVINO™ or through the use of Google Analytics 4. You can opt-out at any time by running the command: ``` bash opt_in_out --opt_out ``` More Information is available at [OpenVINO™ Telemetry](https://docs.openvino.ai/2025/about-openvino/additional-resources/telemetry.html). ## License OpenVINO™ Toolkit is licensed under [Apache License Version 2.0](LICENSE). By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms. --- \* Other names and brands may be claimed as the property of others.