diff --git a/README.md b/README.md index 1fe404d3c1f283..8edbabf3a7499f 100644 --- a/README.md +++ b/README.md @@ -1,176 +1,73 @@ -
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-Open-source software toolkit for optimizing and deploying deep learning models. -

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- 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/2026/_static/download/OpenVINO_Quick_Start_Guide.pdf) and [Key Features](https://docs.openvino.ai/2026/about-openvino/key-features.html) for a quick reference. - - ## Installation -[Get your preferred distribution of OpenVINO](https://docs.openvino.ai/2026/get-started/install-openvino.html) or use this command for quick installation: +### Prerequisites -```sh -pip install -U openvino -``` +* Python 3.6 or later +* pip or conda package manager -Check [system requirements](https://docs.openvino.ai/2026/about-openvino/release-notes-openvino/system-requirements.html) and [supported devices](https://docs.openvino.ai/2026/documentation/compatibility-and-support/supported-devices.html) for detailed information. +### Installation Methods -## Tutorials and Examples +#### pip -[OpenVINO Quickstart example](https://docs.openvino.ai/2026/get-started.html) will walk you through the basics of deploying your first model. +```bash +pip install openvino +``` -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) +#### conda -Discover more examples in the [OpenVINO Samples (Python & C++)](https://docs.openvino.ai/2026/get-started/learn-openvino/openvino-samples.html) and [Notebooks (Python)](https://docs.openvino.ai/2026/get-started/learn-openvino/interactive-tutorials-python.html). +```bash +conda install -c intel openvino +``` -Here are easy-to-follow code examples demonstrating how to run PyTorch and TensorFlow model inference using OpenVINO: +#### Docker -**PyTorch Model** +```bash +docker pull openvino/openvino-toolkit +``` -```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,)) +#### Source Build -# compile the model for CPU device -core = ov.Core() -compiled_model = core.compile_model(ov_model, 'CPU') +```bash +git clone https://github.com/openvinotoolkit/openvino.git +cd openvino +mkdir build +cd build +cmake .. +cmake --build . +``` -# infer the model on random data -output = compiled_model({0: example.numpy()}) -``` - -**TensorFlow Model** +### Quick Start ```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/2026/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/2026/openvino-workflow/model-preparation.html). It includes [APIs](https://docs.openvino.ai/2026/api/api_reference.html) in C++, Python, C, NodeJS, and offers the GenAI API for optimized model pipelines and performance. -## Generative AI with OpenVINO +# Load the model +model = ov.read_model('model.xml') -Get started with the OpenVINO GenAI [installation](https://docs.openvino.ai/2026/get-started/install-openvino/install-openvino-genai.html) and refer to the [detailed guide](https://docs.openvino.ai/2026/openvino-workflow-generative/generative-inference.html) to explore the capabilities of Generative AI using OpenVINO. +# Compile the model +compiled_model = ov.compile_model(model, 'CPU') -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). +# Create an execution engine +exec_engine = ov.ExecutionEngine(compiled_model) -## Documentation +# Run inference +output = exec_engine.run() -[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. +print(output) +``` -[Developer documentation](./docs/dev/index.md) focuses on the OpenVINO architecture and describes [building](./docs/dev/build.md) and [contributing](./CONTRIBUTING.md) processes. +### API Reference -## OpenVINO Ecosystem +https://docs.openvino.ai/latest/index.html -### OpenVINO Tools +## Contributing -- [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) - advanced model optimization techniques including quantization, 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/2026/openvino-workflow/torch-compile.html) - use OpenVINO for Python-native applications by JIT-compiling code into optimized kernels. -- [ExecuTorch](https://github.com/pytorch/executorch/blob/main/backends/openvino/README.md) - use ExecuTorch with OpenVINO to optimize and run AI models efficiently. -- [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/2026/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/2026/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/2026/about-openvino/additional-resources/telemetry.html). +* Fork the repository +* Create a new branch for your feature or bug fix +* Commit your changes +* Push to the branch +* Open a pull request ## 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. +Apache-2.0