- 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 and Key Features for a quick reference.
Installation
Get your preferred distribution of OpenVINO or use this command for quick installation:
pip install -U openvino
Verify Installation
After installation, you can verify OpenVINO is installed correctly by running:
import openvino as ov
print(ov.__version__)
Check system requirements and supported devices for detailed information.
Tutorials and Examples
OpenVINO Quickstart example will walk you through the basics of deploying your first model.
Learn how to optimize and deploy popular models with the OpenVINO Notebooks 📚:
Discover more examples in the OpenVINO Samples (Python & C++) and Notebooks (Python).
Here are easy-to-follow code examples demonstrating how to run PyTorch and TensorFlow model inference using OpenVINO:
PyTorch Model
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
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 and works with models from PyTorch, TensorFlow, ONNX, TensorFlow Lite, PaddlePaddle, and JAX/Flax frameworks. It includes APIs 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 and refer to the detailed guide to explore the capabilities of Generative AI using OpenVINO.
Learn how to run LLMs and GenAI with Samples in the OpenVINO™ GenAI repo. See GenAI in action with Jupyter notebooks: LLM-powered Chatbot and LLM Instruction-following pipeline.
Documentation
User documentation contains detailed information about OpenVINO and guides you from installation through optimizing and deploying models for your AI applications.
Developer documentation focuses on the OpenVINO architecture and describes building and contributing processes.
OpenVINO Ecosystem
Integrations
- 🤗Optimum Intel - grab and use models leveraging OpenVINO within the Hugging Face API.
- Torch.compile - use OpenVINO for Python-native applications by JIT-compiling code into optimized kernels.
- ExecuTorch - use ExecuTorch with OpenVINO to optimize and run AI models efficiently.
- OpenVINO LLMs inference and serving with vLLM - enhance vLLM's fast and easy model serving with the OpenVINO backend.
- OpenVINO Execution Provider for ONNX Runtime - use OpenVINO as a backend with your existing ONNX Runtime code.
- LLMWare - seamlessly build enterprise AI apps, agentic workflows, RAG pipelines, multimodal use cases, and more, using 100+ inference-ready OpenVINO-optimized SLMs.
- LlamaIndex - build context-augmented GenAI applications with the LlamaIndex framework and enhance runtime performance with OpenVINO.
- LangChain - integrate OpenVINO with the LangChain framework to enhance runtime performance for GenAI applications.
- Keras 3 - 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 repository to discover a collection of community-made AI projects based on OpenVINO!
Explore OpenVINO Performance Benchmarks to discover the optimal hardware configurations and plan your AI deployment based on verified data.
Contribution and Support
Check out Contribution Guidelines for more details.
Read the Good First Issues section, if you're looking for a place to start contributing. We welcome contributions of all kinds!
You can ask questions and get support on:
Resources
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:
opt_in_out --opt_out
More Information is available at OpenVINO™ Telemetry.
License
OpenVINO™ Toolkit is licensed under Apache License Version 2.0.
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.
OpenVINO 是 Intel 的开源工具包,用于优化和部署 AI 推理。它可以把训练好的深度学习模型——涵盖视觉、语音、自然语言处理和生成式 AI——跑得更快,从边缘设备一直覆盖到云端服务器。