- 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.
Get your preferred distribution of OpenVINO or use this command for quick installation:
pip install -U openvinoCheck system requirements and supported devices for detailed information.
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??:
- Create an LLM-powered Chatbot using OpenVINO
- YOLOv11 Optimization
- Text-to-Image Generation
- Multimodal assistant with LLaVa and OpenVINO
- Automatic speech recognition using Whisper and OpenVINO
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.
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.
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.
- Neural Network Compression Framework (NNCF) - advanced model optimization techniques including quantization, filter pruning, binarization, and sparsity.
- GenAI Repository and OpenVINO Tokenizers - resources and tools for developing and optimizing Generative AI applications.
- OpenVINO? Model Server (OVMS) - a scalable, high-performance solution for serving models optimized for Intel architectures.
- Intel? Geti? - an interactive video and image annotation tool for computer vision use cases.
- ??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.
- 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.
- 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.
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:
- GitHub Issues.
- OpenVINO channels on the Intel DevHub Discord server.
- The
openvinotag on Stack Overflow*.
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_outMore Information is available at OpenVINO? Telemetry.
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.
