Welcome to OpenVINO™, an open-source software toolkit for optimizing and deploying deep learning models.
Check out the OpenVINO Cheat Sheet for a quick reference.
Get your preferred distribution of OpenVINO or use this command for quick installation:
pip install openvino
Check 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📚:
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 also supports CPU, GPU, and NPU devices and works with models in TensorFlow, PyTorch, ONNX, TensorFlow Lite, PaddlePaddle model formats. With OpenVINO you can do automatic performance enhancements at runtime customized to your hardware (preserving model accuracy), including: asynchronous execution, batch processing, tensor fusion, load balancing, dynamic inference parallelism, automatic BF16 conversion, and more.
Check out the Awesome OpenVINO repository to discover a collection of community-made AI projects based on OpenVINO!
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 how OpenVINO components work and describes building and contributing processes.
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:
openvino
tag on Stack Overflow*.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.
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