OpenVINO™ Project Overview#
This page provides an overview of the most noteworthy tools and components for AI developers, hosted in repositories under the OpenVINO project:
OpenVINO™ GenAI Library simplifies running inference of generative AI models. Check the LLM-powered Chatbot Jupyter notebook to see how GenAI works.
A suite of advanced algorithms for Neural Network inference optimization with minimal accuracy drop. NNCF applies quantization, filter pruning, binarization, and sparsity algorithms to PyTorch and TensorFlow models during training.
A high-performance system that can be used to access the host models via request to the model server.
A collection of Jupyter notebooks for learning and experimenting with the OpenVINO™ Toolkit.
A convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference.
A solution for Model Developers and Independent Software Vendors to use secure packaging and secure model execution.
A cross-platform graphic user interface application for running and testing generative and vision AI models on computers or edge devices.
A framework and a CLI tool for building, transforming, and analyzing datasets.
Intel’s new software for building computer vision models in a fraction of the time and with less data. This software eases laborious data labeling, model training and optimization tasks across the AI model development process, empowering teams to produce custom AI models at scale.
OpenVINO Tokenizers add text processing operations to OpenVINO.
Open Model Zoo includes optimized deep learning models and a set of demos to expedite development of high-performance deep learning inference applications.