- #LAUNCH INTEL GRAPHICS INSTALLER FOR LINUX FROM TERMINAL HOW TO#
- #LAUNCH INTEL GRAPHICS INSTALLER FOR LINUX FROM TERMINAL INSTALL#
- #LAUNCH INTEL GRAPHICS INSTALLER FOR LINUX FROM TERMINAL FULL#
#LAUNCH INTEL GRAPHICS INSTALLER FOR LINUX FROM TERMINAL FULL#
To get a full list of available components for installation, run the.
#LAUNCH INTEL GRAPHICS INSTALLER FOR LINUX FROM TERMINAL INSTALL#
For example, to install only CPU runtime for the Inference Engine, set COMPONENTS=intel-openvino-ie-rt-cpu_x86_64 in silent.cfg. In Option 3 you can select which OpenVINO components will be installed by modifying the COMPONENTS parameter in the silent.cfg file. Sudo sed - i 's/decline/accept/g' silent. For the DL Streamer documentation, see DL Streamer Samples, API Reference, Elements, Tutorial. Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components.
#LAUNCH INTEL GRAPHICS INSTALLER FOR LINUX FROM TERMINAL HOW TO#
OpenCV* community version compiled for Intel® hardwareĪ set of simple command-line applications demonstrating how to utilize specific OpenVINO capabilities in an application and how to perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.Ī set of command-line applications that serve as robust templates to help you implement multi-stage pipelines and specific deep learning scenarios.Ī set of tools to work with your models including Accuracy Checker utility, Post-Training Optimization Tool, Model Downloader and othersĭocumentation for the pre-trained models available in the Open Model Zoo repo. Offers access to hardware accelerated video codecs and frame processing
It includes a set of libraries for an easy inference integration into your applications. This is the engine that runs the deep learning model. Popular frameworks include Caffe*, TensorFlow*, MXNet*, and ONNX*. This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. Kaldi* Statistical Language Model Conversion Toolīy default, the OpenVINO™ Toolkit installation on this page installs the following components: Speech Library and Speech Recognition Demos Reference Implementations For Speech Recognition Apps
Hello NV12 Input Classification C++ SampleĪutomatic Speech Recognition Python* Sample Image Classification Async Python* Sample Quantization Aware Training with NNCF, using TensorFlow Framework Post-Training Quantization with TensorFlow Classification Modelįrom Training to Deployment with TensorFlow and OpenVINO
Quantization Aware Training with NNCF, using PyTorch framework Style Transfer on ONNX Models with OpenVINO Live Inference and Benchmark CT-scan Data with OpenVINO Image Background Removal with U^2-Net and OpenVINO Photos to Anime with PaddleGAN and OpenVINO Super Resolution with PaddleGAN and OpenVINO Single Image Super Resolution with OpenVINO Optical Character Recognition (OCR) with OpenVINO Quantize a Segmentation Model and Show Live Inference Quantize NLP models with OpenVINO Post-Training Optimization Tool Ĭonvert a PaddlePaddle Model to ONNX and OpenVINO IR Post-Training Quantization of PyTorch models with NNCFĬonvert a PyTorch Model to ONNX and OpenVINO IR Install DL Workbench from the Intel® Distribution for OpenVINO™ Toolkit PackageĬonfigure Intel® Vision Accelerator Design with Intel® Movidius™ VPUs on Linux*