Package: openvino-workbench
OpenVINO™ Deep Learning Workbench Python StarterCopyright © 2018-2023 Intel CorporationLEGAL NOTICE: Your use of this software and any required dependent software (the “Software Package”) is subject to the terms and conditions of theApache 2.0 License.IntroductionDeep Learning Workbench is a web-based graphical environment with a convenient user-friendly interface and a wide range of customization options designed to make the development of deep learning models significantly easier.The DL Workbench is an official UI environment of the OpenVINO™ toolkit that enables you to:Learn what neural networks are, how they work, and how to analyze their architectures and performance.Get familiar with the OpenVINO™ ecosystem and its main components without installing it on your system.Measure and interpret model performance.Analyze the quality of your model and visualize output.Optimize your model and prepare it for deployment on the target system.In the DL Workbench, you can use the following OpenVINO™ toolkit components:ComponentDescriptionModel Downloader and Model ConverterModel Downloaderis a tool for getting access to the collection of high-quality pre-trained deep learningpublicandIntel-trained models. The tool downloads model files from online sources and, if necessary, patches them with Model Optimizer.Model Converteris a tool for converting the models stored in a format other than the Intermediate Representation (IR) into that format using Model Optimizer.Model OptimizerModel Optimizerimports, converts, and optimizes models that were trained in certain frameworks to the IR format used in OpenVINO tools.Supported frameworks include TensorFlow*, Caffe*, Kaldi**, MXNet*, and ONNX*.Benchmark ToolBenchmark Applicationallows you to estimate deep learning inference performance on supported devices for synchronous and asynchronous modes.Accuracy CheckerAccuracy Checkeris a deep learning accuracy validation tool that allows you to evaluate accuracy on the given dataset by collecting one or several metric values.Post-Training Optimization ToolPost-Training Optimization Toolallows you to optimize trained models with advanced capabilities, such as quantization and low-precision optimizations, without the need to retrain or fine-tune models.System RequirementsThe complete list of recommended requirements is available in thedocumentation.To successfully run the DL Workbench with Python Starter, install Python 3.6 or higher.PrerequisiteLinux*Windows*macOS*Operating systemUbuntu* 18.04Windows* 10macOS* 10.15 CatalinaAvailable RAM space8 GB**8 GB**8 GB**Available storage space10 GB + space for imported artifacts10 GB + space for imported artifacts10 GB + space for imported artifactsDocker*Docker CE 18.06.1Docker Desktop 2.3.0.3Docker CE 18.06.1Windows*, Linux* and MacOS* support CPU targets. GPU, Intel® Neural Compute Stick 2 and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs are supported only for Linux*.Install the DL Workbench StarterStep 1. Set Up Python Virtual EnvironmentTo avoid dependency conflicts, use a virtual environment. Skip this step only if you do want to install all dependencies globally.Create virtual environment by executing the following commands in your terminal:On Linux and MacOS:python3 -m pip install --user virtualenv
python3 -m venv venvOn Windows:py -m pip install --user virtualenv
py -m venv venvStep 2. Activate Virtual EnvironmentOn Linux and MacOS:source venv/bin/activateOn Windows:venv\Scripts\activateStep 3. Update PIP to the Latest VersionRun the command below:python -m pip install --upgrade pipStep 4. Install the Python Wrapperpip install -U openvino-workbenchStep 5. Verify the InstallationTo verify that the package is properly installed, run the command below:openvino-workbench --helpYou will see the help message for the starting package if installation finished successfully.Use the DL Workbench StarterTo start the latest available version of the DL Workbench, execute the following command:openvino-workbench --image openvino/workbench:latest --force-pullYou can see the list of available arguments with the following command:openvino-workbench --helpRefer to thedocumentationfor additional information.Additional ResourcesRelease NotesDocumentationFeedbackTroubleshooting