One of the common use-cases in the container development workflow is to onboard models trained in popular frameworks such as Caffe, Tensorflow, MXNet or ONNX before building or running your containers. Your development environment comes installed with OpenVINO™ tools such as Model Optimizer, Accuracy Checker, Post-Training Optimization Tool (POT) or downloader and convertor utilities from the OpenVINO™ Model Zoo.
Above tools can be accessed using the installed Python virtual environment from the terminal.
Navigate to CLI
Use the top navigation menu to access the Coding Environment to open the JupyterLab interface in a new browser tab. Use the + button from the Jupyterlab file browser to open a Terminal from the Launcher.
If you not able to access the JupyterLab interface, make sure to Allow Pop-ups in your browser.
Activate virtual environment
In a new JupyterLab terminal, Activate the ov2022.1.0-venv python3 virtual environment.
Your terminal session will reflect the name of your activated virtual environment and the shell will begin with (ov2022.1.0-venv)[build@cliservice-..... .