openvino/tools/pot/docs/InstallationGuide.md

Post-Training Optimization Tool Installation Guide

Prerequisites

• Python* 3.6 or higher
• OpenVINO™

The minimum and the recommended requirements to run the Post-training Optimization Tool (POT) are the same as in OpenVINO™.

There are two ways how to install the POT on your system:

• Installation from PyPI repository
• Installation from Intel® Distribution of OpenVINO™ toolkit package

Install POT from PyPI

The simplest way to get the Post-training Optimization Tool and OpenVINO™ installed is to use PyPI. Follow the steps below to do that:

1. Create a separate Python* environment and activate it
2. To install OpenVINO™, run pip install openvino.
3. To install POT and other OpenVINO™ developer tools, run pip install openvino-dev.

Now the Post-training Optimization Tool is available in the command line by the pot alias. To verify it, run pot -h.

Install and Set Up POT from Intel® Distribution of OpenVINO™ toolkit package

In the instructions below, <INSTALL_DIR> is the directory where the Intel® distribution of OpenVINO™ toolkit is installed. The Post-training Optimization Tool is distributed as a part of the OpenVINO™ release package, and to use the POT as a command-line tool, you need to install OpenVINO™ as well as POT dependencies, namely [Model Optimizer](@ref openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide) and [Accuracy Checker](@ref omz_tools_accuracy_checker). It is recommended to create a separate Python* environment before installing the OpenVINO™ and its components. POT source files are available in <INSTALL_DIR>/deployment_tools/tools/post_training_optimization_toolkit directory after the OpenVINO™ installation.

To set up the Post-training Optimization Tool in your environment, follow the steps below.

Set up the Model Optimizer and Accuracy Checker components

• To set up the [Model Optimizer](@ref openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide):

  1. Go to <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites.
  2. Run the following script to configure the Model Optimizer:
     • Linux:

     sudo ./install_prerequisites.sh
     

     • Windows:

     install_prerequisites.bat
     

  3. To verify that the Model Optimizer is installed, run <INSTALL_DIR>/deployment_tools/model_optimizer/mo.py -h.

• To set up the [Accuracy Checker](@ref omz_tools_accuracy_checker):

  1. Go to <INSTALL_DIR>/deployment_tools/open_model_zoo/tools/accuracy_checker.
  2. Run the following script to configure the Accuracy Checker:

  python setup.py install
  

  3. Now the Accuracy Checker is available in the command line by the accuracy_check alias. To verify it, run accuracy_check -h.

Set up the POT

1. Go to <INSTALL_DIR>/deployment_tools/tools/post_training_optimization_toolkit.

2. Run the following script to configure the POT:

   python setup.py install
   

   In order to enable advanced algorithms such as the Tree-Structured Parzen Estimator (TPE) based optimization, add the following flag to the installation command:

   python setup.py install --install-extras
   

3. Now the POT is available in the command line by the pot alias. To verify it, run pot -h.