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Alexander Kozlov bbeec714aa Moved Post-training Optimization Tool to open-source (#7940 )

* Moved POT to opensource

* Added OMZ as a submodule

* Exclude OMZ from ShellCheck

2021-10-15 16:35:35 +03:00

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Post-Training Optimization Tool Installation Guide

Prerequisites

Python* 3.6 or higher
[OpenVINO™](@ref index)

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

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

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

Installation 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:

Create a separate Python* environment and activate it
To install OpenVINO™ run pip install openvino
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_README). 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_README):
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
```
1. 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

Go to <INSTALL_DIR>/deployment_tools/tools/post_training_optimization_toolkit.
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
```
Now the POT is available in the command line by the pot alias. To verify it, run pot -h.

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