622027bee5
* add sphinx log parsing * fix * fix log * fixes * fixes * fixes * fixes * fixes * fixes * fixes * fixes * fixes * fixes * doxygen-xfail * fixes * fixes * fixes * fixe * fixes * fixes * fix pot * add pot check * fixes * fixes * Fixed POT docs * Fixed POT docs * Fixes * change heading markup * fixes Co-authored-by: azaytsev <andrey.zaytsev@intel.com>
68 lines
3.5 KiB
Markdown
68 lines
3.5 KiB
Markdown
# Post-Training Optimization Tool Installation Guide {#pot_InstallationGuide}
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## Prerequisites
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* Python* 3.6 or higher
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* [OpenVINO™](https://docs.openvino.ai/latest/index.html)
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The minimum and the recommended requirements to run the Post-training Optimization Tool (POT) are the same as in [OpenVINO™](https://docs.openvino.ai/latest/index.html).
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There are two ways how to install the POT on your system:
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- Installation from PyPI repository
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- Installation from Intel® Distribution of OpenVINO™ toolkit package
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## Install POT from PyPI
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The simplest way to get the Post-training Optimization Tool and OpenVINO™ installed is to use PyPI. Follow the steps below to do that:
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1. Create a separate [Python* environment](https://docs.python.org/3/tutorial/venv.html) and activate it
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2. To install OpenVINO™, run `pip install openvino`.
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3. To install POT and other OpenVINO™ developer tools, run `pip install openvino-dev`.
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Now the Post-training Optimization Tool is available in the command line by the `pot` alias. To verify it, run `pot -h`.
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## Install and Set Up POT from Intel® Distribution of OpenVINO™ toolkit package
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In the instructions below, `<INSTALL_DIR>` is the directory where the Intel® distribution of OpenVINO™ toolkit
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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,
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you need to install OpenVINO™ as well as POT dependencies, namely [Model Optimizer](@ref openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide)
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and [Accuracy Checker](@ref omz_tools_accuracy_checker). It is recommended to create a separate [Python* environment](https://docs.python.org/3/tutorial/venv.html) before installing the OpenVINO™ and its components.
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POT source files are available in `<INSTALL_DIR>/deployment_tools/tools/post_training_optimization_toolkit` directory after the OpenVINO™ installation.
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To set up the Post-training Optimization Tool in your environment, follow the steps below.
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### Set up the Model Optimizer and Accuracy Checker components
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- To set up the [Model Optimizer](@ref openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide):
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1. Go to `<INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites`.
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2. Run the following script to configure the Model Optimizer:
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* Linux:
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```sh
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sudo ./install_prerequisites.sh
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```
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* Windows:
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```bat
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install_prerequisites.bat
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```
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3. To verify that the Model Optimizer is installed, run `<INSTALL_DIR>/deployment_tools/model_optimizer/mo.py -h`.
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- To set up the [Accuracy Checker](@ref omz_tools_accuracy_checker):
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1. Go to `<INSTALL_DIR>/deployment_tools/open_model_zoo/tools/accuracy_checker`.
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2. Run the following script to configure the Accuracy Checker:
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```sh
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python setup.py install
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```
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3. Now the Accuracy Checker is available in the command line by the `accuracy_check` alias. To verify it, run `accuracy_check -h`.
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### Set up the POT
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1. Go to `<INSTALL_DIR>/deployment_tools/tools/post_training_optimization_toolkit`.
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2. Run the following script to configure the POT:
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```sh
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python setup.py install
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```
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In order to enable advanced algorithms such as the Tree-Structured Parzen Estimator (TPE) based optimization, add the following flag to the installation command:
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```sh
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python setup.py install --install-extras
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```
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3. Now the POT is available in the command line by the `pot` alias. To verify it, run `pot -h`.
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