Installing Model Optimizer Pre-Requisites¶
cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
./install_prerequisites.sh
cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
virtualenv --system-site-packages -p python3 ./venv
source ./venv/bin/activate # sh, bash, ksh, or zsh
./install_prerequisites.shs
cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisitess
install_prerequisites_caffe.sh
cd <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
install_prerequisites_caffe.shs
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Before running the Model Optimizer, you must install the Model Optimizer pre-requisites for the framework that was used to train the model. This section tells you how to install the pre-requisites either through scripts or by using a manual process.
Using Configuration Scripts¶
You can either configure all three frameworks at the same time or install an individual framework. The scripts delivered with the tool install all required dependencies and provide the fastest and easiest way to configure the Model Optimizer.
To configure all three frameworks, go to the <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
directory and run:
For Linux* OS:
install_prerequisites.sh
Note
This command installs prerequisites globally. If you want to keep Model Optimizer in a separate sandbox, run the following commands instead:
virtualenv --system-site-packages -p python3 ./venv
source ./venv/bin/activate # sh, bash, ksh, or zsh
./install_prerequisites.sh
For Windows* OS:
install_prerequisites.bat
To configure a specific framework, go to the <INSTALL_DIR>/deployment_tools/model_optimizer/install_prerequisites
directory and run:
For Caffe* on Linux:
install_prerequisites_caffe.sh
For Caffe on Windows:
install_prerequisites_caffe.bat
For TensorFlow* 1.x on Linux:
install_prerequisites_tf.sh
For TensorFlow 1.x on Windows:
install_prerequisites_tf.bat
For TensorFlow* 2.x on Linux:
install_prerequisites_tf2.sh
For TensorFlow 2.x on Windows:
install_prerequisites_tf2.bat
For MXNet* on Linux:
install_prerequisites_mxnet.sh
For MXNet on Windows:
install_prerequisites_mxnet.bat
For Kaldi* on Linux:
install_prerequisites_kaldi.sh
For Kaldi on Windows:
install_prerequisites_kaldi.bat
For ONNX* on Linux:
install_prerequisites_onnx.sh
For ONNX on Windows:
install_prerequisites_onnx.bat
Warning
ONLY FOR CAFFE* By default, you do not need to install Caffe to create an Intermediate Representation for a Caffe model, unless you use Caffe for custom layer shape inference and do not write Model Optimizer extensions. To learn more about implementing Model Optimizer custom operations and the limitations of using Caffe for shape inference, see Custom Layers in Model Optimizer.
Using Manual Configuration Process¶
If you prefer, you can manually configure the Model Optimizer for one framework at a time.
Go to the Model Optimizer directory:
cd <INSTALL_DIR>/deployment_tools/model_optimizer/
Strongly recommended for all global Model Optimizer dependency installations : Create and activate a virtual environment. While not required, this step is strongly recommended since the virtual environment creates a Python* sandbox, and dependencies for the Model Optimizer do not influence the global Python configuration, installed libraries, or other components. In addition, a flag ensures that system-wide Python libraries are available in this sandbox. Skip this step only if you do want to install all the Model Optimizer dependencies globally:
Create a virtual environment:
virtualenv -p /usr/bin/python3.6 .env3 --system-site-packages
Activate the virtual environment:
virtualenv -p /usr/bin/python3.6 .env3/bin/activate
Install all dependencies or only the dependencies for a specific framework:
To install dependencies for all frameworks except TensorFlow* 1.x:
pip3 install -r requirements.txt
Note
TensorFlow 1.x and 2.x are incompatible. Use separate virtual environments if you want to install multiple TensorFlow versions.
To install dependencies only for Caffe:
pip3 install -r requirements_caffe.txt
To install dependencies only for TensorFlow 1.x:
pip3 install -r requirements_tf.txt
To install dependencies only for TensorFlow 2.x:
pip3 install -r requirements_tf2.txt
To install dependencies only for MXNet:
pip3 install -r requirements_mxnet.txt
To install dependencies only for Kaldi:
pip3 install -r requirements_kaldi.txt
To install dependencies only for ONNX:
pip3 install -r requirements_onnx.txt
Using the protobuf Library in the Model Optimizer for Caffe*¶
These procedures require:
Access to GitHub and the ability to use git commands
Microsoft Visual Studio* 2013 for Win64* (if using Windows*)
C/C++
Model Optimizer uses the protobuf library to load trained Caffe models. By default, the library executes pure Python* language implementation, which is slow. These steps show how to use the faster C++ implementation of the protobuf library on Windows OS or Linux OS.
Using the protobuf Library on Linux* OS¶
To use the C++ implementation of the protobuf library on Linux, it is enough to set up the environment variable:
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
Using the protobuf Library on Windows* OS¶
On Windows, pre-built protobuf packages for Python versions 3.4, 3.5, 3.6, and 3.7 are provided with the installation package and can be found in the <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites
folder. Please note that they are not installed with the install_prerequisites.bat
installation script due to possible issues with pip
, and you can install them at your own discretion. Make sure that you install the protobuf version that matches the Python version you use:
protobuf-3.6.1-py3.4-win-amd64.egg
for Python 3.4protobuf-3.6.1-py3.5-win-amd64.egg
for Python 3.5protobuf-3.6.1-py3.6-win-amd64.egg
for Python 3.6protobuf-3.6.1-py3.7-win-amd64.egg
for Python 3.7
To install the protobuf package:
Open the command prompt as administrator.
Go to the
install_prerequisites
folder of the OpenVINO toolkit installation directory:cd <INSTALL_DIR>\deployment_tools\model_optimizer\install_prerequisites
Run the following command to install the protobuf for Python 3.6. If you want to install the protobuf for Python 3.4, 3.5, or 3.7, replace
protobuf-3.6.1-py3.6-win-amd64.egg
with the corresponding file name from the list above.python -m easy_install protobuf-3.6.1-py3.6-win-amd64.egg
If the Python version you use is lower than 3.4, you need to update it or build the library manually.
Building the protobuf Library on Windows* OS¶
Note
These steps are optional. If you use Python version 3.4, 3.5, 3.6, or 3.7, you can install the protobuf library using the pre-built packages.
To compile the protobuf library from sources on Windows OS, do the following:
Clone protobuf source files from GitHub:
git clone https://github.com/google/protobuf.git cd protobuf
Create a Visual Studio solution file. Run these commands:
cd C:\Path\to\protobuf\cmake\build mkdir solution cd solution C:\Path\to\protobuf\cmake\build\solution cmake -G "Visual Studio 12 2013 Win64" ../..
Change the runtime library option for
libprotobuf
andlibprotobuf-lite
:Open the project’s Property Pages dialog box
Expand the C/C++ tab
Select the Code Generation property page
Change the Runtime Library property to Multi-thread DLL (/MD)
Build the
libprotoc
,protoc
,libprotobuf
, andlibprotobuf-lite
projects in the Release configuration.Add a path to the build directory to the
PATH
environment variable:set PATH=%PATH%;C:\Path\to\protobuf\cmake\build\solution\Release
Go to the
python
directory:cd C:\Path\to\protobuf\python
Use a text editor to open and change these
setup.py
options:Change from
libraries = ['protobuf']
to
libraries = ['libprotobuf', 'libprotobuf-lite']
Change from
extra_objects = ['../src/.libs/libprotobuf.a', '../src/.libs/libprotobuf-lite.a']
to
extra_objects = ['../cmake/build/solution/Release/libprotobuf.lib', '../cmake/build/solution/Release/libprotobuf-lite.lib']
Build the Python package with the C++ implementation:
python setup.py build –cpp_implementation
Install the Python package with the C++ implementation:
python3 -m easy_install dist/protobuf-3.6.1-py3.6-win-amd64.egg
Set an environment variable to boost the protobuf performance:
set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp