Installation & Deployment¶

One of the main concepts for OpenVINO™ API 2.0 is being “easy to use”, which includes:

Simplification of migration from different frameworks to OpenVINO.
Organization of OpenVINO.
Usage of development tools.
Development and deployment of OpenVINO-based applications.

To accomplish that, the 2022.1 release OpenVINO introduced significant changes to the installation and deployment processes. This guide will walk you through these changes.

The Installer Package Contains OpenVINO™ Runtime Only¶

Since OpenVINO 2022.1, development tools have been distributed only via PyPI, and are no longer included in the OpenVINO installer package. For a list of these components, refer to the installation overview guide. Benefits of this approach include:

simplification of the user experience - in previous versions, installation and usage of OpenVINO Development Tools differed from one distribution type to another (the OpenVINO installer vs. PyPI),
ensuring that dependencies are handled properly via the PIP package manager, and support virtual environments of development tools.

The structure of the OpenVINO 2022.1 installer package has been organized as follows:

The runtime folder includes headers, libraries and CMake interfaces.
The tools folder contains the compile tool, deployment manager, and a set of requirements.txt files with links to the corresponding versions of the openvino-dev package.
The python folder contains the Python version for OpenVINO Runtime.

Installing OpenVINO Development Tools via PyPI¶

Since OpenVINO Development Tools is no longer in the installer package, the installation process has also changed. This section describes it through a comparison with previous versions.

For Versions Prior to 2022.1¶

In previous versions, OpenVINO Development Tools was a part of the main package. After the package was installed, to convert models (for example, TensorFlow), you needed to install additional dependencies by using the requirement files, such as requirements_tf.txt, install Post-Training Optimization tool and Accuracy Checker tool via the setup.py scripts, and then use the setupvars scripts to make the tools available to the following command:

$ mo.py -h

For 2022.1 and After¶

In OpenVINO 2022.1 and later, you can install the development tools only from a PyPI repository, using the following command (taking TensorFlow as an example):

$ python3 -m pip install -r <INSTALL_DIR>/tools/requirements_tf.txt

This will install all the development tools and additional components necessary to work with TensorFlow via the openvino-dev package (see Step 4. Install the Package on the PyPI page for parameters of other frameworks).

Then, the tools can be used by commands like:

$ mo -h
$ pot -h

Installation of any other dependencies is not required. For more details on the installation steps, see the Install OpenVINO Development Tools.

Interface Changes for Building C/C++ Applications¶

The new OpenVINO Runtime with its API 2.0 has also brought some changes for building C/C++ applications.

CMake Interface¶

The CMake interface has been changed as follows:

With Inference Engine of previous versions :

find_package(InferenceEngine REQUIRED)
find_package(ngraph REQUIRED)
add_executable(ie_ngraph_app main.cpp)
target_link_libraries(ie_ngraph_app PRIVATE ${InferenceEngine_LIBRARIES} ${NGRAPH_LIBRARIES})

With OpenVINO Runtime 2022.1 (API 2.0) :

find_package(OpenVINO REQUIRED)
add_executable(ov_app main.cpp)
target_link_libraries(ov_app PRIVATE openvino::runtime)

add_executable(ov_c_app main.c)
target_link_libraries(ov_c_app PRIVATE openvino::runtime::c)

Native Interfaces¶

It is possible to build applications without the CMake interface by using: MSVC IDE, UNIX makefiles, and any other interface, which has been changed as shown here:

With Inference Engine of previous versions :

Include dirs

<INSTALL_DIR>/deployment_tools/inference_engine/include
<INSTALL_DIR>/deployment_tools/ngraph/include

Path to libs

<INSTALL_DIR>/deployment_tools/inference_engine/lib/intel64/Release
<INSTALL_DIR>/deployment_tools/ngraph/lib/

Shared libs

// UNIX systems
inference_engine.so ngraph.so

// Windows
inference_engine.dll ngraph.dll

(Windows) .lib files

ngraph.lib
inference_engine.lib

With OpenVINO Runtime 2022.1 (API 2.0) :

Include dirs

<INSTALL_DIR>/runtime/include

Path to libs

<INSTALL_DIR>/runtime/lib/intel64/Release

Shared libs

// UNIX systems
openvino.so

// Windows
openvino.dll

(Windows) .lib files

openvino.lib

Clearer Library Structure for Deployment¶

OpenVINO 2022.1 introduced a reorganization of the libraries, to make deployment easier. In the previous versions, it was required to use several libraries to perform deployment steps. Now you can just use openvino or openvino_c based on your developing language, with the necessary plugins to complete your task. For example, openvino_intel_cpu_plugin and openvino_ir_frontend plugins will enable loading OpenVINO IRs and performing inference on the CPU device (for more details, see the Local distribution with OpenVINO).

Below are detailed comparisons of the library structure between OpenVINO 2022.1 and the previous versions:

Starting with 2022.1 release, a single core library with all the functionalities (openvino for C++ Runtime, openvino_c for Inference Engine API C interface) is used, instead of the previous core libraries which contained inference_engine, ngraph, inference_engine_transformations and inference_engine_lp_transformations.
The optional inference_engine_preproc preprocessing library (if InferenceEngine::PreProcessInfo::setColorFormat or InferenceEngine::PreProcessInfo::setResizeAlgorithm is used) has been renamed to openvino_gapi_preproc and deprecated in 2022.1. For more details, see the Preprocessing capabilities of OpenVINO API 2.0.
The libraries of plugins have been renamed as follows:
- openvino_intel_cpu_plugin is used for CPU device instead of MKLDNNPlugin.
- openvino_intel_gpu_plugin is used for GPU device instead of clDNNPlugin.
- openvino_auto_plugin is used for Auto-Device Plugin.
The plugins for reading and converting models have been changed as follows:
- openvino_ir_frontend is used to read IRs instead of inference_engine_ir_reader.
- openvino_onnx_frontend is used to read ONNX models instead of inference_engine_onnx_reader (with its dependencies).
- openvino_paddle_frontend is added in 2022.1 to read PaddlePaddle models.