217 lines
9.2 KiB
ReStructuredText
217 lines
9.2 KiB
ReStructuredText
.. {#openvino_docs_install_guides_install_dev_tools}
|
||
|
||
Install OpenVINO™ Development Tools
|
||
=====================================
|
||
|
||
|
||
.. meta::
|
||
:description: Learn how to install OpenVINO™ Development Tools on Windows,
|
||
Linux, and macOS operating systems, using a PyPi package.
|
||
|
||
OpenVINO Development Tools is a set of utilities that make it easy to develop and optimize models and applications for OpenVINO. It provides the following tools:
|
||
|
||
* Model conversion API
|
||
* Benchmark Tool
|
||
* Accuracy Checker and Annotation Converter
|
||
* Model Downloader and other Open Model Zoo tools
|
||
|
||
The instructions on this page show how to install OpenVINO Development Tools. If you are a Python developer, it only takes a few simple steps to install the tools with PyPI. If you are developing in C/C++, OpenVINO Runtime must be installed separately before installing OpenVINO Development Tools.
|
||
|
||
In both cases, Python 3.8 - 3.11 needs to be installed on your machine before starting.
|
||
|
||
.. note::
|
||
|
||
From the 2022.1 release, the OpenVINO™ Development Tools can only be installed via PyPI.
|
||
|
||
.. _python_developers:
|
||
|
||
For Python Developers
|
||
#####################
|
||
|
||
If you are a Python developer, follow the steps in the :ref:`Installing OpenVINO Development Tools <install_dev_tools>` section on this page to install it. Installing OpenVINO Development Tools will also install OpenVINO Runtime as a dependency, so you don’t need to install OpenVINO Runtime separately. This option is recommended for new users.
|
||
|
||
.. _cpp_developers:
|
||
|
||
For C/C++ Developers
|
||
#######################
|
||
|
||
If you are a C/C++ developer, you must first install OpenVINO Runtime separately to set up the C/C++ libraries, sample code, and dependencies for building applications with OpenVINO. These files are not included with the PyPI distribution. See the :doc:`Selector Tool <../../get-started/install-openvino>` page to install OpenVINO Runtime from an archive file for your operating system.
|
||
|
||
Once OpenVINO Runtime is installed, you may install OpenVINO Development Tools for access to tools like ``mo``, Model Downloader, Benchmark Tool, and other utilities that will help you optimize your model and develop your application. Follow the steps in the :ref:`Installing OpenVINO Development Tools <install_dev_tools>` section on this page to install it.
|
||
|
||
.. _install_dev_tools:
|
||
|
||
Installing OpenVINO™ Development Tools
|
||
######################################
|
||
|
||
Follow these step-by-step instructions to install OpenVINO Development Tools on your computer.
|
||
There are two options to install OpenVINO Development Tools: installation into an existing environment with a deep learning framework that was used
|
||
for model training or creation; or installation into a new environment.
|
||
|
||
Installation into an Existing Environment with the Source Deep Learning Framework
|
||
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
|
||
|
||
To install OpenVINO Development Tools (see the :ref:`Install the Package <install_the_package>` section of this article) into an existing environment
|
||
with the deep learning framework used for the model training or creation, run the following command:
|
||
|
||
.. code-block:: sh
|
||
|
||
pip install openvino-dev
|
||
|
||
|
||
Installation in a New Environment
|
||
+++++++++++++++++++++++++++++++++
|
||
|
||
If you do not have an environment with a deep learning framework for the input model or you encounter any compatibility issues between OpenVINO
|
||
and your version of deep learning framework, you may install OpenVINO Development Tools with validated versions of frameworks into a new environment.
|
||
|
||
Step 1. Set Up Python Virtual Environment
|
||
-----------------------------------------
|
||
|
||
Create a virtual Python environment to avoid dependency conflicts. To create a virtual environment, use the following command:
|
||
|
||
.. tab-set::
|
||
|
||
.. tab-item:: Windows
|
||
:sync: windows
|
||
|
||
.. code-block:: sh
|
||
|
||
python -m venv openvino_env
|
||
|
||
.. tab-item:: Linux and macOS
|
||
:sync: linux-and-macos
|
||
|
||
.. code-block:: sh
|
||
|
||
python3 -m venv openvino_env
|
||
|
||
|
||
|
||
Step 2. Activate Virtual Environment
|
||
------------------------------------
|
||
|
||
Activate the newly created Python virtual environment by issuing this command:
|
||
|
||
.. tab-set::
|
||
|
||
.. tab-item:: Windows
|
||
:sync: windows
|
||
|
||
.. code-block:: sh
|
||
|
||
openvino_env\Scripts\activate
|
||
|
||
.. tab-item:: Linux and macOS
|
||
:sync: linux-and-macos
|
||
|
||
.. code-block:: sh
|
||
|
||
source openvino_env/bin/activate
|
||
|
||
.. important::
|
||
|
||
The above command must be re-run every time a new command terminal window is opened.
|
||
|
||
|
||
Step 3. Set Up and Update PIP to the Highest Version
|
||
----------------------------------------------------
|
||
|
||
Make sure `pip` is installed in your environment and upgrade it to the latest version by issuing the following command:
|
||
|
||
.. code-block:: sh
|
||
|
||
python -m pip install --upgrade pip
|
||
|
||
|
||
.. _install_the_package:
|
||
|
||
Step 4. Install the Package
|
||
---------------------------
|
||
|
||
To install and configure the components of the development package together with validated versions of specific frameworks, use the commands below.
|
||
|
||
.. code-block:: sh
|
||
|
||
pip install openvino-dev[extras]
|
||
|
||
|
||
where the ``extras`` parameter specifies the source deep learning framework for the input model
|
||
and is one or more of the following values separated with "," : ``onnx``, ``pytorch``, ``tensorflow``, ``tensorflow2``.
|
||
|
||
For example, to install and configure dependencies required for working with TensorFlow 2.x and ONNX models, use the following command:
|
||
|
||
.. code-block:: sh
|
||
|
||
pip install openvino-dev[tensorflow2,onnx]
|
||
|
||
|
||
.. note::
|
||
|
||
Model conversion API support for TensorFlow 1.x environment has been deprecated. Use the ``tensorflow2`` parameter to install a TensorFlow 2.x environment that can convert both TensorFlow 1.x and 2.x models. If your model isn't compatible with the TensorFlow 2.x environment, use the `tensorflow` parameter to install the TensorFlow 1.x environment. The TF 1.x environment is provided only for legacy compatibility reasons.
|
||
|
||
For more details on the openvino-dev PyPI package, see `pypi.org <https://pypi.org/project/openvino-dev/2023.2.0>`__ .
|
||
|
||
Step 5. Test the Installation
|
||
+++++++++++++++++++++++++++++
|
||
|
||
To verify the package is properly installed, run the command below (this may take a few seconds):
|
||
|
||
.. code-block:: sh
|
||
|
||
mo -h
|
||
|
||
You will see the help message for ``mo`` if installation finished successfully. If you get an error, refer to the :doc:`Troubleshooting Guide <../../get-started/troubleshooting-install-config>` for possible solutions.
|
||
|
||
Congratulations! You finished installing OpenVINO Development Tools with C/C++ capability. Now you can start exploring OpenVINO's functionality through example C/C++ applications. See the "What's Next?" section to learn more!
|
||
|
||
What's Next?
|
||
############
|
||
|
||
Learn more about OpenVINO and use it in your own application by trying out some of these examples!
|
||
|
||
Get started with Python
|
||
+++++++++++++++++++++++
|
||
|
||
.. image:: ../../_static/images/get_started_with_python.gif
|
||
:width: 400
|
||
|
||
Try the `Python Quick Start Example <../../notebooks/vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
|
||
|
||
Visit the :doc:`Tutorials <../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
|
||
|
||
* `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
|
||
* `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
|
||
* `Convert a PyTorch model and use it for image background removal <../../notebooks/vision-background-removal-with-output.html>`__
|
||
|
||
Get started with C++
|
||
++++++++++++++++++++
|
||
|
||
.. image:: ../../_static/images/get_started_with_cpp.jpg
|
||
:width: 400
|
||
|
||
|
||
Try the :doc:`C++ Quick Start Example <../../learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions on building and running a basic image classification C++ application.
|
||
|
||
Visit the :doc:`Samples <../../learn-openvino/openvino-samples>` page for other C++ example applications to get you started with OpenVINO, such as:
|
||
|
||
* :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../learn-openvino/openvino-samples/hello-reshape-ssd>`
|
||
* :doc:`Object classification sample <../../learn-openvino/openvino-samples/hello-classification>`
|
||
|
||
Learn OpenVINO Development Tools
|
||
++++++++++++++++++++++++++++++++
|
||
|
||
* Explore a variety of pre-trained deep learning models in the :doc:`Open Model Zoo <model-zoo>` and deploy them in demo applications to see how they work.
|
||
* Want to import a model from another framework and optimize its performance with OpenVINO? Visit the :doc:`Convert a Model <transition-legacy-conversion-api/legacy-conversion-api>` page.
|
||
* Accelerate your model's speed even further with quantization and other compression techniques using :doc:`Neural Network Compression Framework (NNCF) <../../openvino-workflow/model-optimization-guide/quantizing-models-post-training>`.
|
||
* Benchmark your model's inference speed with one simple command using the :doc:`Benchmark Tool <../../learn-openvino/openvino-samples/benchmark-tool>`.
|
||
|
||
Additional Resources
|
||
####################
|
||
|
||
- `Intel® Distribution of OpenVINO™ toolkit home page <https://software.intel.com/en-us/openvino-toolkit>`__
|
||
- For IoT Libraries & Code Samples, see `Intel® IoT Developer Kit <https://github.com/intel-iot-devkit>`__ .
|
||
|
||
|
||
|