Apply same changes from 22/1 to master (#11035)
* Add Overview page * Revert "Add Overview page" * fix errors & formatting * fix article usage according to the styles * fix errors * update according to PXT comments * CVS-80775 * update support matrix with Python version * fix formatting * fix formatting * CVS-71745 * update formatting * fix formatting * fix formatting * fix links & errors * fix formatting * update bullet points * update * adjust the order * update * update * updates * update references * update * update * apply same updates with 22/1 * minor fix
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Yuan Xu
GitHub
@@ -10,6 +10,88 @@ If you want to download, convert, optimize and tune pre-trained deep learning mo
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> **NOTE**: From the 2022.1 release, the OpenVINO™ Development Tools can only be installed via PyPI.
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## For Python Developers
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If you are a Python developer, you can find the main steps below to install OpenVINO Development Tools. For more details, see <https://pypi.org/project/openvino-dev>.
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While installing OpenVINO Development Tools, OpenVINO Runtime will also be installed as a dependency, so you don't need to install OpenVINO Runtime separately.
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### Step 1. Set Up Python Virtual Environment
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To avoid dependency conflicts, use a virtual environment. Skip this step only if you do want to install all dependencies globally.
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Use the following command to create a virtual environment:
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@sphinxdirective
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.. tab:: Linux and macOS
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.. code-block:: sh
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python3 -m venv openvino_env
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.. tab:: Windows
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.. code-block:: sh
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python -m venv openvino_env
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@endsphinxdirective
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### Step 2. Activate Virtual Environment
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@sphinxdirective
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.. tab:: Linux and macOS
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.. code-block:: sh
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source openvino_env/bin/activate
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.. tab:: Windows
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.. code-block:: sh
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openvino_env\Scripts\activate
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@endsphinxdirective
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### Step 3. Set Up and Update PIP to the Highest Version
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Use the following command:
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```sh
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python -m pip install --upgrade pip
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```
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### Step 4. Install the Package
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To install and configure the components of the development package for working with specific frameworks, use the following command:
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```
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pip install openvino-dev[extras]
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```
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where the `extras` parameter specifies one or more deep learning frameworks via these values: `caffe`, `kaldi`, `mxnet`, `onnx`, `pytorch`, `tensorflow`, `tensorflow2`. Make sure that you install the corresponding frameworks for your models.
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For example, to install and configure the components for working with TensorFlow 2.x and ONNX, use the following command:
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```
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pip install openvino-dev[tensorflow2,onnx]
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```
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> **NOTE**: For TensorFlow, use the `tensorflow2` value as much as possible. The `tensorflow` value is provided only for compatibility reasons.
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### Step 5. Verify the Installation
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To verify if the package is properly installed, run the command below (this may take a few seconds):
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```sh
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mo -h
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```
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You will see the help message for Model Optimizer if installation finished successfully.
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## For C++ Developers
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Note the following things:
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@@ -42,27 +124,24 @@ where the EXTRAS parameter specifies one or more deep learning frameworks via th
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If you have installed OpenVINO Runtime via the installer, to avoid version conflicts, specify your version in the command. For example:
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```
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pip install openvino-dev[tensorflow2,mxnet,caffe]==2022.1
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pip install openvino-dev[tensorflow2,onnx]==2022.1
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```
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> **NOTE**: For TensorFlow, use the `tensorflow2` value as much as possible. The `tensorflow` value is provided only for compatibility reasons.
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For more details, see <https://pypi.org/project/openvino-dev/>.
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## For Python Developers
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## What's Next?
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You can use the following command to install the latest package version available in the index:
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```
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pip install openvino-dev[EXTRAS]
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```
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where the EXTRAS parameter specifies one or more deep learning frameworks via these values: `caffe`, `kaldi`, `mxnet`, `onnx`, `pytorch`, `tensorflow`, `tensorflow2`. Make sure that you install the corresponding frameworks for your models.
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Now you may continue with the following tasks:
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For example, to install and configure the components for working with TensorFlow 2.x, MXNet and Caffe, use the following command:
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```
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pip install openvino-dev[tensorflow2,mxnet,caffe]
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```
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* To convert models for use with OpenVINO, see [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
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* See pre-trained deep learning models in our [Open Model Zoo](../model_zoo.md).
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* Try out OpenVINO via [OpenVINO Notebooks](https://docs.openvino.ai/latest/notebooks/notebooks.html).
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* To write your own OpenVINO™ applications, see [OpenVINO Runtime User Guide](../OV_Runtime_UG/openvino_intro.md).
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* See sample applications in [OpenVINO™ Toolkit Samples Overview](../OV_Runtime_UG/Samples_Overview.md).
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> **NOTE**: For TensorFlow, use the `tensorflow2` value as much as possible. The `tensorflow` value is provided only for compatibility reasons.
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## Additional Resources
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For more details, see <https://pypi.org/project/openvino-dev/>.
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- Intel® Distribution of OpenVINO™ toolkit home page: <https://software.intel.com/en-us/openvino-toolkit>
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- For IoT Libraries & Code Samples, see [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
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