98 lines
3.0 KiB
Markdown
98 lines
3.0 KiB
Markdown
# Install Intel® Distribution of OpenVINO™ Toolkit from PyPI Repository {#openvino_docs_install_guides_installing_openvino_pip}
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You can install both OpenVINO™ Runtime and OpenVINO Development Tools through the PyPI repository. This page provides the main steps for installing OpenVINO Runtime.
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> **NOTE**: From the 2022.1 release, the OpenVINO™ Development Tools can only be installed via PyPI. See [Install OpenVINO Development Tools](installing-model-dev-tools.md) for detailed steps.
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## Installing OpenVINO Runtime
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For system requirements and troubleshooting, see <https://pypi.org/project/openvino/>.
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### Step 1. Set Up Python Virtual Environment
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Use a virtual environment to avoid dependency conflicts.
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To create a virtual environment, use the following command:
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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:: On Linux and macOS
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.. code-block:: sh
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source openvino_env/bin/activate
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.. tab:: On 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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Use the following command:
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```
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pip install openvino
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```
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### Step 5. Verify that the Package Is Installed
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Run the command below:
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```sh
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python -c "from openvino.runtime import Core"
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```
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If installation was successful, you will not see any error messages (no console output).
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## Installing OpenVINO Development Tools
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OpenVINO Development Tools include Model Optimizer, Benchmark Tool, Accuracy Checker, Post-Training Optimization Tool and Open Model Zoo tools including Model Downloader. If you want to install 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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See [Install OpenVINO™ Development Tools](installing-model-dev-tools.md) for detailed steps.
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## What's Next?
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Now you may continue with the following tasks:
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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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## Additional Resources
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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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