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update version numbers & windows installation (#14777)

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Yuan Xu 2022-12-22 17:31:59 +08:00 committed by GitHub
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docs/install_guides/configurations-for-iei-card.md
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@ -19,7 +19,7 @@ Using the IEI Mustang-V100-MX8 requires downloading and installing the most curr
Visit the [IEI Download Center](https://download.ieiworld.com/) for the most current software and documentation.
Search for **Mustang-V100-MX8**.
Download the appropriate software for your system, decompress the downloaded archive, enter the newly created directory, and run the install script:
Download the appropriate software for your system, extract the downloaded archive file, enter the newly created directory, and run the install script:
On **Linux**:
- Run the `install.sh script` with `sudo`, or as `root`.

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docs/install_guides/configurations-for-intel-gpu.md
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@ -11,7 +11,7 @@
## Linux
If you have installed OpenVINO Runtime from the archive file, follow these steps to work with GPU:
If you have installed OpenVINO Runtime from the archive file, APT, or YUM, follow these steps to work with GPU:
1. Go to the install_dependencies directory:
```sh

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docs/install_guides/installing-openvino-from-archive-windows.md
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@ -1,6 +1,6 @@
# Install OpenVINO™ Runtime on Windows from an Archive File {#openvino_docs_install_guides_installing_openvino_from_archive_windows}
With the OpenVINO™ 2022.2 release, you can download and use archive files to install OpenVINO Runtime. The archive files contain pre-built binaries and library files needed for OpenVINO Runtime, as well as code samples.
With the OpenVINO™ 2022.3 release, you can download and use archive files to install OpenVINO Runtime. The archive files contain pre-built binaries and library files needed for OpenVINO Runtime, as well as code samples.
Installing OpenVINO Runtime from archive files is recommended for C++ developers. If you are working with Python, the PyPI package has everything needed for Python development and deployment on CPU and GPUs. See the [Install OpenVINO from PyPI](installing-openvino-pip.md) page for instructions on how to install OpenVINO Runtime for Python using PyPI.
@ -65,29 +65,29 @@ See the [Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNo
```
> **NOTE**: `C:\Program Files (x86)\Intel` is the recommended folder. You may also use a different path if desired or if you don't have administrator privileges on your computer.
2. Download the [OpenVINO Runtime archive file for Windows](https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.2/windows/) to your local `Downloads` folder.
2. Download the [OpenVINO Runtime archive file for Windows](https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3/windows/) to your local `Downloads` folder.
If you prefer using command-lines, run the following commands in the command prompt window you opened:
```sh
cd <user_home>/Downloads
curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.2/windows/w_openvino_toolkit_windows_2022.2.0.7713.af16ea1d79a_x86_64.zip --output openvino_2022.2.0.7713.zip
curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3/windows/w_openvino_toolkit_windows_2022.3.0.9052.9752fafe8eb_x86_64.zip --output openvino_2022.3.0.zip
```
> **NOTE**: A `.sha256` file is provided together with the archive file to validate your download process. To do that, download the `.sha256` file from the same repository and run `CertUtil -hashfile openvino_2022.2.0.7713.zip SHA256`. Compare the returned value in the output with what's in the `.sha256` file: if the values are the same, you have downloaded the correct file successfully; if not, create a Support ticket [here](https://www.intel.com/content/www/us/en/support/contact-intel.html).
> **NOTE**: A `.sha256` file is provided together with the archive file to validate your download process. To do that, download the `.sha256` file from the same repository and run `CertUtil -hashfile openvino_2022.3.0.zip SHA256`. Compare the returned value in the output with what's in the `.sha256` file: if the values are the same, you have downloaded the correct file successfully; if not, create a Support ticket [here](https://www.intel.com/content/www/us/en/support/contact-intel.html).
3. Use your favorite tool to extract the archive file, rename the extracted folder as `openvino_2022.2.0.7713`, and move it to the `C:\Program Files (x86)\Intel` directory.
3. Use your favorite tool to extract the archive file, rename the extracted folder, and move it to the `C:\Program Files (x86)\Intel` directory.
To do this step using command-lines, run the following commands in the command prompt window you opened:
```sh
tar -xf openvino_2022.2.0.7713.zip
ren w_openvino_toolkit_windows_2022.2.0.7713.af16ea1d79a_x86_64 openvino_2022.2.0.7713
move openvino_2022.2.0.7713 "C:\Program Files (x86)\Intel"
tar -xf openvino_2022.3.0.zip
ren w_openvino_toolkit_windows_2022.3.0.9052.9752fafe8eb_x86_64 openvino_2022.3.0
move openvino_2022.3.0 "C:\Program Files (x86)\Intel"
```
4. For simplicity, it is useful to create a symbolic link. Open a command prompt window as administrator (see Step 1 for how to do this) and run the following commands:
```sh
cd C:\Program Files (x86)\Intel
mklink /D openvino_2022 openvino_2022.2.0.7713
mklink /D openvino_2022 openvino_2022.3.0
```
> **NOTE**: If you have already installed a previous release of OpenVINO 2022, a symbolic link to the `openvino_2022` folder may already exist. If you want to override it, nagivate to the `C:\Program Files (x86)\Intel` folder and delete the existing linked folder before running the `mklink` command.
@ -144,16 +144,16 @@ Now that you've installed OpenVINO Runtime, you're ready to run your own machine
@sphinxdirective
.. tab:: Get started with Python
Try the `Python Quick Start Example <https://docs.openvino.ai/2022.2/notebooks/201-vision-monodepth-with-output.html>`_ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
Try the `Python Quick Start Example <https://docs.openvino.ai/2022.3/notebooks/201-vision-monodepth-with-output.html>`_ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
.. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
:width: 400
Visit the :ref:`Tutorials <notebook tutorials>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
* `OpenVINO Python API Tutorial <https://docs.openvino.ai/2022.2/notebooks/002-openvino-api-with-output.html>`_
* `Basic image classification program with Hello Image Classification <https://docs.openvino.ai/2022.2/notebooks/001-hello-world-with-output.html>`_
* `Convert a PyTorch model and use it for image background removal <https://docs.openvino.ai/2022.2/notebooks/205-vision-background-removal-with-output.html>`_
* `OpenVINO Python API Tutorial <https://docs.openvino.ai/2022.3/notebooks/002-openvino-api-with-output.html>`_
* `Basic image classification program with Hello Image Classification <https://docs.openvino.ai/2022.3/notebooks/001-hello-world-with-output.html>`_
* `Convert a PyTorch model and use it for image background removal <https://docs.openvino.ai/2022.3/notebooks/205-vision-background-removal-with-output.html>`_
.. tab:: Get started with C++

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docs/install_guides/installing-openvino-linux.md
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@ -3,6 +3,8 @@
Currently only the following ways are provided to install OpenVINO™:
* [Install OpenVINO Runtime from an Archive File](installing-openvino-from-archive-linux.md)
* [Install OpenVINO Runtime from APT](@ref openvino_docs_install_guides_installing_openvino_apt)
* [Install OpenVINO Runtime from YUM](@ref openvino_docs_install_guides_installing_openvino_yum)
* [Install OpenVINO from PyPI](installing-openvino-pip.md)
* [Install OpenVINO with Docker](installing-openvino-docker-linux.md)
* [Build From Source](https://github.com/openvinotoolkit/openvino/wiki/BuildingCode)

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docs/install_guides/installing-openvino-pip.md
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@ -86,13 +86,13 @@ Now that you've installed OpenVINO Runtime, you're ready to run your own machine
<img src="https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif" width=400>
Try the [Python Quick Start Example](https://docs.openvino.ai/2022.2/notebooks/201-vision-monodepth-with-output.html) to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
Try the [Python Quick Start Example](https://docs.openvino.ai/2022.3/notebooks/201-vision-monodepth-with-output.html) to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
### Get started with Python
Visit the [Tutorials](../tutorials.md) page for more Jupyter Notebooks to get you started with OpenVINO, such as:
* [OpenVINO Python API Tutorial](https://docs.openvino.ai/2022.2/notebooks/002-openvino-api-with-output.html)
* [Basic image classification program with Hello Image Classification](https://docs.openvino.ai/2022.2/notebooks/001-hello-world-with-output.html)
* [Convert a PyTorch model and use it for image background removal](https://docs.openvino.ai/2022.2/notebooks/205-vision-background-removal-with-output.html)
* [OpenVINO Python API Tutorial](https://docs.openvino.ai/2022.3/notebooks/002-openvino-api-with-output.html)
* [Basic image classification program with Hello Image Classification](https://docs.openvino.ai/2022.3/notebooks/001-hello-world-with-output.html)
* [Convert a PyTorch model and use it for image background removal](https://docs.openvino.ai/2022.3/notebooks/205-vision-background-removal-with-output.html)
### Run OpenVINO on accelerated devices
OpenVINO Runtime has a plugin architecture that enables you to run inference on multiple devices without rewriting your code. Supported devices include integrated GPUs, discrete GPUs, NCS2, VPUs, and GNAs. Visit the [Additional Configurations](configurations-header.md) page for instructions on how to configure your hardware devices to work with OpenVINO.

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docs/install_guides/pypi-openvino-dev.md
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@ -119,15 +119,15 @@ For example, to install and configure the components for working with TensorFlow
| Component | Console Script | Description |
|------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Model Optimizer](https://docs.openvino.ai/nightly/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | `mo` |**Model Optimizer** imports, converts, and optimizes models that were trained in popular frameworks to a format usable by OpenVINO components. <br>Supported frameworks include Caffe\*, TensorFlow\*, MXNet\*, PaddlePaddle\*, and ONNX\*. |
| [Benchmark Tool](https://docs.openvino.ai/nightly/openvino_inference_engine_tools_benchmark_tool_README.html)| `benchmark_app` | **Benchmark Application** allows you to estimate deep learning inference performance on supported devices for synchronous and asynchronous modes. |
| [Accuracy Checker](https://docs.openvino.ai/nightly/omz_tools_accuracy_checker.html) and <br> [Annotation Converter](https://docs.openvino.ai/nightly/omz_tools_accuracy_checker_annotation_converters.html) | `accuracy_check` <br> `convert_annotation` |**Accuracy Checker** is a deep learning accuracy validation tool that allows you to collect accuracy metrics against popular datasets. The main advantages of the tool are the flexibility of configuration and a set of supported datasets, preprocessing, postprocessing, and metrics. <br> **Annotation Converter** is a utility that prepares datasets for evaluation with Accuracy Checker. |
| [Post-Training Optimization Tool](https://docs.openvino.ai/nightly/pot_introduction.html)| `pot` |**Post-Training Optimization Tool** allows you to optimize trained models with advanced capabilities, such as quantization and low-precision optimizations, without the need to retrain or fine-tune models. |
| [Model Downloader and other Open Model Zoo tools](https://docs.openvino.ai/nightly/omz_tools_downloader.html)| `omz_downloader` <br> `omz_converter` <br> `omz_quantizer` <br> `omz_info_dumper`| **Model Downloader** is a tool for getting access to the collection of high-quality and extremely fast pre-trained deep learning [public](@ref omz_models_group_public) and [Intel](@ref omz_models_group_intel)-trained models. These free pre-trained models can be used to speed up the development and production deployment process without training your own models. The tool downloads model files from online sources and, if necessary, patches them to make them more usable with Model Optimizer. A number of additional tools are also provided to automate the process of working with downloaded models:<br> **Model Converter** is a tool for converting Open Model Zoo models that are stored in an original deep learning framework format into the OpenVINO Intermediate Representation (IR) using Model Optimizer. <br> **Model Quantizer** is a tool for automatic quantization of full-precision models in the IR format into low-precision versions using the Post-Training Optimization Tool. <br> **Model Information Dumper** is a helper utility for dumping information about the models to a stable, machine-readable format. |
| [Model Optimizer](https://docs.openvino.ai/2022.3/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | `mo` |**Model Optimizer** imports, converts, and optimizes models that were trained in popular frameworks to a format usable by OpenVINO components. <br>Supported frameworks include Caffe\*, TensorFlow\*, MXNet\*, PaddlePaddle\*, and ONNX\*. |
| [Benchmark Tool](https://docs.openvino.ai/2022.3/openvino_inference_engine_tools_benchmark_tool_README.html)| `benchmark_app` | **Benchmark Application** allows you to estimate deep learning inference performance on supported devices for synchronous and asynchronous modes. |
| [Accuracy Checker](https://docs.openvino.ai/2022.3/omz_tools_accuracy_checker.html) and <br> [Annotation Converter](https://docs.openvino.ai/2022.3/omz_tools_accuracy_checker_annotation_converters.html) | `accuracy_check` <br> `convert_annotation` |**Accuracy Checker** is a deep learning accuracy validation tool that allows you to collect accuracy metrics against popular datasets. The main advantages of the tool are the flexibility of configuration and a set of supported datasets, preprocessing, postprocessing, and metrics. <br> **Annotation Converter** is a utility that prepares datasets for evaluation with Accuracy Checker. |
| [Post-Training Optimization Tool](https://docs.openvino.ai/2022.3/pot_introduction.html)| `pot` |**Post-Training Optimization Tool** allows you to optimize trained models with advanced capabilities, such as quantization and low-precision optimizations, without the need to retrain or fine-tune models. |
| [Model Downloader and other Open Model Zoo tools](https://docs.openvino.ai/2022.3/omz_tools_downloader.html)| `omz_downloader` <br> `omz_converter` <br> `omz_quantizer` <br> `omz_info_dumper`| **Model Downloader** is a tool for getting access to the collection of high-quality and extremely fast pre-trained deep learning [public](@ref omz_models_group_public) and [Intel](@ref omz_models_group_intel)-trained models. These free pre-trained models can be used to speed up the development and production deployment process without training your own models. The tool downloads model files from online sources and, if necessary, patches them to make them more usable with Model Optimizer. A number of additional tools are also provided to automate the process of working with downloaded models:<br> **Model Converter** is a tool for converting Open Model Zoo models that are stored in an original deep learning framework format into the OpenVINO Intermediate Representation (IR) using Model Optimizer. <br> **Model Quantizer** is a tool for automatic quantization of full-precision models in the IR format into low-precision versions using the Post-Training Optimization Tool. <br> **Model Information Dumper** is a helper utility for dumping information about the models to a stable, machine-readable format. |
## Troubleshooting
For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](https://docs.openvino.ai/nightly/openvino_docs_get_started_guide_troubleshooting.html). The following sections also provide explanations to several error messages.
For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting.html). The following sections also provide explanations to several error messages.
### Errors with Installing via PIP for Users in China

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docs/install_guides/pypi-openvino-rt.md
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@ -2,7 +2,7 @@
Intel® Distribution of OpenVINO™ toolkit is an open-source toolkit for optimizing and deploying AI inference. It can be used to develop applications and solutions based on deep learning tasks, such as: emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, etc. It provides high-performance and rich deployment options, from edge to cloud.
If you have already finished developing your models and converting them to the OpenVINO model format, you can install OpenVINO Runtime to deploy your applications on various devices. The [OpenVINO™ Runtime](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) Python package includes a set of libraries for an easy inference integration with your products.
If you have already finished developing your models and converting them to the OpenVINO model format, you can install OpenVINO Runtime to deploy your applications on various devices. The [OpenVINO™ Runtime](https://docs.openvino.ai/2022.3/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) Python package includes a set of libraries for an easy inference integration with your products.
## System Requirements
Before you start the installation, check the supported operating systems and required Python* versions. The complete list of supported hardware is available in the [Release Notes](https://www.intel.com/content/www/us/en/developer/articles/release-notes/openvino-relnotes.html).
@ -80,7 +80,7 @@ If installation was successful, you will not see any error messages (no console
## Troubleshooting
For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](https://docs.openvino.ai/nightly/openvino_docs_get_started_guide_troubleshooting.html). The following sections also provide explanations to several error messages.
For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting.html). The following sections also provide explanations to several error messages.
### Errors with Installing via PIP for Users in China