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PyPi install guides improvements (#5756)

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* Update for install guides:

  - Human rights notice
  - Components descriptions update & console scripts
  - Extras requirements definition
  - Change verification step to Model Optimizer call

* Update docs/install_guides/pypi-openvino-dev.md

Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>

* Update docs/install_guides/pypi-openvino-dev.md

Co-authored-by: Roman Donchenko <roman.donchenko@intel.com>

* Update docs/install_guides/pypi-openvino-dev.md

Co-authored-by: Roman Donchenko <roman.donchenko@intel.com>

* Update docs/install_guides/pypi-openvino-dev.md

Co-authored-by: Roman Donchenko <roman.donchenko@intel.com>

* Update docs/install_guides/pypi-openvino-dev.md

Co-authored-by: Roman Donchenko <roman.donchenko@intel.com>

* order

* fix grammar

* Update according to recommendations from InfoDev

* high-quality

* Caffe2*

* Update document style

Add additional verification step

* specify Ubuntu version for troubleshooting

* Add reference  to POT API.

Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com>
Co-authored-by: Roman Donchenko <roman.donchenko@intel.com>
This commit is contained in:
Sergey Lyubimtsev
2021-05-31 14:15:51 +03:00
committed by GitHub
parent 874c07e68e
commit 9a86cbffa7
3 changed files with 54 additions and 26 deletions
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2
docs/install_guides/installing-openvino-pip.md
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@@ -5,7 +5,7 @@ OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applicatio
Intel® Distribution of OpenVINO™ Toolkit provides the following packages available for installation through the PyPI repository:
* Runtime package with the Inference Engine inside: [https://pypi.org/project/openvino/](https://pypi.org/project/openvino/).
* Developers package that includes the runtime package as a dependency, Model Optimizer, Accuracy Checker and Post-Training Optimization Tool: [https://pypi.org/project/openvino-dev](https://pypi.org/project/openvino-dev).
* Developer package that includes the runtime package as a dependency, Model Optimizer and other developer tools: [https://pypi.org/project/openvino-dev](https://pypi.org/project/openvino-dev).
## Additional Resources

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docs/install_guides/pypi-openvino-dev.md
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@@ -4,18 +4,25 @@ Copyright © 2018-2021 Intel Corporation
“Software Package”) is subject to the terms and conditions of the [software license agreements](https://software.intel.com/content/dam/develop/external/us/en/documents/intel-openvino-license-agreements.pdf) for the Software Package, which may also include notices, disclaimers, or
license terms for third party or open source software included in or with the Software Package, and your use indicates your acceptance of all such terms. Please refer to the “third-party-programs.txt” or other similarly-named text file included with the Software Package for additional details.
>Intel is committed to the respect of human rights and avoiding complicity in human rights abuses, a policy reflected in the [Intel Global Human Rights Principles](https://www.intel.com/content/www/us/en/policy/policy-human-rights.html). Accordingly, by accessing the Intel material on this platform you agree that you will not use the material in a product or application that causes or contributes to a violation of an internationally recognized human right.
## Introduction
OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI and deep learning inference deployed from edge to cloud.
**The Developer Package Includes the Following Components Installed by Default:**
The **developer package** includes the following components installed by default:
| Component | Description |
|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Model Optimizer](https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. <br>Popular frameworks include Caffe\*, TensorFlow\*, MXNet\*, and ONNX\*. |
| Additional Tools | A set of tools to work with your models including [Accuracy Checker utility](https://docs.openvinotoolkit.org/latest/omz_tools_accuracy_checker.html), [Post-Training Optimization Tool](https://docs.openvinotoolkit.org/latest/pot_README.html), [Benchmark Tool](../../inference-engine/samples/benchmark_app/README.md) |
| Component | Console Script | Description |
|------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Model Optimizer](https://docs.openvinotoolkit.org/latest/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 Intel tools, especially the Inference Engine. <br>Popular frameworks include Caffe\*, TensorFlow\*, MXNet\*, and ONNX\*. |
| [Benchmark Tool](https://docs.openvinotoolkit.org/latest/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.openvinotoolkit.org/latest/omz_tools_accuracy_checker.html) and <br> [Annotation Converter](https://docs.openvinotoolkit.org/latest/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 an impressive set of supported datasets, preprocessing, postprocessing, and metrics. <br> **Annotation Converter** is a utility for offline conversion of datasets to the format suitable for metric evaluation used in Accuracy Checker. |
| [Post-Training Optimization Tool](https://docs.openvinotoolkit.org/latest/pot_README.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. Optimizations are also available through the [API](https://docs.openvinotoolkit.org/latest/pot_compression_api_README.html). |
| [Model Downloader and other Open Model Zoo tools](https://docs.openvinotoolkit.org/latest/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](https://docs.openvinotoolkit.org/latest/omz_models_group_public.html) and [intel](https://docs.openvinotoolkit.org/latest/omz_models_group_intel.html)-trained models. Use these free pre-trained models instead of training your own models to speed up the development and production deployment process. The principle of the tool is as follows: it downloads model files from online sources and, if necessary, patches them with Model Optimizer to make them more usable. 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 the models stored in a format other than the Intermediate Representation (IR) into that format using Model Optimizer. <br> **Model Quantizer** is a tool for automatic quantization of full-precision IR models into low-precision versions using Post-Training Optimization Tool. <br> **Model Information Dumper** is a helper utility for dumping information about the models in a stable machine-readable format.|
**The Runtime Package Includes the Following Components Installed by Dependency:**
**Developer package** also provides the **runtime package** installed as a dependency. The runtime package includes the following components:
| Component | Description |
|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
@@ -78,35 +85,53 @@ python -m pip install --upgrade pip
### Step 4. Install the Package
Run the command below: <br>
To install and configure the components of the development package for working with specific frameworks, use the `pip install openvino-dev[extras]` command, where `extras` is a list of extras from the table below:
| DL Framework | Extra |
| :------------------------------------------------------------------------------- | :-------------------------------|
| [Caffe*](https://caffe.berkeleyvision.org/) | caffe |
| [Caffe2*](https://caffe2.ai/) | caffe2 |
| [Kaldi*](https://kaldi-asr.org/) | kaldi |
| [MXNet*](https://mxnet.apache.org/) | mxnet |
| [ONNX*](https://github.com/microsoft/onnxruntime/) | onnx |
| [PyTorch*](https://pytorch.org/) | pytorch |
| [TensorFlow* 1.x](https://www.tensorflow.org/versions#tensorflow_1) | tensorflow |
| [TensorFlow* 2.x](https://www.tensorflow.org/versions#tensorflow_2) | tensorflow2 |
For example, to install and configure the components for working with TensorFlow 2.x, MXNet and Caffe, use the following command:
```sh
pip install openvino-dev
pip install openvino-dev[tensorflow2,mxnet,caffe]
```
### Step 5. Verify that the Package is Installed
### Step 5. Verify that the Package Is Installed
Run the command below (this may take a few seconds):
```sh
pot -h
```
- To verify that the **developer package** is properly installed, run the command below (this may take a few seconds):
```sh
mo -h
```
You will see the help message for Model Optimizer if installation finished successfully.
You will see the help message for Post-Training Optimization Tool if installation finished successfully.
- To verify that Inference Engine from the **runtime package** is available, run the command below:
```sh
python -c "from openvino.inference_engine import IECore"
```
You will not see any error messages if installation finished successfully.
## Troubleshooting
#### Error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio"
### Error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio"
On Windows* some dependencies may require compilation from source when installing. To resolve this issue, you need to install [Build Tools for Visual Studio* 2019](https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019) and repeat package installation.
#### ImportError: libpython3.7m.so.1.0: cannot open shared object file: No such file or directory
### ImportError: libpython3.7m.so.1.0: cannot open shared object file: No such file or directory
To resolve missing external dependency on Ubuntu*, execute the following command:
To resolve missing external dependency on Ubuntu* 18.04, execute the following command:
```sh
sudo apt-get install libpython3.7
```
## Additional Resources
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
- [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit)
- [OpenVINO™ toolkit online documentation](https://docs.openvinotoolkit.org)
- [OpenVINO™ Notebooks](https://github.com/openvinotoolkit/openvino_notebooks)

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docs/install_guides/pypi-openvino-rt.md
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@@ -4,6 +4,8 @@ Copyright © 2018-2021 Intel Corporation
“Software Package”) is subject to the terms and conditions of the [software license agreements](https://software.intel.com/content/dam/develop/external/us/en/documents/intel-openvino-license-agreements.pdf) for the Software Package, which may also include notices, disclaimers, or
license terms for third party or open source software included in or with the Software Package, and your use indicates your acceptance of all such terms. Please refer to the “third-party-programs.txt” or other similarly-named text file included with the Software Package for additional details.
>Intel is committed to the respect of human rights and avoiding complicity in human rights abuses, a policy reflected in the [Intel Global Human Rights Principles](https://www.intel.com/content/www/us/en/policy/policy-human-rights.html). Accordingly, by accessing the Intel material on this platform you agree that you will not use the material in a product or application that causes or contributes to a violation of an internationally recognized human right.
## Introduction
OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI and deep learning inference deployed from edge to cloud.
@@ -13,7 +15,7 @@ The Intel® Distribution of OpenVINO™ toolkit\*:
- Supports heterogeneous execution across Intel® CPU, Intel® Integrated Graphics, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
- Speeds time-to-market via an easy-to-use library of computer vision functions and pre-optimized kernels
**The Runtime Package Includes the Following Components Installed by Default:**
The **runtime package** includes the following components installed by default:
| Component | Description |
|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
@@ -77,7 +79,7 @@ Run the command below: <br>
pip install openvino
```
### Step 5. Verify that the Package is Installed
### Step 5. Verify that the Package Is Installed
Run the command below:
```sh
@@ -88,11 +90,11 @@ You will not see any error messages if installation finished successfully.
## Troubleshooting
#### Error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio"
### Error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio"
On Windows* some dependencies may require compilation from source when installing. To resolve this issue, you need to install [Build Tools for Visual Studio* 2019](https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019) and repeat package installation.
#### ImportError: libpython3.7m.so.1.0: cannot open shared object file: No such file or directory
### ImportError: libpython3.7m.so.1.0: cannot open shared object file: No such file or directory
To resolve missing external dependency on Ubuntu*, execute the following command:
```sh
@@ -101,6 +103,7 @@ sudo apt-get install libpython3.7
## Additional Resources
- [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit).
- [OpenVINO™ toolkit online documentation](https://docs.openvinotoolkit.org).
- [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit)
- [OpenVINO™ toolkit online documentation](https://docs.openvinotoolkit.org)
- [OpenVINO™ Notebooks](https://github.com/openvinotoolkit/openvino_notebooks)