Update Get Started Guide structure (#11875)
* 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 * update reference link * fix CVS-71846 * test * add troubleshooting steps * restructure get started home page * update navigation menu * update formatting * fix mistakes * update wording * update * rename configurations files * update wording * adjust the structure * update formatting * reverse the heading * test with formatting * 2nd version of Get Started homepage * add line breaks * change to ordered list * update wording * update content * updates * update DL workbench reference * update wording * update references to pip installations * remove redundant files * update headings * update * update * restructure * rename * updates * remove a comment * correct grammar * correct grammar * update structure * update headings * restructure * fix formatting * change the capitalization * update heading * update PyPI install * updates * update formatting * Update docs/install_guides/troubleshooting-steps.md Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com> * Update docs/install_guides/troubleshooting-steps.md Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com> * integrating comments * update * update * correct an error * correct an error * update * update * update wording * typo * typo * hiding CentOS issues * update headings * update heading * Update docs/get_started/get_started_demos.md Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com> * Update docs/get_started/get_started_demos.md Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com> * Update docs/install_guides/installing-model-dev-tools.md Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com> * Update docs/install_guides/installing-model-dev-tools.md Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com> * Update docs/install_guides/pypi-openvino-dev.md * Update docs/install_guides/pypi-openvino-dev.md Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com> Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com> Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>
This commit is contained in:
Yuan Xu
co-authored by
Helena Kloosterman
Karol Blaszczak
Helena Kloosterman
Karol Blaszczak
parent
5939cb1b36
commit
7963ba20f4
@@ -8,6 +8,7 @@
|
||||
:maxdepth: 1
|
||||
:hidden:
|
||||
|
||||
Basic OpenVINO Workflow <openvino_docs_get_started_get_started_demos>
|
||||
openvino_inference_engine_samples_classification_sample_async_README
|
||||
openvino_inference_engine_ie_bridges_python_sample_classification_sample_async_README
|
||||
openvino_inference_engine_samples_hello_classification_README
|
||||
|
||||
+15
-53
@@ -5,53 +5,22 @@
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:hidden:
|
||||
:caption: Installation & Configuration
|
||||
:caption: Install & Config
|
||||
|
||||
Overview <openvino_docs_install_guides_overview>
|
||||
Installing OpenVINO Runtime <openvino_docs_install_guides_install_runtime>
|
||||
Installing OpenVINO Development Tools <openvino_docs_install_guides_install_dev_tools>
|
||||
Build from Source <https://github.com/openvinotoolkit/openvino/wiki/BuildingCode>
|
||||
Creating a Yocto Image <openvino_docs_install_guides_installing_openvino_yocto>
|
||||
Installing OpenVINO <openvino_docs_install_guides_overview>
|
||||
Additional Configurations <openvino_docs_install_guides_configurations_header>
|
||||
Uninstalling <openvino_docs_install_guides_uninstalling_openvino>
|
||||
|
||||
<!--
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:hidden:
|
||||
:caption: Additional Configurations
|
||||
<!--
|
||||
Configurations for GPU <openvino_docs_install_guides_configurations_for_intel_gpu>
|
||||
Configurations for NCS2 <openvino_docs_install_guides_configurations_for_ncs2>
|
||||
Configurations for VPU <openvino_docs_install_guides_installing_openvino_ivad_vpu>
|
||||
Configurations for GNA <openvino_docs_install_guides_configurations_for_intel_gna>
|
||||
-->
|
||||
Troubleshooting <openvino_docs_get_started_guide_troubleshooting>
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:hidden:
|
||||
:caption: Get Started Guides
|
||||
|
||||
Step-by-step Demo <openvino_docs_get_started_get_started_demos>
|
||||
Python Tutorials <tutorials>
|
||||
Code Samples <openvino_docs_OV_UG_Samples_Overview>
|
||||
Interactive Tutorials (Python) <tutorials>
|
||||
Samples <openvino_docs_OV_UG_Samples_Overview>
|
||||
|
||||
<!--
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:hidden:
|
||||
:caption: OpenVINO Code Samples
|
||||
<!--
|
||||
openvino_docs_OV_UG_Samples_Overview
|
||||
-->
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:hidden:
|
||||
:caption: Troubleshooting
|
||||
|
||||
Installation & Configuration Issues <openvino_docs_get_started_guide_troubleshooting>
|
||||
|
||||
@endsphinxdirective
|
||||
|
||||
@sphinxdirective
|
||||
@@ -62,7 +31,7 @@
|
||||
<p id="GSG_introtext">This guide introduces the installation, configuration, and get started guides of Intel® Distribution of OpenVINO™ toolkit. Check the following steps from left to right:<br />
|
||||
<a href="openvino_docs_install_guides_overview.html" >Install Intel® Distribution<br/>of OpenVINO™ toolkit</a>
|
||||
<a href="openvino_docs_install_guides_configurations_header.html" >Perform <br/>Additional Configurations</a>
|
||||
<a href="#get-started-tutorials" >Try <br/>Get Started Steps</a>
|
||||
<a href="#get-started-tutorials" >Try <br/>Get Started Materials</a>
|
||||
</p>
|
||||
|
||||
<p>If you prefer using a graphical interface, you can <a href="workbench_docs_Workbench_DG_Start_DL_Workbench_in_DevCloud.html" >Run Deep Learning Workbench in the Intel® DevCloud for the Edge</a>. Deep Learning Workbench (DL Workbench) is the web version of OpenVINO developed based on Intel® Distribution of OpenVINO™ toolkit with a similar but slightly different function set.</li>
|
||||
@@ -74,31 +43,31 @@
|
||||
</p>
|
||||
-->
|
||||
|
||||
<h3><a name="get-started-tutorials">Get Started with Tutorials, Demos, and Samples</a></h3>
|
||||
<h3><a name="get-started-tutorials">Tutorials, Samples, and Demo Applications</a></h3>
|
||||
|
||||
<p>After all the installation and configuration steps are done, you are ready to run your first inference and learn the workflow. Here is a set of hands-on demonstrations of various complexity levels to guide you through the process. You can run code samples, demo applications, or Jupyter notebooks.</p>
|
||||
<p>After all the installation and configuration steps are done, you are ready to run your first inference and learn the workflow. Here is a set of hands-on demonstrations of various complexity levels to guide you through the process. You can run Jupyter notebooks, code samples, or demo applications.</p>
|
||||
|
||||
<div id="GSG_nextstepchoice">
|
||||
<a href="openvino_docs_get_started_get_started_demos.html" >
|
||||
<h4>Step-by-step demo </h4>
|
||||
<p>Follow the step-by-step instructions to execute simple tasks with OpenVINO. </p>
|
||||
</a>
|
||||
<a href="tutorials.html" >
|
||||
<h4>Python tutorials </h4>
|
||||
<h4>Interactive Tutorials - Jupyter Notebooks (Python) </h4>
|
||||
<p>Learn from a choice of interactive Python tutorials targeting typical OpenVINO use cases.</p>
|
||||
</a>
|
||||
<a href="openvino_docs_OV_UG_Samples_Overview.html" >
|
||||
<h4>OpenVINO samples </h4>
|
||||
<h4>OpenVINO Samples </h4>
|
||||
<p>See ready-made applications explaining OpenVINO features and various use-cases. </p>
|
||||
</a>
|
||||
<a href="openvino_inference_engine_ie_bridges_python_sample_speech_sample_README.html" >
|
||||
<h4>Reference Implementation For Speech Recognition Apps (Python)</h4>
|
||||
<p>Use a speech recognition demo and Kaldi model conversion tool as reference. </p>
|
||||
</a>
|
||||
<a href="openvino_inference_engine_samples_speech_sample_README.html" >
|
||||
<a href="openvino_inference_engine_samples_speech_sample_README.html" >
|
||||
<h4>Reference Implementation For Speech Recognition Apps (C++)</h4>
|
||||
<p>Use a speech recognition demo and Kaldi model conversion tool as reference. </p>
|
||||
</a>
|
||||
<a href="omz_demos.html" >
|
||||
<h4>Open Model Zoo Demo Applications</h4>
|
||||
<p>See Open Model Zoo demo applications to use robust application templates for implementing specific deep learning scenarios. </p>
|
||||
</a>
|
||||
<a href="http://devcloud.intel.com/edge/" >
|
||||
<h4>Intel® DevCloud </h4>
|
||||
<p>Develop, test, and run your OpenVINO solution for free on a cluster of the latest Intel® hardware. </p>
|
||||
@@ -106,11 +75,4 @@
|
||||
</div>
|
||||
<div style="clear:both;"> </div>
|
||||
|
||||
<!--
|
||||
<a href="workbench_docs_Workbench_DG_Introduction.html" >
|
||||
<h4>DL Workbench </h4>
|
||||
<p>Use a web-based version of OpenVINO with a Graphical User Interface. Installing a DL Workbench container is required. </p>
|
||||
</a>
|
||||
-->
|
||||
|
||||
@endsphinxdirective
|
||||
@@ -1,15 +1,15 @@
|
||||
# Get Started with Sample and Demo Applications {#openvino_docs_get_started_get_started_demos}
|
||||
# Basic OpenVINO™ Workflow {#openvino_docs_get_started_get_started_demos}
|
||||
|
||||
This guide will walk you through a simplified workflow for the Intel® Distribution of OpenVINO™ toolkit using code samples and demo applications.
|
||||
This guide will walk you through a basic workflow for Intel® Distribution of OpenVINO™ toolkit, including how to use code samples.
|
||||
|
||||
This guide assumes you have completed all the installation and preparation steps. If you have not, check out the <a href="prerequisites">Prerequisites</a> section to install OpenVINO Runtime, install OpenVINO Development Tools, or build samples and demos.
|
||||
|
||||
After that, you will perform the following steps:
|
||||
|
||||
1. <a href="#download-models">Use the Model Downloader to download suitable models.</a>
|
||||
2. <a href="#convert-models-to-intermediate-representation">Convert the models with the Model Optimizer.</a>
|
||||
3. <a href="#download-media">Download media files to run inference on.</a>
|
||||
4. <a href="#run-image-classification">Run inference on the sample and see the results:</a>
|
||||
1. <a href="#download-models">Use Model Downloader to download a suitable model.</a>
|
||||
2. <a href="#convert-models-to-intermediate-representation">Convert the model with Model Optimizer.</a>
|
||||
3. <a href="#download-media">Download media files to run inference.</a>
|
||||
4. <a href="#run-image-classification">Run inference on a sample and see the results.</a> The following code sample is used as an example:
|
||||
- <a href="#run-image-classification">Image Classification Code Sample</a>
|
||||
|
||||
## <a name="prerequisites"></a>Prerequisites
|
||||
@@ -286,7 +286,7 @@ The command with most placeholders filled in and FP16 precision:
|
||||
|
||||
@endsphinxdirective
|
||||
|
||||
## <a name="download-media"></a> Step 3: Download a Video or Still Photo as Media
|
||||
## <a name="download-media"></a> Step 3: Download a Video or a Photo as Media
|
||||
|
||||
Many sources are available from which you can download video media to use the code samples and demo applications. Possibilities include:
|
||||
|
||||
@@ -298,7 +298,7 @@ As an alternative, the Intel® Distribution of OpenVINO™ toolkit includes seve
|
||||
- [Sample images and video](https://storage.openvinotoolkit.org/data/test_data/)
|
||||
- [Sample videos](https://github.com/intel-iot-devkit/sample-videos)
|
||||
|
||||
## <a name="run-image-classification"></a>Step 4: Run Inference on the Sample
|
||||
## <a name="run-image-classification"></a>Step 4: Run Inference on a Sample
|
||||
|
||||
|
||||
### Run the Image Classification Code Sample
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
# Configurations for IEI Mustang-V100-MX8-R10 card {#openvino_docs_install_guides_movidius_setup_guide}
|
||||
# Configurations for IEI Mustang-V100-MX8-R10 Card {#openvino_docs_install_guides_movidius_setup_guide}
|
||||
|
||||
> **NOTE**: These steps are only required for **IEI Mustang-V100-MX8-R10** card. **IEI Mustang-V100-MX8-R11** card doesn't require any additional steps and it's completely configured using the [general guidance](configurations-for-ivad-vpu.md).
|
||||
|
||||
|
||||
@@ -12,14 +12,14 @@ This page introduces additional configurations for Intel® Processor Graphics (G
|
||||
|
||||
## Linux
|
||||
|
||||
Once you have your OpenVINO installed, follow the steps to be able to work on GPU:
|
||||
Once you have your OpenVINO Runtime installed, follow the steps to be able to work on GPU:
|
||||
|
||||
1. Go to the install_dependencies directory:
|
||||
```sh
|
||||
cd <INSTALL_DIR>/install_dependencies/
|
||||
```
|
||||
|
||||
2. Install the **Intel® Graphics Compute Runtime for OpenCL™** driver components required to use the GPU plugin and write custom layers for Intel® Integrated Graphics. The drivers are not included in the package. To install, run this script:
|
||||
2. Install the **Intel® Graphics Compute Runtime for OpenCL™** driver components required to use the GPU plugin and write custom layers for Intel® Integrated Graphics. The drivers are not included in the package. To install it, run this script:
|
||||
```sh
|
||||
sudo -E ./install_NEO_OCL_driver.sh
|
||||
```
|
||||
|
||||
@@ -8,7 +8,7 @@
|
||||
:maxdepth: 2
|
||||
:hidden:
|
||||
|
||||
Configurations for IEI Mustang-V100-MX8-R10 card <openvino_docs_install_guides_movidius_setup_guide>
|
||||
IEI Mustang-V100-MX8-R10 Card <openvino_docs_install_guides_movidius_setup_guide>
|
||||
|
||||
@endsphinxdirective
|
||||
|
||||
|
||||
@@ -18,9 +18,9 @@ While installing OpenVINO Development Tools, OpenVINO Runtime will also be insta
|
||||
|
||||
### Step 1. Set Up Python Virtual Environment
|
||||
|
||||
To avoid dependency conflicts, use a virtual environment. Skip this step only if you do want to install all dependencies globally.
|
||||
Use a virtual environment to avoid dependency conflicts.
|
||||
|
||||
Use the following command to create a virtual environment:
|
||||
To create a virtual environment, use the following command:
|
||||
|
||||
@sphinxdirective
|
||||
|
||||
@@ -80,7 +80,7 @@ For example, to install and configure the components for working with TensorFlow
|
||||
pip install openvino-dev[tensorflow2,onnx]
|
||||
```
|
||||
|
||||
> **NOTE**: For TensorFlow, use the `tensorflow2` value as much as possible. The `tensorflow` value is provided only for compatibility reasons.
|
||||
> **NOTE**: Model Optimizer support for TensorFlow 1.x environment has been deprecated. Use TensorFlow 2.x environment to convert both TensorFlow 1.x and 2.x models. Use the `tensorflow2` value as much as possible. The `tensorflow` value is provided only for compatibility reasons.
|
||||
|
||||
|
||||
### Step 5. Verify the Installation
|
||||
@@ -127,7 +127,7 @@ If you have installed OpenVINO Runtime via the installer, to avoid version confl
|
||||
pip install openvino-dev[tensorflow2,onnx]==2022.1
|
||||
```
|
||||
|
||||
> **NOTE**: For TensorFlow, use the `tensorflow2` value as much as possible. The `tensorflow` value is provided only for compatibility reasons.
|
||||
> **NOTE**: Model Optimizer support for TensorFlow 1.x environment has been deprecated. Use TensorFlow 2.x environment to convert both TensorFlow 1.x and 2.x models. The `tensorflow` value is provided only for compatibility reasons, use the `tensorflow2` value instead.
|
||||
|
||||
For more details, see <https://pypi.org/project/openvino-dev/>.
|
||||
|
||||
|
||||
@@ -1,4 +1,17 @@
|
||||
# Overview {#openvino_docs_install_guides_overview}
|
||||
# Installing Intel® Distribution of OpenVINO™ Toolkit {#openvino_docs_install_guides_overview}
|
||||
|
||||
@sphinxdirective
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 3
|
||||
:hidden:
|
||||
|
||||
OpenVINO Runtime <openvino_docs_install_guides_install_runtime>
|
||||
OpenVINO Development Tools <openvino_docs_install_guides_install_dev_tools>
|
||||
Build from Source <https://github.com/openvinotoolkit/openvino/wiki/BuildingCode>
|
||||
Creating a Yocto Image <openvino_docs_install_guides_installing_openvino_yocto>
|
||||
|
||||
@endsphinxdirective
|
||||
|
||||
Intel® Distribution of OpenVINO™ toolkit is a comprehensive toolkit for developing 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. Some of its advantages are:
|
||||
|
||||
@@ -9,6 +22,7 @@ Intel® Distribution of OpenVINO™ toolkit is a comprehensive toolkit for devel
|
||||
## Installation Options
|
||||
|
||||
Since the 2022.1 release, the OpenVINO installation package has been distributed in two parts: OpenVINO Runtime and OpenVINO Development Tools. See the following instructions to choose your installation process.
|
||||
|
||||
### Decide What to Install
|
||||
|
||||
**If you have already finished developing your models and converting them to the OpenVINO model format, you can [install OpenVINO Runtime](installing-openvino-runtime.md) to deploy your applications on various devices**. OpenVINO Runtime contains a set of libraries for easy inference integration with your products.
|
||||
@@ -34,4 +48,11 @@ For C++ developers, you may choose one of the following installation options for
|
||||
* [Raspbian OS](installing-openvino-raspbian.md).
|
||||
|
||||
> **NOTE**: With the introduction of the 2022.1 release, the OpenVINO Development Tools can be installed **only** via PyPI. See [Install OpenVINO Development Tools](installing-model-dev-tools.md) for detailed steps.
|
||||
Source files are also available in the [OpenVINO toolkit GitHub repository](https://github.com/openvinotoolkit/openvino/), so you can build your own package for the supported platforms, as described in [OpenVINO Build Instructions](https://github.com/openvinotoolkit/openvino/wiki/BuildingCode).
|
||||
Source files are also available in the [OpenVINO toolkit GitHub repository](https://github.com/openvinotoolkit/openvino/), so you can build your own package for the supported platforms, as described in [OpenVINO Build Instructions](https://github.com/openvinotoolkit/openvino/wiki/BuildingCode).
|
||||
|
||||
## Next Steps
|
||||
|
||||
- [Install OpenVINO Runtime](installing-openvino-runtime.md)
|
||||
- [Install OpenVINO Development Tools](installing-model-dev-tools.md)
|
||||
- [Build from Source](https://github.com/openvinotoolkit/openvino/wiki/BuildingCode)
|
||||
- [Create a Yocto Image](installing-openvino-yocto.md)
|
||||
|
||||
@@ -10,7 +10,7 @@ For system requirements and troubleshooting, see <https://pypi.org/project/openv
|
||||
|
||||
### Step 1. Set Up Python Virtual Environment
|
||||
|
||||
To avoid dependency conflicts, use a virtual environment. Skip this step only if you do want to install all dependencies globally.
|
||||
To avoid dependency conflicts, use a virtual environment.
|
||||
|
||||
Use the following command to create a virtual environment:
|
||||
|
||||
|
||||
@@ -1,31 +1,12 @@
|
||||
# OpenVINO™ Development Tools
|
||||
|
||||
## Introduction
|
||||
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.
|
||||
|
||||
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:**
|
||||
|
||||
| Component | Console Script | Description |
|
||||
|------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) | `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](../../tools/benchmark_tool/README.md)| `benchmark_app` | **Benchmark Application** allows you to estimate deep learning inference performance on supported devices for synchronous and asynchronous modes. |
|
||||
| [Accuracy Checker](@ref omz_tools_accuracy_checker) and <br> [Annotation Converter](@ref omz_tools_accuracy_checker_annotation_converters) | `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](../../tools/pot/docs/pot_introduction.md)| `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](@ref omz_tools_downloader)| `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.
|
||||
|
||||
The developer package also installs the OpenVINO™ Runtime package as a dependency.
|
||||
|
||||
**The runtime package installs the following components:**
|
||||
|
||||
| Component | Description |
|
||||
|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| [OpenVINO™ Runtime](https://pypi.org/project/openvino) | This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications. |
|
||||
OpenVINO™ Development Tools enables you to download models from Open Model Zoo, convert your own models to OpenVINO IR, as well as optimize and tune pre-trained deep learning models. See [What's in the Package](#whats-in-the-package) for more information.
|
||||
|
||||
## System Requirements
|
||||
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).
|
||||
|
||||
The table below lists the supported operating systems and Python* versions required to run the installation.
|
||||
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).
|
||||
|
||||
| Supported Operating System | [Python* Version (64-bit)](https://www.python.org/) |
|
||||
| :------------------------------------------------------------| :---------------------------------------------------|
|
||||
@@ -43,11 +24,9 @@ The table below lists the supported operating systems and Python* versions requi
|
||||
|
||||
### Step 1. Set Up Python Virtual Environment
|
||||
|
||||
To avoid dependency conflicts, use a virtual environment. Skip this
|
||||
step only if you do want to install all dependencies globally.
|
||||
|
||||
Create virtual environment:
|
||||
Use a virtual environment to avoid dependency conflicts.
|
||||
|
||||
To create a virtual environment, use the following command:
|
||||
```sh
|
||||
python -m pip install --user virtualenv
|
||||
python -m venv openvino_env
|
||||
@@ -76,23 +55,38 @@ python -m pip install --upgrade pip
|
||||
|
||||
### Step 4. Install the Package
|
||||
|
||||
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:
|
||||
There are two options to install OpenVINO Development Tools:
|
||||
|
||||
| DL Framework | Extra |
|
||||
| :------------------------------------------------------------------------------- | :-------------------------------|
|
||||
| [Caffe*](https://caffe.berkeleyvision.org/) | caffe |
|
||||
| [Kaldi*](https://github.com/kaldi-asr/kaldi) | 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 |
|
||||
#### Installing Default Components
|
||||
|
||||
To install [default components in the package](#whats-in-the-package), use the following command:
|
||||
```
|
||||
pip install openvino-dev
|
||||
```
|
||||
|
||||
#### Installing Components for Specific Frameworks
|
||||
|
||||
To install and configure the components of the package for working with specific frameworks, use the following command:
|
||||
```sh
|
||||
pip install openvino-dev[extras]
|
||||
```
|
||||
where `extras` has the following values:
|
||||
|
||||
| Extras Value | DL Framework |
|
||||
| :-------------------------------| :------------------------------------------------------------------------------- |
|
||||
| caffe | [Caffe*](https://caffe.berkeleyvision.org/) |
|
||||
| kaldi | [Kaldi*](https://github.com/kaldi-asr/kaldi) |
|
||||
| mxnet | [Apache MXNet*](https://mxnet.apache.org/) |
|
||||
| onnx | [ONNX*](https://github.com/microsoft/onnxruntime/) |
|
||||
| pytorch | [PyTorch*](https://pytorch.org/) |
|
||||
| tensorflow | [TensorFlow* 1.x](https://www.tensorflow.org/versions#tensorflow_1) |
|
||||
| tensorflow2 | [TensorFlow* 2.x](https://www.tensorflow.org/versions#tensorflow_2) |
|
||||
|
||||
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[tensorflow2,mxnet,caffe]
|
||||
```
|
||||
**NOTE**: Support of MO in TensorFlow 1.x environment is deprecated. Use TensorFlow 2.x environment to convert both TensorFlow 1.x and 2.x models
|
||||
> **NOTE**: Model Optimizer support for TensorFlow 1.x environment has been deprecated. Use TensorFlow 2.x environment to convert both TensorFlow 1.x and 2.x models.
|
||||
|
||||
### Step 5. Verify that the Package Is Installed
|
||||
|
||||
@@ -108,8 +102,23 @@ For example, to install and configure the components for working with TensorFlow
|
||||
```
|
||||
If installation was successful, you will not see any error messages (no console output).
|
||||
|
||||
## What's in the Package?
|
||||
|
||||
> **NOTE**: The openvino-dev package installs [OpenVINO™ Runtime](https://pypi.org/project/openvino) as a dependency, which is the engine that runs the deep learning model and includes a set of libraries for an easy inference integration into your applications.
|
||||
|
||||
**In addition, the openvino-dev package installs the following components by default:**
|
||||
|
||||
| Component | Console Script | Description |
|
||||
|------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) | `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](../../tools/benchmark_tool/README.md)| `benchmark_app` | **Benchmark Application** allows you to estimate deep learning inference performance on supported devices for synchronous and asynchronous modes. |
|
||||
| [Accuracy Checker](@ref omz_tools_accuracy_checker) and <br> [Annotation Converter](@ref omz_tools_accuracy_checker_annotation_converters) | `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](../../tools/pot/docs/pot_introduction.md)| `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](@ref omz_tools_downloader)| `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](./troubleshooting.md). The following sections also provide explanations to several error messages.
|
||||
|
||||
### zsh: no matches found : openvino-dev[...]
|
||||
|
||||
|
||||
@@ -1,15 +1,11 @@
|
||||
# OpenVINO™ Runtime
|
||||
|
||||
## Introduction
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
[OpenVINO™ Runtime](../OV_Runtime_UG/openvino_intro.md) package for Python includes a set of libraries for an easy inference integration into your Python applications and supports of heterogeneous execution across Intel® CPU and Intel® GPU hardware.
|
||||
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](../OV_Runtime_UG/openvino_intro.md) Python package includes a set of libraries for an easy inference integration with your products.
|
||||
|
||||
## System Requirements
|
||||
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).
|
||||
|
||||
The table below lists supported operating systems and Python* versions required to run the installation.
|
||||
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).
|
||||
|
||||
| Supported Operating System | [Python* Version (64-bit)](https://www.python.org/) |
|
||||
| :------------------------------------------------------------| :---------------------------------------------------|
|
||||
@@ -27,10 +23,9 @@ The table below lists supported operating systems and Python* versions required
|
||||
|
||||
### Step 1. Set Up Python Virtual Environment
|
||||
|
||||
To avoid dependency conflicts, use a virtual environment. Skip this
|
||||
step only if you do want to install all dependencies globally.
|
||||
Use a virtual environment to avoid dependency conflicts.
|
||||
|
||||
Create virtual environment:
|
||||
To create a virtual environment, use the following command:
|
||||
```sh
|
||||
python -m pip install --user virtualenv
|
||||
python -m venv openvino_env
|
||||
@@ -76,6 +71,8 @@ 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](./troubleshooting.md). The following sections also provide explanations to several error messages.
|
||||
|
||||
### 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.
|
||||
|
||||
@@ -0,0 +1,201 @@
|
||||
# Issues & Solutions for OpenVINO™ Installation & Configuration {#openvino_docs_get_started_guide_troubleshooting_issues}
|
||||
|
||||
This page lists issues that you may encounter during the installation and configuration of OpenVINO™, as well as their possible solutions.
|
||||
|
||||
## <a name="install-for-prc"></a>Errors with Installing via PIP for PRC Users
|
||||
|
||||
PRC users might encounter errors while downloading sources via PIP during OpenVINO™ installation. To resolve the issues, try one of the following options:
|
||||
|
||||
* Add the download source using the ``-i`` parameter with the Python ``pip`` command. For example:
|
||||
|
||||
``` sh
|
||||
pip install openvino-dev -i https://mirrors.aliyun.com/pypi/simple/
|
||||
```
|
||||
Use the ``--trusted-host`` parameter if the URL above is ``http`` instead of ``https``.
|
||||
You can also run the following command to install specific framework. For example:
|
||||
|
||||
```
|
||||
pip install openvino-dev[tensorflow2] -i https://mirrors.aliyun.com/pypi/simple/
|
||||
```
|
||||
|
||||
* For C++ developers, if you have installed OpenVINO Runtime via APT, YUM, or the installer, and then installed OpenVINO Development Tools via PyPI, you may run into issues. To resolve that, install the components in ``requirements.txt`` by using the following command:
|
||||
|
||||
``` sh
|
||||
pip install -r <INSTALL_DIR>/tools/requirements.txt
|
||||
```
|
||||
|
||||
<!-- this part was from Docker installation -->
|
||||
|
||||
## Issues with Installing OpenVINO on Linux from Docker
|
||||
|
||||
### <a name="proxy-issues"></a>Proxy Issues
|
||||
|
||||
If you met proxy issues during the installation with Docker, you need set up proxy settings for Docker. See the [Set Proxy section in DL Workbench Installation](https://docs.openvino.ai/latest/workbench_docs_Workbench_DG_Prerequisites.html#set-proxy) for more details.
|
||||
|
||||
### Permission Errors for /dev/shm
|
||||
|
||||
If you encounter a permission error for files in `/dev/shm` (see `hddldaemon.log`). A possible cause is that the uid and gid of the container user are different from the uid and gid of the user who created `hddldaemon` service on the host.
|
||||
|
||||
Try one of these solutions:
|
||||
|
||||
* Create the user in the Docker container with the same uid and gid as the HDDL daemon user.
|
||||
* Set the container user umask to 0000: `umask 000`.
|
||||
* (NOT RECOMMENDED) Start HDDL daemon on the host as root and start the container as root with the `-u root:root` option.
|
||||
|
||||
## Issues with Configurations for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
|
||||
|
||||
<!-- this part was taken from original configurations-for-ivad-vpu.md -->
|
||||
|
||||
### Unable to run inference with the MYRIAD Plugin after running with the HDDL Plugin
|
||||
|
||||
Running inference with the MYRIAD Plugin after running with the HDDL Plugin is failed with the following error generated:
|
||||
|
||||
```sh
|
||||
E: [ncAPI] [ 965618] [MainThread] ncDeviceOpen:677 Failed to find a device, rc: X_LINK_ERROR
|
||||
```
|
||||
|
||||
**Possible solutions (use one of the following):**
|
||||
|
||||
* Reboot the host system and run with the MYRIAD Plugin
|
||||
|
||||
* Kill the HDDL Plugin backend service (`hddldaemon`) and reset all Intel® Movidius™ VPUs before running an application that uses the MYRIAD Plugin:
|
||||
```sh
|
||||
kill -9 $(pidof hddldaemon autoboot)
|
||||
pidof hddldaemon autoboot # Make sure none of them is alive
|
||||
source /opt/intel/openvino_2022/setupvars.sh
|
||||
${HDDL_INSTALL_DIR}/bin/bsl_reset
|
||||
```
|
||||
|
||||
---
|
||||
### "No space left on device" error while loading a network
|
||||
When the application runs inference of a network with a big size(>4MB) of input/output or if the system is running out of the DMA buffer,
|
||||
the HDDL Plugin will fall back to use shared memory.
|
||||
In this case, if the application exits abnormally, the shared memory is not released automatically.
|
||||
To release it manually, remove files with the `hddl_` prefix from the `/dev/shm` directory:
|
||||
```sh
|
||||
sudo rm -f /dev/shm/hddl_*
|
||||
```
|
||||
|
||||
---
|
||||
### Solutions to the permission issue
|
||||
|
||||
Make sure that the following udev rules exist:
|
||||
- `/etc/udev/rules.d/97-myriad-usbboot.rules`
|
||||
- `/etc/udev/rules.d/98-hddlbsl.rules`
|
||||
- `/etc/udev/rules.d/99-hddl-ion.rules`
|
||||
- `/etc/udev/rules.d/99-myriad-vsc.rules`
|
||||
|
||||
Also make sure that the current user is included in the users groups. If not, run the command below to include:
|
||||
```sh
|
||||
sudo usermod -a -G users "$(whoami)"
|
||||
```
|
||||
|
||||
<!--
|
||||
### setup.sh doesn't install the driver binaries to /lib/modules on CentOS systems
|
||||
|
||||
As a temporary workaround, run the commands below to install the drivers. This issue will be fixed in future releases.
|
||||
|
||||
```sh
|
||||
sudo mkdir -p /lib/modules/$(uname -r)/kernel/drivers/myd/
|
||||
```
|
||||
```sh
|
||||
sudo cp drv_vsc/myd_vsc.ko /lib/modules/$(uname -r)/kernel/drivers/myd/
|
||||
```
|
||||
```sh
|
||||
sudo cp drv_ion/myd_ion.ko /lib/modules/$(uname -r)/kernel/drivers/myd/
|
||||
```
|
||||
```sh
|
||||
sudo touch /etc/modules-load.d/intel_vision_accelerator.conf
|
||||
```
|
||||
```sh
|
||||
sudo echo "myd_vsc" >> /etc/modules-load.d/intel_vision_accelerator.conf
|
||||
```
|
||||
```sh
|
||||
sudo echo "myd_ion" >> /etc/modules-load.d/intel_vision_accelerator.conf
|
||||
```
|
||||
```sh
|
||||
sudo depmod
|
||||
```
|
||||
```sh
|
||||
sudo modprobe myd_vsc
|
||||
```
|
||||
```sh
|
||||
sudo modprobe myd_ion
|
||||
```
|
||||
-->
|
||||
|
||||
---
|
||||
### Host machine reboots after running an inference application with the HDDL plugin
|
||||
|
||||
**Symptom:** Boot up the host machine, run the inference application with the HDDL plugin. System reboots in a uncertain time.
|
||||
|
||||
**Root Cause:** The I2C address of the reset device of the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs conflicts with another device I2C address in 0x20-0x27 range. If the target Intel® Vision Accelerator Design with Intel® Movidius™ VPUs device needs to be reset (for example, in case of device errors), the `libbsl` library, which is responsible for reset, expects that the target reset device I2C address is in the 0x20-0x27 range on SMBUS. If there is another device on SMBUS in this address range, `libbsl` treats this device as the target reset device and writes an unexpected value to this address. This causes system reboot.
|
||||
|
||||
**Solution:** Detect if there is any I2C device on SMBUS with address in 0x20-0x27 range. If yes, do the following:
|
||||
|
||||
1. Change the DIP switch on the target PCIE card
|
||||
2. Disable autoscan for the reset device by setting `"autoscan": false` in `${HDDL_INSTALL_DIR}/config/bsl.json`
|
||||
3. Set the correct address of the I2C reset device (for example, `0x21`) in `${HDDL_INSTALL_DIR}/config/bsl.json`
|
||||
|
||||
```sh
|
||||
{
|
||||
"autoscan": false,
|
||||
"ioexpander": {
|
||||
"enabled": true,
|
||||
"i2c_addr": [ 33 ]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
### Cannot reset VPU device and cannot find any 0x20-0x27 (Raw data card with HW version Fab-B and before) I2C addresses on SMBUS (using i2c-tools)
|
||||
|
||||
Please contact your motherboard vendor to verify SMBUS pins are connected to the PCIe slot.
|
||||
|
||||
---
|
||||
### "Error: ipc_connection_linux_UDS : bind() failed" in hddldaemon log
|
||||
|
||||
You may have run hddldaemon under another user. Run the command below and try again:
|
||||
```sh
|
||||
sudo rm -rf /var/tmp/hddl_*
|
||||
```
|
||||
|
||||
---
|
||||
### "I2C bus: SMBus I801 adapter at not found!" in hddldaemon log
|
||||
|
||||
Run the following command to check if a SMBUS I801 adapter can be found:
|
||||
```sh
|
||||
i2cdetect -l
|
||||
```
|
||||
Then run:
|
||||
```sh
|
||||
sudo modprobe i2c-i801
|
||||
```
|
||||
---
|
||||
### "open /dev/ion failed!" in hddldaemon log
|
||||
|
||||
Check if `myd_ion` kernel module is installed by running the following command:
|
||||
```sh
|
||||
lsmod | grep myd_ion
|
||||
```
|
||||
If you do not see any output from the command, reinstall the `myd_ion` module.
|
||||
|
||||
---
|
||||
### Constantly getting "\_name\_mapping open failed err=2,No such file or directory" in hddldaemon log
|
||||
|
||||
Check if myd_vsc kernel module is installed by running the following command:
|
||||
```sh
|
||||
lsmod | grep myd_vsc
|
||||
```
|
||||
If you do not see any output from the command reinstall the `myd_vsc` module.
|
||||
|
||||
---
|
||||
### "Required key not available" appears when trying to install the myd_ion or myd_vsc modules
|
||||
|
||||
Run the following commands:
|
||||
```sh
|
||||
sudo apt install mokutil
|
||||
```
|
||||
```sh
|
||||
sudo mokutil --disable-validation
|
||||
```
|
||||
@@ -0,0 +1,75 @@
|
||||
# Troubleshooting Steps for OpenVINO™ Installation and Configurations {#openvino_docs_get_started_guide_troubleshooting_steps}
|
||||
|
||||
If you run into issues while installing or configuring OpenVINO™, you can try the following methods to do some quick checks first.
|
||||
|
||||
## Check the versions of OpenVINO Runtime and Developement Tools
|
||||
|
||||
* To check the version of OpenVINO Development Tools, use the following command:
|
||||
```sh
|
||||
mo --version
|
||||
```
|
||||
* To check the version of OpenVINO Runtime, use the following command:
|
||||
```sh
|
||||
from openvino.runtime import get_version get_version()
|
||||
```
|
||||
|
||||
## Check the versions of Python and PIP
|
||||
|
||||
To check your Python version, run `python -VV` or `python --version`. The supported Python versions should be 64-bit and between 3.7 and 3.9. Note that Python 3.6 is not supported anymore.
|
||||
|
||||
If your Python version does not meet the requirements, update Python:
|
||||
|
||||
* For Windows, **do not install Python from a Windows Store** as it can cause issues. You are highly recommended to install Python from <https://www.python.org/>.
|
||||
* For Ubuntu and other Linux systems, use the Python version comes with the system, or install the `libpython3.X` libraries via the following commands (taking Python 3.7 as an example):
|
||||
```sh
|
||||
sudo apt-get install libpython3.7
|
||||
sudo apt-get install libpython3-dev
|
||||
```
|
||||
* For macOS, download a proper Python version from <https://www.python.org/> and install it. Note that macOS 10.x comes with python 2.7 installed, which is not supported, so you still need install Python from its official website.
|
||||
|
||||
For PIP, make sure that you have installed the latest version. To check and upgrade your PIP version, run the following command:
|
||||
'''sh
|
||||
python -m pip install --upgrade pip
|
||||
'''
|
||||
|
||||
<!--## Check the special tips for Anaconda installation-->
|
||||
|
||||
<!--missing part-->
|
||||
|
||||
|
||||
## Check if required external dependencies are installed
|
||||
|
||||
For Ubuntu and RHEL 8 systems, if you installed OpenVINO Runtime via the installer, APT, or YUM repository, and decided to [install OpenVINO Development Tools](installing-model-dev-tools.md), make sure that you <a href="openvino_docs_install_guides_installing_openvino_linux.html#install-external-dependencies">Install External Software Dependencies</a> first.
|
||||
|
||||
For C++ developers with Windows systems, make sure that Microsoft Visual Studio 2019 with MSBuild and CMake 3.14 or higher (64-bit) are installed. While installing Microsoft Visual Studio 2019, make sure that you have selected **Desktop development with C++** in the **Workloads** tab. If not, launch the installer again to select that option. For more information on modifying the installation options for Microsoft Visual Studio, see its [official support page](https://docs.microsoft.com/en-us/visualstudio/install/modify-visual-studio?view=vs-2019).
|
||||
|
||||
## Check if environment variables are set correctly
|
||||
|
||||
- If you have installed OpenVINO before, you probably have added `setupvars` to your `PATH /.bashrc` or Windows environment variables. After restarting your environment, you should see similar information as below:
|
||||
```sh
|
||||
[setupvars.sh] OpenVINO™ environment initialized
|
||||
```
|
||||
- If you don't see the information, your PATH variable may be configured incorrectly. Check if you have written the correct <INSTALL_DIR> or tried to activate it in the right folder.
|
||||
- If you added it to a `.bashrc` file, make sure that the command is correctly written and the file is found in the `~/.bashrc` folder.
|
||||
|
||||
- For Python developers, if you installed OpenVINO using the installer previously, and now are installing OpenVINO with PIP, remove all the PATH settings and the lines with `setupvars` from `.bashrc`.
|
||||
|
||||
## Verify if OpenVINO is correctly installed
|
||||
|
||||
For Python developers, to verify if OpenVINO is correctly installed, use the following command:
|
||||
```sh
|
||||
python -c "from openvino.runtime import Core"
|
||||
```
|
||||
If OpenVINO was successfully installed, nothing will happen. If not, an error will be displayed.
|
||||
|
||||
## Check if GPU drvier is installed
|
||||
|
||||
[Additional configurations](configurations-header.md) are required in order to use OpenVINO on different hardware.
|
||||
|
||||
To run inference on GPU, make sure that you have installed the correct GPU driver. To check that, see [additional configurations for GPU](configurations-for-intel-gpu.md).
|
||||
|
||||
## Firewall/Network Issues
|
||||
|
||||
Make sure that your firewall and network settings are set correctly. For example, consider configuring system-wide proxy settings and specifying options for using PIP behind the proxy: `pip install --proxy http://address:port --trusted-host pypi.org openvino`.
|
||||
|
||||
For specific issues, see <a href="openvino_docs_get_started_guide_troubleshooting_issues.html#install-for-prc">Errors with Installing via PIP for PRC Users</a> and <a href="openvino_docs_get_started_guide_troubleshooting_issues.html#proxy-issues">proxy issues with installing OpenVINO on Linux from Docker</a>.
|
||||
@@ -1,198 +1,17 @@
|
||||
# Troubleshooting Issues with OpenVINO™ Installation & Configuration {#openvino_docs_get_started_guide_troubleshooting}
|
||||
# Troubleshooting Guide for OpenVINO™ Installation & Configuration {#openvino_docs_get_started_guide_troubleshooting}
|
||||
|
||||
<!-- this part was from Docker installation -->
|
||||
@sphinxdirective
|
||||
|
||||
## Errors with Installing via PIP for PRC Users
|
||||
|
||||
PRC users might encounter errors while downloading sources via PIP during OpenVINO™ installation. To resolve the issues, try one of the following options:
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:hidden:
|
||||
|
||||
* Add the download source using the ``-i`` parameter with the Python ``pip`` command. For example:
|
||||
Troubleshooting Steps <openvino_docs_get_started_guide_troubleshooting_steps>
|
||||
Issues & Solutions <openvino_docs_get_started_guide_troubleshooting_issues>
|
||||
|
||||
``` sh
|
||||
pip install openvino-dev -i https://mirrors.aliyun.com/pypi/simple/
|
||||
```
|
||||
Use the ``--trusted-host`` parameter if the URL above is ``http`` instead of ``https``.
|
||||
You can also run the following command to install specific framework. For example:
|
||||
|
||||
```
|
||||
pip install openvino-dev[tensorflow2] -i https://mirrors.aliyun.com/pypi/simple/
|
||||
```
|
||||
|
||||
* If you run into incompatibility issues between components after installing OpenVINO, try running ``requirements.txt`` with the following command:
|
||||
@endsphinxdirective
|
||||
|
||||
``` sh
|
||||
pip install -r <INSTALL_DIR>/tools/requirements.txt
|
||||
```
|
||||
This guide will provide general troubleshooting steps and possible issues related to the installation and configuration of OpenVINO™.
|
||||
|
||||
## Issues with Installing OpenVINO on Linux from Docker
|
||||
If you met any issues, you are encouraged to run some [Troubleshooting Steps](./troubleshooting-steps.md) first, or look for information on the [Issues & Solutions](./troubleshooting-issues.md) page.
|
||||
|
||||
### Proxy Issues
|
||||
|
||||
If you met proxy issues during the installation with Docker, please set up proxy settings for Docker. See the Proxy section in the [Install the DL Workbench from DockerHub](https://docs.openvino.ai/latest/workbench_docs_Workbench_DG_Prerequisites.html#set-proxy) topic.
|
||||
|
||||
### Permission Errors for /dev/shm
|
||||
|
||||
If you encounter a permission error for files in `/dev/shm` (see `hddldaemon.log`). A possible cause is that the uid and gid of the container user are different from the uid and gid of the user who created `hddldaemon` service on the host.
|
||||
|
||||
Try one of these solutions:
|
||||
|
||||
* Create the user in the Docker container with the same uid and gid as the HDDL daemon user.
|
||||
* Set the container user umask to 0000: `umask 000`.
|
||||
* (NOT RECOMMENDED) Start HDDL daemon on the host as root and start the container as root with the `-u root:root` option.
|
||||
|
||||
## Issues with Configurations for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
|
||||
|
||||
<!-- this part was taken from original configurations-for-ivad-vpu.md -->
|
||||
|
||||
### Unable to run inference with the MYRIAD Plugin after running with the HDDL Plugin
|
||||
|
||||
Running inference with the MYRIAD Plugin after running with the HDDL Plugin is failed with the following error generated:
|
||||
|
||||
```sh
|
||||
E: [ncAPI] [ 965618] [MainThread] ncDeviceOpen:677 Failed to find a device, rc: X_LINK_ERROR
|
||||
```
|
||||
|
||||
**Possible solutions (use one of the following):**
|
||||
|
||||
* Reboot the host system and run with the MYRIAD Plugin
|
||||
|
||||
* Kill the HDDL Plugin backend service (`hddldaemon`) and reset all Intel® Movidius™ VPUs before running an application that uses the MYRIAD Plugin:
|
||||
```sh
|
||||
kill -9 $(pidof hddldaemon autoboot)
|
||||
pidof hddldaemon autoboot # Make sure none of them is alive
|
||||
source /opt/intel/openvino_2022/setupvars.sh
|
||||
${HDDL_INSTALL_DIR}/bin/bsl_reset
|
||||
```
|
||||
|
||||
---
|
||||
### "No space left on device" error while loading a network
|
||||
When the application runs inference of a network with a big size(>4MB) of input/output or if the system is running out of the DMA buffer,
|
||||
the HDDL Plugin will fall back to use shared memory.
|
||||
In this case, if the application exits abnormally, the shared memory is not released automatically.
|
||||
To release it manually, remove files with the `hddl_` prefix from the `/dev/shm` directory:
|
||||
```sh
|
||||
sudo rm -f /dev/shm/hddl_*
|
||||
```
|
||||
|
||||
---
|
||||
### Solutions to the permission issue
|
||||
|
||||
Make sure that the following udev rules exist:
|
||||
- `/etc/udev/rules.d/97-myriad-usbboot.rules`
|
||||
- `/etc/udev/rules.d/98-hddlbsl.rules`
|
||||
- `/etc/udev/rules.d/99-hddl-ion.rules`
|
||||
- `/etc/udev/rules.d/99-myriad-vsc.rules`
|
||||
|
||||
Also make sure that the current user is included in the users groups. If not, run the command below to include:
|
||||
```sh
|
||||
sudo usermod -a -G users "$(whoami)"
|
||||
```
|
||||
|
||||
---
|
||||
### setup.sh doesn't install the driver binaries to /lib/modules on CentOS systems
|
||||
|
||||
As a temporary workaround, run the commands below to install the drivers. This issue will be fixed in future releases.
|
||||
|
||||
```sh
|
||||
sudo mkdir -p /lib/modules/$(uname -r)/kernel/drivers/myd/
|
||||
```
|
||||
```sh
|
||||
sudo cp drv_vsc/myd_vsc.ko /lib/modules/$(uname -r)/kernel/drivers/myd/
|
||||
```
|
||||
```sh
|
||||
sudo cp drv_ion/myd_ion.ko /lib/modules/$(uname -r)/kernel/drivers/myd/
|
||||
```
|
||||
```sh
|
||||
sudo touch /etc/modules-load.d/intel_vision_accelerator.conf
|
||||
```
|
||||
```sh
|
||||
sudo echo "myd_vsc" >> /etc/modules-load.d/intel_vision_accelerator.conf
|
||||
```
|
||||
```sh
|
||||
sudo echo "myd_ion" >> /etc/modules-load.d/intel_vision_accelerator.conf
|
||||
```
|
||||
```sh
|
||||
sudo depmod
|
||||
```
|
||||
```sh
|
||||
sudo modprobe myd_vsc
|
||||
```
|
||||
```sh
|
||||
sudo modprobe myd_ion
|
||||
```
|
||||
|
||||
---
|
||||
### Host machine reboots after running an inference application with the HDDL plugin
|
||||
|
||||
**Symptom:** Boot up the host machine, run the inference application with the HDDL plugin. System reboots in a uncertain time.
|
||||
|
||||
**Root Cause:** The I2C address of the reset device of the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs conflicts with another device I2C address in 0x20-0x27 range. If the target Intel® Vision Accelerator Design with Intel® Movidius™ VPUs device needs to be reset (for example, in case of device errors), the `libbsl` library, which is responsible for reset, expects that the target reset device I2C address is in the 0x20-0x27 range on SMBUS. If there is another device on SMBUS in this address range, `libbsl` treats this device as the target reset device and writes an unexpected value to this address. This causes system reboot.
|
||||
|
||||
**Solution:** Detect if there is any I2C device on SMBUS with address in 0x20-0x27 range. If yes, do the following:
|
||||
|
||||
1. Change the DIP switch on the target PCIE card
|
||||
2. Disable autoscan for the reset device by setting `"autoscan": false` in `${HDDL_INSTALL_DIR}/config/bsl.json`
|
||||
3. Set the correct address of the I2C reset device (for example, `0x21`) in `${HDDL_INSTALL_DIR}/config/bsl.json`
|
||||
|
||||
```sh
|
||||
{
|
||||
"autoscan": false,
|
||||
"ioexpander": {
|
||||
"enabled": true,
|
||||
"i2c_addr": [ 33 ]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
###Cannot reset VPU device and cannot find any 0x20-0x27 (Raw data card with HW version Fab-B and before) I2C addresses on SMBUS (using i2c-tools)
|
||||
|
||||
Please contact your motherboard vendor to verify SMBUS pins are connected to the PCIe slot.
|
||||
|
||||
---
|
||||
### "Error: ipc_connection_linux_UDS : bind() failed" in hddldaemon log
|
||||
|
||||
You may have run hddldaemon under another user. Run the command below and try again:
|
||||
```sh
|
||||
sudo rm -rf /var/tmp/hddl_*
|
||||
```
|
||||
|
||||
---
|
||||
### "I2C bus: SMBus I801 adapter at not found!" in hddldaemon log
|
||||
|
||||
Run the following command to check if a SMBUS I801 adapter can be found:
|
||||
```sh
|
||||
i2cdetect -l
|
||||
```
|
||||
Then run:
|
||||
```sh
|
||||
sudo modprobe i2c-i801
|
||||
```
|
||||
---
|
||||
### "open /dev/ion failed!" in hddldaemon log
|
||||
|
||||
Check if `myd_ion` kernel module is installed by running the following command:
|
||||
```sh
|
||||
lsmod | grep myd_ion
|
||||
```
|
||||
If you do not see any output from the command, reinstall the `myd_ion` module.
|
||||
|
||||
---
|
||||
### Constantly getting "\_name\_mapping open failed err=2,No such file or directory" in hddldaemon log
|
||||
|
||||
Check if myd_vsc kernel module is installed by running the following command:
|
||||
```sh
|
||||
lsmod | grep myd_vsc
|
||||
```
|
||||
If you do not see any output from the command reinstall the `myd_vsc` module.
|
||||
|
||||
---
|
||||
### "Required key not available" appears when trying to install the myd_ion or myd_vsc modules
|
||||
|
||||
Run the following commands:
|
||||
```sh
|
||||
sudo apt install mokutil
|
||||
```
|
||||
```sh
|
||||
sudo mokutil --disable-validation
|
||||
```
|
||||
|
||||
Reference in New Issue
Block a user