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[DOCS]Changed DL WB related docs and tips (#6318)

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* changed DL WB related docs and tips

* added two tips to benchmark and changed layout

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* changed tips

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* updated diagram and hints

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* changed DL WB description

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Tatiana Savina authored and GitHub committed 2021-06-28 16:39:15 +03:00
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docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md
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* <code>.bin</code> - Contains the weights and biases binary data.
> **TIP**: You also can work with the Model Optimizer inside the OpenVINO™ [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench).
> [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare
> performance of deep learning models on various Intel® architecture
> configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components.
> <br>
> Proceed to an [easy installation from Docker](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) to get started.
> **TIP**: You can quick start with the Model Optimizer inside the OpenVINO™ [Deep Learning Workbench](@ref
> openvino_docs_get_started_get_started_dl_workbench) (DL Workbench).
> [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is the OpenVINO™ toolkit UI that enables you to
> import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for
> deployment on various Intel® platforms.
## What's New in the Model Optimizer in this Release?
docs/doxygen/ie_docs.xml
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<tab type="user" title="Convert ONNX* GPT-2 Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_GPT2"/>
<tab type="user" title="[DEPRECATED] Convert DLRM ONNX* Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_DLRM"/>
<tab type="usergroup" title="Converting Your PyTorch* Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch">
<tab type="user" title="Convert PyTorch* QuartzNet Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_QuartzNet"/>
<tab type="user" title="Convert PyTorch* RNN-T Model " url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RNNT"/>
<tab type="user" title="Convert PyTorch* YOLACT Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_YOLACT"/>
<tab type="user" title="Convert PyTorch* F3Net Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_F3Net"/>
<tab type="user" title="Convert PyTorch* RCAN Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RCAN">
<tab type="user" title="Convert PyTorch* BERT-NER Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_Bert_ner"/>
<tab type="user" title="Convert PyTorch* QuartzNet Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_QuartzNet"/>
<tab type="user" title="Convert PyTorch* RNN-T Model " url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RNNT"/>
<tab type="user" title="Convert PyTorch* YOLACT Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_YOLACT"/>
<tab type="user" title="Convert PyTorch* F3Net Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_F3Net"/>
<tab type="user" title="Convert PyTorch* RCAN Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RCAN"/>
<tab type="user" title="Convert PyTorch* BERT-NER Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_Bert_ner"/>
</tab>
</tab>
<tab type="user" title="Model Optimizations Techniques" url="@ref openvino_docs_MO_DG_prepare_model_Model_Optimization_Techniques"/>
docs/doxygen/openvino_docs.xml
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<tab type="user" title="Install Intel® Distribution of OpenVINO™ toolkit for Linux* from a Docker* Image" url="@ref openvino_docs_install_guides_installing_openvino_docker_linux"/>
<tab type="user" title="Install Intel® Distribution of OpenVINO™ toolkit for Windows* from a Docker* Image" url="@ref openvino_docs_install_guides_installing_openvino_docker_windows"/>
</tab>
<tab type="user" title="Docker with DL Workbench" url="./workbench_docs_Workbench_DG_Install_from_Docker_Hub.html"/><!-- Link to the original Workbench topic -->
<tab type="user" title="Docker with DL Workbench" url="./workbench_docs_Workbench_DG_Run_Locally.html"/><!-- Link to the original Workbench topic -->
<tab type="user" title="APT" url="@ref openvino_docs_install_guides_installing_openvino_apt"/>
<tab type="user" title="YUM" url="@ref openvino_docs_install_guides_installing_openvino_yum"/>
<tab type="user" title="Anaconda Cloud" url="@ref openvino_docs_install_guides_installing_openvino_conda"/>
@@ -57,7 +57,7 @@ limitations under the License.
<tab type="user" title="Windows" url="@ref openvino_docs_get_started_get_started_windows"/>
<tab type="user" title="macOS" url="@ref openvino_docs_get_started_get_started_macos"/>
<tab type="user" title="Raspbian" url="@ref openvino_docs_get_started_get_started_raspbian"/>
<tab type="user" title="Get Started with OpenVINO via DL Workbench" url="@ref openvino_docs_get_started_get_started_dl_workbench"/>
<tab type="user" title="DL Workbench: Quick Start with OpenVINO™ Toolkit" url="@ref openvino_docs_get_started_get_started_dl_workbench"/>
<tab type="user" title="Legal Information" url="@ref openvino_docs_Legal_Information"/>
</tab>
<!-- Configuration for Hardware -->
docs/get_started/dl_workbench_img/active_projects_page.png
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docs/get_started/dl_workbench_img/openvino_in_dl_wb.png
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docs/get_started/get_started_dl_workbench.md
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# Get Started with OpenVINO™ Toolkit via Deep Learning Workbench {#openvino_docs_get_started_get_started_dl_workbench}
# Quick Start with OpenVINO™ Toolkit via Deep Learning Workbench {#openvino_docs_get_started_get_started_dl_workbench}
The OpenVINO™ toolkit optimizes and runs Deep Learning Neural Network models on Intel® hardware. This guide helps you get started with the OpenVINO™ toolkit via the Deep Learning Workbench (DL Workbench) on Linux\*, Windows\*, or macOS\*.
The OpenVINO™ toolkit is a comprehensive toolkit for optimizing pretrained deep learning models to achieve high performance and prepare them for deployment on Intel® platforms. Deep Learning Workbench (DL Workbench) is the OpenVINO™ toolkit UI designed to make the production of pretrained deep learning models significantly easier.
In this guide, you will:
* Learn the OpenVINO™ inference workflow.
* Start DL Workbench on Linux. Links to instructions for other operating systems are provided as well.
* Create a project and run a baseline inference.
Start working with the OpenVINO™ toolkit right from your browser: import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment in a matter of minutes. DL Workbench will take you through the full OpenVINO™ workflow, providing the opportunity to learn about various toolkit components.
[DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is a web-based graphical environment that enables you to easily use various sophisticated
OpenVINO™ toolkit components:
* [Model Downloader](@ref omz_tools_downloader) to download models from the [Intel® Open Model Zoo](@ref omz_models_group_intel)
with pre-trained models for a range of different tasks
* [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) to transform models into
the Intermediate Representation (IR) format
* [Post-training Optimization Tool](@ref pot_README) to calibrate a model and then execute it in the
INT8 precision
* [Accuracy Checker](@ref omz_tools_accuracy_checker) to determine the accuracy of a model
* [Benchmark Tool](@ref openvino_inference_engine_samples_benchmark_app_README) to estimate inference performance on supported devices
![](./dl_workbench_img/openvino_in_dl_wb.png)
![](./dl_workbench_img/DL_Workbench.jpg)
## User Goals
DL Workbench supports the following scenarios:
1. [Calibrate the model in INT8 precision](@ref workbench_docs_Workbench_DG_Int_8_Quantization)
2. [Find the best combination](@ref workbench_docs_Workbench_DG_View_Inference_Results) of inference parameters: [number of streams and batches](../optimization_guide/dldt_optimization_guide.md)
3. [Analyze inference results](@ref workbench_docs_Workbench_DG_Visualize_Model) and [compare them across different configurations](@ref workbench_docs_Workbench_DG_Compare_Performance_between_Two_Versions_of_Models)
4. [Implement an optimal configuration into your application](@ref workbench_docs_Workbench_DG_Deploy_and_Integrate_Performance_Criteria_into_Application)
* Learn what neural networks are, how they work, and how to examine their architectures with more than 200 deep learning models.
* Measure and interpret model performance right after the import​.
* Tune the model for enhanced performance.
* Analyze the quality of your model and visualize output.
* Use preconfigured JupyterLab\* environment to learn OpenVINO™ workflow.
## Prerequisites
Prerequisite | Linux* | Windows* | macOS*
:----- | :----- |:----- |:-----
Operating system|Ubuntu\* 18.04. Other Linux distributions, such as Ubuntu\* 16.04 and CentOS\* 7, are not validated.|Windows\* 10 | macOS\* 10.15 Catalina
CPU | Intel® Core™ i5| Intel® Core™ i5 | Intel® Core™ i5
GPU| Intel® Pentium® processor N4200/5 with Intel® HD Graphics | Not supported| Not supported
HDDL, MYRIAD| Intel® Neural Compute Stick 2 <br> Intel® Vision Accelerator Design with Intel® Movidius™ VPUs| Not supported | Not supported
Available RAM space| 4 GB| 4 GB| 4 GB
Available storage space | 8 GB + space for imported artifacts| 8 GB + space for imported artifacts| 8 GB + space for imported artifacts
Docker\*| Docker CE 18.06.1 | Docker Desktop 2.1.0.1|Docker CE 18.06.1
Web browser| Google Chrome\* 76 <br> Browsers like Mozilla Firefox\* 71 or Apple Safari\* 12 are not validated. <br> Microsoft Internet Explorer\* is not supported.| Google Chrome\* 76 <br> Browsers like Mozilla Firefox\* 71 or Apple Safari\* 12 are not validated. <br> Microsoft Internet Explorer\* is not supported.| Google Chrome\* 76 <br>Browsers like Mozilla Firefox\* 71 or Apple Safari\* 12 are not validated. <br> Microsoft Internet Explorer\* is not supported.
Resolution| 1440 x 890|1440 x 890|1440 x 890
Internet|Optional|Optional|Optional
Installation method| From Docker Hub <br> From OpenVINO™ toolkit package|From Docker Hub|From Docker Hub
## Start DL Workbench
This section provides instructions to run the DL Workbench on Linux from Docker Hub.
Use the command below to pull the latest Docker image with the application and run it:
```bash
wget https://raw.githubusercontent.com/openvinotoolkit/workbench_aux/master/start_workbench.sh && bash start_workbench.sh
```
DL Workbench uses [authentication tokens](@ref workbench_docs_Workbench_DG_Authentication) to access the application. A token
is generated automatically and displayed in the console output when you run the container for the first time. Once the command is executed, follow the link with the token. The **Get Started** page opens:
![](./dl_workbench_img/Get_Started_Page-b.png)
For details and more installation options, visit the links below:
* [Install DL Workbench from Docker Hub* on Linux* OS](@ref workbench_docs_Workbench_DG_Install_from_DockerHub_Linux)
* [Install DL Workbench from Docker Hub on Windows*](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub_Win)
* [Install DL Workbench from Docker Hub on macOS*](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub_mac)
* [Install DL Workbench from the OpenVINO toolkit package on Linux](@ref workbench_docs_Workbench_DG_Install_from_Package)
## <a name="workflow-overview"></a>OpenVINO™ DL Workbench Workflow Overview
The simplified OpenVINO™ DL Workbench workflow is:
1. **Get a trained model** for your inference task. Example inference tasks: pedestrian detection, face detection, vehicle detection, license plate recognition, head pose.
2. **Run the trained model through the Model Optimizer** to convert the model to an Intermediate Representation, which consists of a pair of `.xml` and `.bin` files that are used as the input for Inference Engine.
3. **Run inference against the Intermediate Representation** (optimized model) and output inference results.
## Run Baseline Inference
This section illustrates a sample use case of how to infer a pre-trained model from the [Intel® Open Model Zoo](@ref omz_models_group_intel) with an autogenerated noise dataset on a CPU device.
\htmlonly
<iframe width="560" height="315" src="https://www.youtube.com/embed/9TRJwEmY0K4" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<iframe width="560" height="315" src="https://www.youtube.com/embed/on8xSSTKCt8" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
\endhtmlonly
Once you log in to the DL Workbench, create a project, which is a combination of a model, a dataset, and a target device. Follow the steps below:
## Run DL Workbench
### Step 1. Open a New Project
You can [run DL Workbench](@ref workbench_docs_Workbench_DG_Install) on your local system or in the Intel® DevCloud for the Edge. Ensure that you have met the [prerequisites](@ref workbench_docs_Workbench_DG_Prerequisites).
On the the **Active Projects** page, click **Create** to open the **Create Project** page:
![](./dl_workbench_img/create_configuration.png)
Run DL Workbench on your local system by using the installation form. Select your options and run the commands on the local machine:
### Step 2. Choose a Pre-trained Model
\htmlonly
<iframe style="width: 100%; height: 620px;" src="https://openvinotoolkit.github.io/workbench_aux/" frameborder="0" allow="clipboard-write;"></iframe>
\endhtmlonly
Click **Import** next to the **Model** table on the **Create Project** page. The **Import Model** page opens. Select the squeezenet1.1 model from the Open Model Zoo and click **Import**.
![](./dl_workbench_img/import_model_02.png)
Once DL Workbench is set up, open the http://127.0.0.1:5665 link.
### Step 3. Convert the Model into Intermediate Representation
![](./dl_workbench_img/active_projects_page.png)
The **Convert Model to IR** tab opens. Keep the FP16 precision and click **Convert**.
![](./dl_workbench_img/convert_model.png)
Watch the video to learn more detailed information on how to run DL Workbench:
You are directed back to the **Create Project** page where you can see the status of the chosen model.
![](./dl_workbench_img/model_loading.png)
\htmlonly
<iframe width="560" height="315" src="https://www.youtube.com/embed/JBDG2g5hsoM" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
\endhtmlonly
### Step 4. Generate a Noise Dataset
Congratulations, you have installed DL Workbench. Your next step is to [Get Started with DL Workbench](@ref workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets) and create your first project.
Scroll down to the **Validation Dataset** table. Click **Generate** next to the table heading.
![](./dl_workbench_img/validation_dataset.png)
The **Autogenerate Dataset** page opens. Click **Generate**.
![](./dl_workbench_img/generate_dataset.png)
You are directed back to the **Create Project** page where you can see the status of the dataset.
![](./dl_workbench_img/dataset_loading.png)
### Step 5. Create the Project and Run a Baseline Inference
On the **Create Project** page, select the imported model, CPU target, and the generated dataset. Click **Create**.
![](./dl_workbench_img/selected.png)
The inference starts and you cannot proceed until it is done.
![](./dl_workbench_img/inference_banner.png)
Once the inference is complete, the **Projects** page opens automatically. Find your inference job in the **Projects Settings** table indicating all jobs.
![](./dl_workbench_img/inference_complete.png)
Congratulations, you have performed your first inference in the OpenVINO DL Workbench. Now you can proceed to:
* [Select the inference](@ref workbench_docs_Workbench_DG_Run_Single_Inference)
* [Visualize statistics](@ref workbench_docs_Workbench_DG_Visualize_Model)
* [Experiment with model optimization](@ref workbench_docs_Workbench_DG_Int_8_Quantization)
and inference options to profile the configuration
For detailed instructions to create a new project, visit the links below:
* [Select a model](@ref workbench_docs_Workbench_DG_Select_Model)
* [Select a dataset](@ref workbench_docs_Workbench_DG_Select_Datasets)
* [Select a target and an environment](@ref workbench_docs_Workbench_DG_Select_Environment). This can be your local workstation or a remote target. If you use a remote target, [register the remote machine](@ref workbench_docs_Workbench_DG_Add_Remote_Target) first.
## Additional Resources
* [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
## See Also
* [Get Started with DL Workbench](@ref workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets)
* [DL Workbench Overview](@ref workbench_docs_Workbench_DG_Introduction)
* [DL Workbench Educational Resources](@ref workbench_docs_Workbench_DG_Additional_Resources)
* [OpenVINO™ Toolkit Overview](../index.md)
* [DL Workbench Installation Guide](@ref workbench_docs_Workbench_DG_Install_Workbench)
* [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
* [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
* [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
* [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)
docs/install_guides/installing-openvino-images.md
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You may install Intel® Distribution of OpenVINO™ toolkit from images and repositories using the **Install OpenVINO™** button above or directly from the [Get the Intel® Distribution of OpenVINO™ Toolkit](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit/download.html) page. Use the documentation below if you need additional support:
* [Docker](installing-openvino-docker-linux.md)
* [Docker with DL Workbench](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub)
* [Docker with DL Workbench](@ref workbench_docs_Workbench_DG_Run_Locally)
* [APT](installing-openvino-apt.md)
* [YUM](installing-openvino-yum.md)
* [Anaconda Cloud](installing-openvino-conda.md)
docs/install_guides/installing-openvino-linux.md
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> - An internet connection is required to follow the steps in this guide.
> - [Intel® System Studio](https://software.intel.com/en-us/system-studio) is an all-in-one, cross-platform tool suite, purpose-built to simplify system bring-up and improve system and IoT device application performance on Intel® platforms. If you are using the Intel® Distribution of OpenVINO™ with Intel® System Studio, go to [Get Started with Intel® System Studio](https://software.intel.com/en-us/articles/get-started-with-openvino-and-intel-system-studio-2019).
> **TIP**: If you want to [quick start with OpenVINO™ toolkit](@ref
> openvino_docs_get_started_get_started_dl_workbench), you can use
> the OpenVINO™ [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench). DL Workbench is the OpenVINO™ toolkit UI
> that enables you to import a
> model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment
> on various Intel® platforms. Begin your OpenVINO™ journey with [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Install).
## 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.
@@ -31,19 +38,6 @@ The Intel® Distribution of OpenVINO™ toolkit for Linux\*:
| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel) | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo). |
| Deep Learning Streamer (DL Streamer) | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Elements), [Tutorial](https://github.com/openvinotoolkit/dlstreamer_gst/wiki/DL-Streamer-Tutorial). |
**Could Be Optionally Installed**
[Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench) is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare performance of deep learning models on various Intel® architecture
configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components:
* [Model Downloader](@ref omz_tools_downloader)
* [Intel® Open Model Zoo](@ref omz_models_group_intel)
* [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
* [Post-training Optimization Tool](@ref pot_README)
* [Accuracy Checker](@ref omz_tools_accuracy_checker)
* [Benchmark Tool](../../inference-engine/samples/benchmark_app/README.md)
Proceed to an [easy installation from Docker](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) to get started.
## System Requirements
**Hardware**
docs/install_guides/installing-openvino-macos.md
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> - The Intel® Distribution of OpenVINO™ is supported on macOS\* 10.15.x versions.
> - An internet connection is required to follow the steps in this guide. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
> **TIP**: If you want to [quick start with OpenVINO™ toolkit](@ref
> openvino_docs_get_started_get_started_dl_workbench), you can use
> the OpenVINO™ [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench). DL Workbench is the OpenVINO™ toolkit UI
> that enables you to import a
> model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment
> on various Intel® platforms. Begin your OpenVINO™ journey with [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Install).
## Introduction
The Intel® Distribution of OpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNN), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance.
@@ -31,18 +38,6 @@ The following components are installed by default:
| Additional Tools | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other |
| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel) | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo) |
**Could Be Optionally Installed**
[Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench) is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare performance of deep learning models on various Intel® architecture
configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components:
* [Model Downloader](@ref omz_tools_downloader)
* [Intel® Open Model Zoo](@ref omz_models_group_intel)
* [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
* [Post-training Optimization Tool](@ref pot_README)
* [Accuracy Checker](@ref omz_tools_accuracy_checker)
* [Benchmark Tool](../../inference-engine/samples/benchmark_app/README.md)
Proceed to an [easy installation from Docker](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) to get started.
## Development and Target Platform
docs/install_guides/installing-openvino-windows.md
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> - This guide applies to Microsoft Windows\* 10 64-bit. For Linux* OS information and instructions, see the [Installation Guide for Linux](installing-openvino-linux.md).
> - [Intel® System Studio](https://software.intel.com/en-us/system-studio) is an all-in-one, cross-platform tool suite, purpose-built to simplify system bring-up and improve system and IoT device application performance on Intel® platforms. If you are using the Intel® Distribution of OpenVINO™ with Intel® System Studio, go to [Get Started with Intel® System Studio](https://software.intel.com/en-us/articles/get-started-with-openvino-and-intel-system-studio-2019).
> **TIP**: If you want to [quick start with OpenVINO™ toolkit](@ref
> openvino_docs_get_started_get_started_dl_workbench), you can use
> the OpenVINO™ [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction). DL Workbench is the OpenVINO™ toolkit UI
> that enables you to import a
> model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for deployment
> on various Intel® platforms. Begin your OpenVINO™ journey with [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Install).
## Introduction
> **IMPORTANT**:
@@ -64,18 +71,6 @@ The following components are installed by default:
| Additional Tools | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other |
| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel) | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/openvinotoolkit/open_model_zoo) |
**Could Be Optionally Installed**
[Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench) is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare performance of deep learning models on various Intel® architecture
configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components:
* [Model Downloader](@ref omz_tools_downloader)
* [Intel® Open Model Zoo](@ref omz_models_group_intel)
* [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
* [Post-training Optimization Tool](@ref pot_README)
* [Accuracy Checker](@ref omz_tools_accuracy_checker)
* [Benchmark Tool](../../inference-engine/samples/benchmark_app/README.md)
Proceed to an [easy installation from Docker](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) to get started.
### System Requirements
inference-engine/samples/benchmark_app/README.md
+5 -6
View File
@@ -4,12 +4,11 @@ This topic demonstrates how to use the Benchmark C++ Tool to estimate deep learn
> **NOTE:** This topic describes usage of C++ implementation of the Benchmark Tool. For the Python* implementation, refer to [Benchmark Python* Tool](../../tools/benchmark_tool/README.md).
> **TIP**: You also can work with the Benchmark Tool inside the OpenVINO™ [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench).
> [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare
> performance of deep learning models on various Intel® architecture
> configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components.
> <br>
> Proceed to an [easy installation from Docker](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) to get started.
> **TIP**: You can quick start with the Benchmark Tool inside the OpenVINO™ [Deep Learning Workbench](@ref openvino_docs_get_started_get_started_dl_workbench) (DL Workbench).
> [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is the OpenVINO™ toolkit UI you to
> import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for
> deployment on various Intel® platforms.
## How It Works
inference-engine/tools/benchmark_tool/README.md
+4 -6
View File
@@ -4,12 +4,10 @@ This topic demonstrates how to run the Benchmark Python* Tool, which performs in
> **NOTE:** This topic describes usage of Python implementation of the Benchmark Tool. For the C++ implementation, refer to [Benchmark C++ Tool](../../samples/benchmark_app/README.md).
> **TIP**: You also can work with the Benchmark Tool inside the OpenVINO™ [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench).
> [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare
> performance of deep learning models on various Intel® architecture
> configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components.
> <br>
> Proceed to an [easy installation from Docker](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) to get started.
> **TIP**: You can quick start with the Benchmark Tool inside the OpenVINO™ [Deep Learning Workbench](@ref openvino_docs_get_started_get_started_dl_workbench) (DL Workbench).
> [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is the OpenVINO™ toolkit UI you to
> import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for
> deployment on various Intel® platforms.
## How It Works