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[DOCS]Changed DL WB related docs and tips (openvinotoolkit#6318) * changed DL WB related docs and tips * added two tips to benchmark and changed layout * changed layout * changed links * page title added * changed tips * ie layout fixed * updated diagram and hints * changed tooltip and ref link * changet tooltip link * changed DL WB description * typo fix # Conflicts: # docs/doxygen/ie_docs.xml # thirdparty/ade
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| # Get Started with OpenVINO™ Toolkit via Deep Learning Workbench {#openvino_docs_get_started_get_started_dl_workbench} | ||
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| 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\*. | ||
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| 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. | ||
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| [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 | ||
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| 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) | ||
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| ## Prerequisites | ||
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| 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 | ||
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| ## Start DL Workbench | ||
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| This section provides instructions to run the DL Workbench on Linux from Docker Hub. | ||
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| Use the command below to pull the latest Docker image with the application and run it: | ||
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| ```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: | ||
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| 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) | ||
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| ## <a name="workflow-overview"></a>OpenVINO™ DL Workbench Workflow Overview | ||
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| 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. | ||
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| ## Run Baseline Inference | ||
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| 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> | ||
| \endhtmlonly | ||
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| 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: | ||
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| ### Step 1. Open a New Project | ||
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| On the the **Active Projects** page, click **Create** to open the **Create Project** page: | ||
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| ### Step 2. Choose a Pre-trained Model | ||
| # Quick Start with OpenVINO™ Toolkit via Deep Learning Workbench {#openvino_docs_get_started_get_started_dl_workbench} | ||
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| 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**. | ||
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| 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. | ||
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| ### Step 3. Convert the Model into Intermediate Representation | ||
| 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. | ||
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| The **Convert Model to IR** tab opens. Keep the FP16 precision and click **Convert**. | ||
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| You are directed back to the **Create Project** page where you can see the status of the chosen model. | ||
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| ## User Goals | ||
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| ### Step 4. Generate a Noise Dataset | ||
| * 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. | ||
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| Scroll down to the **Validation Dataset** table. Click **Generate** next to the table heading. | ||
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| \htmlonly | ||
| <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 | ||
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| The **Autogenerate Dataset** page opens. Click **Generate**. | ||
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| ## Run DL Workbench | ||
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| You are directed back to the **Create Project** page where you can see the status of the dataset. | ||
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| 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). | ||
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| ### Step 5. Create the Project and Run a Baseline Inference | ||
| Run DL Workbench on your local system by using the installation form. Select your options and run the commands on the local machine: | ||
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| On the **Create Project** page, select the imported model, CPU target, and the generated dataset. Click **Create**. | ||
|  | ||
| \htmlonly | ||
| <iframe style="width: 100%; height: 620px;" src="https://openvinotoolkit.github.io/workbench_aux/" frameborder="0" allow="clipboard-write;"></iframe> | ||
| \endhtmlonly | ||
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| The inference starts and you cannot proceed until it is done. | ||
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| Once DL Workbench is set up, open the http://127.0.0.1:5665 link. | ||
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| Once the inference is complete, the **Projects** page opens automatically. Find your inference job in the **Projects Settings** table indicating all jobs. | ||
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| 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 | ||
| Watch the video to learn more detailed information on how to run DL Workbench: | ||
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| 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. | ||
| \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 | ||
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| ## Additional Resources | ||
| 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. | ||
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| * [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) |
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