[40929] DL Workbench in Get Started (openvinotoolkit#2740)

* Initial commit * Added the doc * More instructions and images * Added slide * Borders for screenshots * fixes * Fixes * Added link to Benchmark app * Replaced the image * tiny fix * tiny fix
plaidml · tzerrell · Dec 18, 2020 · Oct 26, 2020 · Oct 30, 2020 · Oct 30, 2020
commit 751ef424243e5a0783df4796f49565c0a0d0d934
diff --git a/docs/doxygen/openvino_docs.xml b/docs/doxygen/openvino_docs.xml
@@ -37,6 +37,7 @@
                 <tab type="user" title="Linux" url="@ref openvino_docs_get_started_get_started_linux"/>
                 <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="Get Started with OpenVINO via DL Workbench" 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 -->

diff --git a/docs/get_started/dl_workbench_img/DL_Workbench.jpg b/docs/get_started/dl_workbench_img/DL_Workbench.jpg
diff --git a/docs/get_started/dl_workbench_img/Get_Started_Page-b.png b/docs/get_started/dl_workbench_img/Get_Started_Page-b.png
diff --git a/docs/get_started/dl_workbench_img/convert_model.png b/docs/get_started/dl_workbench_img/convert_model.png
diff --git a/docs/get_started/dl_workbench_img/create_configuration.png b/docs/get_started/dl_workbench_img/create_configuration.png
diff --git a/docs/get_started/dl_workbench_img/dataset_loading.png b/docs/get_started/dl_workbench_img/dataset_loading.png
diff --git a/docs/get_started/dl_workbench_img/generate_dataset.png b/docs/get_started/dl_workbench_img/generate_dataset.png
diff --git a/docs/get_started/dl_workbench_img/import_model_01.png b/docs/get_started/dl_workbench_img/import_model_01.png
diff --git a/docs/get_started/dl_workbench_img/import_model_02.png b/docs/get_started/dl_workbench_img/import_model_02.png
diff --git a/docs/get_started/dl_workbench_img/inference_banner.png b/docs/get_started/dl_workbench_img/inference_banner.png
diff --git a/docs/get_started/dl_workbench_img/inference_complete.png b/docs/get_started/dl_workbench_img/inference_complete.png
diff --git a/docs/get_started/dl_workbench_img/model_loading.png b/docs/get_started/dl_workbench_img/model_loading.png
diff --git a/docs/get_started/dl_workbench_img/selected.png b/docs/get_started/dl_workbench_img/selected.png
diff --git a/docs/get_started/dl_workbench_img/validation_dataset.png b/docs/get_started/dl_workbench_img/validation_dataset.png
diff --git a/docs/get_started/get_started_dl_workbench.md b/docs/get_started/get_started_dl_workbench.md
@@ -0,0 +1,140 @@
+# Get Started with OpenVINO™ Toolkit via Deep Learninig 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\*. 
+
+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.     
+
+[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_README) to download models from the [Intel® Open Model Zoo](@ref omz_models_intel_index) 
+with pretrained 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 toolkit](@ref pot_README) to calibrate a model and then execute it in the
+ INT8 precision
+* [Accuracy Checker](@ref omz_tools_accuracy_checker_README) 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/DL_Workbench.jpg)
+
+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)   
+
+## 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 pretrained model from the [Intel® Open Model Zoo](@ref omz_models_intel_index) with an autogenerated noise dataset on a CPU device.
+
+<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>
+
+
+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:
+
+### Step 1. Open a New Project 
+
+On the the **Active Projects** page, click **Create** to open the **Create Project** page:
+![](./dl_workbench_img/create_configuration.png)
+
+### Step 2. Choose a Pretrained Model
+
+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)
+
+### Step 3. Convert the Model into Intermediate Representation
+
+The **Convert Model to IR** tab opens. Keep the FP16 precision and click **Convert**.
+![](./dl_workbench_img/convert_model.png)
+
+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)
+
+### Step 4. Generate a Noise Dataset
+
+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)
+* [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)
+* [OpenVINO™ Hello World Face Detection Exercise](https://github.com/intel-iot-devkit/inference-tutorials-generic)