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:
DL Workbench is a web-based graphical environment that enables you to easily use various sophisticated OpenVINO™ toolkit components:
DL Workbench supports the following scenarios:
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 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 Browsers like Mozilla Firefox* 71 or Apple Safari* 12 are not validated. Microsoft Internet Explorer* is not supported. | Google Chrome* 76 Browsers like Mozilla Firefox* 71 or Apple Safari* 12 are not validated. Microsoft Internet Explorer* is not supported. | Google Chrome* 76 Browsers like Mozilla Firefox* 71 or Apple Safari* 12 are not validated. 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 From OpenVINO™ toolkit package | From Docker Hub | From Docker Hub |
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:
DL Workbench uses authentication tokens 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:
For details and more installation options, visit the links below:
The simplified OpenVINO™ DL Workbench workflow is:
.xml
and .bin
files that are used as the input for Inference Engine.This section illustrates a sample use case of how to infer a pretrained model from the Intel® Open Model Zoo with an autogenerated noise dataset on a CPU device.
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:
On the the Active Projects page, click Create to open the Create Project page:
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.
The Convert Model to IR tab opens. Keep the FP16 precision and click Convert.
You are directed back to the Create Project page where you can see the status of the chosen model.
Scroll down to the Validation Dataset table. Click Generate next to the table heading.
The Autogenerate Dataset page opens. Click Generate.
You are directed back to the Create Project page where you can see the status of the dataset.
On the Create Project page, select the imported model, CPU target, and the generated dataset. Click Create.
The inference starts and you cannot proceed until it is done.
Once the inference is complete, the Projects page opens automatically. Find your inference job in the Projects Settings table indicating all jobs.
Congratulations, you have performed your first inference in the OpenVINO DL Workbench. Now you can proceed to:
For detailed instructions to create a new project, visit the links below: