Ubuntu* 16.04, Ubuntu 18.04
Other Linux distributions, such as CentOS* 7, are not validated.
|Toolkit||Original *.tgz for l_openvino_toolkit|
|Container||Docker* CE (all options require docker)|
|Storage Space||5 GB|
Google Chrome* 72+
Other browsers, like Mozilla Firefox* 65 or Apple Safari* 12, are not validated.
Microsoft Internet Explorer* is not recommended
|Skills||Familiarity with Linux* commands|
The Deep Learning Workbench was introduced with the Intel® Distribution of OpenVINO™ toolkitlkit recently. In this article, we will setup and demo the deep learning workbench.
The instructions are broken up into 6 different steps.
|1||Install the Deep Learning Workbench|
|2||Configure - Import Model(s)|
|3||Configure - Import or Create Dataset|
|4||Configure - Select Environment|
Stage 1: Install the workbench
Note: The workbench included in the Intel® Distribution of OpenVINO™ toolkitlkit 2019 r3 and 2020 r1 require docker*.
Open a terminal. Run the following command:
sudo docker run -p 127.0.0.1:5665:5665 --name workbench --privileged -v /dev/bus/usb:/dev/bus/usb -v /dev/dri:/dev/dri -e PORT=5665 -e PROXY_HOST_ADDRESS=0.0.0.0 -it openvino/workbench:latest
This command sets up the environment for the DL Workbench Docker* image, builds the Docker image, and runs the Docker container with the DL Workbench.
Once the command is executed, you may see a message similar to:
Proxy server listening on: http://0.0.0.0:5665 new session: ID
Once you verify a session is running, use the browser to access:
The DL Workbench Get Started page displays.
Step 2: Configure - Import Model(s)
Only one model can be imported at a time with r3 and 2020 r1 workbench. After import, the models are available. However, note the models may take time to import one at a time. Either Upload an Original Model or select a model to import from the Open Model Zoo.
Select the Import Model button.
The frameworks available include:
- Apache* MxNet*
Import from Open Model Zoo
- Select Open Model Zoo
- Select the model to import: face-detection-retail-004
- Framework: squeezenet - OpenVINO IR
- Type: object detection
- Precision: FP16
3. Select the + sign to see more details.
4. The details display.
5. Select Import
Progress of the import displays under Status.
The import completes.
3. Configure - Import or Create Dataset
Add a dataset. The options are:
- Import Local Dataset
- Autogenerate Dataset in ImageNet format
Select Autogenerate Dataset in ImageNet format if you do not have a local dataset to import.
The default settings are 100 images at 124x124.
There is an option to select more images. The more images you select to include in the dataset, the longer this will take to autogenerate.
The dataset is complete.
In this example, the squeezenet model will not work with an imagenet dataset, but a caffenet model will.
Import a caffenet model.
After importing caffenet, select caffenet with an autogenerated dataset.
4. Configure Environment
Select an environment from the list.
- Core i7
Note: For the purposes of this article, the Up* Xtreme was used and the listed options are available when using the Up Xtreme. Your environment options may vary.
After clicking GO, the results display.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.