In this video, we will learn how to install DL Workbench on your machine so that you can
start experimenting with your model and how it works on different types of the hardware
on the CPU, on the GPU, neural compute stick 2, and the HDDL.
So once we have decided to install the OpenVINO DL Workbench, we can read more about this
tool in the official documentation for that we need to go to the docs.Openvinotoolkit.org,
and there we can easily navigate to the sections that describe the OpenVINO DL Workbench.
So here we can read more about the deep learning workbench.
What are the main components, how it is organized.
And then we can proceed to the installation guide there.
We will read about the prerequisites that are required to be installed in the system.
So in short,in order to have the DL Workbench running on your system, you need to have Docker
installed in this video.
We will learn how to install Docker on all major OSes like Mac, Linux, and windows.
So we begin with installation of Docker on Mac.
And for that, we need to download their Docker desktop for Mac, which is the traditional
Mac application that you need to download put in the applications folder.
And after you have done that, you need to run the application.
And then I take a look at the status bar at the top of this screen.
If you see the small whale there and when you hover over this whale and see that the
Docker desktop is running, then you're good to go and proceed to running DL Workbench
on your system.
Now, let's talk a bit about how to install Docker on windows.
The situation is pretty the same.
You download the installer, install it as a plain windows application, and then you
need to run this application for that.
Just find it in the windows search.
After that go to the status bar.
And if you recognize that little whale and when you hover, it says the Docker desktop
is running, then you're good to go and proceed with running deal where bench only a windows
machine installing Docker on Ubuntu and on Linux systems in general is a bit more sophisticated.
However, there is a nice tutorial from the Docker team that we just follow in order to
install Docker on the Ubuntu 20.04.
So we remove all old installations of Docker.
Then we proceed to add in the new repository and installing Docker from that repository.
Once we have installed the Docker, we need to make sure that it is working and we can
do that by pulling the sample.
Hello World image.
And once we've done that, we can see that it successfully downloaded and it means the
Docker hasn't stopped.
However, there is an extremely important final step that a you need to follow and, uh, it
is adding your user to the Docker group.
So we do that by using this special command.
And after that, you're able to run Docker without sudo.
It is highly recommended way and you will need this when running DL Workbench on the
system.
So we have installed Docker on our operation system.
What is next?
And the next step of course is running the DL Workbench.
Before we start running DL Workbench, we need to understand one simple thing.
If we want to experiment with numerous accelerators, like I said, GPU.
Neural compute stick 2, HDDL cards.
Then we need Ubuntu system.
If we are working on Mac and windows, only CPU is available as the accelerator.
So now we can start running the DL Workbench on our system.
So first of all, we need to understand what is the latest version of the OpenVINO DL Workbench
for that?
We go to the Docker hub.
And, uh, see that there are several tags and the most recent one is 2021.2.
And this version we are going to use in this video.
In order to run DL Workbench on the Mac system, we need to pull the image first for that.
We copy this command and, uh, it is recommended to provide the tag that we just saw on the
Docker hub page.
So we pull the image, check that it is in the list, and now we need to run the DL Workbench.
We're going to use one of the commands available in the instructions.
I will use the most simple one.
Here.
It is just copied paste into the terminal.
And in few moments we see that DL Workbench has started and there is a special link in
the terminal that we need to follow.
And here you are, here is the DL Workbench running on your system.
When we want to run DL Workbench on Windows,situation is pretty much the same.
We need to pull the image by selecting the command from the documentation and providing
the proper tag.
Once it's there, we need to, to select one of the commands in the instructions.
There are several instructions, but we will use the simplest one.
We copied and execute in the terminal.
After we do that in a few moments, we can see the DL Workbench is running on their local
host four to five, six, six, five, and then we visit this page and see that DL Workbench
is running and is available for your experiments.
So as we discussed the most, a complete list of available accelerators is on the Linux
system.
So to run the DL Workbench on the Linux system is pretty much the same as we did that on
Mac and windows.
However, as there are more accelerators available, there are more options to be used.
And, uh, we have made a special script that encapsulates all the complexity of running
Workbench on the Linux system.
So we need to obtain the script that I was talking about, which is called start workbench
-sh after that, uh, we need to make sure that it is executable and, uh, then we can, uh,
review all the available options, how to run Docker container.
On the Linux system.
So make sure that you have selected the proper instructions for the accelerators that you
have on your machine, and then copy the command and execute it in the terminal.
After you have done that DL Workbench appears and, uh, you can start experimenting and uploading
your model or selecting one of the models from the Open Model Zoo.
So, congratulations, you have your Workbench running on your operation system, whether
it is Mac, Linux, or windows.
And now you can start experimenting with your model and benchmarking it, optimizing it for
various accelerators, preparing your model for deployment, or even analyzing how our
model works.
What is its output, how it is optimized and how it is executed on the target hardware.
If you have some special feedback or you want to, to make some special feature requests,
maybe you have some troubles working with the DL Workbench.
Please let us know by using the official forum, the link to the forum is available in the
description.
So thank you again.
Bye