Creating and Connecting VM on Microsoft Azure

Python AI and Machine Learning for Production and Development Provisioning VM on Leading Cloud Platforms
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Transcript

Hello everyone so in this video we are going to see how we can get the Python AI and machine learning kit from us marketplace. For that, you know there are multiple ways you can provision the machine. First one is you can go to tech letters dotnet you can go to products and you can click on you know it will show you all the products you can click on Python a and machine learning kit. It will open the product page you can click on Get on a zoo. So this is one way. The other ways you can directly go to the zoo marketplace.

So for example, here, I'm directly going to the zoo marketplace. And on this zoom marketplace you can search for by AI and then the drop down you will see Python AI and machine learning suite by title it is dotnet if you're not sure about the name, you can just search for particular test dotnet and you know it will list all the available VMs by ticular test, and then you can select them via once you have the VM displayed, you can click on get it now. Click on Continue, then it will take you to the Azure portal. It may ask you for your user ID and password in case you are not logged in into Azure Portal. Once you are there on the portal, it will show you the details about the VM offer. And then you can click on Create and here you need to provide the configuration for your VM.

Alright, so first is you need to provide the resource group you need to select the subscription. Then, in my case, I will go with my current company so you can create a new or use your existing one To give it some name. So in my case I'd say by a ml VM, you can give any name select the region, go with the default option for other things and the size so by default, it will come with two virtual CPUs, eight GB RAM This is sufficient to get started. If you think that you know, you need more beefy virtual machine, you can click on change size and selected as per your requirement, but this should be enough to get started. The next one is very important step you need to select the password option for authentication because this virtual machine comes with remote desktop and that authentication mechanism for remote desktop is password.

So here, a unit to select password for you authentication type this is very important. The other important thing is for the username you need to select a boon to because the VM is configured with this username so this has to be open to and then password you can select as per your requirement sorry, some password over here. Next click on the disk configuration then you can go with standard HDD because that will cost you less. So this option is the cheapest one. If you want a faster harness, you can go with standard or premium SSD but note that that will have an extra cost associated with it. So I will recommend to use standard HDD you don't need any data disk you just need one was disk.

That's enough. Under advance options, you don't need to have anything selected or you know you can go with not to use manage disk. Okay, so this is up to you. In my case, I will just go with the monitors. Next is networking. In the networking, you can go with any virtual network, whatever you have, go with the default one, it will give you a public IP as well take the default option.

Configure network security group now or here. You need to make sure that you know you have certain ports open so it is creating a new security group. We'll go with that. extroverted networking. We don't need it Load Balancing also, we don't need it. Okay.

Next is management. Boot diagnostic, you don't want it again, you know, it comes with its own overhead. So we'll go with without it. Other option we'll go with off auto shutdown. You know, this is another feature if you want to have your VM auto shutdown in general with that in the advance. We don't need anything over here.

Tags if you want to add any tags you can add. So let's say your name. Yeah. We have something on there are tough. And then you're going to create another thing is in the networking unit to make sure that certain ports are open. So for that did create the VM and then we'll see, by default it opens the port.

Because the VM configuration has that. If not, then we'll open those ports. So I will click on review and create and then it will go and create a virtual machine. It will take some time, so hang in there. And once the VM is created, it will do the validation once the validation is done, then you can click on Create. So it will start provisioning your virtual machine.

So you can see the deployment is underway. And once it is done, you should be able to access the virtual machine. So now you can see that the deployment is complete. So we can click on Go to resource and it will show us our virtual machine Which is running over here. Now the VM is up and running. The next thing is how do we connect to it.

So for that, you need to take the public IP address. So I can click on this. The other way is this public IP address might change in your subsequent restart of the VM. So the other better option is you configure a DNS name. So I can click on this Configure. So you configure a DNS name for our VM.

So that way, we can refer to the VM using the the DNS name so for that, we'll just say Python AI ml. It will validate the name. Okay, if the name is not taken, then you know, it will show this tick mark, then you can save otherwise you need to give a new name to your DNS. So once that one is saved, then we can refer to our VM using this fully qualified DNS name, you need to add this domain name to your DNS name. Okay, so the complete name will be Python, ai ml Doc, this whole domain. So what I'll do is I'll just copy it in when notepad for reference, and then I'll go back to our overview tab.

So in the overview tab, now you will see that the virtual machine has the DNS name. So you got the public IP or you got the DNS name, we can use it to connect to the VM. So we will copy the DNS name then I can go to drama mode. dekstop open a Remote Desktop Connection put your DNS name or public IP select this option. Now one thing you will notice that you know for me it is giving an error Let me see. So now you can see it is connecting to the virtual machine it will ask you for the user ID and password.

So it is the same user ID and password you provided while creating the virtual machine. So, and I mentioned that you have to use the username as open to password you can give it whatever you want. And then click OK and it will connect to the desktop environment. Alright, so here you can see this is our pattern machine learning dekstop environment for our Watson And let me just navigate you through the virtual machine here at the deck stop. So by default, you will log into the tech stop. And on the next tab you will see this photo Python Machine Learning Kit.

This folder has got all the things you need four for this virtual machine. In this folder, you will you will have the demos for for the virtual machine. So, here you can see the folder called code and in that you have all these demos we will go through you know what this demos are for what the cover. So, this is where you will find all the sample codes. Then you got Visual Studio code, this you can use it for your development purpose. Then you got the term In a lower here, you can use this terminal this terminal comes with a new shell called fish friendly interactive shell, which has auto completion a lot of other features.

So Visual Studio code for your development, the terminal with fish shell for your shell environment. And then the set of all the setups for this VM are available under slash home slash Anaconda. So if you go to the home directory so this is your home slash home slash Ubuntu, you can go to the File System. Go back to the home directory over here and here you see this Anaconda folder. So this is where you will find all your Anaconda setup and within this Anaconda setup, we have all the modules required for Running the AI and machine learning sample codes as well as you know any new application or script you develop then all the required packages are already installed in this Anaconda environment okay. So, this is the desktop environment through which you can, you know, access and work or develop your own application.

This is one way you can use the virtual machine. But the better ways to capitalize or use the iPod ipython environment which this VM comes with. Now, that I Bhutan environment will be running by default. So, if you see on a command prompt if you just search for by Tom process, you will see that there is This Python process running and this Python process is running our IPython notebook. Okay, and those iPad Python notebook are basically using something called as Jupiter hub. We'll get into other videos about what Jupiter hub is.

But at this point, all you need to understand is you can access your VM and all the code for AI ml through the desktop environment. Or there is another way, the better way is from anywhere. You can access the environment using the browser using the ipython. Okay, so how are you going to access that fun? Again, you will use your DNS name or your public IP. I already have the public IP, all you need to do is just copy paste that public IP.

Make sure you are using HTTP protocol and hit enter and It should open your IPython notebook. Okay, and here you can use the same username and sign in. And as soon as you sign in using this Jupiter hub ipython environment, it will directly take you to the n machine learning folder which I showed you on the desktop. So on the desktop, we had one folder where I told you that you will have all your sample demos and everything on that one. If you log in through your IPython notebook or Jupiter hub environment, it will directly take you to this environment or to the sample code. Okay.

So now here you see you're directly logged in. And here you have your home directory. So the Jupiter hub or iPad font is configured to this home directory, which is your slash home slash wouldn't do slash textile slash Python machine learning folder. And in this, you can click and then you will see the code, the document images and everything. So this is how you are going to provision your virtual machine on a zoo. Note that this virtual machine is also available on Google Cloud as well as AWS.

So if you want to have it on other cloud platform, we have separate videos on how to provision your virtual machine in those cloud environment. So thanks a lot for watching and hope this has been helpful for you

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