Name: SAP Artificial Intelligence Onboarding : S3 Bucket Creation
Uploaded: 2023-9-7
Duration: 644 s

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<div><div><div>In this video tutorial, Tahir Hussain Babar (Bob) shows how to onboard SAP Artificial Intelligence & how to setup pre-requisites. This video shows how to create an S3 Bucket in SAP BTP using the Object Store Service Plan. We'll then look at installing the AWS S3 CLI, so we can connect to the S3 Bucket we created, where we will upload a small dataset.  <br><br>00:00 - Intro<br>02:10 - Object Store Creation<br>03:31 - Object Store Key Creation<br>04:31 - AWS CLI Install<br>05:36 - S3 Bucket Connection<br>06:29 - Bucket Commands (rm, ls, cp, sync)<br>07:10 - Bucket Test Data Creation<br><br>SAP AI in BTP: <a href="https://www.sap.com/products/artificial-intelligence.html" rel="nofollow noopener noreferrer" target="_blank">https://www.sap.com/products/artificial-intelligence.html</a><br><br>Please note:<br>                     This video was originally published on the SAP HANA Academy YouTube Channel                      and therefore may contain references to SAP HANA Academy and YouTube.                      The tech world is always evolving and the SAP HANA Academy YouTube channel                      is set to close on December 31, 2023.                      At that time, references will no longer be valid                      and will gradually be removed when the videos are updated.<br>Also, this video includes automatically generated captions                      and therefore might misrepresent the spoken content or display incorrect product names.</div></div></div>

SAP Artificial Intelligence Onboarding : S3 Bucket Creation

Welcome to the SAP HANA Academy. My name is Bob, and in this video series we'll be looking at SAP Artificial Intelligence. In this video series we'll be looking at onboarding and in this video we'll be looking at how to create S3 buckets within the SAP Business Technology Platform and how to upload data to that S3 bucket. In the previous video, we looked at how we can link a GitHub repository to our SAP AI Core service. We looked at how to create an application within that GitHub repository. And then lastly we looked at linking Docker via what's called a Docker registry secret which would connect to your Docker repository and of course we linked that again to SAP AI Core. So in this video we're going to concentrate on our data storage in the form of an S3 bucket. There are different ways you can connect to S3 buckets. Of course there are different types in different platforms. We're going to be looking at S3 buckets, which of course can be in AWS, or you can create them within SAP's business technology platform, which is what I'm going to focus on. As you can imagine, the S3 bucket is for data storage. So we're going to use it to store our source data. And I'll demonstrate this by creating a simple CSV file which I'll upload to that S3 bucket. Of course, eventually later on in another video series, we'll store a massive data set of both images and also of sound files. As well as data storage for our source data, we'll use these S3 buckets to store our trained models. So later on in the video series again you'll see objects are created after you've trained your model, which are then accessed when you serve your model. And we'll look at those concepts later on when we need to. Because we're using BTP, as you can imagine, we'll need to have the rights to access an Object Store. So for us, if we go to our entitlements, what we can do is configure our entitlements, we can add our service plans, and of course I'm going to search for Object Store. I'll select it. In my plan I've only got one available plan, although there are different plans for different providers. So I'll select it and I'll click on add one service plan and save. Of course if you don't see your service plan there for the object store, you'll need to contact your account manager. So now if I go to my instances what we'll do is we'll create an instance using the object store. I'll create an instance, so I'll search for it and I'll select object store. We're using the S3 standard plan and we just need to give it a name so I'll call it underscore dev underscore os and then I'll select next. We don't need to add anything here so I'll select create. We'll see that it's been created by going down to our list of instances, and here you can see the object store being created. So while it's creating, we can still go ahead and create some other objects. What I'll do is I'll create myself a key. So if I select the three dots and we'll say create service key, we'll need to give it a name, so I'm going to call it the similar name which is underscore dev underscore os underscore s key for service key. If I go to my service keys, now we'll see that that key is created. Like when we connected SAP AI Core to the Launchpad, we'll use that service key for authorization when we access the bucket. So as you know, you've got all the configuration parameters within that file, which means it's a good idea to save it somewhere. So now we've got the file downloaded. What we'll want to do is test connectivity to our bucket. So we'll do this via command line. That's the easiest way to do this. So that means I'll need to install some tools and I'll do this via a command line. So I'll go back to my Windows box and I'll launch a command window. And there's a few different ways we can get hold of this executable from AWS. It's the AWS command line interface. So I'm going to use msi exec executable to get hold of the file and this is the path for the MSI file that you'll need to install. So the Slash I will just install it automatically. So I'll click next, then I'll click next. We don't need to change the directory where it installs to. And it will only take a few seconds to install that Klee. There's other ways you can obviously connect to your S3 bucket, but I found this is the easiest way to do it, to run some simple commands. So I'll select finish, and then what we want to do is make sure that it's installed correctly. So the command to do this is AWS dash dash version. If you don't get a response all you need to do is restart your command window because it's probably not in the path of that command window yet. So I'll launch the command prompt again and again I'll do an AWS dash dash version and we should get a response. So firstly, we need to connect to the S3 bucket based on the information in our service key. The way we do this is with an AWS space configure command. What you'll then be is prompted for the various parts of that service key. So we'll firstly need to import the AWS key. We'll press return. The next thing to do is enter the AWS secret access key, which is essentially a password. So I'll paste that in and press return. The next thing is to include the default region. Again, this is in your service key text file. For me, it's US East-1. And for us, we don't need a default output format. Today I'm going to show you how to create a bucket with a default output format. Now that we've connected of course what we'll want to do is test the bucket. So the bucket name is in that service key and we'll use an AWS. And what we want to do is, when you run any command, is you start the command with aws s3 and we're going to list the contents of our bucket, which again we get from that service key. So we'll use an ls s3 colon forward slash and then we'll paste in the name of that bucket. As expected, the bucket should be empty. So what we're going to do is test by creating a file locally, uploading that file, viewing the contents and then removing the file. So to do this what we can do is the following command. The first thing we'll need to do is create a file locally. So we can do this with an echo command. So I'm going to echo the values 1, 2, 3 in the text file locally called test.csv. If we output the contents of test.csv, here you can see I've maybe run it one or two times to append to that file. So that's our source file that we'll want to get into our S3 bucket. So how do we do it? Well, within the bucket you can have various folders. So again if I list the contents we can see that the bucket at the moment is empty. To copy all we need to do is run a CP command just like in Linux. We'll copy the test.csv file which is local in this folder. And again what we'll do is we'll just paste in the name of my bucket. With the forward slash it puts it at the root level, but of course you can have a folder structure, although it's not really folders. If I list the contents of the bucket here you can see we've got that test.csv file. And as you can imagine, what we can also do is delete the contents from the file with a similar Linux command. So again, with AWS S3RM for remove, what we'll do, of course, we don't want to remove the bucket. So we'll paste in the name of our bucket and put a forward slash test.csv. We'll execute it, we can see it's deleted. And of course if we do an ls, we'll see that the bucket is indeed empty. So if I go back to my browser to summarize, what we've done is we've created an object store in SAP BTP, which is our S3 bucket. As of now, there's nothing needed to be added to our SAP AI Launchpad. If we look down into one of our machine learning operations, you'll see there's an option for data sets, but we'll cover that in a future video series. To summarize what we've done in this onboarding video series, we've created a sub account within BTP. We then look at subscribing to SAP AI Launchpad and creating an instance of SAP AI Core. We then looked at Jupyter to connect via the artificial intelligence API to SAP AI Core from a notebook. And what we did is to test that that connectivity works, we linked firstly a GitHub repository to our SAP AI Core service. That will contain our workflows, which will be synced, our workflow model templates and our serving templates. We then looked at connecting Docker to our SAP AI Core service. And then lastly, we've looked at connecting our S3 buckets, whether it's an AWS or BTP, to our SAP AI core service. In the next video series, of course, we're going to look at some machine learning. We're going to have a much bigger data set. We're going to do some machine learning. We're going to build a model. We're going to train it. Today, I'm going to show you how to train it and of course the ultimate aim is to have an app or a report to analyze that data. And here is the app...