1_Setup.md

1_Setup:

Estimated Time: 30-40 minutes

Note: If you have been provided an environment for completing this bootcamp, log in to the Azure Portal (in a private window) with the provided credentials. Connect to your VM. Next, complete Lab 1.1 Steps 2-4. Additionally, you need to make note of the keys provided (should be sent to the email address you provided) and add them to the appropriate sections in settings.json (see the beginning of Lab 1.2).

Lab: Setting up your Azure Account

You may activate an Azure free trial at https://azure.microsoft.com/en-us/free/.

If you have been given an Azure Pass to complete this lab, you may go to http://www.microsoftazurepass.com/ to activate it. Please follow the instructions at https://www.microsoftazurepass.com/howto, which document the activation process. A Microsoft account may have one free trial on Azure and one Azure Pass associated with it, so if you have already activated an Azure Pass on your Microsoft account, you will need to use the free trial or use another Microsoft account.

Lab 1.1: Setting up your Data Science Virtual Machine

After creating an Azure account, you may access the Azure portal. From the portal, create a Resource Group for this lab. Detailed information about the Data Science Virtual Machine can be found online, but we will just go over what's needed for this workshop. We are creating a VM and not doing it locally to ensure that we are all working from the same environment. This will make troubleshooting much easier. In your Resource Group, deploy and connect to a "Data Science Virtual Machine - Windows 2016", with a size of 2-4 vCPUs and 8-12 GB RAM, some examples include but are not limited to D4S_V3, B4MS, DS3, DS3_V2, etc. Put in the location that is closest to you: West US 2, East US, West Europe, Southeast Asia. All other defaults are fine.

Note: Testing was completed on both West US 2 D4S_V3 and Southeast Asia D4S_V3.

Connect to your VM. Once you're connected, there are several things you need to do to set up the DSVM for the workshop:

1. In the Cortana search bar, type "git bash" and select "Git Bash Desktop App", or type "cmd" and select "Command Prompt". Next, type cd c:// then enter, and git clone https://github.com/Azure/LearnAI-Bootcamp.git then enter. This copies down all of the files from the GitHub site to C:\LearnAI-Bootcamp.
   Validation step: Go to C:\LearnAI-Bootcamp and confirm it exists.
2. From File Explorer, open "ImageProcessing.sln" which is under C:\LearnAI-Bootcamp\lab01.1-computer_vision\resources\code\Starting-ImageProcessing. It may take a while for Visual Studio to open for the first time, and you will have to log in to your account. The account you use to log in should be the same as your Azure subscription account. Note: If your company has two factor authentication, you may not be able to use your pin to log in. Use your password and mobile phone authentication to log in instead.
   Validation step: In the top right corner of Visual Studio, confirm that you see your name and account information.
3. After Visual Studio loads and the solution is open, right-click on TestCLI and select "Set as StartUp Project."
   Validation step: TestCLI should appear bold in the Solution Explorer
   Note: If you get a message that TestCLI is unable to load, right-click on TestCLI and select "Install Missing Features". This will prompt you to install .Net Desktop Development. Click Install, then Install again. You may get an error because Visual Studio needs to be closed to install updates. Close Visual Studio and then select Retry. It should only take 1-2 minutes to install. Reopen "ImageProcessing.sln", confirm that you are able to expand TestCLI and see its contents. Then, right-click on TestCLI and select "Set as StartUp Project".
4. Right-click on the solution in Solution Explorer and select "Build Solution".
   Validation step: When you build the solution, the only errors you receive are related to ImageProcessor in Program.cs. You do not need to worry about yellow warning messages.

Lab 1.2: Collecting the Keys

Over the course of this lab, we will collect Cognitive Services keys and storage keys. You should save all of them in a text file so you can easily access them in future labs.

• Cognitive Services Keys

  • Computer Vision API:

• Storage Keys

  • Azure Blob Storage Connection String:
  • Cosmos DB URI:
  • Cosmos DB key:

In addition, you will need to add the keys to the settings.json file which is under C:\LearnAI-Bootcamp\lab01.1-computer_vision\resources\code\Starting-ImageProcessing\TestCLI\settings.json. You will have to replace VisionKeyHere, ConnectionStringHere, CosmosURIHere, and CosmosKeyHere with their corresponding keys that you collect in the next section. Do not change the blob container (images), the database name (images), or the collection name (metadata).

Getting Cognitive Services API Keys

Within the Portal, we'll first create keys for the Cognitive Services we'll be using. We'll primarily be using the Computer Vision Cognitive Service, so let's create an API key for that first.

In the Portal, click the "+ New" button (when you hover over it, it will say Create a resource) and then enter computer vision in the search box and choose Computer Vision API:

This will lead you to fill out a few details for the API endpoint you'll be creating, choosing the API you're interested in and where you'd like your endpoint to reside (!!put in the West US region or it will not work!!), as well as what pricing plan you'd like. We'll be using S1 so that we have the throughput we need for the tutorial. Use the same Resource Group that you used to create your DSVM. We'll also use this resource group for Blob Storage and Cosmos DB. Pin to dashboard so that you can easily find it. Since the Computer Vision API stores images internally at Microsoft (in a secure fashion), to help improve future Cognitive Services Vision offerings, you'll need to check the box that states you're ok with this before you can create the resource.

Double check that you put your Computer Vision service in West US

The code in the following labs has been set up to use West US for calling the Computer Vision API. In your future endeavors, you can learn how to call other regions here.

Modifying settings.json, part one

Once you have created your new API subscription, you can grab the keys from the appropriate section of the blade and add them to your TestCLI's settings.json file.

Note: there are two keys for each of the Cognitive Services APIs you will create. Either one will work. You can read more about multiple keys here.

Setting up Storage

We'll be using two different stores in Azure for this project - one for storing the raw images, and the other for storing the results of our Cognitive Service calls. Azure Blob Storage is made for storing large amounts of data in a format that looks similar to a file-system, and it is a great choice for storing data like images. Azure Cosmos DB is our resilient NoSQL PaaS solution and is incredibly useful for storing loosely structured data like we have with our image metadata results. There are other possible choices (Azure Table Storage, SQL Server), but Cosmos DB gives us the flexibility to evolve our schema freely (like adding data for new services), query it easily, and integrate quickly into Azure Search.

Azure Blob Storage

Detailed "Getting Started" instructions can be found online, but let's just go over what you need for this lab.

In the Portal, click the "+ New" button (when you hover over it, it will say Create a resource) and then enter storage in the search box and choose Storage account. Select create.

Once you click it, you'll be presented with some fields to fill out.

• Choose your storage account name (lowercase letters and numbers),
• Set Account kind to Blob storage,
• Set Replication to Locally-Redundant storage (LRS) (this is just to save money),
• Use the same Resource Group as above, and
• Set Location to the region that is closest to you from the following list: East US, West US, Southeast Asia, West Europe. (The list of Azure services that are available in each region is at https://azure.microsoft.com/en-us/regions/services/). Pin to dashboard so that you can easily find it.
• All other defaults are fine

Modifying settings.json, part two

Now that you have an Azure Storage account, let's grab the Connection String and add it to your TestCLI's settings.json.

Cosmos DB

Detailed "Getting Started" instructions can be found online, but we'll walk through what you need for this lab.

In the Portal, click the "+ New" button (when you hover over it, it will say Create a resource) and then enter cosmos db in the search box and choose Azure Cosmos DB and click Create.

Once you click this, you'll have to fill out a few fields as you see fit. Set Location to the region that is closest to you from the following list: East US, West US, Southeast Asia, West Europe.

In our case, select the ID you'd like, subject to the constraints that it needs to be lowercase letters, numbers, or dashes. We will be using the SQL API so we can create a document database that is queryable using SQL syntax, so select SQL as the API. Let's use the same Resource Group as we used for our previous steps, and the same location, select Pin to dashboard to make sure we keep track of it and it's easy to get back to, and hit Create.

Modifying settings.json, part three

Once creation is complete, open the panel for your new database and select the Keys sub-panel.

You'll need the URI and the PRIMARY KEY for your TestCLI's settings.json file, so copy those into there and you're now ready to store images and data into the cloud.

Note: Be sure to turn off your DSVM from the portal after you have completed the Setup lab. When the workshop begins, you will need to start your DSVM from the portal to begin the labs. We recommend turning off your DSVM at the end of each day, and deleting all of the resources you create at the end of the workshop. Alternatively, you can set up auto-shutdown for your DSVM. Be sure to set the correct time zone.

You have completed the prerequisites.

Continue to 2_ImageProcessor

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