DEV Community: Anthony Bartolo

How to Use Cloud Shell in Visual Studio Code

Anthony Bartolo — Thu, 20 Jun 2019 11:48:18 +0000

Originally posted by @ThomasMaurer on ITOpsTalk.com

As you may know, I am a huge fan of the Azure Cloud Shell. I use it often directly in the Azure Portal, on Microsoft Docs, in the Azure Mobile App or on shell.azure.com. A lot of times I am editing files and writing code and Azure Resource Manager (ARM) templates in Visual Studio Code and in that case I need to either use a local Azure PowerShell or Azure CLI installation or switch from Visual Studio Code back in the browser to use Cloud Shell. However, there is also a third option, which allows me to run Cloud Shell directly within Visual Studio Code.

To set up Cloud Shell in Visual Studio Code you need to do two things. First, you need to install nodeJS and the Azure Account extension.

On Windows: Requires Node.js 6 or later to be installed (https://nodejs.org).
Visual Studio Code Azure Account extension.

Next, you can log in to Azure and open PowerShell or Bash in Cloud Shell:

Press CTRL+SHIFT+P
Sign in to Microsoft Azure, by typing Azure: Sign In
A browser window will open to login to Azure
Press CTRL+ SHIFT+P
Type Open PowerShell in Cloud Shell or Open Bash in Cloud Shell
This will connect you directly to your Cloud Shell running in Azure.

I hope this gives you an overview of how you can run Cloud Shell directly in Visual Studio Code. If you have any questions, leave a commement below.

How to access Azure Linux virtual machines with Azure Active Directory

Anthony Bartolo — Tue, 26 Mar 2019 07:01:00 +0000

Written By @nepeters

Using Azure AD credentials for accessing Azure Linux Virtual Machines improves security by:

Centrally controlling and enforcing access policies on Azure AD credentials
Reducing the reliance on local access accounts
Integration with multi-factor authentication

In this blog post, I will quickly walk through the basic configuration steps for accessing Azure Linux virtual machines using Azure AD credentials. For detailed steps and documentation, see Log into a Linux Virtual machine in Azure using Azure Active Directory authentication.

Create a Virtual Machine

First things first, you need an Azure Linux virtual machine. This blog uses the Azure CLI to create the virtual machine however any method for deploying virtual machine will work. If you already have an Azure Linux virtual machine, this section can be skipped.

Create a resource group using the az group create command.

az group create --name myResourceGroup --location eastus

Create a virtual machine using the az vm create command. Notice here that I have neither used the --admin-username argument to create a local user account nor used any arguments to create or provide SSH keys.

az vm create --resource-group myResourceGroup --name linuxVM --image UbuntuLTS

Here is where the magic happens. Use the az vm extension set command to install the Active Directory Linux SSH extension. This extension is responsible for the configuration of the Azure AD integration.

az vm extension set --publisher Microsoft.Azure.ActiveDirectory.LinuxSSH --name AADLoginForLinux --resource-group myResourceGroup --vm-name linuxVM

Configure Role-Based Access

Before logging into the virtual machine with an Azure AD account, the Azure AD access must be configured. To do so, we will create a role binding between the Azure AD account, the "Virtual Machines Administrators Login" AD role, and the virtual machine.

First, get the ID of the virtual machine using the az vm show command. In this example, the ID is stored in a variables name VMID.

VMID=$(az vm show --resource-group myResourceGroup --name linuxVM --query id -o tsv)

Create the role binding using the az role assignment create command. Notice here that the --assignee would be the Azure AD account or group for which the access is established.

az role assignment create --role "Virtual Machine Administrator Login" --assignee user@contoso.com --scope $VMID

Access the VM

With the VM created, and the access established, you can now access the VM using SSH. First, get the public IP address of the virtual machine. This can be done with the azvm show command.

az vm show -d --resource-group myResourceGroup --name linuxVM --query publicIps -o tsv

Now create the SSH connection. In this example, I am using SSH from a terminal. Take note that the Azure AD user account is specified in the command.

ssh user@contoso.com @137.117.88.113

Once completed, you are prompted to open up a browser and complete the authentication. Follow the instructions and press ENTER when done.

To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code AJ9GDRXBQ to authenticate. Press ENTER when ready.

At this point, the SSH connection should have been successfully created. Feel free to reach out in comments or on Twitter at @nepeters.

Step-By-Step: Introduction to Azure Command Line Interface (CLI) with Azure Arm Templates

Anthony Bartolo — Tue, 05 Mar 2019 08:01:00 +0000

Written By @jaydestro

Working with Azure Resource Manager Templates provides you with a way to codify your infrastructure using JSON! In this tutorial we'll show you how to get started with using different parameters along side with your template.

From the Azure Resource Manager Overview:

Azure Resource Manager is the deployment and management service for Azure. It provides a consistent management layer that enables you to create, update, and delete resources in your Azure subscription. You can use its access control, auditing, and tagging features to secure and organize your resources after deployment.

When you take actions through the portal, PowerShell, Azure CLI, REST APIs, or client SDKs, the Azure Resource Manager API handles your request. Because all requests are handled through the same API, you see consistent results and capabilities in all the different tools. All capabilities that are available in the portal are also available through PowerShell, Azure CLI, REST APIs, and client SDKs. Functionality initially released through APIs will be represented in the portal within 180 days of initial release.

The following image shows how all the tools interact with the Azure Resource Manager API. The API passes requests to the Resource Manager service, which authenticates and authorizes the requests. Resource Manager then routes the requests to the appropriate service.

With Resource Manager, you can create a template (in JSON format) that defines the infrastructure and configuration of your Azure solution. By using a template, you can repeatedly deploy your solution throughout its lifecycle and have confidence your resources are deployed in a consistent state.

To follow along with this tutorial it's recommended you have:

Azure Account
Some Bash shell experience
General understanding of scripting/programming

Important links you'll need to have:

Parameters section of Azure Resource Manager templates

Deploy resources with Resource Manager templates and Azure CLI

ubuntu-apache-test-page GitHub Repo

To create a resource group:

az group create -l centralus -n \<insert your preferred name here\>

Basic deploy of an ARM template:

az group deployment create --name \<your-deploy-name\> --resource-group \<your resource group name\> --template-file azuredeploy.json

Deploy with specified parameters:

az group deployment create --name \<your deployment group\> --resource-group=\<your resource group name\> --template-file azuredeploy.json --parameter location=eastus testPageTitle="demo jay" testPageBody="\<p\>This is a really cool test page.\</p\>" adminUsername=\<your preferred username\> adminPassword=\<your preferred password\> dnsNameForPublicIP=\<your preferred hostname\>

Have additional questions about this tutorial? Feel free to leave a message below.

Step-By-Step: Getting Started with Azure Machine Learning

Anthony Bartolo — Thu, 07 Feb 2019 12:58:16 +0000

Artificial Intelligence (AI) study and use is on the rise. Tools to enable AI are becoming more readily available, simpler to use and easier to implement. What's more is that the definition of AI itself has been broken down into ingredients that, when later applied into a recipe (or process), can provide multiple desired outcomes. One of the more important ingredients used in most recipes is Machine Learning. Machine Learning in essence is a way of teaching computers to provide more accurate predictions on provided data. These predictions can also make apps and devices smarter by providing recommendations as an outcome to the data.

In the pursuit of making roads safer, Toyota Canada has been capturing data from mechanics in all of Toyota Canada's 300 dealerships on the vehicles they repair. In the past, the repair data was extracted from Toyota Canada's service application manually and stored in databases on premise to later be analyzed. While parts of the analytics process were automated, the entire process took over 6 months to process the reams of data to provide a part replacement recommendation.

Toyota Canada wanted to reduce the process time and so approached Microsoft to collaborate in a Machine Learning Hackfest to come up with a solution. While we are unable to detail the exact process undertaken by Toyota Canada and Microsoft as completed during the Hackfest itself, this post will walk through steps accomplishing a similar exercise to enable further understanding of the Machine Learning process. The step-by-step detailed below will set up a pricing prediction of specific vehicles. Lets get started.

_ _

Step 1: Accessing Machine Learning Studio

To begin this exercise, navigate to https://studio.azureml.net and select Sign up here. Next choose between free and paid options to complete this exercise. NOTE: Select Sign In if you have already completed a Machine Learning experiment previously and simple enter your credentials. You are ready to begin the exercise once you are able to access the Microsoft Azure Machine Learning Studio.

Step 2: Getting the Data to Analyze

Next you'll need to acquire data to analyze. Machine Learning Studio has many sample datasets to choose from or you can even import your own dataset from almost any source. In keeping with the automotive theme, the Automobile price data (Raw) dataset will be used in this exercise. This dataset provides data on various cars including make, model, price and specifications The first thing you need to perform machine learning is data. There are several sample datasets included with Machine Learning Studio that you can use, or you can import data from many sources. For this example, we'll use the sample dataset, Automobile price data (Raw), that's included in your workspace. This dataset includes entries for various individual automobiles, including information such as make, model, technical specifications, and price. NOTE: All data used in this exercise is factitious and does not represent the current automotive market. Let's now capture the dataset for this experiment.

Click +NEW located at the bottom of the Machine Learning Studio window to create a new experiment
Select EXPERIMENT > Blank Experiment
Name the experiment Automotive Price Prediction Exercise by selecting and replacing the text found at the top
In the Search box located in the top left hand side, enter automobile to find the dataset labeled Automobile price data (Raw)
Drag the dataset to the experiment canvas NOTE: Click the output port at the bottom of the automobile dataset, and then select Visualize to see what the automotive dataset looks like

Step 3: Preparation of the Data

Preprocessing the dataset is needed to ensure missing values are addressed prior to running the prediction exercise. As noted in the newly added automotive dataset, the normalized-losses column is missing many values and will have to be excluded to provide a better prediction.

In the Search box located in the top left hand side, enter select columns and located the Select Columns in Dataset module
Drag the module to the newly created experiment canvas NOTE: This module allows for the selection of columns of data to be included or excluded in this exercise
Connect the output port of the Automobile price data (Raw) dataset to the input port of the Select Columns in Dataset module
Select the Select Columns in Dataset module
Click Launch column selector in the Properties pane
Click With rules located on the left
Under Begin With , click All columns. This directs Select Columns in Dataset to pass through all the columns (except those columns we're about to exclude).
From the drop-downs, select Exclude and column names , and then click inside the text box. A list of columns is displayed. Select normalized-losses , and it's added to the text box.
Click the check mark to close the column selector NOTE: The properties pane for Select Columns in Dataset now shows that all columns from the dataset will pass through except normalized-losses
Drag the Clean Missing Data module to the experiment canvas and connect it to the Select Columns in Dataset module
In the Properties pane, select Remove entire row under Cleaning mode** NOTE:** This directs Clean Missing Data to clean the data by removing rows that have any missing values.
Double-click the module and type the comment Remove missing value rows
Click RUN at the bottom of the page

Step 4: Defining Features

Machine Leaning Features are individual measurable properties that are of interest. In Automotive Price dataset, each row represents one car, and each column is a feature of that vehicle. Experimentation and knowledge about the problem you want to solve are needed to find a good set of features to create a predictive model. This experiment will build a model that uses a subset of the features in the automotive dataset. These features include: make, body-style, wheel-base, engine-size, horsepower, peak-rpm, highway-mpg, price

Find and drag another Select Columns in the Dataset module to the experiment canvas
Connect the left output port of the Clean Missing Data module to the input of the Select Columns in Dataset module
Double-click the module and type Select features for prediction
Click Launch column selector in the Properties pane
Click With rules
Click No columns u nder Begin With
Select Include and column names in the filter row
Select the list of column names (as listed above prior to the start of Step 3's steps) in the text box
Click the check mark button to confirm the selection

Step 5: Selecting and Applying a Learning Algorithm With the appropriate data now repaired, training and testing of a predictive model can now commence. The data will now be uses to train the model and test the model to review price prediction. For this experiment the regression machine learning algorithm will be used. Regression is used to predict a number which will come in handing when predicting pricing. More specifically, this experiment will use the simple linear regression model. The data itself will be used for both training the model and testing. This is completed by splitting the data into separate training and testing datasets.

Find, select and drag the Split Data module to the experiment canvas
Connect the Split Data module to the last Select Columns in Dataset module
Click the Split Data module
In the Properties pane to the right of the canvas, find the Fraction of rows in the first output dataset () and set it to 0.75
Run the experiment
Expand the Machine Learning category in the module palette to the left of the canvas to select the learning algorithm
Expand Initialize Model NOTE: This displays several categories of modules that can be used to initialize machine learning algorithms
Select the Linear Regression module under the Regression category and drag it to the experiment canvas
Find and drag the Train Model module to the experiment canvas
Connect the output of the Linear Regression module to the left input of the Train Model module, and connect the training data output (left port) of the Split Data module to the right input of the Train Model module NOTE: Please pay attention to the port utilized as the experiment will not work if connected incorrectly
Click the Train Model module
Select Launch column selector in the Properties pane
Select the price column and move it to the Selected columns list (This is the value that the experiment is going to predict)
Click the check mark button to confirm the selection
Run the experiment

Step 6: Predict New Automobile Pricing

The experiment can now score the 25 percent of data to how the model functions being trained on the other 75 percent.

Find and drag the Score Model module to the experiment canvas
Connect the output of the Train Model module to the left input port of Score Model
Connect the test data output (right port) of the Split Data module to the right input port of Score Model
Run the experiment
Click the output port of Score Model and select Visualize to view the output from the Score Model module NOTE: The output shows the predicted values for price and the known values from the test data

Congratulations as you have now completed your first machine learning experiment. Next steps would be to try an improve the prediction and then deploy it as a predictive web service. Experiment further by adding multiple machine learning algorithms, modifying the properties of the Linear Regression algorithm or trying a different algorithm altogether.